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          Machine learning predicts electronic properties at relatively low computational cost      Cache   Translate Page   Web Page Cache   
Algorithm aims for the accuracy of more computationally intensive approaches
          How Edge Computing Gives You an Edge Over Cloud Computing      Cache   Translate Page   Web Page Cache   

Edge computing is a term that’s regularly coming up in technology conversations, and being touted as the next big thing after cloud computing. There might be a great deal of truth to that given the fact that a MarketsandMarkets report forecasts edge computing to grow at a CAGR of 35%, and reach $6.72 billion USD by 2022.

So, what exactly is edge computing?

Research firm IDC describes edge computing as a “mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, in a footprint of less than 100 square feet”.

Basically, it’s processing data at the edge of a network, at or near the point of origin. This ‘micro data center’ could be a sensor device itself or a device with predefined computational powers that will locally process time-sensitive data and relay it back into the system. The rest of the data is then moved to the cloud for further processing.

How is it better than cloud computing?

Edge computing brings in certain distinct benefits over cloud computing, but that requires us to first acknowledge the rising importance of IoT across industries.

IoT demands a complete ecosystem of connected sensors and devices that are continuously gathering a massive volume of data. Giving this data a round-trip to the cloud/central data centre is slow and costly. What follows is where edge computing has an ‘edge’ over cloud computing.

No Latency

Sending and receiving data from the cloud, especially when data centres are physically located miles apart, can be slow. Not for us, but definitely slow for enterprises whose businesses run on the speed of data processing.

With IoT, real-time data processing has become a requirement. Sensors monitoring manufacturing lines, or cars navigating via GPS and sensor data, need to process information in real-time to be able to correctly respond to situations. And hence, waiting for the data to get back from the cloud is not a feasible option.

With edge computing, critical data is processed near to these IoT devices. This eliminates latency issues and vastly improves response rates across enterprise operations.

Cost Savings

It’s predicted that by 2020, there will be over 50 billion IoT devices collecting more than 1.44 billion data points per plant, per day. First up, that is a massive amount of data to be transferred over the network, leading to increased loads. Secondly, even if it’s done, it will be hugely expensive for businesses. There will be the cost of acquiring additional bandwidth to transfer this volume of data. Add to that the fact that they have to increase investments in load balancing and frequent maintenance of the network and data centres.

With edge computing, on the other hand, the majority of the time-sensitive data is processed locally, and relayed back to the IoT devices for further action. That leaves a manageable amount of data that needs to be transferred to the cloud, and hence does not demand huge expenditure.

Reliability

With data travelling long distances to reach the cloud, there are high chances of data getting corrupted. This can cause data loss, system crashes, and also financial loss due to incorrect data processing.

In the case of edge computing, the path from an IoT device to micro data centre is extremely short. This ensures that there is a slim-to-none chance of data corruption, or network jitters to cause data packets to reach unevenly. The data transmission is reliable and hence the data processing and insights are also more reliable.

Security

Edge computing gives businesses the option of not just locally processing, but also locally storing sensitive data. This is definitely a step beyond having to store data in public or hybrid clouds where it is slightly more prone to security risks.

Because edge computing capabilities are always close to the data source, they are almost always within premises controlled by the enterprises themselves. This means they can build and deploy custom security measures as per their standards of compliance.

Will edge computing replace cloud computing?

While edge computing definitely has its upside, one must remember that it’s a network of ‘micro’ data centres. They are great for processing time-sensitive data or storing critical data; but that is only a portion of the data being produced by enterprises. There’s still a huge amount of data that needs to be stored and processed at normal speeds. And there’s nothing better than cloud computing to achieve that.

So no, cloud computing will definitely not be replaced by edge computing. What will happen, though, is that both these methods will become indispensable and complementary parts of an enterprise data strategy. So it is advisable for businesses to start understanding and investing in building edge computing capabilities, so they get a headstart in the game.

Author Bio Sriram Sitaraman: Practice Head for Analytics and Data Science at Srijan Technologies. With over 20 years of experience in designing and delivering innovative business solutions, Sriram leverages his expertise in machine learning, statistical modelling, and business intelligence to enable digital transformation in industries as diverse as healthcare, manufacturing, retail, banking, and more.


          How to Develop a Skillful Machine Learning Time Series Forecasting Model      Cache   Translate Page   Web Page Cache   

You are handed data and told to develop a forecast model. What do you do? This is a common situation; far more common than most people think. Perhaps you are sent a CSV file. Perhaps you are given access to a database. Perhaps you are starting a competition. The problem can be reasonably well defined: […]

The post How to Develop a Skillful Machine Learning Time Series Forecasting Model appeared first on Machine Learning Mastery.


          Business Strategy, Sr. Manager - Hortonworks - Dallas, TX      Cache   Translate Page   Web Page Cache   
Business Strategy, Leadership Opportunity. Experience in the Software and/or Business Impact of Analytics, Big Data, Machine Learning/AI, Cloud is a plus....
From Hortonworks - Mon, 23 Jul 2018 20:31:09 GMT - View all Dallas, TX jobs
          Business Strategy, Sr. Manager - Hortonworks - Atlanta, GA      Cache   Translate Page   Web Page Cache   
Business Strategy, Leadership Opportunity. Experience in the Software and/or Business Impact of Analytics, Big Data, Machine Learning/AI, Cloud is a plus....
From Hortonworks - Mon, 23 Jul 2018 20:31:09 GMT - View all Atlanta, GA jobs
          NASA Satellites Assist in Estimating Abundance of Key Wildlife Species      Cache   Translate Page   Web Page Cache   

Climate and land-use change are shrinking natural wildlife habitats around the world. Yet despite their importance to rural economies and natural ecosystems, remarkably little is known about the geographic distribution of most wild species – especially those that migrate seasonally over large areas.

By combining NASA satellite imagery with wildlife surveys conducted by state natural resources agencies, a team of researchers at Utah State University and the University of Maryland, and the U.S. Geological Survey modeled the effects of plant productivity on populations of mule deer and mountain lions. Specifically, they mapped the abundance of both species over a climatically diverse region spanning multiple western states. The findings were published in Global Change Biology.

These models provide new insights into how differences in climate are transmitted through the food chain, from plants to herbivores and then to predators. Prey and predator abundance both increased with plant productivity, which is governed by precipitation and temperature. 

Conversely, animals responded to decreases in food availability by moving and foraging over larger areas, which could lead to increased conflict with humans.

“Climatically driven changes in primary production propagate through trophic levels,” said David Stoner, lead author of the study and researcher in Wildland Resources at USU. “We expected to see that satellite measurements of plant productivity would explain the abundance of deer. However, we were surprised to see how closely the maps of productivity also predicted the distribution of the mountain lion, their major predator.” 

The study also reveals a disruption in the way scientists study the biosphere. 

“Up until about a decade ago, we were limited to analyzing landscapes through highly simplified maps representing a single point in time,” said Joseph Sexton, chief scientist of terraPulse, Inc. and a coauthor on the study. “This just doesn’t work in regions experiencing rapid economic or environmental change—the map is irrelevant by the time it’s finished.” 

Now, given developments in machine learning, “big data” computation and the “cloud,” ecologists and other scientists are studying large, dynamic ecosystems in ever-increasing detail and resolution. 

“We’re now mining global archives of satellite imagery spanning nearly forty years, we’re updating our maps in pace with ecosystem changes and we’re getting that information out to government agencies and private land managers working in the field,” Stoner said.

The authors predict that, by enabling land managers to monitor rangeland and agricultural productivity, forest loss and regrowth, urban growth and the dynamics of wildlife habitat, the expanding stream of information will help humanity adapt to climate and other environmental changes. “State wildlife agencies are tasked with estimating animal abundance in remote and rugged habitats, which is difficult and expensive,” Stoner said. “Integration of satellite imagery can help establish baseline population estimates, monitor environmental conditions and identify populations at risk to climate and land-use change.”

Related Links:
S.J. and Jessie E. Quinney College of Natural Resources at Utah State University
Wildland Resources Department at Utah State University

Contact: David Stoner, 435-797-9147, david.stoner@usu.edu 
Public Information Officer : Traci Hillyard, 435-797-2452, traci.hillyard@usu.edu 
 


          Senior Software Developer      Cache   Translate Page   Web Page Cache   
NY-New York, Senior Software Developer Location: New York, NY Perm Position If you are interested please send me your resume: elizabeth.naraine@nttdata.com The Senior Software Developer will play a key role in designing and developing the probabilistically working device recognition- and tracking technology to support accuracy and scalability for extremely high volumes by using machine learning techniques. Req
          Radar Perzeption fr automatisiertes Fahren mit Machine Learnin      Cache   Translate Page   Web Page Cache   
Konzipieren neuer Methoden zur Umfelderfassung unter Verwendung von Radardaten fr das automatisierte FahrenEntwicklung von Algorithmen zur Objekterkennung, -klassifikation und Tracking mit Machine Learning-VerfahrenBewertung der bertragbarkeit/Generalisierung der Anstze fr andere Umfelderfassungstechnologien (z.B. Lidar)Umsetzung der entwickelten Anstze und Integration in Gesamtradarsystem einschlielich Evaluation im PrototypQualifikationenSehr guter Hochschulabschluss im Bereich Informatik, Robotik, Elektrotechnik o
          Machine Learning Engineer - Technica Corporation - Dulles, VA      Cache   Translate Page   Web Page Cache   
Technica Corporation is seeking a Machine Learning Engineer to support our internal Innovation, Research and Development (IRD) team....
From Technica Corporation - Wed, 11 Jul 2018 06:07:15 GMT - View all Dulles, VA jobs
          Machine learning technique reconstructs images passing through a multimode fiber      Cache   Translate Page   Web Page Cache   
(The Optical Society) Through innovative use of a neural network that mimics image processing by the human brain, a research team reports accurate reconstruction of images transmitted over optical fibers for distances of up to a kilometer.
          AI and Machine Learning: Separating Fact from Fiction      Cache   Translate Page   Web Page Cache   
Are AI and ML really changing the game, or are they just fancy buzzwords?
          MACHINE LEARNING ENGINEER FOR SPEECH RELATED APPLICATIONS - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Engineer for Speech related Applications (6 months contract)....
From Huawei Canada - Mon, 18 Jun 2018 23:46:16 GMT - View all Montréal, QC jobs
          MACHINE LEARNING INTERN FOR SPEECH RELATED APPLICATIONS - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Intern for Speech related Applications....
From Huawei Canada - Mon, 18 Jun 2018 17:50:57 GMT - View all Montréal, QC jobs
          MACHINE LEARNING HARDWARE RESEARCHER OR DEVELOPER - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Hardware Researcher or Developer....
From Huawei Canada - Wed, 06 Jun 2018 23:47:32 GMT - View all Montréal, QC jobs
          Machine Learning Software Developer - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. ML Software developer....
From Huawei Canada - Wed, 06 Jun 2018 23:47:31 GMT - View all Montréal, QC jobs
          Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Director, Data & AI - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Senior Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:23 GMT - View all Montréal, QC jobs
          Senior Software Developer      Cache   Translate Page   Web Page Cache   
NY-New York, Senior Software Developer Location: New York, NY Perm Position If you are interested please send me your resume: elizabeth.naraine@nttdata.com The Senior Software Developer will play a key role in designing and developing the probabilistically working device recognition- and tracking technology to support accuracy and scalability for extremely high volumes by using machine learning techniques. Req
          Product Manager, Marketplace Growth - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sat, 14 Jul 2018 06:23:30 GMT - View all New York, NY jobs
          QA Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Fri, 08 Jun 2018 16:35:13 GMT - View all New York, NY jobs
          Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Tue, 05 Jun 2018 16:15:49 GMT - View all New York, NY jobs
          Back End Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sun, 03 Jun 2018 06:21:49 GMT - View all New York, NY jobs
          UI Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sun, 27 May 2018 20:27:03 GMT - View all New York, NY jobs
          AI Conversation Designer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sat, 14 Apr 2018 06:15:32 GMT - View all New York, NY jobs
          Vector is a Robot by Anki that can Answer your Questions and Play Games with You for $250      Cache   Translate Page   Web Page Cache   
AI and Machine Learning are buzzwords we’ve been hearing a lot of late, especially with regards to smartphones. But hey, there’s much more to AI than just scene detection on your camera app! While robots haven’t really taken over much work from humans yet, there are products like robot vacuum cleaners which do a hassle-free […]
          Stop getting screwed: Using AI to prevent game fraud (VB Live)      Cache   Translate Page   Web Page Cache   

There are 2.2 billion active gamers, and 100 percent of them are at risk from fraudsters. Also at risk: your game's reputation, customer retention, and your bottom line. Learn how machine learning and AI can keep your game and players safe online criminals, don’t miss this VB Live event.


          Innovation Developer - TeamSoft - Sun Prairie, WI      Cache   Translate Page   Web Page Cache   
Are you interested in topics like machine learning, IoT, Big data, data science, data analysis, satellite imagery or mobile telematics?...
From Dice - Thu, 19 Jul 2018 08:35:55 GMT - View all Sun Prairie, WI jobs
          Automate your feature engineering      Cache   Translate Page   Web Page Cache   

In machine learning, a feature is another word for an attribute or input, or an independent variable. What is feature engineering? Feature engineering is a process of preparing inputs for machine learning models. The goal of feature engineering is to to improve classification accuracy by considering the limitations of the [...]

The post Automate your feature engineering appeared first on SAS Blogs.


          Data Scientist / Operations Research Engineer - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page   Web Page Cache   
Work closely with the business units to identify Machine Learning applications, define the strategic and tactical needs and drive the appropriate business...
From Advanced Micro Devices, Inc. - Thu, 12 Jul 2018 07:32:54 GMT - View all Austin, TX jobs
          ISV Technology Director - AI and ML - 67511 - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page   Web Page Cache   
AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. What you do at AMD changes everything....
From Advanced Micro Devices, Inc. - Sat, 07 Jul 2018 01:32:18 GMT - View all Austin, TX jobs
          ISV Technology Director - AI and ML - 67453 - Advanced Micro Devices, Inc. - Santa Clara, CA      Cache   Translate Page   Web Page Cache   
AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. What you do at AMD changes everything....
From Advanced Micro Devices, Inc. - Sat, 07 Jul 2018 01:32:16 GMT - View all Santa Clara, CA jobs
          Sentiment Analysis      Cache   Translate Page   Web Page Cache   
I need you to improve a research article on sentiment analysis (Budget: $30 - $250 USD, Jobs: Algorithm, Artificial Intelligence, Machine Learning, Mathematics, Research Writing)
          Vice President, Data Science - Machine Learning - Wunderman - Dallas, TX      Cache   Translate Page   Web Page Cache   
Goldman Sachs, Microsoft, Citibank, Coca-Cola, Ford, Pfizer, Adidas, United Airlines and leading regional brands are among our clients....
From Wunderman - Thu, 26 Apr 2018 16:49:34 GMT - View all Dallas, TX jobs
          Terminator: Arnold Schwarzenegger allena Gabriel Luna nella nuova immagine      Cache   Translate Page   Web Page Cache   

Arnold Schwarzenegger ha pubblicato sul suo account Twitter un’immagine nella quale potete vederlo allenare Gabriel Luna. I due attori compariranno nel nuovo film: Terminator. Schwarzenegger tornerà nel ruolo che lo ha reso celebre mentre Gabriel Luna sarà il nuovo Terminator. Machine learning. pic.twitter.com/ppTR9RqF7q — Arnold (@Schwarzenegger) August 9, 2018 Il film sarà prodotto da James Cameron, […]

L'articolo Terminator: Arnold Schwarzenegger allena Gabriel Luna nella nuova immagine proviene da Videogiochi, Fumetti, Cinema, Serie TV, Tech, Eventi | NerdPlanet.it.


          Data Scientist / Operations Research Engineer - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page   Web Page Cache   
Work closely with the business units to identify Machine Learning applications, define the strategic and tactical needs and drive the appropriate business...
From Advanced Micro Devices, Inc. - Thu, 12 Jul 2018 07:32:54 GMT - View all Austin, TX jobs
          ISV Technology Director - AI and ML - 67511 - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page   Web Page Cache   
AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. What you do at AMD changes everything....
From Advanced Micro Devices, Inc. - Sat, 07 Jul 2018 01:32:18 GMT - View all Austin, TX jobs
          ISV Technology Director - AI and ML - 67453 - Advanced Micro Devices, Inc. - Santa Clara, CA      Cache   Translate Page   Web Page Cache   
AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. What you do at AMD changes everything....
From Advanced Micro Devices, Inc. - Sat, 07 Jul 2018 01:32:16 GMT - View all Santa Clara, CA jobs
          Linux Foundation and Kernel News      Cache   Translate Page   Web Page Cache   

read more


          Red Hat and Fedora      Cache   Translate Page   Web Page Cache   
  • Women in IT Awards USA winner: Margaret Dawson, Red Hat

    Margaret has led teams at companies ranging from startups to Fortune 500 firms including Amazon, Microsoft and HP. She grew up in Detroit and began her career in the automotive industry, an experience that helped her feel at home in the similarly male-dominated technology sector.

    She has a passion for mentoring women in technology and has made it her mission to share the lessons she has learned with others and to mentor them in their own journeys.

  • How do tools affect culture?

    Most of the DevOps community talks about how tools don’t matter much. The culture has to change first, the argument goes, which might modify how the tools are used.

    I agree and disagree with that concept. I believe the relationship between tools and culture is more symbiotic and bidirectional than unidirectional. I have discovered this through real-world transformations across several companies now. I admit it’s hard to determine whether the tools changed the culture or whether the culture changed how the tools were used.

  • GPU Accelerated SQL queries with PostgreSQL & PG-Strom in OpenShift-3.10

    In the OpenShift 3.9 GPU blog, we leveraged machine learning frameworks on OpenShift for image recognition. And in the How To Use GPUs with DevicePlugin in OpenShift 3.10 blog, we installed and configured an OpenShift cluster with GPU support. In this installment, we will create a more sophisticated workload on the cluster – accelerating databases using GPUs.

    One of the key parts of any machine learning algorithm is the data (often referred to as the data lake/warehouse, stored as structured, semi-structured or unstructured data).

    A major part of machine learning pipelines is the preparation, cleaning, and exploration of this data. Specifically removing NAs (missing values), transformations, normalization, subsetting, sorting, and a lot of plotting.

  • Red Hat, Inc. (RHT) stock returned -15.52% negative Quarterly performance
  • Red Hat Inc (RHT) CEO & President James M Whitehurst Sold $6.3 million of Shares
  • Sigma Planning Corp Increases Position in Red Hat Inc (RHT)
  • PHPUnit 7.3

    RPM of PHPUnit version 7.3 are available in remi repository for Fedora ≥ 25 and for Enterprise Linux (CentOS, RHEL...).

  • Reducing the use of non-glibc allocators in Fedora

    Memory allocation for applications is a bit of a balancing act between various factors including CPU performance, memory efficiency, and how the memory is actually being allocated and deallocated by the application. Different programs may have diverse needs, but it is often the kind of workload that the application is expected to handle that determines which memory allocator performs best. That argues for a diversity of memory allocators (and allocation strategies) but, on the other hand, that complicates things for Linux distributions. As a result, Fedora is discussing ways to rein in the spread of allocators used by its packages.

  • Copr has a brand new API

    New Copr version is here and after several months of discussions and development, it finally brings a brand new API. In this article, we are going to see why it was needed, how it is better than previous API versions (i.e. why you should be happy about it) and try some code samples.

read more


          Sentiment Analysis      Cache   Translate Page   Web Page Cache   
I need you to improve a research article on sentiment analysis (Budget: $30 - $250 USD, Jobs: Algorithm, Artificial Intelligence, Machine Learning, Mathematics, Research Writing)
          Sentiment Analysis      Cache   Translate Page   Web Page Cache   
I need you to improve a research article on sentiment analysis (Budget: $30 - $250 USD, Jobs: Algorithm, Artificial Intelligence, Machine Learning, Mathematics, Research Writing)
          Fuzzy SVM-Based Coding Unit Decision in HEVC      Cache   Translate Page   Web Page Cache   
The latest video compression standard, High Efficiency Video Coding (HEVC), has greatly improved the coding efficiency compared to the predecessor H.264/AVC. However, equipped with the quadtree structure of coding tree unit partition and other sophisticated coding tools, HEVC brings a significant increase in the computational complexity. To address this issue, a coding unit (CU) decision method based on fuzzy support vector machine (SVM) is proposed for rate-distortion-complexity (RDC) optimization, where the process of CU decision is formulated as a cascaded multi-level classification task. The optimal feature set is selected according to a defined misclassification cost and a risk area is introduced for an uncertain classification output. To further improve the RDC performance, different regulation parameters in SVM are adopted and outliers in training samples are eliminated. Additionally, the proposed CU decision method is incorporated into a joint RDC optimization framework, where the width of risk area is adaptively adjusted to allocate flexible computational complexity to different CUs, aiming at minimizing computational complexity under a configurable constraint in terms of RD performance degradation. Experimental results show that the proposed approach can reduce 58.9% and 55.3% computational complexity on average with the values of Bjønteggard delta peak-signal-to-noise ratio as −0.075 dB and −0.085 dB and the values of Bjøntegaard delta bit rate as 2.859% and 2.671% under low delay ${P}$ and random access configurations, respectively, which has outperformed the state-of-the-art fast algorithms based on statistical information and machine learning.
          E746: Talla CEO Rob May on using A.I. & machine learning to automate HR & IT, the power of bots, the future of jobs, & the age of verticalized A.I.      Cache   Translate Page   Web Page Cache   
none
          E730: Inside LAUNCH Incubator, PT1: Transported: Virtual Reality Tours for Real Estate & FitBod: Personalized strength-training powered by machine learning      Cache   Translate Page   Web Page Cache   
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          E704: Napster co-founder Jordan Ritter & his new Atlas Informatics are redefining search to solve digital chaos in the age of contextual intelligence; the future of cybersecurity, privacy, AI/machine learning, & political turbulence      Cache   Translate Page   Web Page Cache   
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          Machine Learning/AI Engineer - Groom & Associates - Montréal, QC      Cache   Translate Page   Web Page Cache   
Expérience avec tensorflow ou d'autres backends, keras ou autres frameworks, scikit-learn, OpenCV, Pandas. Experience with tensorflow or other backends, keras...
From Groom & Associates - Thu, 07 Jun 2018 14:58:16 GMT - View all Montréal, QC jobs
          Data Scientists / AI & Machine Learning Engineer - IVADO Labs - Montréal, QC      Cache   Translate Page   Web Page Cache   
Experience implementing AI/data science algorithms using one or more of the modern programming languages/frameworks (e.g., Python, Pandas, Scikit-learn,...
From IVADO Labs - Sat, 05 May 2018 03:10:45 GMT - View all Montréal, QC jobs
          E683: Founder Nancy Lublin (DoSomething.org, Dress for Success) takes on suicide prevention with Crisis Text Line, partnering the power of human empathy with machine learning to save lives      Cache   Translate Page   Web Page Cache   
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          Machine Learning with R Step by Step Guide for Newbies      Cache   Translate Page   Web Page Cache   

Machine Learning with R Step by Step Guide for Newbies

Machine Learning With R: Step by Step Guide For Newbies Kindle Edition
by Dominic Lordy

English | 2018 | ISBN: 1720424608 | 114 Pages | EPUB | 690 KB


          Understand the significance for large scale machine learning      Cache   Translate Page   Web Page Cache   
Managing AI projects is no more a difficult job. With the help of AI platform, you can easily manage your code, projects or data at the same time. If you need help with scaling artificial intelligence or need to manage your high-end AI projects effectively then make sure you prefer ClusterOne.
          Machine Learning Adaptive Receiver for PAM-4 Modulated Optical Interconnection Based on Silicon Microring Modulator      Cache   Translate Page   Web Page Cache   
Modulation nonlinearity can severely distort multilevel modulation, and signal processing to mitigate the distortion is highly desirable. In this paper, we demonstrated a machine learning method for adaptive detection of 4-level pulse amplitude modulation (PAM-4) signals modulated by silicon micro-ring modulator (Si-MRM). The very limited linear modulation range of Si-MRM leads to serious modulation nonlinearity distortion for high-level modulations like PAM-4 with the consideration of wavelength drift. Our approach is based on the support vector machine (SVM) method, which can learn the nonlinear distortion of Si-MRM during PAM-4 modulation. Thus, the detection can be made adaptive for PAM-4 signals with nonlinear levels and level dependent noise. The modulation nonlinearity distortion of PAM-4 has been characterized in terms of level deviation (LD) with respect to wavelength drift. Up to 2.7-dB receiver sensitivity gain is obtained at about 26% LD by using the proposed SVM machine learning method. The receiver sensitivity-float range can be squeezed to be within 0.3 dB even with up to 30% LD, which indicates a stable detection of PAM-4 signals along with wavelength drift. Up to 3.63-dB receiver sensitivity improvement has been experimentally achieved at 50 Gbps for PAM-4 signals modulated by a Si-MRM and after 2-km standard single mode fiber transmission. The stable operation of Si-MRM is very difficult and very important. The proof-of-concept results indicate the very promising capability of machine learning method for stable detection of PAM-4 signals modulated by Si-MRM, which is of great significance for practical application of Si-MRM in optical interconnection.
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Virtual machine switching); Large scale distributed systems, real-time data analysis, machine learning, windows internals (networking stack and other OS...
From Microsoft - Thu, 09 Aug 2018 04:41:50 GMT - View all Redmond, WA jobs
          Economist - Forecasting - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Experience with machine learning applications. We are breaking fresh ground, pioneering in a program that is crucial for future Amazon growth, and our business...
From Amazon.com - Wed, 27 Jun 2018 07:21:23 GMT - View all Seattle, WA jobs
          Computing’s Hippocratic oath is here      Cache   Translate Page   Web Page Cache   

Computing professionals are on the front lines of almost every aspect of the modern world. They’re involved in the response when hackers steal the personal information of hundreds of thousands of people from a large corporation. Their work can protect–or jeopardize–critical infrastructures, such as electrical grids and transportation lines. And the algorithms they write may determine who gets a job, who is approved for a bank loan, or who gets released on bail.

Technological professionals are the first, and last, lines of defense against the misuse of technology. Nobody else understands the systems as well, and nobody else is in a position to protect specific data elements or ensure that the connections between one component and another are appropriate, safe, and reliable. As the role of computing continues its decades-long expansion in society, computer scientists are central to what happens next.

That’s why the world’s largest organization of computer scientists and engineers, the Association for Computing Machinery, of which I am president, has issued a new code of ethics for computing professionals. And it’s why ACM is taking other steps to help technologists engage with ethical questions.

[Photo: Hero Images/Getty Images]

Serving the public interest

A code of ethics is more than just a document on paper. There are hundreds of examples of the core values and standards to which every member of a field is held–including for organist guilds and outdoor-advertising associations. The world’s oldest code of ethics is also its most famous: The Hippocratic oath that medical doctors take, promising to care responsibly for their patients.

I suspect that one reason for the Hippocratic oath’s fame is how personal medical treatment can be, with people’s lives hanging in the balance. It’s important for patients to feel confident their medical caregivers have their interests firmly in mind.

Technology is, in many ways, similarly personal. In modern society, computers, software, and digital data are everywhere. They’re visible in laptops and smartphones, social media and video conferencing, but they’re also hidden inside the devices that help manage people’s daily lives, from thermostats to timers on coffeemakers. New developments in autonomous vehicles, sensor networks, and machine learning mean computing will play an even more central role in everyday life in coming years.

[Photo: Hero Images/Getty Images]

A changing profession

As the creators of these technologies, computing professionals have helped usher in the new and richly vibrant rhythms of modern life. But as computers become increasingly interwoven into the fabric of life, we in the profession must personally recommit to serving society through ethical conduct.

ACM’s last code of ethics was adopted in 1992, when many people saw computing work as purely technical. The internet was in its infancy and people were just beginning to understand the value of being able to aggregate and distribute information widely. It would still be years before artificial intelligence and machine learning had applications outside research labs.

Today, technologists’ work can affect the lives and livelihoods of people in ways that may be unintended, even unpredictable. I’m not an ethicist by training, but it’s clear to me that anyone in today’s computing field can benefit from guidance on ethical thinking and behavior.

[Photo: Hero Images/Getty Images]

Updates to the code

ACM’s new ethics code has several important differences from the 1992 version. One has to do with unintended consequences. In the 1970s and 1980s, technologists built software or systems whose effects were limited to specific locations or circumstances. But over the past two decades, it has become clear that as technologies evolve, they can be applied in contexts very different from the original intent.

For example, computer vision research has led to ways of creating 3D models of objects–and people–based on 2D images, but it was never intended to be used in conjunction with machine learning in surveillance or drone applications. The old ethics code asked software developers to be sure a program would actually do what they said it would. The new version also exhorts developers to explicitly evaluate their work to identify potentially harmful side effects or potential for misuse.

Another example has to do with human interaction. In 1992, most software was being developed by trained programmers to run operating systems, databases, and other basic computing functions. Today, many applications rely on user interfaces to interact directly with a potentially vast number of people. The updated code of ethics includes more detailed considerations about the needs and sensitivities of very diverse potential users–including discussing discrimination, exclusion, and harassment.

More and more software is being developed to run with little or no input or human understanding, producing analytical results to guide decision making, such as when to approve bank loans. The outputs can have completely unintended social effects, skewed against whole classes of people–as in recent cases where data-mining predictions of who would default on a loan showed biases against people who seek longer-term loans or live in particular areas. There are also the dangers of what are called “false positives,” when a computer links two things that shouldn’t be connected–as when facial-recognition software recently matched members of Congress to criminals’ mug shots. The revised code exhorts technologists to take special care to avoid creating systems with the potential to oppress or disenfranchise whole groups of people.

[Photo: Hero Images/Getty Images]

Living ethics in technology

The code was revised over the course of more than two years, including ACM members and people outside the organization and even outside the computing and technological professions. All of these perspectives made the code better. For example, a government-employed weapons designer asked whether that job inherently required violating the code; the wording was changed to clarify that systems must be “consistent with the public good.”

Now that the code is out, there’s more to do. ACM has created a repository for case studies, showing how ethical thinking and the guidelines can be applied in a variety of real-world situations. The group’s “Ask An Ethicist” blog and video series invites the public to submit scenarios or quandaries as they arise in practice. Word is also underway to develop teaching modules so that concepts can be integrated into computing education from primary school through university.

Feedback has been overwhelmingly positive. My personal favorite was the comment from a young programmer after reading the code: “Now I know what to tell my boss if he asks me to do something like that again.”

The ACM Code of Ethics and Professional Conduct begins with the statement “Computing professionals’ actions change the world.” We don’t know if our code will last as long as the Hippocratic oath. But it highlights how important it is that the global computing community understands the impact our work has–and takes seriously our obligation to the public good.

Cherri M. Pancake is Professor Emeritus of Electrical Engineering & Computer Science at Oregon State University. This post originally appeared on The Conversation.

          I Know First Forex Forecasts Featured In EuroMoney Article      Cache   Translate Page   Web Page Cache   

On August 2, Euromoney posted an article about how machine learning use grows, but lags in HFT in foreign exchange trading. In it, they explain how demand for machine learning use has grown in the world of high frequency currency trading. To find out more, they spoke to I Know First CEO Yaron Golgher who […]

The post I Know First Forex Forecasts Featured In EuroMoney Article appeared first on Stock Forecast Based On a Predictive Algorithm | I Know First |.


          MACHINE LEARNING ENGINEER FOR SPEECH RELATED APPLICATIONS - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Engineer for Speech related Applications (6 months contract)....
From Huawei Canada - Mon, 18 Jun 2018 23:46:16 GMT - View all Montréal, QC jobs
          MACHINE LEARNING INTERN FOR SPEECH RELATED APPLICATIONS - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Intern for Speech related Applications....
From Huawei Canada - Mon, 18 Jun 2018 17:50:57 GMT - View all Montréal, QC jobs
          MACHINE LEARNING HARDWARE RESEARCHER OR DEVELOPER - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Hardware Researcher or Developer....
From Huawei Canada - Wed, 06 Jun 2018 23:47:32 GMT - View all Montréal, QC jobs
          Machine Learning Software Developer - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. ML Software developer....
From Huawei Canada - Wed, 06 Jun 2018 23:47:31 GMT - View all Montréal, QC jobs
          How AI will Reinvent the Market Research Industry      Cache   Translate Page   Web Page Cache   
What kind of opportunities will AI bring to market research? Which tasks and activities are likely to be “outsourced” to machine learning in the coming years?

Qualtrics surveyed 250 verified market research decision makers to understand how they think AI will change the industry, and whether that change is creative or destructive.

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          New Feature: Short Query Acceleration is automatically enabled      Cache   Translate Page   Web Page Cache   
Amazon Redshift short query acceleration (SQA) is now enabled by default to speed up execution of short-running queries such as reports, dashboards, and interactive analysis. SQA uses machine learning to provide higher performance, faster results, and...
          Aug 15, 2018: Jupyter Notebooks at Snell Library      Cache   Translate Page   Web Page Cache   

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

This session will introduce you to Jupyter Notebooks and we will spend time installing Jupyter Notebooks locally.

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          Best Machine Learning Training Institute in BTM Bangalore-Ascent      Cache   Translate Page   Web Page Cache   
Ascent to learn Technology is one of the best Machine Learning Training Institutes in Bangalore. We are offering best Placement Assistance for ML Training in Bangalore.ML integral for making sense of large volumes of data.Call for free demo today -9182346740 or visit us our website.
          Schwarzenegger Shows Gabriel Luna How to Get Pumped for Terminator 6      Cache   Translate Page   Web Page Cache   
Arnold Schwarzenegger is teaching machine learning to Gabriel Luna for Terminator 6.

          The Defense Department has produced the first tools for catching deepfakes      Cache   Translate Page   Web Page Cache   

Deepfakes, as they are known, are videos that use machine learning to superimpose one person’s face into the video of another person. It has been used to make fake pornographic videos of celebrities, and with the right editing, it can be entirely convincing.

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          Product Manager, Marketplace Growth - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sat, 14 Jul 2018 06:23:30 GMT - View all New York, NY jobs
          QA Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Fri, 08 Jun 2018 16:35:13 GMT - View all New York, NY jobs
          Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Tue, 05 Jun 2018 16:15:49 GMT - View all New York, NY jobs
          Back End Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sun, 03 Jun 2018 06:21:49 GMT - View all New York, NY jobs
          UI Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sun, 27 May 2018 20:27:03 GMT - View all New York, NY jobs
          AI Conversation Designer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sat, 14 Apr 2018 06:15:32 GMT - View all New York, NY jobs
          Software Development Engineer - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
2 years experience working on machine learning based models. The engineer will play a pivotal role in the expansion of pricing software, with the mission to...
From Amazon.com - Fri, 27 Jul 2018 19:19:19 GMT - View all Seattle, WA jobs
          Technical Program Manager, Links Machine Learning - Google - Seattle, WA      Cache   Translate Page   Web Page Cache   
You plan requirements with internal customers and usher projects through the entire project lifecycle. We build the technologies that transform the way we think...
From Google - Thu, 26 Jul 2018 08:23:26 GMT - View all Seattle, WA jobs
          Chemical Engineers Simplify Models Via AI       Cache   Translate Page   Web Page Cache   

Nikolaos Sahinidis

Researchers in Carnegie Mellon University’s Department of Chemical Engineering are using a novel machine learning approach, called ALAMO, to build simple, but accurate models for applications that can be used to make sense of massive amounts of data quickly.

“We don't just use algorithms that others develop,” said Nikolaos Sahinidis, the John E. Swearingen Professor of Chemical Engineering, developer of ALAMO and a CMU alumnus. “In our group, we also develop the algorithms ourselves, and then we apply them to many application domains, both within and outside of process systems engineering.”

Process systems engineering involves making decisions about chemical processes — from designing molecules to designing entire supply chains. In all of these domains there are decision-making problems in which algorithms are useful for optimizing these processes.

While deep neural networks provide accurate models, these models are very complex. Leveraging mathematical optimization techniques, ALAMO was developed as a new methodology to simply and accurately represent complex processes and account for physical constraints.

“What we started looking at back seven years ago was the modeling and optimization of very complex processes for which we don't have analytical models,” Sahinidis said. “So then the question was, ‘can we use data to build mathematical models that we can then use to analyze and optimize these processes?’”

To create these models, the ALAMO methodology uses a small set of experimental or simulation data and builds models that are as simple as possible. In the development process the team has found how to enforce physical constraints of processes in the modeling process.

A number of students in Sahinidis’ group are applying the ALAMO methodology to multiple chemical engineering problems.

Fifth-year Ph.D. student Zachary Wilson is using the ALAMO method to work in reactions engineering. Wilson uses the ALAMO approach to create models that can predict what reactions or reaction mechanisms are occurring inside a chemical reactor, based on process data. In many problems, such as in computer vision and other problems that computer scientists tackle, the main goal of a model is to generalize and predict well. Understanding and interpreting the inner workings of the model often becomes a secondary priority. But in engineering, the parameters that researchers need to estimate are imperative, often having physical meaning.

“We’ve taken the integer programming methodology in ALAMO, which discreetly considers sub-models, and have applied it to these engineering domains,” Wilson said.

Another application is in thermodynamics. Third-year Ph.D. student Marissa Engle is extending the ALAMO approach to incorporate all of the datasets measuring different properties of the same fluid, creating one big picture to characterize its thermodynamic properties. Using data on pressure, volume, temperature, heat capacities and speed of sound, Engle is developing machine learning techniques to find one optimized equation.

“The problem with these equations is that they get very complex,” Engle said. “Using an ALAMO-like approach, we can suggest basis functions and limit how many terms are being used. We want to improve on these empirical equations so that they are simple, but accurate in the regions where new technologies are starting to push into areas where the thermodynamics get complicated, so we can accurately represent them and control them.”

Artificial intelligence and machine learning are providing new avenues for scientists and engineers to do their work better. But not all types of machine learning work for every problem. ALAMO is one example of how engineers are leveraging these techniques in order to accurately solve the problems that face engineers of every discipline.

“In some cases you can model from first principles,” Sahinidis said. “If the problem is too complex or too modern for first principles, then that's where we see the potential usefulness of machine learning.”


          Product Manager, Marketplace Growth - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sat, 14 Jul 2018 06:23:30 GMT - View all New York, NY jobs
          QA Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Fri, 08 Jun 2018 16:35:13 GMT - View all New York, NY jobs
          Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Tue, 05 Jun 2018 16:15:49 GMT - View all New York, NY jobs
          Back End Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sun, 03 Jun 2018 06:21:49 GMT - View all New York, NY jobs
          UI Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sun, 27 May 2018 20:27:03 GMT - View all New York, NY jobs
          AI Conversation Designer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sat, 14 Apr 2018 06:15:32 GMT - View all New York, NY jobs
          UI Developer/Designer - Cerebri AI - Toronto, ON      Cache   Translate Page   Web Page Cache   
Cerebri AI, a venture-backed pioneer in artificial intelligence and machine learning, is the creator of Cerebri Values™, the industry’s first universal measure...
From Indeed - Thu, 09 Aug 2018 23:28:23 GMT - View all Toronto, ON jobs
          Why Automated Feature Engineering Will Change the Way You Do Machine Learning      Cache   Translate Page   Web Page Cache   
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          BrandPost: CIO Interview with Michael S. Carlin, Vice President Business Technology Solutions/Global CIO at AbbVie      Cache   Translate Page   Web Page Cache   

What are some of the major technological initiatives under way at AbbVie?

Since our separation from Abbott, we’ve spent time setting the technical foundation for AbbVie.  The burning platform for us was clear – get an infrastructure and application environment set up that would allow us to operate as an independent company.  Now looking back, that monumental initiative seems small compared to the activities we now have going on as we partner with all of our business areas.  The underlying foundation of recent technological initiatives is data and information.  In IT we are establishing core capabilities and platforms in data, analytics, robots and machine learning to help support critical business initiatives. 

To read this article in full, please click here


          On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization. (arXiv:1808.02941v1 [cs.LG])      Cache   Translate Page   Web Page Cache   

Authors: Xiangyi Chen, Sijia Liu, Ruoyu Sun, Mingyi Hong

This paper studies a class of adaptive gradient based momentum algorithms that update the search directions and learning rates simultaneously using past gradients. This class, which we refer to as the "Adam-type", includes the popular algorithms such as the Adam, AMSGrad and AdaGrad. Despite their popularity in training deep neural networks, the convergence of these algorithms for solving nonconvex problems remains an open question. This paper provides a set of mild sufficient conditions that guarantee the convergence for the Adam-type methods. We prove that under our derived conditions, these methods can achieve the convergence rate of order $O(\log{T}/\sqrt{T})$ for nonconvex stochastic optimization. We show the conditions are essential in the sense that violating them may make the algorithm diverge. Moreover, we propose and analyze a class of (deterministic) incremental adaptive gradient algorithms, which has the same $O(\log{T}/\sqrt{T})$ convergence rate. Our study could also be extended to a broader class of adaptive gradient methods in machine learning and optimization.


          Auto-Scaling Network Resources using Machine Learning to Improve QoS and Reduce Cost. (arXiv:1808.02975v1 [cs.NI])      Cache   Translate Page   Web Page Cache   

Authors: Sabidur Rahman, Tanjila Ahmed, Minh Huynh, Massimo Tornatore, Biswanath Mukherjee

Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Hence, auto-scaling (of resources without human intervention) has been receiving attention. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto-scaling, our study explores how the properties (e.g., start-up time) of underlying virtualization technology impacts Quality of Service (QoS) and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier using real data collected from a private ISP. We report in-depth analysis of the learning process (learning-curve analysis), feature ranking (feature selection, Principal Component Analysis (PCA), etc.), impact of different sets of features, training time, and testing time. Our results show how the proposed methods improve QoS and reduce operational cost for network owners. We also demonstrate a practical use-case example (Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to show that our ML methods save significant cost for network service leasers.


          Object Detection in Satellite Imagery using 2-Step Convolutional Neural Networks. (arXiv:1808.02996v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Hiroki Miyamoto, Kazuki Uehara, Masahiro Murakawa, Hidenori Sakanashi, Hirokazu Nosato, Toru Kouyama, Ryosuke Nakamura

This paper presents an efficient object detection method from satellite imagery. Among a number of machine learning algorithms, we proposed a combination of two convolutional neural networks (CNN) aimed at high precision and high recall, respectively. We validated our models using golf courses as target objects. The proposed deep learning method demonstrated higher accuracy than previous object identification methods.


          A Survey on Sentiment and Emotion Analysis for Computational Literary Studies. (arXiv:1808.03137v1 [cs.CL])      Cache   Translate Page   Web Page Cache   

Authors: Evgeny Kim, Roman Klinger

Emotions have often been a crucial part of compelling narratives: literature tells about people with goals, desires, passions, and intentions. In the past, classical literary studies usually scrutinized the affective dimension of literature within the framework of hermeneutics. However, with emergence of the research field known as Digital Humanities (DH) some studies of emotions in literary context have taken a computational turn. Given the fact that DH is still being formed as a science, this direction of research can be rendered relatively new. At the same time, the research in sentiment analysis started in computational linguistic almost two decades ago and is nowadays an established field that has dedicated workshops and tracks in the main computational linguistics conferences. This leads us to the question of what are the commonalities and discrepancies between sentiment analysis research in computational linguistics and digital humanities? In this survey, we offer an overview of the existing body of research on sentiment and emotion analysis as applied to literature. We precede the main part of the survey with a short introduction to natural language processing and machine learning, psychological models of emotions, and provide an overview of existing approaches to sentiment and emotion analysis in computational linguistics. The papers presented in this survey are either coming directly from DH or computational linguistics venues and are limited to sentiment and emotion analysis as applied to literary text.


          Building a Kannada POS Tagger Using Machine Learning and Neural Network Models. (arXiv:1808.03175v1 [cs.CL])      Cache   Translate Page   Web Page Cache   

Authors: Ketan Kumar Todi, Pruthwik Mishra, Dipti Misra Sharma

POS Tagging serves as a preliminary task for many NLP applications. Kannada is a relatively poor Indian language with very limited number of quality NLP tools available for use. An accurate and reliable POS Tagger is essential for many NLP tasks like shallow parsing, dependency parsing, sentiment analysis, named entity recognition. We present a statistical POS tagger for Kannada using different machine learning and neural network models. Our Kannada POS tagger outperforms the state-of-the-art Kannada POS tagger by 6%. Our contribution in this paper is three folds - building a generic POS Tagger, comparing the performances of different modeling techniques, exploring the use of character and word embeddings together for Kannada POS Tagging.


          Data-driven polynomial chaos expansion for machine learning regression. (arXiv:1808.03216v1 [stat.ML])      Cache   Translate Page   Web Page Cache   

Authors: E. Torre, S. Marelli, P. Embrechts, B. Sudret

We present a regression technique for data driven problems based on polynomial chaos expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ), where it is typically used to replace a runnable but expensive computational model subject to random inputs with an inexpensive-to-evaluate polynomial function. The metamodel obtained enables a reliable estimation of the statistics of the output, provided that a suitable probabilistic model of the input is available.

In classical machine learning (ML) regression settings, however, the system is only known through observations of its inputs and output, and the interest lies in obtaining accurate pointwise predictions of the latter. Here, we show that a PCE metamodel purely trained on data can yield pointwise predictions whose accuracy is comparable to that of other ML regression models, such as neural networks and support vector machines. The comparisons are performed on benchmark datasets available from the literature. The methodology also enables the quantification of the output uncertainties and is robust to noise. Furthermore, it enjoys additional desirable properties, such as good performance for small training sets and simplicity of construction, with only little parameter tuning required. In the presence of statistically dependent inputs, we investigate two ways to build the PCE, and show through simulations that one approach is superior to the other in the stated settings.


          OBOE: Collaborative Filtering for AutoML Initialization. (arXiv:1808.03233v1 [cs.LG])      Cache   Translate Page   Web Page Cache   

Authors: Chengrun Yang, Yuji Akimoto, Dae Won Kim, Madeleine Udell

Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in machine learning. The number of machine learning applications is growing much faster than the number of machine learning experts, hence we see an increasing demand for efficient automation of learning processes. Here, we introduce OBOE, an algorithm for time-constrained model selection and hyperparameter tuning. Taking advantage of similarity between datasets, OBOE finds promising algorithm and hyperparameter configurations through collaborative filtering. Our system explores these models under time constraints, so that rapid initializations can be provided to warm-start more fine-grained optimization methods. One novel aspect of our approach is a new heuristic for active learning in time-constrained matrix completion based on optimal experiment design. Our experiments demonstrate that OBOE delivers state-of-the-art performance faster than competing approaches on a test bed of supervised learning problems.


          Variational inference for the multi-armed contextual bandit. (arXiv:1709.03163v2 [stat.ML] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Iñigo Urteaga, Chris H. Wiggins

In many biomedical, science, and engineering problems, one must sequentially decide which action to take next so as to maximize rewards. One general class of algorithms for optimizing interactions with the world, while simultaneously learning how the world operates, is the multi-armed bandit setting and, in particular, the contextual bandit case. In this setting, for each executed action, one observes rewards that are dependent on a given 'context', available at each interaction with the world. The Thompson sampling algorithm has recently been shown to enjoy provable optimality properties for this set of problems, and to perform well in real-world settings. It facilitates generative and interpretable modeling of the problem at hand. Nevertheless, the design and complexity of the model limit its application, since one must both sample from the distributions modeled and calculate their expected rewards. We here show how these limitations can be overcome using variational inference to approximate complex models, applying to the reinforcement learning case advances developed for the inference case in the machine learning community over the past two decades. We consider contextual multi-armed bandit applications where the true reward distribution is unknown and complex, which we approximate with a mixture model whose parameters are inferred via variational inference. We show how the proposed variational Thompson sampling approach is accurate in approximating the true distribution, and attains reduced regrets even with complex reward distributions. The proposed algorithm is valuable for practical scenarios where restrictive modeling assumptions are undesirable.


          Principal Market Validation Specialist - PTC - Needham, MA      Cache   Translate Page   Web Page Cache   
Advance knowledge and experience with Machine Learning / Data Science / Analytics. Customer Satisfaction focus, both internal and external, with strong...
From PTC - Wed, 16 May 2018 14:29:21 GMT - View all Needham, MA jobs
          Así crea refranes un ordenador: "Cuando la muerte venga no tendrá ovejas"      Cache   Translate Page   Web Page Cache   

Ronda de refranes. Pensemos en tres de esas frases solemnes que llevan entre nosotros siglos: un buen yunque no es el que más ruido hace; cría cuervos y te sacarán los ojos; no por mucho madrugar amanece más temprano. Pues bien, uno de estos refranes no proviene de la tradición española.

Si las máquinas son capaces de reconocer caras o de 'escribir' poemas, ¿por qué no un refrán que suene a verdad de generaciones pasadas? Janelle Shane es una investigadora y creativa estadounidense conocida por sus trabajos con redes neuronales. Lo hace siempre con un toque de humor y extravagancia. Por ejemplo, creó un robot que ideaba frases para cortejar a alguien, con resultado estrambótico, como "si me dieran una rosa cada vez que pienso en ti, tengo un precio ajustado" o "tengo que darte un libro, porque eres la única cosa en tus ojos". También ha creado disfraces para Halloween o nombres de colores: verde azúcar, rosa fastidioso…

Incluso, sus redes neuronales se han atrevido a crear nombres de grupos de heavy metal, como Arena Inhumana, que según la inteligencia artificial haría death metal melódico desde Rusia, o Cielo Clónico Negro, cuyo black metal nos llegaría desde Grecia. "Una de las cosas que me gustan del machine learning es lo impredecible que puede ser. Puedes sorprenderte por resultados que el ordenador se inventa y no sabes en absoluto cómo lo hace, pero de alguna manera son mejores o diferentes a lo que una inteligencia humana pudiera haber inventado", cuenta Shane a HojadeRouter.com.

Así son los refranes de una máquina

Sin embargo, nada tan divertido como los proverbios. En esta ocasión, recibió la ayuda de Anthony Mandelli, un joven coleccionista de refranes clásicos que montó la base de datos con la que la inteligencia artificial, llamada char-rnn, dio a luz sus creaciones. En total, 2000 dichos en inglés. El resultado fueron secuencias de palabras que se pueden leer y entender (tienen su verbo, sus artículos determinantes, sus complementos…), pero sin sentido o significado, que los hablantes tenemos que dar ahora. 

El listado resultante suena a anciano, a algo que ha transmitido la tradición oral. El favorito de Shane es Death when it comes will have no sheep ("cuando la muerte venga no tendrá ovejas"). Pero hay muchos más: A good anvil does not make the most noise ("una buena excusa es tan buena como un descanso"), a good face is a letter to get out of the fire ("una buena cara es una carta para salir del fuego") o there is no smoke without the best sin ("no hay humo sin el mejor pecado"). Junto a ellos, a good anvil does not make the most noise ("un buen yunque no es el que más ruido hace")" o a good excuse is as good as a rest ("una buena excusa es tan buena como un descanso").

El buey (ox en inglés) y el zorro (fox) son dos animales que se repiten mucho en los refranes de Shane. La artista cree que, al ser común la secuencia -ox en los refranes de la base de datos, la máquina pudo entender que eran importantes para hacer combinaciones. Así, tenemos an ox is not fill when he will eat forever ("un buey no está lleno cuando comerá para siempre") o a fox smells it better than a fool’s for a day ("un zorro lo huele mejor que un tonto por un día").

Aunque cueste encontrar sentido a muchas combinaciones de palabras (¿valdría como refrán "un tonto en una taza de té es un silencio por una aguja en la venta"?), parece que la máquina entendió el armazón exterior que tienen muchos refranes, pues la mayoría cumplen una lógica de "si haces A, entonces B" o "la C es un/a D". "Se podría decir que la red entiende un poco la estructura de los refranes", explica Mandelli a HojadeRouter.com, que asegura además haber incluido en su vida diaria a good wine makes the best sermon ("un buen vino hace el mejor sermón"), an ox is never known till needed ("no conoces a un buey hasta que lo necesitas") y el ya citado "cuando la muerte venga no tendrá ovejas".

El del vino y los sermones lo usa cuando está tomando algo con amigos: "Nunca me preguntan por sus orígenes, ya que suena como un refrán legítimo y funciona por contexto cuando brindas antes de beber". El del buey le parece muy profundo: para él significa que "no sabemos qué es necesario para nuestras vidas hasta que se vuelve evidente que está faltando". Por eso es su favorito.

Mendelli no es es el único que trata de buscar significado a los refranes. Con Shane han contactado otras personas que les pretenden dar aplicaciones: "Quieren usarlos específicamente para escritura de ficción; para fantasía, en concreto", porque, según le dicen, necesitan algunos proverbios que suenen antiguos. Además, las frases ya decoran pósteres motivacionales paródicos, como si de citas célebres se tratara.

La inteligencia artificial chat-rnn ya había creado en el pasado recetas de cocina ("nada deliciosas", recuerda la investigadora). Incluso se atrevió a parir pokémones con la misma red que luego usaría para los refranes. Los resultados fueron un disparate (había una tortuga con una especie de lago o estaque en el caparazón y un pájaro con una bolsa de papel en la cabeza), pero seguro que alguno podría pertenecer al ya largo listado oficial de criaturas de Nintendo. Otras redes se han usado para escribir fan fiction de Harry Potter y diseñar nombres de cervezas.

Shane no descarta seguir trabajando en los refranes, mientras que Mandelli se formaba en inteligencia artificial y machine learning para acompañarla. Para él, el trabajo con redes neuronales contribuye a mejorar nuestro entendimiento de las posibilidades de los ordenadores para interpretar grandes cantidades de datos y realizar con ellos "tareas complejas". Ahora solo queda que los humanos demos significado a todos esos nuevos refranes.

--------------

Las imágenes son propiedad del Portland Guinea Pig Rescue y Steve Jurvetson


          How Mature Are Your Cyber Defender Strategies?       Cache   Translate Page   Web Page Cache   

Our latest research examines real-world vulnerability assessment practices at 2,100 organizations to understand how defenders are approaching this crucial step in cyber hygiene.

For our latest research study, "Cyber Defender Strategies: What Your Vulnerability Assessment Practices Reveal," we explore how organizations are practicing vulnerability assessment (VA), and what these practices teach us about cyber maturity.

Our curiosity was piqued by our previous study, “Quantifying the Attacker's First-Mover Advantage,” which found it takes attackers a median of five days to gain access to a functioning exploit. In contrast, we learned, defenders take a median 12 days to assess for a vulnerability. The difference between the two results is a median seven-day window of opportunity for an attacker to strike, during which a defender isn’t even aware they’re vulnerable. This led us to consider how defenders are performing in the all-important discovery and assess phases of the Cyber Exposure Lifecycle.

Our Cyber Defender Strategies Report specifically focuses on key performance indicators (KPIs) associated with the Discover and Assess stages of the five-phase Cyber Exposure Lifecycle. During the first phase – Discover – assets are identified and mapped for visibility across any computing environment. The second phase – Assess – involves understanding the state of all assets, including vulnerabilities, misconfigurations, and other health indicators. While these are only two phases of a longer process, together they decisively determine the scope and pace of subsequent phases, such as prioritization and remediation.

We wanted to learn more about how end users are conducting vulnerability assessment in the real world, what this tells us about their overall maturity level, and how this varies based on demographics.

Cyber Defender Strategies: Understanding Vulnerability Assessment KPIs

For our Cyber Defender Strategies Report, we analyzed five key performance indicators (KPIs) based on real-world end user vulnerability assessment behavior. These KPIs correlate to four VA maturity styles: Diligent, Investigative, Surveying and Minimalist.

We discovered about half (48%) of the enterprises included in the data set are practicing very mature (exhibiting a Diligent or Investigative style) vulnerability assessment strategies. However, just over half (52%) exhibit moderate- to low-level VA maturity (exhibiting a Surveying or Minimalist style). We’ll tell you more about what all this means in a moment. First, let’s take a quick look at the methodology we applied to arrive at these results.

To identify our four VA Styles, we trained a machine learning algorithm called archetypal analysis (AA) with anonymized scan telemetry data from more than 2,100 individual organizations in 66 countries. We analyzed just over 300,000 scans during a three-month period from March to May 2018. We identified a number of idealized VA behaviors within this data set and assigned organizations to groups defined by the archetype to which they most closely relate. The vulnerability assessment characteristics for each defender style are described in the table below.

Four Vulnerability Assessment Styles: What They Reveal

VA Style

VA Maturity Level

Characteristics

Diligent

High

The Diligent conducts comprehensive vulnerability assessment, tailoring scans as required by use case, but only authenticates selectively.

Investigative

Medium to High

The Investigator executes vulnerability assessments with a high level of maturity, but only assesses selective assets.

Surveying

Low to Medium

The Surveyor conducts frequent broad-scope vulnerability assessments, but focuses primarily on remote and network-facing vulnerabilities.

Minimalist

Low

The Minimalist executes bare minimum vulnerability assessments, typically as required by compliance mandates.

Source: Tenable Cyber Defender Strategies Report, August 2018.

Here’s what we learned about each vulnerability assessment style:

  • Only five percent of enterprises follow the Diligent style, displaying a high level of maturity across the majority of KPIs. Diligent followers conduct frequent vulnerability assessments with comprehensive asset coverage, as well as targeted, customized assessments for different asset groups and business units.
  • Forty-three percent follow the Investigative style, indicating a medium to high level of maturity. These organizations display a good scan cadence, leverage targeted scan templates, and authenticate most of their assets.
  • Nineteen percent of enterprises follow the Surveying style, placing them at a low to medium maturity level. Surveyors conduct broad-scope assessments, but with little authentication and little customization of scan templates.
  • Thirty-three percent of enterprises are at a low maturity, following the Minimalist style and conducting only limited assessments of selected assets.

Tenable Cyber Defender Strategies Report: Key Findings

Tenable Cyber Defender Strategies Report Key Findings August 2018

Source: Tenable, Cyber Defender Strategies Report, August 2018.

Vulnerability Assessment Matters at Every Maturity Level

By now, you’re probably forming an opinion about how your vulnerability assessment strategies stack up. If your organization seems to be leaning toward the lower-maturity Surveying or Minimalist styles, fear not. There is nothing wrong with being at a low maturity. What is wrong is choosing to remain there.

If you’re a later adopter, it means you have more work to do to catch up. It also means you can learn from the mistakes and experiences of early adopters. Rather than having your organization serve as a testing bed for untried, novel and immature solutions, you’ll benefit from the availability of tried-and-tested offerings. There’s also an existing pool of expertise you can tap into, rather than trying to develop your strategies from scratch. Skipping the experimentation phase, you are poised to jump right into optimization and innovation.

And, if you identify with the most mature vulnerability assessment strategies highlighted here, it doesn’t mean you can take a lengthy sabbatical. Even the most sophisticated defenders know their work is never done.

The ultimate objective – regardless of which style most closely aligns to your own – is to always keep evolving toward a higher level of maturity. We know it isn’t easy. Cybersecurity professionals are hauling a lot of historical baggage. You’re dealing with legacy technology and dependencies alongside the complexities of managing a growing portfolio of continuously evolving and emerging technologies. Meanwhile, the threat environment has escalated noticeably over the past few years. And all of this is happening against a backdrop of competitive business pressures.

When it comes to cybersecurity, we have hit escape velocity, and most organizations now get it.

Our Cyber Defender Strategies Report provides recommendations for each VA style, to help you advance to the next maturity level. We also explore how these four VA styles are distributed across major industry verticals and by organization size, so you can compare yourself with your peers. Click to download the full report.

Learn More:


          Update: Kivi Doc (Medical)      Cache   Translate Page   Web Page Cache   

Kivi Doc 3.3.0


Device: iOS Universal
Category: Medical
Price: Free, Version: 3.2.3 -> 3.3.0 (iTunes)

Description:

Is your Clinic ready to become Gen-Next One? With introduction of Artificial Intelligence and Machine Learning in field of medicines, most of the clinics are unaware of the resources that integrate this technological trend. KiviDoc App is a leading healthcare marketplace dedicated to improve patient doctor engagement . It is synced with Kivihealth platform-a one stop for Healthcare Digitization. Every task is automated- from writing prescription to sending Messages. Doctors can view all the appointments, search and view the patients to update their Digital Medical reports. App is a doctor’s partner-keeps them updated about their clinic and patients on the go.

Features-

1)View and manage all the appointments and create new ones, add all the medical reports for further followups.

2)Get a easy and handy access to medical records of a patient,no need to fear about losing the patient records.

3)Easy access to your patient database, leading to better patient interaction. Send messages to patients about their appointments, obtain feedback and express your gratitude to patients on birthdays.

4)Access your practice offline even when phone is not connected to the Internet. Easily sync your practice data between cloud storage and your mobile. Manage multiple practices by using the App.

5)Prescribe medicines and send patients messages mentioning the dosage and duration of the course.

Specialties Delivered to-General Practitioners, Dentist, Ophthalmologist, Dermatologist, Radiologist, Gynaecologist, Diabetologist, Nephrologist, Heptalogist,etc.

Available in Cities- Ahmedabad, Mumbai, Delhi, Jaipur, Surat, Bikaner, Vadodara, Pune, Jammu, Valsad, Ludhiana, Daman, Hyderabad, etc.

Love using our KiviDoc App? Help us serve you better - Review the App. For any queries mail us at info@kivihealth.com.

What's New

Book multiple appointments support.
Add doctor referral and patient referral while adding new patient.
cancel appointment bug fixed.
Search patient bug fixed.

Kivi Doc


          Loan Risk Analysis with XGBoost and Databricks Runtime for Machine Learning      Cache   Translate Page   Web Page Cache   

For companies that make money off of interest on loans held by their customer, it’s always about increasing the bottom line. Being able to assess the risk of loan applications can save a lender the cost of holding too many risky assets. It is the data scientist’s job to run analysis on your customer data […]

The post Loan Risk Analysis with XGBoost and Databricks Runtime for Machine Learning appeared first on Databricks.


          Factualities for Wednesday, August 8, 2018      Cache   Translate Page   Web Page Cache   
Beyond Search noted these factualities in the last week. Believe ‘em or not: TGI Fridays, The home of the loaded baked potato, allegedly doubled business and grew “engagement” by 500 percent with… artificial intelligence. Source: Venture Beat Machine learning is like medieval alchemy. Source: Guardian According to Internet Live Stats, Google conducts 40,000 searches per […]
          Data Architect - Remote West coast - Insight Enterprises, Inc. - Dallas, TX      Cache   Translate Page   Web Page Cache   
R, Azure Machine Learning. 2017 Arizona’s Most Admired Companies (AZ Business Magazine), 2016 Best Places to Work (Phoenix Business Journal)....
From Insight - Mon, 14 May 2018 23:57:10 GMT - View all Dallas, TX jobs
          Senior Manager, Software Engineering - DELL - Austin, TX      Cache   Translate Page   Web Page Cache   
Experience with machine learning and artificial intelligence. Learn more about Diversity and Inclusion at Dell here....
From Dell - Wed, 18 Jul 2018 11:23:18 GMT - View all Austin, TX jobs
          Director, Software Engineering - DELL - Austin, TX      Cache   Translate Page   Web Page Cache   
Experience with machine learning and artificial intelligence. Learn more about Diversity and Inclusion at Dell here....
From Dell - Sat, 07 Jul 2018 11:22:08 GMT - View all Austin, TX jobs
          Software Development Principal Engineer – Data Scientist - DELL - Austin, TX      Cache   Translate Page   Web Page Cache   
Learn more about Diversity and Inclusion at Dell here. Selecting features, building and optimizing classifiers using machine learning techniques....
From Dell - Sat, 07 Jul 2018 11:22:08 GMT - View all Austin, TX jobs
          Cloud Solution Architect - Microsoft - Philadelphia, PA      Cache   Translate Page   Web Page Cache   
Machine Learning (SAS, R, Python). Problem-solving mentality leveraging internal and/or external resources....
From Microsoft - Tue, 17 Apr 2018 18:34:17 GMT - View all Philadelphia, PA jobs
          Distilled News      Cache   Translate Page   Web Page Cache   
Ultimate guide to handle Big Datasets for Machine Learning using Dask (in Python) Have you ever tried working with a …

Continue reading


          Principal Market Validation Specialist - PTC - Needham, MA      Cache   Translate Page   Web Page Cache   
Advance knowledge and experience with Machine Learning / Data Science / Analytics. Customer Satisfaction focus, both internal and external, with strong...
From PTC - Wed, 16 May 2018 14:29:21 GMT - View all Needham, MA jobs
          «Терминатор». Арнольд Шварценеггер тренирует Габриэла Луну      Cache   Translate Page   Web Page Cache   
Терминатор
Старый Терминатор тренирует нового. В Instagram у Арнольда Шварценеггера появилось фото, на котором мы видим, как Железный Арни тренирует Габриэла Луну, исполнителя роли Терминатора в новом фильме серии. Получается, что Терминатор тренирует Терминатора: Machine learning. A post shared by Arnold Schwarzenegger (@schwarzenegger) on Aug 9, 2018 at 8:15am PDT Уже давно известно, что Луна сыграет […]
          Artificial Intelligence and Machine Learning      Cache   Translate Page   Web Page Cache   
Participants will learn to develop artificial intelligence (AI) applications to address real-world business problems using tools such as Python, ...
          Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm.      Cache   Translate Page   Web Page Cache   
Related Articles

Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm.

Am J Surg. 2018 Jul 24;:

Authors: Bartz-Kurycki MA, Green C, Anderson KT, Alder AC, Bucher BT, Cina RA, Jamshidi R, Russell RT, Williams RF, Tsao K

Abstract
BACKGROUND: Machine-learning can elucidate complex relationships/provide insight to important variables for large datasets. This study aimed to develop an accurate model to predict neonatal surgical site infections (SSI) using different statistical methods.
METHODS: The 2012-2015 National Surgical Quality Improvement Program-Pediatric for neonates was utilized for development and validations models. The primary outcome was any SSI. Models included different algorithms: full multiple logistic regression (LR), a priori clinical LR, random forest classification (RFC), and a hybrid model (combination of clinical knowledge and significant variables from RF) to maximize predictive power.
RESULTS: 16,842 patients (median age 18 days, IQR 3-58) were included. 542 SSIs (4%) were identified. Agreement was observed for multiple covariates among significant variables between models. Area under the curve for each model was similar (full model 0.65, clinical model 0.67, RF 0.68, hybrid LR 0.67); however, the hybrid model utilized the fewest variables (18).
CONCLUSIONS: The hybrid model had similar predictability as other models with fewer and more clinically relevant variables. Machine-learning algorithms can identify important novel characteristics, which enhance clinical prediction models.
SUMMARY: This study evaluated risk factors associated with neonatal surgical site infection (SSI) utilizing multiple logistic regression and a random forest classifier. Operative time, open surgical technique, and preoperative supplemental nutrition were associated with SSI. A hybrid multiple logistic regression model was developed based on the random forest and clinical knowledge, and predicted neonatal SSI as well as the other models while being more feasible.

PMID: 30078669 [PubMed - as supplied by publisher]


          SQL Server 2017 Machine Learning services with R book      Cache   Translate Page   Web Page Cache   

This blog post is slighty different, since it brings you the tittle of the book , that my dear friend Julie Koesmarno ( blog | twitter ) and I have written in and it was published in March 2018 at Packt Publishing .


SQL Server 2017 Machine Learning services with R book

Book covers the aspect of the R Machine Learning services available in Microsoft SQL Server 2017 (and 2016), how to start, handle and operationalise R code, deploy and manage your predictive models and how to bring the complete solution to your enterprise environment. Exploring the CD/CI, diving into examples supporting RevoScaleR algorithms , bringing closer the data science to database administrators and data analysts.

More specifically, content of the book is following (as noted in table of content):

1: Introduction to R and SQL Server

2: Overview of Microsoft Machine Learning Server and SQL Server

3: Managing Machine Learning Services for SQL Server 2017 and R

4: Data Exploration and Data Visualization

5: RevoScaleR Package

6: Predictive Modeling

7: Operationalizing R Code

8: Deploying, Managing, and Monitoring Database Solutions containing R Code

9: Machine Learning Services with R for DBAs

10: R and SQL Server 2016/2017 Features Extended

My dear friend, co-author and long time SQL Server community dedicated tech and data science lover, Julie and myself, we had great time working on this book, sharing the code, the ideas and collaborating on what was the great end product. Thank you, Julie.

I would also like to thank all the people involved, with their help, expertise, inspirations, people at the Packt Publishing, to Hamish Watson and also a special thanks, to you, Marlon Ribunal ( blog | twitter ), for your reviews and comments in the time of the writing and your review and to you, dear David Wentzel ( website | linkedin ) for your chapter comments and your review .

Finally, thank you Microsoft SQL Server community, SQL friends and SQL family, R community and R Consortium , and the Revolution Analytics community, gather and led by David Smith ( twitter ). Not only did this concept of R in Microsoft SQL Server, but also the intersection of technologies brought together so many beautiful people, minds and ideas, that will in future time help so many business and industries world-wide.

Much appreciated!

Book is available on Amazon or you can get your copy at the Packt .

Happy reading and coding!


          How Siri finds local destinations in your language      Cache   Translate Page   Web Page Cache   
'Accurately recognizing named entities, like small local businesses, has remained a performance bottleneck.' — Siri Speech Recognition Team Personal assistants like Siri have gotten better and better at recognizing what we're saying, at least in general. When it comes to recognizing names, including business names, especially regional names, the challenge has been greater. Apple's Machine Learning Journal describes how the Siri team has been tackling it: Generally, virtual assistants correctly recognize and understand the names of high-profile businesses and chain stores like Starbucks, but have a harder time recognizing the names of the millions of smaller, local POIs that users ask about. In ASR, there's a known performance bottleneck when it comes to accurately recognizing named entities, like small local businesses, in the long tail of a frequency distribution. We decided to improve Siri's ability to recognize names of local POIs by incorporating knowledge of the u...
          Principal Program Manager - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Our internal customers use machine learning models to analyze multi-exabyte datasets. The Big Data team builds solutions that enable customers to tackle...
From Microsoft - Sat, 28 Jul 2018 02:13:20 GMT - View all Redmond, WA jobs
          Senior Software Engineer - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Experience with leveraging machine learning and AI for Analytics. The Big Data Fundamentals team focuses on Engineering systems, Advanced data Analytics /...
From Microsoft - Fri, 27 Apr 2018 19:10:03 GMT - View all Redmond, WA jobs
          Software Development Manager - Core Video Delivery Technologies, Prime Video - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Strong business and technical vision. Experience in machine learning technologies and big data is a plus. We leverage Amazon Web Services (AWS) technologies...
From Amazon.com - Thu, 02 Aug 2018 19:21:25 GMT - View all Seattle, WA jobs
          Solutions Architect - Amazon Web Services - Amazon.com - San Francisco, CA      Cache   Translate Page   Web Page Cache   
DevOps, Big Data, Machine Learning, Serverless computing etc. High level of comfort communicating effectively across internal and external organizations....
From Amazon.com - Thu, 26 Jul 2018 08:17:05 GMT - View all San Francisco, CA jobs
          Machine learning technique reconstructs images passing through a multimode fiber      Cache   Translate Page   Web Page Cache   
Through innovative use of a neural network that mimics image processing by the human brain, a research team reports accurate reconstruction of images transmitted over optical fibers for distances of up to a kilometer.
          Conference Report: Fullstack 2018 London      Cache   Translate Page   Web Page Cache   

I recently attended Fullstack 2018, “The Conference on JavaScript, Node & Internet of Things” with my colleagues from the Canonical Web Team in London. Fullstack attempts to cover the full spectrum of the JS ecosystem – frontend, backend, IoT, machine learning and a number of other topics. While I attended a broad range of talks, […]

The post Conference Report: Fullstack 2018 London appeared first on Ubuntu Blog.


          Apple erklärt, wie Siri den Namen örtlicher Geschäfte und Restaurants besser verstehen kann      Cache   Translate Page   Web Page Cache   
In regelmäßigen Abständen gibt Apple Einblicke, wie man den digitalen Sprachassistenten Siri verbessert. Mit einem neuen Eintrag im Machine Learning Journal widmet sich der Hersteller nun den Möglichkeiten, wie Siri den Namen von örtlichen Geschäften und Restaurants besser verstehen kann. So will Apple Siri verbessern Kurzum: Apple hat angepasste Sprachmodelle entwickelt, die die Erkenntnis zum […]
          How AI will Reinvent the Market Research Industry      Cache   Translate Page   Web Page Cache   
What kind of opportunities will AI bring to market research? Which tasks and activities are likely to be “outsourced” to machine learning in the coming years?

Qualtrics surveyed 250 verified market research decision makers to understand how they think AI will change the industry, and whether that change is creative or destructive.

Request Free!

          CSI-INFRA - SQL Server DBA - CSI Jobs - Charlotte, NC      Cache   Translate Page   Web Page Cache   
Configured 3M MVS database systems. Worked on Power BI tools and machine learning tools to admin and development data ware house internal projects....
From CSI Jobs - Thu, 09 Aug 2018 21:01:53 GMT - View all Charlotte, NC jobs
          Sr. Product Marketing Manager - Automation Anywhere - San Jose, CA      Cache   Translate Page   Web Page Cache   
Experience in artificial intelligence, analytics, machine learning or business process management software especially in the enterprise space is a big plus but...
From Automation Anywhere - Sat, 16 Jun 2018 05:57:32 GMT - View all San Jose, CA jobs
          Director, Product Marketing - Security - Automation Anywhere - San Jose, CA      Cache   Translate Page   Web Page Cache   
Experience in artificial intelligence, analytics, machine learning or business process management software especially in the enterprise space is a big plus but...
From Automation Anywhere - Sat, 16 Jun 2018 05:57:32 GMT - View all San Jose, CA jobs
          Director, Product Marketing - Enterprise - Automation Anywhere - San Jose, CA      Cache   Translate Page   Web Page Cache   
Experience in artificial intelligence, analytics, machine learning or business process management software especially in the enterprise space is a big plus but...
From Automation Anywhere - Sat, 16 Jun 2018 05:57:32 GMT - View all San Jose, CA jobs
          Machine learning technique reconstructs images passing through a multimode fiber      Cache   Translate Page   Web Page Cache   
Through innovative use of a neural network that mimics image processing by the human brain, a research team reports accurate reconstruction of images transmitted over optical fibers for distances of up to a kilometer.
          iOS Developer - PGS SOFTWARE - Rzeszów, podkarpackie      Cache   Translate Page   Web Page Cache   
Augmented Reality, Machine Learning, iBeacons, Top Level Security. Elastyczne godziny pracy....
Od PGS SOFTWARE - Wed, 08 Aug 2018 14:51:19 GMT - Pokaż wszystkie Rzeszów, podkarpackie oferty pracy
          (USA-TX-Plano) Advertising & Analytics - Principal Data Scientist (AdCo)      Cache   Translate Page   Web Page Cache   
The Data Scientist will be responsible for designing and implementing processes and layouts for complex, large- scale data sets used for modeling, data mining, and research purposes. The purpose of this role is to conceptualize, prototype, design, develop and implement large scale big data science solutions in the cloud and on premises, in close collaboration with product development teams, data engineers and cloud enterprise teams. Competencies in implementing common and new machine learning, text mining and other data science driven solutions on cloud based technologies such as AWS are required. The data scientist will be knowledgeable and skilled in the emerging data science trends and must be able to provide technical guidance to the other data scientists in implementing emerging and advanced techniques. The data scientist must also be able to work closely with the product and business teams to conceptualize appropriate data science models and methods that meet the requirements. Key Roles and Responsibilities + Uses known and emerging techniques and methods in data science (including statistical, machine learning, deep learning, text and language analytics and visualization) in big data and cloud based technologies to conceptualize, prototype, design, code, test, validate and tune data science centric solutions to address business and product requirements + Conceptualizes data science enablers required for supporting future product features based on business and product roadmaps, and guides cross functional teams in prototyping and validating these enablers + Mentors and guides other data scientists + Uses a wide range of existing and new data science and machine learning tools and methods as required to solve the problem on hand. Skilled in frameworks and libraries using but not limited to R, python, spark, scala, pig, hive, mllib, mxnet, tensorflow, keras, theanos etc. + Is aware of industry trends an collaborates with the platform and engineering teams to update the data science development stack for competitive advantage + Collaborates with third party data science capability vendors and provides appropriate recommendations to the product development teams + Works in a highly agile environment **Experience** Typically requires 10 or more years experience or PhD in an approved field with a minimum of 6 years of relevant experience. **Education** Preferred Masters of Science in Computer Science, Math or Scientific Computing; Data Analytics, Machine Learning or Business Analyst nanodegree; or equivalent experience.
          (USA-MA-Bedford) Data Scientist - must be software savvy      Cache   Translate Page   Web Page Cache   
**Data Scientist \- must be software savvy** **Description** MITRE is different from most technology companies\. We are a not\-for\-profit corporation chartered to work for the public interest, with no commercial conflicts to influence what we do\. The R&D centers we operate for the government create lasting impact in fields as diverse as cybersecurity, healthcare, aviation, defense, and enterprise transformation\. We're making a difference every day—working for a safer, healthier, and more secure nation and world\. Join the Data Analytics team where you will provide software development, algorithm development, and data analytics \(to include big data analytics, data mining, and data science\) to enable data\-driven decisions and insights\. Experience with analytic techniques and methods \(e\.g\., supervised and unsupervised machine learning, link analysis, and text mining\) as well as software languages is a must\. Software languages and big data technologies needed include: Java, Python, R, C\#, C, SAS, analytic engines, Hadoop, parallelized analytic algorithms, and NoSQL and massively parallel processing databases\. The successful candidate will have the ability to formulate problems, prototype solutions, and to analyze results\. * Formulate data analytic problems * Get and cleanse data * Employ analytic methods and techniques * Develop analytic algorithms * Analyze data **Qualifications** Required Qualifications Must be a US citizen able to obtain and maintain a DoD clearance Completed BS degree in Computer Science, Data Science, or similar technical degree\. New grads must have strong academic record of 3\.0 GPA\. Experience will include: * Hands\-on software development skills \(Java, R, C , C\#, python, JavaScript\) with analytic applications and technologies\. * Capture and cleansing data raw data, data storage and retrieval \(relational and NoSQL\), data analytics and visualization, and cloud\-based technologies\. * Proficiency with the Map Reduce programming model and technologies such as Hadoop, Hive, and Pig is a plus\. \ * \ * \ * \ * Preference given to candidates with active clearances\. **Job** SW Eng, Comp Sci & Mathematics **Primary Location** United States\-Virginia\-McLean **Other Locations** United States\-Massachusetts\-Bedford **This requisition requires a clearance of** Secret **Travel** Yes, 10 % of the Time **Job Posting** Aug 9, 2018, 11:05:43 AM **Req ID:** 00050915
          (USA-VA-McLean) Data Scientist - must be software savvy      Cache   Translate Page   Web Page Cache   
**Data Scientist \- must be software savvy** **Description** MITRE is different from most technology companies\. We are a not\-for\-profit corporation chartered to work for the public interest, with no commercial conflicts to influence what we do\. The R&D centers we operate for the government create lasting impact in fields as diverse as cybersecurity, healthcare, aviation, defense, and enterprise transformation\. We're making a difference every day—working for a safer, healthier, and more secure nation and world\. Join the Data Analytics team where you will provide software development, algorithm development, and data analytics \(to include big data analytics, data mining, and data science\) to enable data\-driven decisions and insights\. Experience with analytic techniques and methods \(e\.g\., supervised and unsupervised machine learning, link analysis, and text mining\) as well as software languages is a must\. Software languages and big data technologies needed include: Java, Python, R, C\#, C, SAS, analytic engines, Hadoop, parallelized analytic algorithms, and NoSQL and massively parallel processing databases\. The successful candidate will have the ability to formulate problems, prototype solutions, and to analyze results\. * Formulate data analytic problems * Get and cleanse data * Employ analytic methods and techniques * Develop analytic algorithms * Analyze data **Qualifications** Required Qualifications Must be a US citizen able to obtain and maintain a DoD clearance Completed BS degree in Computer Science, Data Science, or similar technical degree\. New grads must have strong academic record of 3\.0 GPA\. Experience will include: * Hands\-on software development skills \(Java, R, C , C\#, python, JavaScript\) with analytic applications and technologies\. * Capture and cleansing data raw data, data storage and retrieval \(relational and NoSQL\), data analytics and visualization, and cloud\-based technologies\. * Proficiency with the Map Reduce programming model and technologies such as Hadoop, Hive, and Pig is a plus\. \ * \ * \ * \ * Preference given to candidates with active clearances\. **Job** SW Eng, Comp Sci & Mathematics **Primary Location** United States\-Virginia\-McLean **Other Locations** United States\-Massachusetts\-Bedford **This requisition requires a clearance of** Secret **Travel** Yes, 10 % of the Time **Job Posting** Aug 9, 2018, 11:05:43 AM **Req ID:** 00050915
          (USA-CA-Pleasanton) Data Scientist      Cache   Translate Page   Web Page Cache   
We are looking for a **Data Scientist to be** a key contributor responsible for designing, developing and maintaining aerospace operational models specific to the Panasonic Avionics Corporation product suite including inflight consumer engagement platforms and inflight connectivity systems. **Major Responsibilities include;** **Data Science** + Advance team’s capability to bring vision to life, support roadmap development and prioritization, which may include items like a strategic KPI framework, customer portfolio optimization and planning, strategic site analysis (segments and pathing), measurement and attribution, AI platform evaluation and journey analytics + Continuously innovate by staying abreast of and bringing recommendations on the latest tools and techniques (and evaluate options) associated with consumer personalization, AI/Machine learning, real time decisioning, and digital analytics. + Iterate quickly in an agile development process. + Support projects from start to finish & produce data-driven results with appropriate techniques to answer key business questions + Use machine learning and predictive modeling to develop data driven solutions that drive substantial business value in key PAC product areas. + Program and support analytic solutions to improve and optimize business performance and minimize risk. + Program and support machine learning algorithms for model training and deployment. + Lead junior team members to develop solutions. **KNOWLEDGE/SKILL REQUIREMENTS** + Understands department’s mission and vision and the ability to execute on that vision + Able to define the correct data, analysis and interpretation to achieve complex design and marketing initiatives. + Experience with cloud solutions for products and services. + Experience with designing, building and managing large scale ML and analytics platforms + Proven technical ability with a variety of tools including SQL, Python and R. Commanding knowledge of statistics and or machine learning techniques. Applications in the game industry a plus. + Knowledge of advanced statistical techniques suitable for analysis of highly skewed populations + Proven experience in predictive analytics, segmentation, experimental design and related areas + Experience designing, deploying and maintaining cloud based big data technology stacks (Amazon Redshift experience preferred) + Experience with traditional Business Intelligence relational database modeling, tools and processes + Familiarity with the design and implementation of data telemetry systems **EDUCATION/EXPERIENCE REQUIREMENTS** + BS in Statistics, Operations Research, Economics or similar degree with a focus on statistical methodology + 10 years+ experience in data science and analytics environment; should include experience in consumer/CRM analytics methods, measurement, attribution, test planning and rapid testing, strong knowledge of media analytics and addressable media measurement and testing, digital analytics, some B2B2C experience or knowledge, omni-channel lifecycle marketing orientation + Strong communication and collaboration skills with ability to build consensus and drive cross-functional teams forward to execution against project goals and timelines + Experience with statistical modeling, machine learning, digital analytics, media analytics. + In depth specialization in mathematical analysis methods, predictive modeling, statistical analysis, machine learning, and technologies like Python, R and Hadoop. Or equivalent experience. + Experience with time-series models Bayesian modeling, Generalized Linear Models and/or Limited Dependent Variables + Expertise with R, SAS, Python, Hadoop, DMPs, and digital platforms + Proven ability to design and code new algorithms from scratch\#LI-POST
          AI and Machine Learning: Separating Fact from Fiction      Cache   Translate Page   Web Page Cache   
Are AI and ML really changing the game, or are they just fancy buzzwords?
          Machine Learning Engineer - Technica Corporation - Dulles, VA      Cache   Translate Page   Web Page Cache   
Technica Corporation is seeking a Machine Learning Engineer to support our internal Innovation, Research and Development (IRD) team....
From Technica Corporation - Wed, 11 Jul 2018 06:07:15 GMT - View all Dulles, VA jobs
          Azure HDInsight Interactive Query: Ten tools to analyze big data faster      Cache   Translate Page   Web Page Cache   

Customers use HDInsight Interactive Query (also called Hive LLAP, or Low Latency Analytical Processing) to query data stored in Azure storage & Azure Data Lake Storage in super-fast manner. Interactive query makes it easy for developers and data scientist to work with the big data using BI tools they love the most. HDInsight Interactive Query supports several tools to access big data in easy fashion. In this blog we have listed most popular tools used by our customers:

Microsoft Power BI

Microsoft Power BI Desktop has a native connector to perform direct query against HDInsight Interactive Query cluster. You can explore and visualize the data in interactive manner. To learn more see Visualize Interactive Query Hive data with Power BI in Azure HDInsight and Visualize big data with Power BI in Azure HDInsight .


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Apache Zeppelin

Apache Zeppelin interpreter concept allows any language/data-processing-backend to be plugged into Zeppelin. You can access Interactive Query from Apache Zeppelin using a JDBC interpreter. To learn more please see Use Zeppelin to run Hive queries in Azure HDInsight .


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Visual Studio Code

With HDInsight Tools for VS Code, you can submit interactive queries as well at look at job information in HDInsight interactive query clusters. To learn more please see Use Visual Studio Code for Hive, LLAP or pySpark .


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Visual Studio

Visual Studio integration helps you create and query tables in visual fashion. You can create a Hive tables on top of data stored in Azure Data Lake Storage or Azure Storage. To learn more please see Connect to Azure HDInsight and run Hive queries using Data Lake Tools for Visual Studio .


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Ambari Hive View

Hive View is designed to help you author, optimize, and execute queries. With Hive Views you can:

Browse databases. Write queries or browse query results in full-screen mode, which can be particularly helpful with complex queries or large query results. Manage query execution jobs and history. View existing databases, tables, and their statistics. Create/upload tables and export table DDL to source control. View visual explain plans to learn more about query plan.

To learn more please see Use Hive View with Hadoop in Azure HDInsight .


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Beeline

Beeline is a Hive client that is included on the head nodes of HDInsight cluster. Beeline uses JDBC to connect to HiveServer2, a service hosted on HDInsight cluster. You can also use Beeline to access Hive on HDInsight remotely over the internet. To learn more please see Use Hive with Hadoop in HDInsight with Beeline .


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Hive ODBC

Open Database Connectivity (ODBC) API, a standard for the Hive database management system, enables ODBC compliant applications to interact seamlessly with Hive through a standard interface. Learn more about how HDInsight publishes HDInsight Hive ODBC driver .


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Tableau

Tableau is very popular data visualization tool. Customers can build visualizations by connecting Tableau with HDInsight interactive Query.


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Apache DBeaver

Apache DBeaver is SQL client and a database administration tool. It is free and open-source (ASL). DBeaver use JDBC API to connect with SQL based databases. To learn more, see How to use DBeaver with Azure #HDInsight .


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Excel

Microsoft Excel is the most popular data analysis tool and connecting it with big data is even more interesting for our customers. Azure HDInsight Interactive Query cluster can be integrated with Excel with ODBC connectivity.To learn more, see Connect Excel to Hadoop in Azure HDInsight with the Microsoft Hive ODBC driver .


Azure HDInsight Interactive Query: Ten tools to analyze big data faster
Try HDInsight now

We hope you will take full advantage fast query capabilities of HDInsight Interactive Query using your favorite tools. We are excited to see what you will build with Azure HDInsight. Read this developer guide and follow the quick start guide to learn more about implementing these pipelines and architectures on Azure HDInsight. Stay up-to-date on the latest Azure HDInsight news and features by following us on Twitter #HDInsight and @AzureHDInsight . For questions and feedback, please reach out to AskHDInsight@microsoft.com .

About HDInsight

Azure HDInsight is Microsoft’s premium managed offering for running open source workloads on Azure. Azure HDInsight powers mission critical applications ranging in a wide variety of sectors including, manufacturing, retail education, nonprofit, government, healthcare, media, banking, telecommunication, insurance, and many more industries ranging in use cases from ETL to Data Warehousing, from Machine Learning to IoT, and more.

Additional resources Get started with HDInsight Interactive Query Cluster in Azure . Zero
          One-to-One at Scale: The Confluence of Behavioral Science and Technology and How It’s ...      Cache   Translate Page   Web Page Cache   

Consumer and business customers have increasing expectations that businesses provide products and services customized for their unique needs. Adaptive intelligence and machine learning technology, combined with insights into behavior, make this customization possible. The financial services industry is moving aggressively to take advantage of these new capabilities. In March 2018, Bank of America launched Erica, a virtual personal assistant—a chatbot—powered by AI. In just three months, Erica surpassed one million users.

But to achieve personalization at scale requires an IT infrastructure that can handle huge amounts of data and process it in real time. Engineered systems purpose-built for these cognitive workloads provide the foundation that helps make this one-to-one personalization possible.

Bradley Leimer, Managing Director and Head of Fintech Strategy at Explorer Advisory & Capital, provides consulting and investment advisory services to start-ups, accelerators, and established financial services companies. As the former Head of Innovation and Fintech Strategy at Santander U.S., his team connected the bank to the fintech ecosystem. Bradley spoke with us recently about how behavioral science is evolving in the financial services industry and how new technological capabilities, when tied to human behavior, are changing the way organizations respond to customer needs.

I know you’re fascinated by behavioral science. How does it frame what you do in the financial sector?

Behavioral science is fascinating because the study of human behavior itself is so intriguing. One of the many books I was influenced by early in my career was Paco Underhill’s 1999 book Why We Buy. The science around purchase behavior and how companies leverage our behavior to create buying decisions that fall in their favor—down to where products are placed and the colors that are used to attract the eye—these are techniques that have been used since before the Mad Men era of advertising.

I’m intrigued by the psychology behind the decisions we make. People are a massive puzzle to solve at scale. Humans are known to be irrational, but they are irrational in predictable ways. Leveraging behavioral science, along with things like design thinking and human-computer interaction, have been a part of building products and customer experiences in financial services for some time. To nudge customers to sign up for a service or take an additional product or to perform behaviors that are sometimes painful like budgeting, saving more, investing, consolidating, or optimizing the use of credit all involve deeply understanding human behavior.

Student debt reached $1.5 trillion in Q1 2018. Can behavioral analytics be used to help students better manage their personal finances?

What’s driving this intersection between behavioral science and fintech?

Companies have been using the ideas of behavioral science in strategic planning and marketing for some time, but it’s only been in the last decade that the technology to act upon the massive amount of new data we collect has been available. The type of data we used to struggle to plug into a mainframe through data reels now flies freely within a cloud of shared service layers. So beyond new analytic tools and AI, there are few other things that are important.

People interact with brands differently now. To become a customer now in financial services, it most often means that you’re interacting through an app, or a website, not in any physical form. It’s not necessarily how a branch is laid out anymore; it’s how the navigation works in your application, and what you can do in how few steps, how quickly you can onboard. This is what is really driving the future of revenue opportunity in the financial space.

At the same time, the competition for customers is increasing. Investments in the behavioral science area are a must-have now because the competition gets smarter every day and the applications to understand human behavior are simultaneously getting more accessible. We use behavioral science to understand and refine our precious opportunities to build empathy and relationships. 

You’ve mentioned the evolution of behavioral science in the financial services industry. How is it evolving and what’s the impact?

Behavioral science is nothing without the right type of pertinent, clean data. We have entered the era of engagement banking: a marketing, sales, and service model that deploys technology to achieve customer intimacy at scale. But humans are not just 1’s and 0’s. You need a variety of teams within banks and fintechs to leverage data in the right way, to make sure it addresses real human needs.

The real impact of these new tools has only started to be really felt. We have an opportunity to broaden the global use of financial services to reduce the number of the underbanked, to open new markets for payments and credit, to optimize every unit of currency for our customers more fully and lift up a generation by ending poverty and reducing wealth inequality.

40% of Americans could not come up with $400 for an emergency expense. Behavioral science can help move people move out of poverty and reduce wealth inequality.

How does artificial intelligence facilitate this evolution?

Financial institutions are challenged with innovating a century-old service model, and the addition of advanced analytics, artificial intelligence tools and how they can be used within the enterprise is still a work in progress. Our metamorphosis has been slowed by the dual weight of digital transformation and the broader implications of ever-evolving customers.

Banks have vast amounts of unstructured and disparate data throughout their complicated, mostly legacy, systems. We used to see static data modeling efforts based on hundreds of inputs. That’s transitioned to an infinitely more complex set of thousands of variables. In response, we are developing and deploying applications that make use of machine learning, deep learning, pattern recognition, and natural language processing among other functionalities.

Using AI applications, we have seen efficiency gains in customer onboarding/know-your-customer (KYC), automation of credit decisioning and fraud detection, personalized and contextual messaging, supply-chain improvements, infinitely tailored product development, and more effective communication strategies based on real-time, multivariate data. AI is critical to improving the entire lifecycle of the customer experience.

What’s the role of behavioral analytics in this trend?

Behavioral analytics combines specific user data: transaction histories, where people shop, how they manage their spending and savings habits, the use of credit, historical trends in balances, how they use digital applications, how often they use different channels like ATMs and branches, along with technology usage data like navigation path, clicks, social media interactions, and responsiveness to marketing. It takes a more holistic and human view of data, connecting individual data points to tell us not only what is happening, but also how and why it is happening.

You’ve built out these customization and personalization capabilities in banks and fintechs. Tell us about the basic steps any enterprise can take to build these capabilities.

As an organization, you need to clearly define your business goals. What are the metrics you want to improve? Is it faster onboarding, lower cost of acquisition, quicker turn toward profitable products, etc.? And how can a more customer-centric, personalized experience assist those goals?

As you develop these, make sure you understand who needs to be in the room. Many banks don’t have a true data science team, or they are a sort of hybrid analytical marketing team that has many masters. That’s a mistake. You need deep understanding of advanced analytics to derive the most efficiencies out of these projects. Then you need a strong collaborative team that includes marketing, digital banking, customer experience, and representation from those teams that interacts with clients. Truly user-centric teams leverage data to create a complete understanding of their users’ challenges. They develop insight into what features their customers use and what they don’t and build knowledge of how customers get the most value out of their products. And then they continually iterate and adjust.

You also need to look at your partnerships, including those with fintechs. There are several lessons derived from fintech platforms such as attention to growth through business model flexibility, devotion to speed-to-market, and a focus on creating new forms of customer value through leveraging these tools to customize everything from onboarding to the new user experience as well as how they communicate and customize the relationship over time.

What would be the optimum technology stack to support real-time contextual messages, products, or services?

Choosing the right technology stack for behavioral analytics is not that different than for any other type of application. You have to find the solution that maps most economically and efficiently to your particular problem set. This means implementing a technology that can solve the core business problems, can be maintained and supported efficiently, and minimizes your total cost of ownership.

In banking, it has to reduce risk while maximizing your opportunities for success. The legacy systems that many banks still deploy were built on relational databases and not designed for real-time processing, providing access via Restful APIs and the cloud-based data lakes we see today. Nor did they have the ability to connect and analyze any form of data. The types of data we now have to consider is just breathtaking and growing daily. In choosing technology partners, you want to make sure what you’re buying is built for this new world from the beginning, that the platform is flexible. You have to be able to migrate between on-premises solutions to the cloud, along with a variety of virtual machines being used today.

If I can paraphrase what you’re saying, it’s that financial services companies need a big data solution to manage all these streams of structured and unstructured data coming in from AI/ML, and other advanced applications. Additionally, a big data solution that simplifies deployment by offering identical functionality on-premises, in the cloud, and in the Oracle public Cloud behind your firewall would also be a big plus.

Are there any other must-haves in terms of performance, analytics, etc., to build an effective AI-based solution?

Must-haves include flexibility to consume all types of data, especially that which is gathered from the web and from digital applications. It needs to be very good at data aggregation—that is, reducing large data sets down to more manageable proportions that are still representative. It must be good at transitioning from aggregation to the detail level and back to optimize different analytical tools. It should be strong in quickly identifying cardinality—how many types of variables can there be within a given field.

Some other things to look for in a supporting infrastructure are direct access through query tools (SQL), support for data transformation within the platform (ETL and ELT tools), flexible data model or unstructured access to all data, algorithmic data transformation, ability to add and access one-off data sets simply (like through ODBC), flexible ways to use APIs to load and extract information, that kind of thing. A good system needs to be real time to help customers in taking the most optimized journey within digital applications. 

To wrap up our discussion, what three tips would you give the enterprise IT chief about how to incorporate these new AI capabilities to help the organization reach its goals around delivering a better customer experience?

First, realize that this isn’t just a technology problem—it will require engineers, data scientists, system architects and data specialists sure, but you also need a collaborative team that involves many parts of the business and builds tools that are accessible.

Start with simple KPIs to improve. Reducing the cost of acquisition or improving onboarding workflows, improving release time for customer-facing applications, reducing particular types of unnecessary customer churn—these are good places to start. They improve efficiencies and impact the bottom line. They help build the case around necessary new technology spend and create momentum.

Understand that the future of the financial services model is all about the customer—understanding their needs and helping the business meet them. Our greatest source of innovation is, in the end, our empathy.

You’ve given us a lot to think about, Bradley. Based on our discussion, it seems that the world of financial services is changing and banks today will require an effective AI-based solution that leverages behavioral science and personalization capabilities.

Additionally, in order for banks to sustain a competitive advantage and lead in the market, they need to invest an effective big data warehousing strategy. Therefore, business and IT leaders need a solution that can store, acquire, process large data workloads at scale, and has cognitive workload capabilities to give you the advanced insights needed to run your business most effectively. It is also important that the technology is tailor-made for advancing businesses’ analytical capabilities that leverage familiar big data and analytics open source tools. And Oracle Big Data Appliance provides that high-performance, cloud-ready secure platform for running diverse workloads using Hadoop, Spark, and NoSQL systems. 


          8 Python Machine Learning Algorithms You Must LEARN      Cache   Translate Page   Web Page Cache   
1. Objective

Previously, we discussed the techniquesof machine learning with python . Going deeper, today, we will talk about and implement 8 top Python Machine Learning Algorithms.

Let’s begin the journey of Machine Learning Algorithms in Python Programming.


8 Python Machine Learning Algorithms   You Must LEARN

8 Python Machine Learning Algorithms You Must LEARN

2. Python Machine Learning Algorithms

Followings are the Algorithms of Python Machine Learning:

a. Linear Regression

Linear regression is one of the supervised Python Machine learning algorithms that observes continuous features and predicts an outcome. Depending on whether it runs on a single variable or on many features, we can call it simple linear regression or multiple linear regression.

This is one of the most popular Python ML algorithms and often under-appreciated. It assigns optimal weights to variables to create a line ax+b to predict the output. We often use linear regression to estimate real values like a number of calls and costs of houses based on continuous variables. The regression lineis the best line that fits Y=a*X+b to denote a relationship between independent and dependent variables.

Let’s plot this for the diabetes dataset.

>>> import matplotlib.pyplot as plt >>> import numpy as np >>> from sklearn import datasets,linear_model >>> from sklearn.metrics import mean_squared_error,r2_score >>> diabetes=datasets.load_diabetes() >>> diabetes_X=diabetes.data[:,np.newaxis,2] >>> diabetes_X_train=diabetes_X[:-30] #splitting data into training and test sets >>> diabetes_X_test=diabetes_X[-30:] >>> diabetes_y_train=diabetes.target[:-30] #splitting targets into training and test sets >>> diabetes_y_test=diabetes.target[-30:] >>> regr=linear_model.LinearRegression() #Linear regression object >>> regr.fit(diabetes_X_train,diabetes_y_train) #Use training sets to train the model

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

>>> diabetes_y_pred=regr.predict(diabetes_X_test) #Make predictions >>> regr.coef_ array([941.43097333]) >>> mean_squared_error(diabetes_y_test,diabetes_y_pred)

3035.0601152912695

>>> r2_score(diabetes_y_test,diabetes_y_pred) #Variance score

0.410920728135835

>>> plt.scatter(diabetes_X_test,diabetes_y_test,color ='lavender')

<matplotlib.collections.PathCollection object at 0x0584FF70>

>>> plt.plot(diabetes_X_test,diabetes_y_pred,color='pink',linewidth=3) [<matplotlib.lines.Line2D object at 0x0584FF30>] >>> plt.xticks(()) ([], <a list of 0 Text xticklabel objects>) >>> plt.yticks(()) ([], <a list of 0 Text yticklabel objects>) >>> plt.show()
8 Python Machine Learning Algorithms   You Must LEARN

Python Machine LearningAlgorithm Linear Regression

b. Logistic Regression

Logistic regression is a supervised classification Python Machine Learning algorithms that finds its use in estimating discrete values like 0/1, yes/no, and true/false. This is based on a given set of independent variables. We use a logistic function to predict the probability of an event and this gives us an output between 0 and 1.

Although it says ‘regression’, this is actually a classification algorithm. Logistic regression fits data into a logit function and is also called logit regression . Let’s plot this.

>>> import numpy as np >>> import matplotlib.pyplot as plt >>> from sklearn import linear_model >>> xmin,xmax=-7,7 #Test set; straight line with Gaussian noise >>> n_samples=77 >>> np.random.seed(0) >>> x=np.random.normal(size=n_samples) >>> y=(x>0).astype(np.float) >>> x[x>0]*=3 >>> x+=.4*np.random.normal(size=n_samples) >>> x=x[:,np.newaxis] >>> clf=linear_model.LogisticRegression(C=1e4) #Classifier >>> clf.fit(x,y) >>> plt.figure(1,figsize=(3,4)) <Figure size 300x400 with 0 Axes> >>> plt.clf() >>> plt.scatter(x.ravel(),y,color='lavender',zorder=17)

<matplotlib.collections.PathCollection object at 0x057B0E10>

>>> x_test=np.linspace(-7,7,277) >>> def model(x): return 1/(1+np.exp(-x)) >>> loss=model(x_test*clf.coef_+clf.intercept_).ravel() >>> plt.plot(x_test,loss,color='pink',linewidth=2.5) [<matplotlib.lines.Line2D object at 0x057BA090>] >>> ols=linear_model.LinearRegression() >>> ols.fit(x,y)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

>>> plt.plot(x_test,ols.coef_*x_test+ols.intercept_,linewidth=1) [<matplotlib.lines.Line2D object at 0x057BA0B0>] >>> plt.axhline(.4,color='.4')

<matplotlib.lines.Line2D object at 0x05860E70>

>>> plt.ylabel('y')

Text(0,0.5,’y’)

>>> plt.xlabel('x')

Text(0.5,0,’x’)

>>> plt.xticks(range(-7,7)) >>> plt.yticks([0,0.4,1]) >>> plt.ylim(-.25,1.25)

(-0.25, 1.25)

>>> plt.xlim(-4,10)

(-4, 10)

>>> plt.legend(('Logistic Regression','Linear Regression'),loc='lower right',fontsize='small')

<matplotlib.legend.Legend object at 0x057C89F0>

>>> plt.show()
8 Python Machine Learning Algorithms   You Must LEARN

Machine LearningAlgorithm Logistic Regreesion

c. Decision Tree

A decision tree falls under supervised Python Machine Learning learning and comes of use for both classification and regression- although mostly for classification. This model takes an instance, traverses the tree, and compares important features with a determined conditional statement. Whether it descends to the left child branch or the right depends on the result. Usually, more important features are closer to the root.

This Python Machine Learning algorithms can work on both categorical and continuous dependent variables. Here, we split a population into two or more homogeneous sets. Let’s see the algorithm for this-

>>> from sklearn.cross_validation import train_test_split >>> from sklearn.tree import DecisionTreeClassifier >>> from sklearn.metrics import accuracy_score >>> from sklearn.metrics import classification_report >>> def importdata(): #Importing data balance_data=pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-'+ 'databases/balance-scale/balance-scale.data', sep= ',', header = None) print(len(balance_data)) print(balance_data.shape) print(balance_data.head()) return balance_data >>> def splitdataset(balance_data): #Splitting data x=balance_data.values[:,1:5] y=balance_data.values[:,0] x_train,x_test,y_train,y_test=train_test_split( x,y,test_size=0.3,random_state=100) return x,y,x_train,x_test,y_train,y_test >>> def train_using_gini(x_train,x_test,y_train): #Training with giniIndex clf_gini = DecisionTreeClassifier(criterion =
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Seattle, WA      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:37:13 GMT - View all Seattle, WA jobs
          Data Architect - Remote West coast - Insight Enterprises, Inc. - Dallas, TX      Cache   Translate Page   Web Page Cache   
R, Azure Machine Learning. 2017 Arizona’s Most Admired Companies (AZ Business Magazine), 2016 Best Places to Work (Phoenix Business Journal)....
From Insight - Mon, 14 May 2018 23:57:10 GMT - View all Dallas, TX jobs
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Portland, OR      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:41:10 GMT - View all Portland, OR jobs
          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page   Web Page Cache   
Database architecture, Big Data, Machine Learning, Business Intelligence, Advanced Analytics, Data Mining, ETL. Internal teammate application guidelines:....
From Insight - Thu, 12 Jul 2018 01:56:10 GMT - View all Chicago, IL jobs
          How to Optimize Elasticsearch Machine Learning Job Configurations using Job Validation      Cache   Translate Page   Web Page Cache   
none
          How Primary Care Data and Machine Learning Can Detect Dementia      Cache   Translate Page   Web Page Cache   
The researchers built a model to predict whether a patient had dementia, based on information known
          Artificial Intelligence: Key to Mood Disorders Diagnosis      Cache   Translate Page   Web Page Cache   
Machine learning was found to predict medication response in patients with complex mood disorders, revealed collaborative study by Lawson Health Research

          Crescendo Global - Recruitment Consultant - Analytics & Machine Learning Domain (2-5 yrs)      Cache   Translate Page   Web Page Cache   
Delhi - Gurgaon, Haryana - Applications:415 Rec. Actions:Recruiter Actions:415 Crescendo Global - Recruitment Consultant - Analytics & Machine Learning Domain (2-5 yrs) Delhi... recruitment firm with a reputation for exceptional delivery, account management and customer service. In India, we recruit for our international...
          CSI-INFRA - SQL Server DBA - CSI Jobs - Charlotte, NC      Cache   Translate Page   Web Page Cache   
Configured 3M MVS database systems. Worked on Power BI tools and machine learning tools to admin and development data ware house internal projects....
From CSI Jobs - Thu, 09 Aug 2018 21:01:53 GMT - View all Charlotte, NC jobs
          Sr. Product Marketing Manager - Automation Anywhere - San Jose, CA      Cache   Translate Page   Web Page Cache   
Experience in artificial intelligence, analytics, machine learning or business process management software especially in the enterprise space is a big plus but...
From Automation Anywhere - Sat, 16 Jun 2018 05:57:32 GMT - View all San Jose, CA jobs
          Director, Product Marketing - Security - Automation Anywhere - San Jose, CA      Cache   Translate Page   Web Page Cache   
Experience in artificial intelligence, analytics, machine learning or business process management software especially in the enterprise space is a big plus but...
From Automation Anywhere - Sat, 16 Jun 2018 05:57:32 GMT - View all San Jose, CA jobs
          Director, Product Marketing - Enterprise - Automation Anywhere - San Jose, CA      Cache   Translate Page   Web Page Cache   
Experience in artificial intelligence, analytics, machine learning or business process management software especially in the enterprise space is a big plus but...
From Automation Anywhere - Sat, 16 Jun 2018 05:57:32 GMT - View all San Jose, CA jobs
          TPx Communications and CounterTack Partner to Deliver Best-in-Class Managed Endp ...      Cache   Translate Page   Web Page Cache   
Emerging Managed Security Service Available from TPx Communications
Delivers Breadth of Coverage for Customers that Extends Beyond EDR and
Traditional Endpoint Protection.

WALTHAM, Mass. (BUSINESS WIRE) #ETP CounterTack, the leading provider of Predictive Endpoint Security for

the enterprise, today announced a partnership with TPx, a premier

nationwide managed services provider. CounterTack has established itself

as the only true behavior-based predictive endpoint solution with

in-memory analysis, multi-tenancy, and scalability that can manage

hundreds of thousands of endpoints in a single deployment.

Under the agreement, TPx will deploy CounterTack’s Endpoint Protection

Platform (EPP) to broaden its Managed Endpoint offering later this year,

giving even more depth and capability to the

managed

TPx just rolled out. This industry-leading package

helps businesses worldwide, ranging from enterprise organizations to

SMBs, pinpoint potential infiltrations and neutralize advanced endpoint

threats. The dynamic prevention and protection at scale offered by EPP

will round out TPx service offering with proven capabilities that allow

enterprises worldwide to detect and respond to unknown malware, insider

threats, data loss prevention, fileless malware, forensics and more.

CounterTack

has established itself as the leading endpoint detection,

response and prevention solution with a comprehensive suite of features.

EPP includes Digital DNA with its one-of-a-kind, patented real-time

binary analysis for processes running in memory. With the ability to

behaviorally detect and act on advanced malware, zero-days, insider

threats, movement of sensitive files, multi-tenancy, cloud or

on-premises deployment, easily scalable infrastructure that supports

deployments of hundreds of thousands of endpoints, and an underlying

architecture that can ingest and process massive volumes of endpoint

telemetry data with real-time analytics, EPP was the clear choice for

TPx.

TPx Managed Endpoints is a key part of its just released comprehensive

security

that provides the essential protection businesses

need today. This comprehensive workstation and server solution includes

application patching for critical updates, next-gen endpoint protection

software, system performance monitoring, inventory reporting and more.

Critical customer systems remain healthy and stable with the help of

TPx’s 100% U.S-based engineering and support teams, freeing in-house

resources to focus on other needed tasks.

“Teaming with CounterTack to enhance our MSx Endpoints product with

Managed Detection and Response will help our installed base and

potential customers have more visibility and control over the mounting

and dangerous endpoint threats they face,” said Jared Martin, VP of MSx

Managed Services at TPx. “Integrating the continuous, behavior-based

detection and response capabilities from CounterTack helps TPx

communications reduce risk, promote business resiliency and challenge

attackers, at a time when endpoints are the most targeted and vulnerable

element of the business infrastructure.”

As a result, TPx and CounterTack customers can benefit from early

detection, preventative controls, and automated response capabilities,

coupled with highly intelligent correlation capabilities before endpoint

threats can fully execute or escalate in severity.

“TPx and CounterTack are partnering to counter today’s advanced,

targeted threats and deliver a new class of endpoint protection services

for customers,” said Neal Creighton, CEO, CounterTack. “When advanced,

unknown threats attempt to penetrate our customer’s networks, TPx and

CounterTack will have already identified and mitigated those threats and

eliminated the opportunity for them to impact business operations.”

About TPx

TPx is the premier managed services provider,

redefining the way enterprises grow, compete and communicate. TPx’s

unified communications, managed IT services, continuity and connectivity

solutions all work together to “reach a higher state of connectedness”

with customers, employees, clients, suppliers, locations, applications

and more. TPx can provide guaranteed performance wherever there’s a

broadband connection, erasing the limitations of geography, incumbent

providers and capital expenditure. Headquartered in Los Angeles, with

major locations across the country, TPx has delivered 16 years of

consecutive revenue growth, driven by a DNA of obsessive customer

service and being a trusted advisor to its customers. For more

information, go to www.tpx.com .

About CounterTack

CounterTack is the leading provider of

Predictive Endpoint Detection, Response and Next Gen Antivirus, which

together, is coined by Gartner as Endpoint Protection Platform (EPP) for

the enterprise. CounterTack’s EPP delivers multi-vector detection,

prevention, and response by applying a unique combination of behavioral

analysis, memory forensics, machine learning, and reputational

techniques to counter the most advanced threats. CounterTack detects and

analyzes threats based on behaviors observed in the operating system, in

memory and on-disk, leveraging analytics that examine the cause and

effect of endpoint state changes. CounterTack empowers security teams

with the tools, information, and context they require to prevent and

neutralize threats across the entire threat spectrum before they damage

the business. CounterTack currently has over 300 customers globally and

powers the Managed Detection and Response (MDR) offerings of a growing

ecosystem of the world’s leading MSSPs.

To learn more, please visit: http://www.countertack.com/ or

follow us on Twitter at @CounterTack .

Contacts

TPx

Douglass Brownstone, 310-567-2578

dbrownstone@tpx.com

or

CounterTack

Madeline

Lee, 781-215-9427

mlee@countertack.com
TPx Communications and CounterTack Partner to Deliver Best-in-Class Managed Endp ...
Do you think you can beat this Sweet post? If so, you may have what it takes to become a Sweetcode contributor...Learn More.
          Is it possible for an organisation to learn?      Cache   Translate Page   Web Page Cache   

Can organisations learn, or can only people learn? Some thoughts on the subject.

from creative commons images

We often hear about "organisational learning" but is learning something that organisations actually can do? Or is learning the province of people and animals? (Let's put machine learning aside for the moment - that is another discussion).

There is a school of though that learning is a human attribute- that only humans are able to learn. After all, learning  requires a memory in which new knowledge can be stored. Humans  have a memory, but do organisations?

You could argue that organisations have two memories - one if the collective memory of the individuals in the organisation, often reinforced through stories and "shared experience"

The second memory is held in "the way we work" - the processes, procedures, doctrines, structures, norms, behaviours, organigrams, and the stories that are told. As one project person said to me - "our standard process is made up of all the lessons we have learned over the years".

The first memory comes and goes with the people, and the effect of this can be observed in the cycles of unlearning you see in some organisations, where the same mistakes are repeated on a 5 to 10 year cycle as the older staff retire. The second memory is potentially longer-term, and survives the changeover of staff, but is also slower to build up and slower to respond to events.

However I would argue that this deeper slower memory is where real organisational learning can take place. An organisation, through activating learning activities and learning cycles, can steadily but surely change the way it operates, in response to events and to new experiences.

So, yes, organisations can learn. Organisations can modify their behaviour as a result of experience, and that, surely, is a form of learning. Maybe its more mechanistic than intuitive learning, maybe they don't learn as fast or as well as a human does, maybe they learn more like the way a dog learns.

However I believe that organisations can learn if they develop a structure for learning. The bigger question is why don't they learn better, and more often?


          Understand the significance for large scale machine learning      Cache   Translate Page   Web Page Cache   
Managing AI projects is no more a difficult job. With the help of AI platform, you can easily manage your code, projects or data at the same time. If you need help with scaling artificial...

[[ This is a content summary only. Visit my website for full links, other content, and more! ]]

          Data Science Analyst - Strategic Data Solutions - Apple - Austin, TX      Cache   Translate Page   Web Page Cache   
We apply data science and machine learning to drive strategic impact across multiple lines of business at Apple....
From Apple - Fri, 06 Jul 2018 13:47:35 GMT - View all Austin, TX jobs
          Sr Software Engineer - Applied Machine Learning - Apple - Austin, TX      Cache   Translate Page   Web Page Cache   
We work on many high-impact projects that serve various Apple lines of business. Understanding of machine learning, statistics....
From Apple - Fri, 15 Jun 2018 01:48:26 GMT - View all Austin, TX jobs
          Director, Customer Service Product and Tools - Kabam - Austin, TX      Cache   Translate Page   Web Page Cache   
Enthusiastic about the latest mobile trends and emerging technologies (IE Machine Learning, AI). Providing leadership and supporting for the technology...
From Kabam - Thu, 24 May 2018 02:31:26 GMT - View all Austin, TX jobs
          Research Scientist Machine Learning - Intel - Hillsboro, OR      Cache   Translate Page   Web Page Cache   
Intel Labs engages the leading thinkers in academia and industry in addition to partnering closely with Intel business units. Inside this Business Group....
From Intel - Fri, 27 Jul 2018 12:08:26 GMT - View all Hillsboro, OR jobs
          Healthcare General Manager - DataRobot - New York, NY      Cache   Translate Page   Web Page Cache   
DataRobot is defining a category called automated machine learning and has already raised more than $122M from the likes of NEA, Intel Ventures and Accomplice...
From DataRobot - Thu, 26 Jul 2018 02:23:40 GMT - View all New York, NY jobs
          Senior Sales Engineer, Financial Services - GoodData Corporation - New York, NY      Cache   Translate Page   Web Page Cache   
Experience conducting business value assessments. Experience in Predictive Analytics and/or Machine Learning....
From GoodData Corporation - Thu, 19 Jul 2018 07:11:19 GMT - View all New York, NY jobs
          Healthcare General Manager - DataRobot - Boston, MA      Cache   Translate Page   Web Page Cache   
DataRobot is defining a category called automated machine learning and has already raised more than $122M from the likes of NEA, Intel Ventures and Accomplice...
From DataRobot - Thu, 26 Jul 2018 02:23:39 GMT - View all Boston, MA jobs
          Insurance General Manager - DataRobot - Boston, MA      Cache   Translate Page   Web Page Cache   
DataRobot is defining a category called automated machine learning and has already raised more than $122M from the likes of NEA, Intel Ventures and New York...
From DataRobot - Wed, 20 Jun 2018 20:26:05 GMT - View all Boston, MA jobs
          Senior Sales Engineer, Financial Services - GoodData Corporation - Boston, MA      Cache   Translate Page   Web Page Cache   
Experience conducting business value assessments. Experience in Predictive Analytics and/or Machine Learning....
From GoodData Corporation - Thu, 19 Jul 2018 07:11:21 GMT - View all Boston, MA jobs
          Hiring Solution Architect with Python & Machine Learning - RR Donnelley India Outsource Private Limited - Chennai, Tamil Nadu      Cache   Translate Page   Web Page Cache   
Graduates / Post graduates in Mathematics/Statistics/Data science / Actuarial science or any other degree that is considered suitable to perform the required...
From Monster IN - Tue, 07 Aug 2018 14:34:59 GMT - View all Chennai, Tamil Nadu jobs
          How Siri finds local destinations in your language      Cache   Translate Page   Web Page Cache   
'Accurately recognizing named entities, like small local businesses, has remained a performance bottleneck.' — Siri Speech Recognition Team Personal assistants like Siri have gotten better and better at recognizing what we're saying, at least in general. When it comes to recognizing names, including business names, especially regional names, the challenge has been greater. Apple's Machine Learning Journal describes how the Siri team has been tackling it: Generally, virtual assistants correctly recognize and understand the names of high-profile businesses and chain stores like Starbucks, but have a harder time recognizing the names of the millions of smaller, local POIs that users ask about. In ASR, there's a known performance bottleneck when it comes to accurately recognizing named entities, like small local businesses, in the long tail of a frequency distribution. We decided to improve Siri's ability to recognize names of local POIs by incorporating knowledge of the u...
          microsoft-r-open (3.5.1)      Cache   Translate Page   Web Page Cache   
World’s most powerful programming language for statistical computing, machine learning and graphics as well as a thriving global community of users, developers and contributors.

          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Virtual machine switching); Large scale distributed systems, real-time data analysis, machine learning, windows internals (networking stack and other OS...
From Microsoft - Thu, 09 Aug 2018 04:41:50 GMT - View all Redmond, WA jobs
          Economist - Forecasting - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Experience with machine learning applications. We are breaking fresh ground, pioneering in a program that is crucial for future Amazon growth, and our business...
From Amazon.com - Wed, 27 Jun 2018 07:21:23 GMT - View all Seattle, WA jobs
          Product Designer 2      Cache   Translate Page   Web Page Cache   
WA-Redmond, RESPONSIBILITIES: Our Kforce client is a cutting-edge provider of world class software, solutions and services. Employing over 100,000 passionate people worldwide, they empower every person and every organization on the planet to achieve their dreams. Their best in class advancements in cloud, mobile, machine learning, and AI are changing the way people go about their lives. Doing business in 170
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Seattle, WA      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:37:13 GMT - View all Seattle, WA jobs
          Data Architect - Remote West coast - Insight Enterprises, Inc. - Dallas, TX      Cache   Translate Page   Web Page Cache   
R, Azure Machine Learning. 2017 Arizona’s Most Admired Companies (AZ Business Magazine), 2016 Best Places to Work (Phoenix Business Journal)....
From Insight - Mon, 14 May 2018 23:57:10 GMT - View all Dallas, TX jobs
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Portland, OR      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:41:10 GMT - View all Portland, OR jobs
          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page   Web Page Cache   
Database architecture, Big Data, Machine Learning, Business Intelligence, Advanced Analytics, Data Mining, ETL. Internal teammate application guidelines:....
From Insight - Thu, 12 Jul 2018 01:56:10 GMT - View all Chicago, IL jobs
          EasemlSchema added to PyPI      Cache   Translate Page   Web Page Cache   
Schema which is used to define the type of a machine learning data set.
          pulib added to PyPI      Cache   Translate Page   Web Page Cache   
Positive-Unlabeled machine learning package
          Proof of Concept Malware Uses Intelligence      Cache   Translate Page   Web Page Cache   
I know executives who are alarmed when they see programmers or major companies outline how new kinds of Malware can be built.  Isn't it bad enough?  But it does help to outline the vulnerabilities to plan for them. This example might be Outlined:  If we can use intelligence to feel you out and trick you as to who we are , we can more likely be successful.    All Phishing does this.   Recent demos of Google Duplex hint at this.   Somewhat unusual to see IBM doing this.   Expect to see more of this.

IBM's proof-of-concept malware uses AI for spear phishing   in V3
The neural network running DeepLocker hides its intent until it finds the right victim  The world is beginning to transition from the cloud era to the artificial intelligence (AI) era, as systems and networks grow and learn. But just as the web and cloud eras had their own threats, the same applies to this new landscape - and it is AI itself.

Excitement and confusion abound over AI, but despite - or perhaps because of - this, the technology can pose a real danger to computer users.

"As machine learning matures into AI, nascent use of AI for cyber threat defense will likely be countered by threat actors using AI for offense," Rick Hemsley, managing director at Accenture Security, told us earlier this year.

So on the face of it, IBM's development of DeepLocker - ‘a new breed of highly targeted and evasive attack tools powered by AI' - seems like it sets a dangerous precedent.

There is method to the madness. IBM reasons that cybercriminals are already working to weaponise AI, and the best way to counter such a threat is to watch how it works ... " 

Here is the report on Deeplocker by IBM.


          Tax Declaration Mobile Assistant Taxfix Raises $13M in Series A      Cache   Translate Page   Web Page Cache   

Taxfix, a Berlin, Germany-based provider of a mobile assistant for tax declarations, completed a $13M (€11.4M) Series A funding round. The round was led by Valar Ventures with participation from existing investors Creandum and Redalpine. The company intends to use the funds to expand into international markets and invest in machine learning to improve its […]

The post Tax Declaration Mobile Assistant Taxfix Raises $13M in Series A appeared first on FinSMEs.


          Can a selfie diagnose your skin condition?      Cache   Translate Page   Web Page Cache   
AI and machine learning-based tools are being increasingly used in dermatology to analyze skin conditions ranging from acne to STDs. (Source: bizjournals.com Health Care:Pharmaceuticals headlines)
          Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Director, Data & AI - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Senior Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:23 GMT - View all Montréal, QC jobs
          Generalist Software Developer - AIRY:3D - Montréal, QC      Cache   Translate Page   Web Page Cache   
Computer vision, image processing, computational imaging, 2D/3D computer graphics, or machine learning algorithms and toolkits (OpenCV, OpenGL, CUDA, TensorFlow...
From Indeed - Wed, 01 Aug 2018 17:08:19 GMT - View all Montréal, QC jobs
          Apple erklärt, wie Siri den Namen örtlicher Geschäfte und Restaurants besser verstehen kann      Cache   Translate Page   Web Page Cache   
In regelmäßigen Abständen gibt Apple Einblicke, wie man den digitalen Sprachassistenten Siri verbessert. Mit einem neuen Eintrag im Machine Learning Journal widmet sich der Hersteller nun den Möglichkeiten, wie Siri den Namen von örtlichen Geschäften und Restaurants besser verstehen kann. So will Apple Siri verbessern Kurzum: Apple hat angepasste Sprachmodelle entwickelt, die die Erkenntnis zum […]
          eSentire and Secure Infrastructure Provider Cyxtera Partner to Bring Zero-Trust ...      Cache   Translate Page   Web Page Cache   

LAS VEGAS AND CAMBRIDGE, ONTARIO (PRWEB)AUGUST 08, 2018

eSentire, Inc. , the largest pure-play Managed Detection and Response (MDR) provider, and Cyxtera Technologies , the secure infrastructure company, today announced a strategic partnership to bring customers comprehensive security solutions designed to secure today’s modern hybrid IT environment.

The partnership combines eSentire’s 24×7 MDR with Cyxtera’s AppGate SDP zero-trust network offering, delivering industry-leading prevention and detection capabilities across customers’ hybrid IT environments. The two companies will jointly go-to-market to maximize customer reach for this mid-sized enterprise offering.

“Digital transformation of our customers is accelerating the adoption of hybrid IT, fundamentally changing the enterprise perimeter and the methods required to secure it,” said Kerry Bailey, eSentire CEO. “Cyxtera and eSentire share a common vision that traditional security solutions and methods are challenged to secure the new highly distributed enterprise. With this partnership, our joint customers gain modern security solutions that were built specifically for modern hybrid IT environments that span prevention, protection, and detection and response capabilities. eSentire established and leads the MDR security category; our customers look for our continued innovation to help them operate securely now and into the future. Solutions like AppGate SDP deliver on our commitment to our customers and partners.”

eSentire MDR and its Security Operations Centers (SOC) investigate and respond in real-time to known and unknown threats that bypass its customers’ traditional security controls. eSentire MDR ingests mass amounts of security data, leveraging advanced tools, like machine learning, to detect threats and respond to them on the customer’s behalf. Customers can now include AppGate SDP with eSentire MDR service, delivering improved cloud policy enforcement, fine-grained user access controls, and better user behavior-based response capabilities.

“Our analysts leverage the richest data sets available to make fast, informed decisions on behalf of our customers,” said Bailey. “AppGate SDP enriches those toolsets with user behavior and access control data that extends our response capabilities.”

“Organizations understand the need to modernize their security programs to keep up with transformative IT initiatives,” said Manuel D. Medina, CEO of Cyxtera. “Cyxtera is committed to securing network access in a perimeter-less, hybrid environment. Our AppGate SDP solution combined with eSentire’s MDR services can significantly lower the risk of a security event. eSentire’s security analysts have the benefit of AppGate SDP’s fine-grained identity-centric access controls and dynamic policy enforcement a quantum leap over outdated, static IP-based access control methods so they can quickly detect and respond to threats.”

eSentire and Cyxtera are exhibiting at Black Hat USA, August 4-9, 2018 in Las Vegas, NV, and available to discuss the partnership: eSentire booth #2010; Cyxtera booth #244.

For more information on eSentire Managed Detection and Response, visit: https://www.esentire.com/what-we-do/managed-detection-and-response/ .

For more information on AppGate SDP, visit: https://www.cyxtera.com/secure-access/appgate-sdp .

About Cyxtera:

Cyxtera Technologies combines a worldwide footprint of 50+ best-in-class data centers with a portfolio of modern, cloud- and hybrid-ready security and analytics offerings providing more than 3,500 enterprises, government agencies, and service providers an integrated, secure, and cyber-resilient infrastructure platform for critical applications and systems. For more information about Cyxtera, visit http://www.cyxtera.com/ .

About eSentire:

eSentire is the largest pure-play Managed Detection and Response (MDR) service provider, keeping organizations safe from constantly evolving cyber-attacks that technology alone cannot prevent. Its 24×7 Security Operations Center (SOC), staffed by elite security analysts, hunts, investigates, and responds in real-time to known and unknown threats before they become business disrupting events. Protecting more than $5.7 trillion in corporate assets, eSentire absorbs the complexity of cybersecurity, delivering enterprise-grade protection and the ability to comply with growing regulatory requirements. For more information, visit http://www.esentire.com and follow @eSentire .

PR Contacts:


          Business Strategy, Sr. Manager - Hortonworks - Dallas, TX      Cache   Translate Page   Web Page Cache   
Business Strategy, Leadership Opportunity. Experience in the Software and/or Business Impact of Analytics, Big Data, Machine Learning/AI, Cloud is a plus....
From Hortonworks - Mon, 23 Jul 2018 20:31:09 GMT - View all Dallas, TX jobs
          Business Strategy, Sr. Manager - Hortonworks - Atlanta, GA      Cache   Translate Page   Web Page Cache   
Business Strategy, Leadership Opportunity. Experience in the Software and/or Business Impact of Analytics, Big Data, Machine Learning/AI, Cloud is a plus....
From Hortonworks - Mon, 23 Jul 2018 20:31:09 GMT - View all Atlanta, GA jobs
          How Siri finds local destinations in your language      Cache   Translate Page   Web Page Cache   
'Accurately recognizing named entities, like small local businesses, has remained a performance bottleneck.' — Siri Speech Recognition Team Personal assistants like Siri have gotten better and better at recognizing what we're saying, at least in general. When it comes to recognizing names, including business names, especially regional names, the challenge has been greater. Apple's Machine Learning Journal describes how the Siri team has been tackling it: Generally, virtual assistants correctly recognize and understand the names of high-profile businesses and chain stores like Starbucks, but have a harder time recognizing the names of the millions of smaller, local POIs that users ask about. In ASR, there's a known performance bottleneck when it comes to accurately recognizing named entities, like small local businesses, in the long tail of a frequency distribution. We decided to improve Siri's ability to recognize names of local POIs by incorporating knowledge of the u...
          Bell Labs - Integrated Photonics Researcher - NOKIA - Holmdel, NJ      Cache   Translate Page   Web Page Cache   
Nokia is a global leader in the technologies that connect people and things. Investigate and implement machine learning based optimization to control large...
From Nokia - Mon, 18 Jun 2018 15:55:57 GMT - View all Holmdel, NJ jobs
          AA Chief SW Architect - NOKIA - San Jose, CA      Cache   Translate Page   Web Page Cache   
Analytics, AI, and machine learning. Presenting to customers, industry forums, analysts and internal audiences....
From Nokia - Mon, 18 Jun 2018 15:51:40 GMT - View all San Jose, CA jobs
          Senior Software Development Engineer - Distributed Computing Services (Hex) - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Knowledge and experience with machine learning technologies. We enable Amazon’s internal developers to improve time-to-market by allowing them to simply launch...
From Amazon.com - Thu, 26 Jul 2018 19:20:25 GMT - View all Seattle, WA jobs
          Senior Site Reliability Engineer - Sift Science - Seattle, WA      Cache   Translate Page   Web Page Cache   
The Sift Science Trust PlatformTM uses real-time machine learning to accurately predict which users businesses can trust, and which ones they can’t....
From Sift Science - Fri, 22 Jun 2018 20:18:59 GMT - View all Seattle, WA jobs
          Python Developer - MJDP Resources, LLC - Radnor, PA      Cache   Translate Page   Web Page Cache   
Assemble large, complex data sets that meet business requirements and power machine learning algorithms. EC2, Lambda, ECS, S3.... $30 - $40 an hour
From Indeed - Wed, 13 Jun 2018 13:41:07 GMT - View all Radnor, PA jobs
          Data Engineer - PYTHON - MJDP Resources, LLC - Devon, PA      Cache   Translate Page   Web Page Cache   
Assemble large, complex data sets that meet business requirements and power machine learning algorithms. EC2, Lambda, ECS, S3.... $100,000 - $120,000 a year
From Indeed - Tue, 31 Jul 2018 14:44:04 GMT - View all Devon, PA jobs
          Executive Director- Machine Learning & Big Data - JP Morgan Chase - Jersey City, NJ      Cache   Translate Page   Web Page Cache   
We would be partnering very closely with individual lines of business to build these solutions to run on either the internal and public cloud....
From JPMorgan Chase - Fri, 20 Jul 2018 13:57:18 GMT - View all Jersey City, NJ jobs
          Senior Software Engineer - Revenue Optimization - WeWork Global Technology - New York, NY      Cache   Translate Page   Web Page Cache   
Experience building Linear Programming solutions and Machine learning applications highly desired. Deep understanding of Amazon Web Services including ECS,...
From WeWork - Wed, 06 Jun 2018 05:19:01 GMT - View all New York, NY jobs
          Senior DevOps Engineer - Info Group NW - Beaverton, OR      Cache   Translate Page   Web Page Cache   
Design and implement public and internal APIs. Build cloud infrastructure for machine learning following the DevOps model....
From Info Group NW - Sat, 28 Jul 2018 06:52:49 GMT - View all Beaverton, OR jobs
          Senior Backend Engineer - Tinder Trust - Tinder - West Hollywood, CA      Cache   Translate Page   Web Page Cache   
Implemented machine learning algorithms in production. Experience with Docker containers along with Kubernetes or ECS....
From Tinder - Sat, 23 Jun 2018 00:30:11 GMT - View all West Hollywood, CA jobs
          Machine Learning Architect - Epsilon - San Diego, CA      Cache   Translate Page   Web Page Cache   
Excellent understanding of machine learning techniques and algorithms. Experience implementing at least two Machine Learning pipelines in production....
From Epsilon - Wed, 18 Jul 2018 19:16:16 GMT - View all San Diego, CA jobs
          Senior Machine Learning Architect - AllianceData - San Diego, CA      Cache   Translate Page   Web Page Cache   
Excellent understanding of machine learning techniques and algorithms. Experience implementing at least two to three Machine Learning pipelines in production....
From AllianceData - Wed, 18 Jul 2018 16:07:56 GMT - View all San Diego, CA jobs
          Improving Machine Learning with Continuous Learning Models      Cache   Translate Page   Web Page Cache   
Improving Machine Learning with Continuous Learning Models

Improving Machine Learning with Continuous Learning Models
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 222 MB
Genre: eLearning | Language: English

          [ASAP] General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen      Cache   Translate Page   Web Page Cache   

TOC Graphic

Langmuir
DOI: 10.1021/acs.langmuir.8b01791

          New Course: Fundamentals of Bayesian Data Analysis in R      Cache   Translate Page   Web Page Cache   
Here is the course link. Course Description Bayesian data analysis is an approach to statistical modeling and machine learning that is becoming more and more popular. It provides a uniform framework to build problem specific models that can be used...
          (USA-CA-San Jose) Python Engineer Software 2      Cache   Translate Page   Web Page Cache   
At Northrop Grumman, our work with **cutting-edge technology** is driven by something **human** : **the lives our technologies protects** . It's the value of innovation that makes a difference today and tomorrow. Here you'll have the opportunity to connect with coworkers in an environment that's uniquely caring, diverse, and respectful; where employees share experience, insights, perspectives and creative solutions through integrated product & cross-functional teams, and employee resource groups. Don't just build a career, build a life at Northrop Grumman. The Cyber Intelligence Mission Solutions team is seeking an Engineer Software 2 to join our team in San Jose as we kick off a new 10 year program to protect our nation's security. You will be using your Python skills to perform advanced data analytics on a newly architected platform. Hadoop, Spark, Storm, and other big data technologies will be used as the basic framework for the program's enterprise. **Roles and Responsibilities:** + Python development of new functionality and automation tools using Agile methodologies + Build new framework using Hadoop, Spark, Storm, and other big data technologies + Migrate legacy enterprise to new platform + Test and troubleshoot using Python and some Java on Linux + Function well as a team player with great communication skills **Basic Qualifications:** + Bachelor Degree in a STEM discipline (Science, Technology, Engineering or Math)from an accredited institutionwith 2+ years of relevant work experience, or Masters in a STEM discipline with 0+ years of experience + 1+ years of Python experience in a work setting + Active SCI clearance **Preferred Qualifications:** + Machine learning / AI / Deep Learning / Neural Networks + Familiar withHadoop, Spark or other Big Data technologies + Familiar with Agile Scrum methodology + Familiar withRally, GitHub, Jenkins, Selenium applications Northrop Grumman is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. For our complete EEO/AA statement, please visit www.northropgrumman.com/EEO . U.S. Citizenship is required for most positions.
          (USA-TN-Memphis) Manager Data Analytics      Cache   Translate Page   Web Page Cache   
Our growing team seeks individuals who are passionate about using data to help drive informed decisions for the institution. As a Data Analytics Manager, you'll have a direct impact on analytics needs utilizing state-of-the-art technologies. Responsible for the management and overall day-to-day operations of the data analytics team staff, programs and technologies, projects, and resource assignment within Information Services. Works collaboratively to provide data analytical solutions across St. Jude. Will work with all users to define and deploy analytical models, metrics, data warehouses, dashboards, and reports that support all operations and areas of St. Jude. Participates and leads in the requirements, implementation, evaluation and ongoing first line support. Remains current in Data Analytic trends and practices through ongoing educational and informal relationships and incorporates this knowledge into system planning and deployment. Responsible for integration of systems across departmental boundaries to ensure solutions are consistent with the broader goals and objectives of the overall strategic plan. Provide management of end user training programs, user documentation and continuous improvement initiatives. + Leads and acts as project manager for the development and implementation of assigned Data Analytics projects + Project management responsibilities include initiating, planning, organizing and directing assigned projects to achieve established objectives, optimal technology solutions, and overall project success + Expanded responsibility includes budget and resource planning, utilization, management and reporting + Establishes project plans with milestones, deliverables and budgets; provides regular status reports + Ability to manage and direct professional staff which directly report to him/her + Serve as a strong advocate to improve analytical capability across the organization + Bachelor's degree in Management Information Systems, Administrative/Financial specialty, or related area is required + Master's degree is preferred + Eight (8) years progressive experience in IT or administrative or financial area that includes five (5) years of IT experience and three (3) years in a leadership role + Hospital Information System experience preferred + Experience with IBM Cognos, dimensional modeling, Microsoft SQL Server Integration Services (SSIS) and SQL Server Analysis Services (SSAS), dimension insight, or Tableau is preferred + Experience with advanced analytics to develop forecasting and statisical modeling (machine learning) a strong plus + Project Management Professional (PMP) certification is preferred St. Jude is an Equal Opportunity Employer No Search Firms: St. Jude Children's Research Hospital does not accept unsolicited assistance from search firms for employment opportunities. Please do not call or email. All resumes submitted by search firms to any employee or other representative at St. Jude via email, the internet or in any form and/or method without a valid written search agreement in place and approved by HR will result in no fee being paid in the event the candidate is hired by St. Jude. Posted Job Title: Manager Data Analytics WITS Req ID: 38964 Street: 262 Danny Thomas Place
          (USA) Software Engineer- Infrastructure      Cache   Translate Page   Web Page Cache   
Software Engineer- Infrastructure Job Summary Apply Now + Job:18968-MKAI + Location:US-MA-Natick + Department:Product Development We are looking for a versatile, enthusiastic computer scientist or engineer capable of multi-tasking to join the Control & Identification team. You will help develop software tools to facilitate the application of reinforcement learning to practical industrial application in areas such as robotics and other autonomous systems. You will need skills that cross traditional domain boundaries in areas such as machine learning, optimization, object-oriented programming, and graphical user interface design. Responsibilities + Develop and implement new software tools to help our customers apply reinforcement learning to their applications. + Work on improving the integration and deployment of reinforcement learning tools with workflows utilizing GPUs, parallel computing and cloud computing. + Contribute to all aspects of the product development process from writing functional specifications to designing software architecture to implementing software features. + Work with quality engineering, documentation, and usability teams to develop state-of-the-art software tools. Minimum Qualifications + A bachelor's degree and 3 years of professional work experience (or a master's degree) is required. Additional Qualifications In addition, a combination of some of the follow skills is important: + Knowledge of numerical algorithms. + Experience with MATLAB or Simulink. + Experience with machine learning. + Experience with neural networks. + Experience with object-oriented design and programming. + Experience with GPUs and parallel computing. + Experience with IoT and cloud computing is a plus. + Experience with software development lifecycle is a plus. + Experience with other programming languages is a nice to have. Why MathWorks? It’s the chance to collaborate with bright, passionate people. It’s contributing to software products that make a difference in the world. And it’s being part of a company with an incredible commitment to doing the right thing – for each individual, our customers, and the local community. MathWorks develops MATLAB and Simulink, the leading technical computing software used by engineers and scientists. The company employs 4000 people in 16 countries, with headquarters in Natick, Massachusetts, U.S.A. MathWorks is privately held and has been profitable every year since its founding in 1984. Apply Now Contact usif you need reasonable accommodation because of a disability in order to apply for a position. The MathWorks, Inc. is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other protected characteristics. View TheEEO is the Law posterandits supplement. The pay transparency policy is availablehere. MathWorks participates in E-Verify. View the E-Verify postershere. Apply Now + Job:18968-MKAI + Location:US-MA-Natick + Department:Product Development
          Document worth reading: “Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems”      Cache   Translate Page   Web Page Cache   
Security, privacy, and fairness have become critical in the era of data science and machine learning. More and more we …

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          (USA-MA-Boston) Software Engineer (Node.js/Python)      Cache   Translate Page   Web Page Cache   
Software engineers on our team work closely with Machine Learning engineers to create a smarter, personalized learning journey for our users. You will be working on a cross-functional team with a Product Manager, UX Designer, DevOps Engineer, Machine Learning Engineers, and Software Engineers. You’ll be part of a team that is user focused, has a mentality for experimentation, and iterates quickly. *Ways we work:* * Autonomous & responsible teams - making their own product & dev choices (https://www.pluralsight.com/tech-blog/team-responsibilty) * Data first - we work with large volumes of data to build scalable solutions for our products * Software Craftsmanship - we want to be proud of our work o Pair programming - we value collaborative development o Test-driven development - we take responsibility for our code without QA engineers o Continuous delivery - teams independently ship code to production every day o Kanban & Lean - no more backlog grooming, no more T-shirt size estimating o Continual improvement - we hold weekly retrospectives in order to assess and improve system processes *What we create with:* * Backend - Node.js/Python * Testing - Mocha/Pytest * Declarative UI - React * Styling - CSS Modules <3 * Messaging - RabbitMQ * Database - PostgreSQL * Source Control - Github * Frameworks - Airflow/TensorFlow EOE Statement Be Yourself. Pluralsight is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
          Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies, Third Edition      Cache   Translate Page   Web Page Cache   
скачать Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies, Third Edition бесплатно
Название: Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies, Third Edition
Автор: Steven Finlay
Страниц: 192
Формат: EPUB, MOBI, RTF, PDF
Размер: 10.18 MB
Качество: Отличное
Язык: Английский
Год издания: 2018


 Artificial Intelligence (AI) and Machine Learning are now mainstream business tools. They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Organizations which understand these tools and know how to use them are benefiting at the expense of their rivals.
          Hands-on Machine Learning for Data Mining      Cache   Translate Page   Web Page Cache   
скачать Hands-on Machine Learning for Data Mining бесплатно
Название: Hands-on Machine Learning for Data Mining
Автор: Jesus Salcedo
Страниц: Duration: 2h 44m
Формат: HDRip
Размер: 821,2 mb
Качество: Отличное
Язык: Английский
Жанр: Video Course
Год издания: 2018


Improve your data mining capabilities with advanced predictive modelling


          Software Development Engineer - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
2 years experience working on machine learning based models. The engineer will play a pivotal role in the expansion of pricing software, with the mission to...
From Amazon.com - Fri, 27 Jul 2018 19:19:19 GMT - View all Seattle, WA jobs
          Technical Program Manager, Links Machine Learning - Google - Seattle, WA      Cache   Translate Page   Web Page Cache   
You plan requirements with internal customers and usher projects through the entire project lifecycle. We build the technologies that transform the way we think...
From Google - Thu, 26 Jul 2018 08:23:26 GMT - View all Seattle, WA jobs
          Research Scientist Machine Learning - Intel - Hillsboro, OR      Cache   Translate Page   Web Page Cache   
Intel Labs engages the leading thinkers in academia and industry in addition to partnering closely with Intel business units. Inside this Business Group....
From Intel - Fri, 27 Jul 2018 12:08:26 GMT - View all Hillsboro, OR jobs
          Healthcare General Manager - DataRobot - New York, NY      Cache   Translate Page   Web Page Cache   
DataRobot is defining a category called automated machine learning and has already raised more than $122M from the likes of NEA, Intel Ventures and Accomplice...
From DataRobot - Thu, 26 Jul 2018 02:23:40 GMT - View all New York, NY jobs
          Senior Sales Engineer, Financial Services - GoodData Corporation - New York, NY      Cache   Translate Page   Web Page Cache   
Experience conducting business value assessments. Experience in Predictive Analytics and/or Machine Learning....
From GoodData Corporation - Thu, 19 Jul 2018 07:11:19 GMT - View all New York, NY jobs
          Healthcare General Manager - DataRobot - Boston, MA      Cache   Translate Page   Web Page Cache   
DataRobot is defining a category called automated machine learning and has already raised more than $122M from the likes of NEA, Intel Ventures and Accomplice...
From DataRobot - Thu, 26 Jul 2018 02:23:39 GMT - View all Boston, MA jobs
          Insurance General Manager - DataRobot - Boston, MA      Cache   Translate Page   Web Page Cache   
DataRobot is defining a category called automated machine learning and has already raised more than $122M from the likes of NEA, Intel Ventures and New York...
From DataRobot - Wed, 20 Jun 2018 20:26:05 GMT - View all Boston, MA jobs
          Senior Sales Engineer, Financial Services - GoodData Corporation - Boston, MA      Cache   Translate Page   Web Page Cache   
Experience conducting business value assessments. Experience in Predictive Analytics and/or Machine Learning....
From GoodData Corporation - Thu, 19 Jul 2018 07:11:21 GMT - View all Boston, MA jobs
          From Tableau to Elastic: How Samtec Streamlined Business Intelligence & Analytics      Cache   Translate Page   Web Page Cache   

Let’s get this out of the way: we’re not the typical Elastic users. While most use Elasticsearch for logging, security, or search; we use Kibana and Elasticsearch for business intelligence across our enterprise from sales to manufacturing. Also, we have applications using Elasticsearch as a primary data store, and our “production” cluster is often running pre-releases to take advantage of the newest functionality. That being so, our origin with Elastic is likely the same as yours — it all started with needing a place to dump a whole bunch of logs.

Samtec builds electrical interconnects found in products ranging from medical devices, servers to self-driving cars. Our group — Smart Platform Group — was born from an area of Samtec that builds high-speed copper and optical interconnects. Samtec’s FireFly™ family of interconnects are a great example. They have 192 Gbps of bandwidth packed onto a device the size of a dime.

samtec_1.png

The process to manufacture Firefly™ requires hundreds of steps. Almost every step gets performed by equipment that produces logs containing volumes of data. For example, one step of the process is to place silicon die onto a printed circuit board. The placement gets measured in microns (1/1000th of a millimeter), and the machine that does the placement holds onto each die with a small amount of vacuum pressure. That pressure gets logged for every placement. Those logs look something like this:

"die_count_x" => "integer"
"die_count_y" => "integer"
"commanded_pick_force" => "float"
"actual_pick_force" => "float"
"commanded_place_force" => "float"
"actual_place_force" => "float

We needed to start with capturing the data that would be lost over time as the machines rotated their logs. Logstash, and by extension Elasticsearch, were determined to be the quickest and most cost-effective solution, requiring the least development and maintenance effort. So we wrote some Logstash configs and started dumping data into Elasticsearch. It may not have been pretty, but it worked and we more-or-less forgot about it. That was late 2015.

Three months went by, then six, then a year — and the data was never touched. Finally, one fateful day a customer request came in with an early device failure “in the field”. We needed to look back to when that customer’s order was running on a specific machine to see if everything looked normal in the production process.

Cue our first Kibana visualization, a simple line chart showing the force used to pick up that small silico die. We knew the build date of the device, so we filtered our chart by date and got something similar to the below.

samtec_2.jpg

Each dip in the top line represents a period where the force seems to have dropped suspiciously low. Naturally, we had lots of questions, but the most pressing was: did the device in question get built during one of those periodic dips? The exact times each order and piece got ran was stored in our SQL database, and we needed to bring the two pieces of data together. Each machine had a vastly different data structure, and the logistics of moving them all into SQL seemed problematic. However, moving tabular SQL data into Elasticsearch was a well-documented path with Logstash. So we wrote another Logstash config to bring in the SQL data and leveraged some Kibana-fu to overlay orders and serial numbers onto some time series charts.

samtec_3.jpg

Given the above, it looked like there were a couple of transactions that occurred during a time of “low pick force”. We were going to have to review those in more depth.

What started as one or two Logstash configs was threatening to turn into hundreds as the number of data sources we wanted to pull into Elasticsearch grew. Rather than continue to spend developer time creating Logstash configs, a product idea was born focused on making Elasticsearch data imports more accessible. Moreover, we wanted to enable self-service Business Intelligence using the Elastic Stack.

Enter Conveyor

Conveyor is an open source plugin that we wrote with data analysts and business users in mind. We wanted a graphical, interactive way for loading data into Elasticsearch. We wanted it to be easily extended to support different data sources, and we wanted it to integrate well into the Elastic Stack.

samtec_4.jpg

So now that any data is just a few buttons away from being in Elastic, what kind of possibilities are within an easy reach?

We’ve replaced our Tableau Dashboards with Kibana ones. In this case a heads-up display for showing order status in a manufacturing center. Auto-refresh meant the display showed new orders near real-time.

samtec_5_6_2.png#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

We’ve used Conveyor to pull in bill of materials and inventory data. We put it all in to Graph to quickly trace suspect lots through manufacturing and to identify other affected lots. In this case, we were able to easily determine customer orders (green circles) that consumed a suspect lot (red circle) even though there was a sub-assembly process that occurred midstream (pink circles).

samtec_7.jpg

We’ve also used Conveyor to pull in process control data and analyzed it for anomalies using Elastic machine learning. In this case, we analyze a metric that gauges the health of a test station — and we can easily identify or be alerted when it isn’t as expected.

samtec_8.jpg

Finally, we use Conveyor and Kibana together to build powerful dashboards on all sorts of business metrics, like GitHub Repository Statistics, for our open source chatbot platform called Articulate to cheer-on the increasing downloads and stars!

samtec_9.jpg

What’s Next

We’ve now been using Elasticsearch as a primary data store for our projects for more than four years and though we don’t have big data like some users of Elastic we have broad data. With the introduction of Conveyor, as well as new Kibana functionality like Canvas and Vega visualizations, we strongly believe that the Elastic Stack is the best open source business intelligence platform. To reinforce that we’ll leave you with three thoughts:

  • Joining disparate data sources in a single analytical tool is extremely powerful and compounds the value of your data.
  • Dynamic mappings and re-index capabilities make Elasticsearch an excellent collect now, analyze later data store.
  • Cross-index search enables powerful information gathering on business data. (Want all sales orders, shipments, and contacts for a customer? Just search for their name across multiple indices).

We hope to be back to share more in-depth walkthroughs on using Conveyor with Vega and Canvas, as well as our experience with the new Elastic User Interface (EUI) library. We’re happy to share our experiences so keep an eye out for those here or on our site at https://blog.spg.ai. Also, be sure to check out Conveyor


Caleb Keller, Woo Ee, and Mike Lutz head a team of data lovers, tinkerers, and technology enthusiasts building open-source solutions for Samtec's Smart Platform Group


          Sr Director, Growth Marketing Technology - eBay Inc. - Bellevue, WA      Cache   Translate Page   Web Page Cache   
Further, the Marketing Tech Leader will apply the latest data analysis and machine learning technologies to innovate applications in both BI analysis and...
From eBay Inc. - Fri, 01 Jun 2018 08:04:49 GMT - View all Bellevue, WA jobs
          Software Engineer - Machine Learning - Convoy - Seattle, WA      Cache   Translate Page   Web Page Cache   
Today, we use machine learning to figure out freight prices, shipment relevance for carriers, auction bidding strategy, and other internal processes....
From Convoy - Sat, 19 May 2018 10:13:22 GMT - View all Seattle, WA jobs
          Global Embedded AI Computing Platforms Market insights by Growth, Size, Supply, Demand, Comparative Analysis, Competitive market share forecast      Cache   Translate Page   Web Page Cache   
Global embedded AI computing Platforms market report take overview  of existing efforts by media, research firms, opportunity, development, price trade and others who have attempted to move from the eagle view of the AI industry to categorizing technologies under the grand umbrella. The AI marketplace is positioned to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection and many others. Global Embedded AI...
          Approximate Computing      Cache   Translate Page   Web Page Cache   
This special issue of IEEE Micro explores exciting, new ideas in the vast design space of approximate computing. We present articles that range from programming languages to circuits and cover important application domains such as machine learning and the Internet of Things.
          SiMul: An Algorithm-Driven Approximate Multiplier Design for Machine Learning      Cache   Translate Page   Web Page Cache   
The need to support various machine learning (ML) algorithms on energy-constrained computing devices has steadily grown. In this article, we propose an approximate multiplier, which is a key hardware component in various ML accelerators. Dubbed SiMul, our approximate multiplier features user-controlled precision that exploits the common characteristics of ML algorithms. SiMul supports a tradeoff between compute precision and energy consumption at runtime, reducing the energy consumption of the accelerator while satisfying a desired inference accuracy requirement. Compared with a precise multiplier, SiMul improves the energy efficiency of multiplication by 11.6x to 3.2x while achieving 81.7-percent to 98.5-percent precision for individual multiplication operations (96.0-, 97.8-, and 97.7-percent inference accuracy for three distinct applications, respectively, compared to the baseline inference accuracy of 98.3, 99.0, and 97.7 percent using precise multipliers). A neural accelerator implemented with our multiplier can provide 1.7x (up to 2.1x) higher energy efficiency over one implemented with the precise multiplier with a negligible impact on the accuracy of the output for various applications.
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Virtual machine switching); Large scale distributed systems, real-time data analysis, machine learning, windows internals (networking stack and other OS...
From Microsoft - Thu, 09 Aug 2018 04:41:50 GMT - View all Redmond, WA jobs
          Economist - Forecasting - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Experience with machine learning applications. We are breaking fresh ground, pioneering in a program that is crucial for future Amazon growth, and our business...
From Amazon.com - Wed, 27 Jun 2018 07:21:23 GMT - View all Seattle, WA jobs
          Machine Learning Engineer - Flex A.I. - Vancouver, BC      Cache   Translate Page   Web Page Cache   
Keep in mind our wages will only temporarily be at this level until the company skyrockets in growth in the next year, at which point we will likely provide... $70,000 - $120,000 a year
From Indeed - Sun, 15 Jul 2018 01:01:22 GMT - View all Vancouver, BC jobs
          Machine Learning Engineer - Technica Corporation - Dulles, VA      Cache   Translate Page   Web Page Cache   
Technica Corporation is seeking a Machine Learning Engineer to support our internal Innovation, Research and Development (IRD) team....
From Technica Corporation - Wed, 11 Jul 2018 06:07:15 GMT - View all Dulles, VA jobs
          How Primary Care Data and Machine Learning Can Detect Dementia      Cache   Translate Page   Web Page Cache   
The researchers built a model to predict whether a patient had dementia, based on information known as Read data, which are routinely collected by primary care doctors in the United Kingdom’s National Health System. They found that of nearly 25,000…

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          Microsoft, AT&T and Nike on AI      Cache   Translate Page   Web Page Cache   

We’re on the threshold of a new era, where rapid advances in artificial intelligence, the internet of things, cloud computing, and automation will transform how we live and work.

Ethical Corporation has just published a new 40-page briefing that delves into the impact of AI on business and society and I wanted to send this across to you -you can access the report here.

There’s 40-pages of expert response and analysis from the likes of Danone, Nike, Flex, AT&T, PwC, Infosys, Microsoft, Sodexo and many more on:

  • All change: How AI is disrupting business

  • The reskilling challenge: Who will mind the robots?

  • Apocalypse soon? Fears rise of AI arms race

  • AI for good: How tech could transform sustainability

  • Machine learning: Automation case studies

Click here to receive the 40-page briefing


          Machine Learning SW Engineer      Cache   Translate Page   Web Page Cache   
MD-Rockville, Our client now has an open Machine Learning SW Engineer opening. It is a SW Engineering role with Machine Learning. If the right candidate had Deep Learning concepts (Algorithm using Neural Networks) that would be a plus but at least Machine Learning. Mission: There is a NEW Data Strategy for our client to enable to solve DATA problems more efficiently. DAY to DAY: Will be working with Product Tea
          Amazon Redshift automatically enables short query acceleration      Cache   Translate Page   Web Page Cache   

Amazon Redshift now enables short query acceleration by default to speed up execution of short-running queries such as reports, dashboards, and interactive analysis. Short query acceleration uses machine learning to provide higher performance, faster results, and better predictability of query execution times. 


          iOS Developer - PGS SOFTWARE - Rzeszów, podkarpackie      Cache   Translate Page   Web Page Cache   
Augmented Reality, Machine Learning, iBeacons, Top Level Security. Elastyczne godziny pracy....
Od PGS SOFTWARE - Wed, 08 Aug 2018 14:51:19 GMT - Pokaż wszystkie Rzeszów, podkarpackie oferty pracy
          Algorithm Developer      Cache   Translate Page   Web Page Cache   
We are looking for a candidate that will be responsible for developing algorithms which will form the basis of our mathematical models for our understanding of sports betting markets, which will be used for automation. The candidate MUST have a strong background in Machine Learning and Algorithm Development experience. Our ideal candidate should have: Degree/Diploma […]
          vulcan 0.1.4      Cache   Translate Page   Web Page Cache   
Terminal-based flashcard application, for developers, that uses machine learning to schedule reviews
          Smart Device & AI Device Usage Statistics      Cache   Translate Page   Web Page Cache   
Artificial intelligence is becoming a bigge part of our lives with every passing day. Machine learning programs are gathering ever more data, which they use as feedback to improve their functionality and drive progress. AI technology continues to grow and improve, and so does its applicability in the business world. CEOs and other executives at … Continue reading "Smart Device & AI Device Usage Statistics"
          Innovation Developer - TeamSoft - Sun Prairie, WI      Cache   Translate Page   Web Page Cache   
Are you interested in topics like machine learning, IoT, Big data, data science, data analysis, satellite imagery or mobile telematics?...
From Dice - Thu, 19 Jul 2018 08:35:55 GMT - View all Sun Prairie, WI jobs
          Vice President, Data Science - Machine Learning - Wunderman - Dallas, TX      Cache   Translate Page   Web Page Cache   
Goldman Sachs, Microsoft, Citibank, Coca-Cola, Ford, Pfizer, Adidas, United Airlines and leading regional brands are among our clients....
From Wunderman - Thu, 26 Apr 2018 16:49:34 GMT - View all Dallas, TX jobs
          [עושים תוכנה] מצילים חיי אדם באמצעות Deep Learning      Cache   Translate Page   Web Page Cache   

אם אתם מסתובבים בעולם התוכנה וכנראה שגם אם לא, אתם שומעים כמה פעמים ביום את צמד המילים Machine Learning.
פה ושם כנראה גם שמעתם את צמד המילים של המגניבות החדשות בשכונה : Deep Learning

אבל..האם טרחתם להתעמק (מצחיק!) ולהבין מה זה אומר? האם העזתם להתנסות בתחום בעצמכם?

היחס בין הכמות שנאמרות המילים הללו לבין השימוש בהם בפועל בצורה אמיתית ונכונה הוא מקרי בהחלט.
לכן, כדי שתוכלו להבין קצת מעבר , הבאנו בפרק החדש של ״עושים תוכנה״ את גיא ריינר אחד המייסדים של חברת aidoc.

גיא ביחד עם שותפיו הנהדרים אלעד וולך ומיכאל ברגינסקי, פיתחו מערכת שעוזרת לרדיולוגים לנתח צילומי CT ורנטגן ובעצם עוזרת לייעל תהליכים ואולי אפילו מצילה חיי אדם.
בלי השימוש בDeep Learning לא בטוח שהם היו מצליחים לעשות זאת ותהיו בטוחים שהדרך שהם עשו בשנים האחרונות לא הייתה קלה בכלל.

The post [עושים תוכנה] מצילים חיי אדם באמצעות Deep Learning appeared first on עושים היסטוריה.


          Extracting value from social and news data      Cache   Translate Page   Web Page Cache   
The use of machine learning and AI techniques has opened new avenues for quantitative fund managers to derive value from traditional and non-traditional data sources everywhere in the world.
          Software Development Engineer - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
2 years experience working on machine learning based models. The engineer will play a pivotal role in the expansion of pricing software, with the mission to...
From Amazon.com - Fri, 27 Jul 2018 19:19:19 GMT - View all Seattle, WA jobs
          Technical Program Manager, Links Machine Learning - Google - Seattle, WA      Cache   Translate Page   Web Page Cache   
You plan requirements with internal customers and usher projects through the entire project lifecycle. We build the technologies that transform the way we think...
From Google - Thu, 26 Jul 2018 08:23:26 GMT - View all Seattle, WA jobs
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Seattle, WA      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:37:13 GMT - View all Seattle, WA jobs
          Data Architect - Remote West coast - Insight Enterprises, Inc. - Dallas, TX      Cache   Translate Page   Web Page Cache   
R, Azure Machine Learning. 2017 Arizona’s Most Admired Companies (AZ Business Magazine), 2016 Best Places to Work (Phoenix Business Journal)....
From Insight - Mon, 14 May 2018 23:57:10 GMT - View all Dallas, TX jobs
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Portland, OR      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:41:10 GMT - View all Portland, OR jobs
          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page   Web Page Cache   
Database architecture, Big Data, Machine Learning, Business Intelligence, Advanced Analytics, Data Mining, ETL. Internal teammate application guidelines:....
From Insight - Thu, 12 Jul 2018 01:56:10 GMT - View all Chicago, IL jobs
          109: Neural Network C# Predictions for Everyone      Cache   Translate Page   Web Page Cache   
It is that time again for more machine learning! This time it is actually something that you can totally build and something that Frank shipped inside of his application to do code prediction using Python, Keras, PlaidML, and CoreML! We talk about the main use case, the route Frank took to create the machine learning model, what hardware and software he used, and the final outcome to predict code while you type. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us (https://itunes.apple.com/us/podcast/merge-conflict/id1133064277?mt=2&ls=1) ⭐⭐ SUPPORT US ON PATREON: patreon.com/mergeconflictfm Special Thanks to Syncfusion: Download their e-bools: * Xamarin.Forms Succinctly (https://www.syncfusion.com/ebooks/xamarin_forms_succinctly?utm_source=podcasts&utm_medium=list&utm_campaign=mergexampodcy18) * Xamarin.Forms for macOS Succinctly (https://www.syncfusion.com/ebooks/xamarin_forms_for_mac_os_succinctly?utm_source=podcasts&utm_medium=list&utm_campaign=mergexampodcy18)
          Innovation Developer - TeamSoft - Sun Prairie, WI      Cache   Translate Page   Web Page Cache   
Are you interested in topics like machine learning, IoT, Big data, data science, data analysis, satellite imagery or mobile telematics?...
From Dice - Thu, 19 Jul 2018 08:35:55 GMT - View all Sun Prairie, WI jobs
          tf-nightly-gpu 1.11.0.dev20180810      Cache   Translate Page   Web Page Cache   
TensorFlow is an open source machine learning framework for everyone.
          tf-nightly 1.11.0.dev20180810      Cache   Translate Page   Web Page Cache   
TensorFlow is an open source machine learning framework for everyone.
          How Primary Care Data and Machine Learning Can Detect Dementia      Cache   Translate Page   Web Page Cache   
The researchers built a model to predict whether a patient had dementia, based on information known
          PRACTICAL SSRS 2017 REALTIME Online Training (DELHI)      Cache   Translate Page   Web Page Cache   
SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning ...
          LIVE Online Training ON SSRS 2017 WITH PROJECT (DELHI)      Cache   Translate Page   Web Page Cache   
SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning ...
          Business Strategy, Sr. Manager - Hortonworks - Dallas, TX      Cache   Translate Page   Web Page Cache   
Business Strategy, Leadership Opportunity. Experience in the Software and/or Business Impact of Analytics, Big Data, Machine Learning/AI, Cloud is a plus....
From Hortonworks - Mon, 23 Jul 2018 20:31:09 GMT - View all Dallas, TX jobs
          Business Strategy, Sr. Manager - Hortonworks - Atlanta, GA      Cache   Translate Page   Web Page Cache   
Business Strategy, Leadership Opportunity. Experience in the Software and/or Business Impact of Analytics, Big Data, Machine Learning/AI, Cloud is a plus....
From Hortonworks - Mon, 23 Jul 2018 20:31:09 GMT - View all Atlanta, GA jobs
          CSI-INFRA - SQL Server DBA - CSI Jobs - Charlotte, NC      Cache   Translate Page   Web Page Cache   
Configured 3M MVS database systems. Worked on Power BI tools and machine learning tools to admin and development data ware house internal projects....
From CSI Jobs - Thu, 09 Aug 2018 21:01:53 GMT - View all Charlotte, NC jobs
          Sr. Product Marketing Manager - Automation Anywhere - San Jose, CA      Cache   Translate Page   Web Page Cache   
Experience in artificial intelligence, analytics, machine learning or business process management software especially in the enterprise space is a big plus but...
From Automation Anywhere - Sat, 16 Jun 2018 05:57:32 GMT - View all San Jose, CA jobs
          Director, Product Marketing - Security - Automation Anywhere - San Jose, CA      Cache   Translate Page   Web Page Cache   
Experience in artificial intelligence, analytics, machine learning or business process management software especially in the enterprise space is a big plus but...
From Automation Anywhere - Sat, 16 Jun 2018 05:57:32 GMT - View all San Jose, CA jobs
          Director, Product Marketing - Enterprise - Automation Anywhere - San Jose, CA      Cache   Translate Page   Web Page Cache   
Experience in artificial intelligence, analytics, machine learning or business process management software especially in the enterprise space is a big plus but...
From Automation Anywhere - Sat, 16 Jun 2018 05:57:32 GMT - View all San Jose, CA jobs
          The Top Five Data Innovations Transforming Wildlife Conservation: Artificial Intelligence for Wildlife Conservation Workshop 2018      Cache   Translate Page   Web Page Cache   
As part of the ICML and IJCAI conferences, I was honored to give the invited keynote talk at the Artificial Intelligence for Wildlife Conservation (AIWC) workshop.  It contained a strong agenda of many compelling speakers presenting their work using machine learning to solve challenges in wildlife conservation.  The topic of my talk was innovations in...
          Fixed Income Software Engineer      Cache   Translate Page   Web Page Cache   
NY-NEW YORK CITY, A prominent, data based global technology firm is currently seeking a Senior Software Engineer to join their team in New York. The firm's systems are very large and highly distributed, and engineers are always looking for creative solutions to solve problems, including employing a variety of modern programming languages, open source and big data technologies, as well as Machine Learning and Natura
          Resume for Computer Vision or Human Computer Interaction - Upwork      Cache   Translate Page   Web Page Cache   
Need a resume which deals with computer vision and human computer interaction projects. I need this resume so that I could apply for jobs. So, a 4 years experience resume on AI needed.

Budget: $20
Posted On: August 10, 2018 09:59 UTC
ID: 213914444
Category: Data Science & Analytics > Machine Learning
Skills: Artificial Intelligence, Computer Vision
Country: India
click to apply
          Senior QA Engineer      Cache   Translate Page   Web Page Cache   
CA-Santa Clara, If you are a Senior QA Engineer with strong frontend experience, please read on! We are a health and wellness startup that applies data science, machine learning and other innovative technologies to deliver comprehensive data and analytics. What You Will Be Doing You will design and implement test processes, manage bug fixes and document QA metrics according to industry standards. - Develop test a
          Software Developer (Machine Learning) - Lincoln Electric - Cleveland, OH      Cache   Translate Page   Web Page Cache   
Experience with popular languages (C++, C#, Java, Python, and R). 3 - 10 years of experience with Windows, Linux, or Java platforms....
From The Lincoln Electric Company - Thu, 19 Apr 2018 18:30:28 GMT - View all Cleveland, OH jobs
          Data Scientist      Cache   Translate Page   Web Page Cache   
NSW-Sydney, Data Scientist - SYD46781 The Role We are looking for Data Scientists. Through the application of data mining, predictive analytics and machine learning techniques they will support teams across the bank. The ideal candidate is adept at using large data sets and streaming data to find opportunities to help optimise portfolio performance, identify customer needs and test the effectiveness of differ
          Solutions Architect - NVIDIA - Washington State      Cache   Translate Page   Web Page Cache   
Assist field business development in through the enablement process for GPU Computing products, technical relationship and assisting machine learning/deep...
From NVIDIA - Fri, 20 Apr 2018 08:02:03 GMT - View all Washington State jobs
          Embedded ML Developer - Erwin Hymer Group North America - Virginia Beach, VA      Cache   Translate Page   Web Page Cache   
NVIDIA VisionWorks, OpenCV. Game Development, Accelerated Computing, Machine Learning/Deep Learning, Virtual Reality, Professional Visualization, Autonomous...
From Indeed - Fri, 22 Jun 2018 17:57:58 GMT - View all Virginia Beach, VA jobs
          US Federal CTO - Solution Architect - NVIDIA - Virginia      Cache   Translate Page   Web Page Cache   
Build and cultivate internal understanding of Federal customer data analytics and machine learning requirements among the NVIDIA technical community including...
From NVIDIA - Tue, 07 Aug 2018 01:54:32 GMT - View all Virginia jobs
          Solutions Architect - NVIDIA - Virginia      Cache   Translate Page   Web Page Cache   
Build and cultivate internal understanding of data analytics and machine learning among the NVIDIA technical community....
From NVIDIA - Sun, 03 Jun 2018 08:00:36 GMT - View all Virginia jobs
          Software Engineer | Python Backend (New York) - BenevolentAI - New York, NY      Cache   Translate Page   Web Page Cache   
Machine Learning Squads. All employment is decided on the basis of qualifications, merit, and business need. Fun internal events (boat parties, karting, Oktober...
From BenevolentAI - Thu, 28 Jun 2018 21:30:28 GMT - View all New York, NY jobs
          Senior Solution Architect - Cyber Security - NVIDIA - Maryland      Cache   Translate Page   Web Page Cache   
You will also be an internal champion for Cyber Security and Machine Learning among the NVIDIA technical community....
From NVIDIA - Wed, 01 Aug 2018 07:57:49 GMT - View all Maryland jobs
          Solutions Architect - NVIDIA - Maryland      Cache   Translate Page   Web Page Cache   
You will also be an internal champion for Data Analytics and Machine Learning among the NVIDIA technical community....
From NVIDIA - Sun, 08 Jul 2018 07:55:18 GMT - View all Maryland jobs
          Vice President of Engineering - IDx LLC - Coralville, IA      Cache   Translate Page   Web Page Cache   
Work with world-renowned doctors who are pushing the limits of machine learning in medicine. Build a performance driven Engineering team through the development...
From IDx LLC - Tue, 17 Apr 2018 23:17:11 GMT - View all Coralville, IA jobs
          Solutions Architect, Accelerated Computing - NVIDIA - Santa Clara, CA      Cache   Translate Page   Web Page Cache   
Assist field business development in through the enablement process for GPU Computing products, technical relationship and assisting machine learning/deep...
From NVIDIA - Tue, 24 Jul 2018 07:56:24 GMT - View all Santa Clara, CA jobs
          Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Director, Data & AI - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Senior Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:23 GMT - View all Montréal, QC jobs
          Architecte de solutions, Groupe d'analyse de données - PwC - Montréal, QC      Cache   Translate Page   Web Page Cache   
They understand business, are conversant across a number of modern data &amp; analytics domains such as Big Data, advanced analytics, machine learning, advanced...
From PwC - Fri, 13 Jul 2018 10:27:51 GMT - View all Montréal, QC jobs
          Solution Architect, Data Analytics Group - PwC - Montréal, QC      Cache   Translate Page   Web Page Cache   
They understand business, are conversant across a number of modern data &amp; analytics domains such as Big Data, advanced analytics, machine learning, advanced...
From PwC - Fri, 13 Jul 2018 10:26:41 GMT - View all Montréal, QC jobs
          Alles, was du über KI wissen musst – unsere t3n Podcasts zum Thema      Cache   Translate Page   Web Page Cache   
Künstliche Intelligenz wird die Welt stärker verändern als das Internet, glaubt Salesforce-Chefwissenschaftler Richard  Socher. Kein anderes Thema bewegt die IT-Welt so stark. Auch beim t3n Podcast ist es ein Schwerpunkt-Thema – wir zeigen euch alle bisherigen Folgen zu KI.

Wie setzen Unternehmen KI ein?

Wenn Experten, Wissenschaftler und Journalisten über die künstliche Intelligenz diskutieren, geht es oft um grundsätzliche Fragen: Was unterscheidet Mensch und Maschine oder wird es eines Tages KIs geben, die intelligenter sind als Menschen? Dabei bleibt oftmals der Umstand auf der Strecke, dass intelligente Algorithmen in der Wirtschaft vor allem dann zum Einsatz kommen, wenn eine Steigerung der Effizienz möglich ist. In dieser Episode soll deshalb ein pragmatischer Blick auf künstliche Intelligenz geworfen werden. Print-Chefredakteur Luca Caracciolo spricht dazu mit dem Leiter der Machine-Learning-Foundation von SAP, Sebastian Wieczorek, über konkrete Anwedungsfälle von Machine Learning in Unternehmenssoftware und wie das die tägliche Arbeit verändert.

Salesforce-Chefwissenschaftler: „KI wird einen noch größeren Einfluss auf die Menschheit haben als das Internet“

Richard Socher, geboren und aufgewachsen in Deutschland, ist Professor für künstliche Intelligenz an der Stanford University und Chefwissenschaftler von Salesforce. Im t3n Podcast berichtet er, was KI heute wirklich kann – und welche Alternativen zu Machine Learning existieren.

USA versus China versus Europa: Der große Wettlauf um die Vorherrschaft bei künstlicher Intelligenz

Künstliche Intelligenz gilt als Schlüsseltechnologie der kommenden Jahre. Deshalb ist es nicht verwunderlich, dass Unternehmen und Staaten auf der ganzen Welt um die klügsten Köpfe buhlen.

FDP-Chef Christian Lindner fordert CERN für künstliche Intelligenz in Europa

FDP-Chef Christian Lindner fordert im t3n Podcast eine gemeinsame europäische Anstrengung, um die Grundlagenforschung im Bereich der künstlichen Intelligenz voranzubringen. Vorbild könnte das CERN sein.

Ranga Yogeshwar erklärt, wie künstliche Intelligenz auch dein Leben verändert

Wie wird künstliche Intelligenz unser Leben verändern? Und was unterscheidet uns Menschen eigentlich von den Maschinen? Im t3n Podcast liefert Ranga Yogeshwar die Antworten.

Bedroht künstliche Intelligenz unsere Demokratie?

Künstliche Intelligenz verändert zunehmend, wie wir arbeiten und leben. Was aber, wenn sie die Grundlagen unseres Zusammenlebens bedroht?

Weniger arbeiten, mehr erreichen: Neue Freiräume durch künstliche Intelligenz und Automatisierung

Weniger arbeiten, mehr erreichen: Das könnte mit dem zunehmenden Einsatz von künstlicher Intelligenz gelingen. Wie nutzen Unternehmen aber die neu gewonnen Freiräume sinnvoll?

Sebastian Thrun: „KI wird alle repetitiven Aufgaben des Menschen ersetzen“

Sebastian Thrun hat für Google an selbstfahrenden Autos geforscht und will mit Udacity die Bildung revolutionieren. Im Podcast spricht er über die Chancen der KI und warum im Silicon Valley jeder mit Deep-Learning-Fähigkeiten einen Job bekommt.

Künstliche Intelligenz: Hat die Entwicklung bereits ihren Höhepunkt erreicht?

Wenn heute über Digitalisierung gesprochen wird, geht es meist auch um KI. Wir diskutieren im Podcast, was von der Technologie in Zukunft zu erwarten ist.

Deutscher Microsoft-Technikchef: Wohin führt die Explosion der künstlichen Intelligenz?

Der Boom von Machine Learning, insbesondere Deep Learning, ist weiter eines der größten Hype-Themen der Tech-Branche. Im t3n-Filterblase-Podcast erläutert Microsofts Technikchef für Deutschland die Hintergründe.

Droht das Menschheitsende durch die Superintelligenz?

Starke und schwache KI, Machine Learning, Neuronale Netze – was steckt eigentlich dahinter? t3n.de-Chefredakteur Stephan Dörner und Luca Caracciolo, Chefredakteur des Print-Magazins, erklären die wichtigsten Begriffe und diskutieren Anwendungsbeispiele und Einsatzszenarien der einzelnen KI-Disziplinen in der Wirtschaft. Abschließend geht es um die Superintelligenz – was passiert eigentlich, wenn KI-Systeme eines Tages die kognitiven Fähigkeiten des Menschen übersteigen?

t3n Podcast abonnieren

Ihr könnt den t3n Podcast bequem in der Podcast-App eurer Wahl abonnieren. In der Regel findet ihr den Podcast, wenn ihr ihn dort einfach sucht. Ansonsten könnt ihr auch den RSS-Feed manuell in der App eingeben.
          Parks Associates: 42% of Consumers 50 and Older Are Very Interested in Home Systems That Sense Emergencies      Cache   Translate Page   Web Page Cache   
...necessary people if it allows them to continue living independently. The IoT research firm will explore new use cases for healthcare emerging from smart home, AI, and machine learning innovations at its fifth-annual Connected Health Summit: Engaging Consumers , August 28 ...


          Zögerlicher Einsatz von Machine Learning      Cache   Translate Page   Web Page Cache   
Maschinelles Lernen (ML) hat deutliche Fortschritte gemacht, ist im Unternehmensumfeld aber nach wie vor Mangelware. Das hat viele Gründe.
          Delivering Real Time Analytics with Sitecore and DataStax      Cache   Translate Page   Web Page Cache   

In one of myearlier articles, I talked about why your company or organization should adopt Sitecore as your Experience Platform. Its a good platform for users, content authors, and developers to create compelling and engaging digital experiences as well as collect information on website traffic. Machine learning and analytics in personalized content are two of the most compelling features of Sitecore. In today’s world, companies particularly the Fortune 500, require real-time analytics to help drive stakeholder goals.

It’s Tradition

Traditionally Sitecore used MongoDB as their experience database (xDB) of choice for storing and retrieving analytics. However, with the latest version of Sitecore, the company is moving to more options for development teams to use to fit their needs especially if they require real-time analytics. There are now options for using SQL Server’s new provider for NoSQL data. In fact, at the time of the writing, the only option for Sitecore 9 xDB deployment is the SQL Server Provider. The company has planned support for MongoDB but sent a clear message with their change of xDB choice in the latest version. The platform is also looking at expanding to higher end distributed NoSQL databases such as Microsoft Azure CosmosDB. This would require an Azure subscription but would offer features to support distributed analytics.

Why DataStax?

DataStax Enterprise (DSE) is analways-on, distributed cloud database built on Apache Cassandra and designed for the hybrid cloud. Our firm is making the argument that Apache Cassandra, and more importantly DataStax, should be used as your Analytics xDB option if you are building experiences for the Right-Now Economy . These are usually systems which use IoT ( internet of things ) or have global demand from a user audience of hundreds of millions and thus can never fail. That goes double for the analytical operations you run on the real-time data you are storing.

DataStax over the Competition

Cassandra and DataStax clearly outperform MongoDB and other rivals in Throughput by Workload and Load Process benchmarks. They also provide no single point of failure and more consistency models to support high-level operations. Cassandra is completely free and open source and supports both cloud or on-premise (translation you won’t need an Azure subscription like CosmosDB) but the real special sauce is with DataStax. DataStax is a commercial product, however, it is almost always used if Cassandra is being deployed on an enterprise level scale. DSE integrates Cassandra with graph, search, analytics, administration, developer tooling, and monitoring all in one platform. With Mongo or other NoSQL competitors, developers would have to piece together these functionalities with third-party options instead of native out of the box support. Developers can also create Spark jobs and see analytical data or personalize content in real time no matter how many users are viewing the experience. Other systems support Spark, however, they are usually deployed in a master to slave or parent to child relationship providing points of failure for both your users and your analytical operations. Furthermore, they tend to face challenges when an application needs to be global.

OurBusiness Platform Services

Need help with a Business Platform implementation or guidance in creating a tailor-fit design & architecture? Our team has decades of Business Platform experience and can help you transition onto the next phase of your technology eco-system, whether it be using Sitecore and DataStax, or simply a combination of common SaaS software like WordPress and Salesforce. Don’t know where to start? Check out ourservices or send us aquick email!

Resources DataStax Corporate DSE vs MongoDB

Photo by Carlos Muza on Unsplash


          Cloudera CCA 175 Spark Developer Certification: Hadoop Based      Cache   Translate Page   Web Page Cache   

Cloudera CCA 175 Spark Developer Certification: Hadoop Based
Description

Featured on: Aug 2, 2018

Get Hands-on Experience as to how they themselves can become Spark Application Developers. Become masters at working with Spark DataFrames, HiveQL, and Spark SQL. Understand how to control importing and exporting of Data in Spark through Apache Sqoop in the exact format that is needed. Learn all Spark RDDs Transformations and Actions needed to analyze Big Data. Become absolutely ready for the Cloudera Spark CCA 175 Certification Exam. This course is designed to cover the end-to-end implementation of the major components of Spark. I will be giving you hands on experience and insight into how big data processing works and how it is applied in the real world. We will explore Spark RDDs, which are the most dynamic way of working with your data. They allow you to write powerful code in a matter of minutes and accomplish whatever tasks that might be required of you. They, like DataFrames, leverage the Spark Lazy Evaluation and Directed Acyclic Graphs (DAG) to give you 100x better functionality than MapReduce while writing less than a tenth of the code. You can execute all the Joins, Aggregations,Transformations and even Machine Learning you want on top of Spark RDDs. We will explore these in depth in the course and I will equip you with all the tools necessary to do anything you want with your data.
          First Class GPUs support in Apache Hadoop 3.1, YARN & HDP 3.0      Cache   Translate Page   Web Page Cache   

This blog is also co-authored by Zian Chen and Sunil Govindan from Hortonworks.

Introduction Apache Hadoop 3.1, YARN, & HDP 3.0
First Class GPUs support in Apache Hadoop 3.1, YARN &amp; HDP 3.0
Without speed up with GPUs, some computations take forever! (Image from Movie “Howl’s Moving Castle”)

GPUs are increasingly becoming a key tool for many big data applications. Deep-learning / machine learning, data analytics , Genome Sequencing etc all have applications that rely on GPUs for tractable performance. In many cases, GPUs can get up to 10x speedups. And in some reported cases (like this ), GPUs can get up to 300x speedups! Many modern deep-learning applications directly build on top of GPU libraries like cuDNN (CUDA Deep Neural Network library). It’s not a stretch to say that many applications like deep-learning cannot live without GPU support.

Starting Apache Hadoop 3.1 and with HDP 3.0, we have a first-class support for operators and admins to be able to configure YARN clusters to schedule and use GPU resources.

Previously, without first-class GPU support, YARN has a not-so-comprehensive story around GPU support. Without this new feature, users have to use node-labels ( YARN-796 ) to partition clusters to make use of GPUs, which simply puts machines equipped GPUs to a different partition and requires jobs to be submitted that need GPUs to the specific partition. For a detailed example of this pattern of GPU usage, see Yahoo!’s blog post about Large Scale Distributed deep-learning on Hadoop Clusters .

Without a native and more comprehensive GPU support, there’s no isolation of GPU resources also! For example, multiple tasks compete for a GPU resource simultaneously which could cause task failures / GPU memory exhaustion, etc.

To this end, the YARN community looked for a comprehensive solution to natively support GPU resources on YARN.

First class GPU support on YARN GPU scheduling using “extensible resource-types “in YARN

We need to recognize GPU as a resource type when doing scheduling. YARN-3926 extends the YARN resource model to a more flexible model which makes it easier to add new countable resource-types. It also considers the related aspect of “resource profiles” which allow users to easily specify the resources they need for containers. Once we have GPUs type added to YARN, YARN can schedule applications on GPU machines. By specifying the number of requested GPU to containers, YARN can find machines with available GPUs to satisfy container requests.


First Class GPUs support in Apache Hadoop 3.1, YARN &amp; HDP 3.0
GPU isolation

With GPU scheduling support, containers with GPU request can be placed to machines with enough available GPU resources. We still need to solve the isolation problem: When multiple applications use GPU resources on the same machine, they should not affect each other.

Even if GPU has many cores, there’s no easy isolation story for processes sharing the same GPU. For instance, Nvidia Multi-Process Service (MPS) provides isolation for multiple process access the same GPU, however, it only works for Volta architecture, and MPS is not widely support by deep learning platforms yet. ,So our isolation, for now, is per-GPU device: each container can ask for an integer number of GPU devices along with memory, vcores (for example 4G memory, 4 vcores and 2 GPUs). With this, each application uses their assigned GPUs exclusively .

We use cgroups to enforce the isolation. This works by putting a YARN container a process tree into a cgroup that allows access to only the prescribed GPU devices. When Docker containers are used on YARN, nvidia-docker-plugin an optional plugin that admins have to configure is used to enforce GPU resource isolation.

GPU discovery

For properly doing scheduling and isolation, we need to know how many GPU devices are available in the system. Admins can configure this manually on a YARN cluster. But it may also be desirable to discover GPU resources through the framework automatically. Currently, we’re using Nvidia system management interface (nvidia-smi) to get number of GPUs in each machine and usages of these GPU devices. An example output of nvidia-smi looks like below:


First Class GPUs support in Apache Hadoop 3.1, YARN &amp; HDP 3.0
Web UI

We also added GPU information to the new YARN web UI. On ResourceManager page, we show total used and available GPU resources across the cluster along with other resources like memory / cpu.


First Class GPUs support in Apache Hadoop 3.1, YARN &amp; HDP 3.0

On NodeManager page, YARN shows per-GPU device usage and metrics:


First Class GPUs support in Apache Hadoop 3.1, YARN &amp; HDP 3.0
Configurations

To enable GPU support in YARN, administrators need to set configs for GPU Scheduling and GPU isolation.

GPU Scheduling

(1) yarn.resource-types in resource.type.xml

This gives YARN a list of available resource types supported for user to use. We need to add “yarn.io/gpu” here if we want to support GPU as a resource type

(2) yarn.scheduler.capacity.resource-calculator in capacity-scheduler.xml

DominantResourceCalculator MUST be configured to enable GPU scheduling. It has to be set to, org.apache.hadoop.yarn.util.resource.DominantResourceCalculator

GPU Isolation

(1) yarn.nodemanager.resource-plugins in yarn-site.xml

This is to enable GPU isolation module on NodeManager side. By default, YARN will automatically detect and config GPUs when above config is set. It should also add “yarn.io/gpu”

(2) yarn.nodemanager.resource-plugins.gpu.allowed-gpu-devices in yarn-site.xml

Specify GPU devices which can be managed by YARN NodeManager, split by comma Number of GPU devices will be reported to RM to make scheduling decisions. Set to auto (default) to let YARN automatically discover GPU resource from system.

Manually specify GPU devices if auto detect GPU device failed or admin only wants a s
          Software Engineering Lead - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Preferred experience with at least one of the Machine Learning related technologies (SAS, SPSS, RevR, Azure ML, MapR). Do you have a passion for Data?...
From Microsoft - Wed, 18 Jul 2018 02:08:52 GMT - View all Redmond, WA jobs
          Architect - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Preferred experience with at least one of the Machine Learning related technologies (SAS, SPSS, RevR, Azure ML, MapR)....
From Microsoft - Wed, 25 Apr 2018 01:08:05 GMT - View all Redmond, WA jobs
          How AI will Reinvent the Market Research Industry      Cache   Translate Page   Web Page Cache   
What kind of opportunities will AI bring to market research? Which tasks and activities are likely to be “outsourced” to machine learning in the coming years?

Qualtrics surveyed 250 verified market research decision makers to understand how they think AI will change the industry, and whether that change is creative or destructive.

Request Free!

          Achal Negi posted a blog post      Cache   Translate Page   Web Page Cache   
Achal Negi posted a blog post

Drone Security & Surveillance Operations Made Easy

Drones have already been in use in the security and surveillance industry, bringing a significant change in how the operations are carried out. However, most current aerial security and surveillance systems are either tied to a particular drone hardware, or need significant manual intervention during operation. These solutions lack critical features and software capabilities, such as, AI and machine learning for automated alerts, automatic mission scheduling, compatibility with wide-range of drone hardware, etc. This makes it expensive, and often infeasible, to deploy the drone-based security/surveillance solutions at scale.Drones have already established the value that they bring to the table, in terms of mobility, unrestricted bird’s eye view and accessibility. The focus is now on efficiencies and realising a meaningful return on investment for wide commercial adoption. This calls for integration of “intelligence” and “connectivity” with drones, to build completely automated and integrated workflows.FlytSecurity offers a plug-and-play, drone-agnostic, SaaS platform to quickly deploy and scale drone-based automated security operations. This significantly cuts down the cost of development and time to market, translating into an attractive ROI for the drone security service providers. With a wide range of features, like, 4G/LTE connectivity over unlimited range, live video, control and telemetry, fleet management (for simultaneous coverage of a large, distributed facilities), AI/ML for automated alerts, automated mission schedules, FlytSecurity enables fully-automated 24×7 operations at scale. Compatibility with any drone hardware, further makes FlytSecurity easy to adapt to variety of customer requirements (large/small drone, long/short endurance, quad-planes/multicopters, thermal/RGB sensor, etc.), and makes it easy to upgrade hardware at any time.For early access to FlytSecurity, please visit: https://flytsecurity.aiRead how FlytSecurity is transforming drone security & surveillance operationsSee More

          Data Architect - Remote West coast - Insight Enterprises, Inc. - Dallas, TX      Cache   Translate Page   Web Page Cache   
R, Azure Machine Learning. 2017 Arizona’s Most Admired Companies (AZ Business Magazine), 2016 Best Places to Work (Phoenix Business Journal)....
From Insight - Mon, 14 May 2018 23:57:10 GMT - View all Dallas, TX jobs
          Senior Manager, Software Engineering - DELL - Austin, TX      Cache   Translate Page   Web Page Cache   
Experience with machine learning and artificial intelligence. Learn more about Diversity and Inclusion at Dell here....
From Dell - Wed, 18 Jul 2018 11:23:18 GMT - View all Austin, TX jobs
          Director, Software Engineering - DELL - Austin, TX      Cache   Translate Page   Web Page Cache   
Experience with machine learning and artificial intelligence. Learn more about Diversity and Inclusion at Dell here....
From Dell - Sat, 07 Jul 2018 11:22:08 GMT - View all Austin, TX jobs
          Software Development Principal Engineer – Data Scientist - DELL - Austin, TX      Cache   Translate Page   Web Page Cache   
Learn more about Diversity and Inclusion at Dell here. Selecting features, building and optimizing classifiers using machine learning techniques....
From Dell - Sat, 07 Jul 2018 11:22:08 GMT - View all Austin, TX jobs
          Cloud Solution Architect - Microsoft - Philadelphia, PA      Cache   Translate Page   Web Page Cache   
Machine Learning (SAS, R, Python). Problem-solving mentality leveraging internal and/or external resources....
From Microsoft - Tue, 17 Apr 2018 18:34:17 GMT - View all Philadelphia, PA jobs
          A little bit of Machine Learning: Playing with Google's Prediction API      Cache   Translate Page   Web Page Cache   
Before we get started, let’s begin by making clear that this isn’t going to be a deep dive on TensorFlow, neural networks, inductive logic, Bayesian networks, genetic algorithms or any other sub-heading from the Machine Learning Wikipedia article. Nor is this really a Go-heavy article, but rather an introduction to machine learning via a simple consumption of the Google Prediction API. How the Google Prediction API works The Google Prediction API attempts to guess answers to questions by either predicting a numeric value between 0 and 1 for that item based on similar valued examples in its training data (“regression”), or choosing a category that describes it given a set of similar categorized items in its training data (“categorical”).
          Predicting genetic diseases with CloudForest      Cache   Translate Page   Web Page Cache   
CloudForest is a machine learning project dedicated to the construction of Random Forests built entirely in Go. It was created by Ryan Bressler. Random Forests are a machine learning algorithm based around the construction of many single classification trees, each splitting both the training set and the features available to train the model randomly. Each single tree is different from the others due to this random split and the ensemble of all the trees together is able to classify the data better than any single tree could do by itself.
          Senior Software Development Engineer - Distributed Computing Services (Hex) - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Knowledge and experience with machine learning technologies. We enable Amazon’s internal developers to improve time-to-market by allowing them to simply launch...
From Amazon.com - Thu, 26 Jul 2018 19:20:25 GMT - View all Seattle, WA jobs
          Senior Site Reliability Engineer - Sift Science - Seattle, WA      Cache   Translate Page   Web Page Cache   
The Sift Science Trust PlatformTM uses real-time machine learning to accurately predict which users businesses can trust, and which ones they can’t....
From Sift Science - Fri, 22 Jun 2018 20:18:59 GMT - View all Seattle, WA jobs
          Python Developer - MJDP Resources, LLC - Radnor, PA      Cache   Translate Page   Web Page Cache   
Assemble large, complex data sets that meet business requirements and power machine learning algorithms. EC2, Lambda, ECS, S3.... $30 - $40 an hour
From Indeed - Wed, 13 Jun 2018 13:41:07 GMT - View all Radnor, PA jobs
          Data Engineer - PYTHON - MJDP Resources, LLC - Devon, PA      Cache   Translate Page   Web Page Cache   
Assemble large, complex data sets that meet business requirements and power machine learning algorithms. EC2, Lambda, ECS, S3.... $100,000 - $120,000 a year
From Indeed - Tue, 31 Jul 2018 14:44:04 GMT - View all Devon, PA jobs
          Senior Software Engineer - Revenue Optimization - WeWork Global Technology - New York, NY      Cache   Translate Page   Web Page Cache   
Experience building Linear Programming solutions and Machine learning applications highly desired. Deep understanding of Amazon Web Services including ECS,...
From WeWork - Wed, 06 Jun 2018 05:19:01 GMT - View all New York, NY jobs
          Executive Director- Machine Learning & Big Data - JP Morgan Chase - Jersey City, NJ      Cache   Translate Page   Web Page Cache   
We would be partnering very closely with individual lines of business to build these solutions to run on either the internal and public cloud....
From JPMorgan Chase - Fri, 20 Jul 2018 13:57:18 GMT - View all Jersey City, NJ jobs
          Senior Backend Engineer - Tinder Trust - Tinder - West Hollywood, CA      Cache   Translate Page   Web Page Cache   
Implemented machine learning algorithms in production. Experience with Docker containers along with Kubernetes or ECS....
From Tinder - Sat, 23 Jun 2018 00:30:11 GMT - View all West Hollywood, CA jobs
          Machine Learning Architect - Epsilon - San Diego, CA      Cache   Translate Page   Web Page Cache   
Excellent understanding of machine learning techniques and algorithms. Experience implementing at least two Machine Learning pipelines in production....
From Epsilon - Wed, 18 Jul 2018 19:16:16 GMT - View all San Diego, CA jobs
          Senior Machine Learning Architect - AllianceData - San Diego, CA      Cache   Translate Page   Web Page Cache   
Excellent understanding of machine learning techniques and algorithms. Experience implementing at least two to three Machine Learning pipelines in production....
From AllianceData - Wed, 18 Jul 2018 16:07:56 GMT - View all San Diego, CA jobs
          Sales Engineer - Hitachi Vantara - New York, NY      Cache   Translate Page   Web Page Cache   
Account Managers, internal specialists and customers. Understanding of Data Science and Machine Learning....
From Hitachi Vantara - Sat, 04 Aug 2018 04:47:47 GMT - View all New York, NY jobs
          Machine Learning Engineer - Technica Corporation - Dulles, VA      Cache   Translate Page   Web Page Cache   
Technica Corporation is seeking a Machine Learning Engineer to support our internal Innovation, Research and Development (IRD) team....
From Technica Corporation - Wed, 11 Jul 2018 06:07:15 GMT - View all Dulles, VA jobs
          Product Manager, Marketplace Growth - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sat, 14 Jul 2018 06:23:30 GMT - View all New York, NY jobs
          QA Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Fri, 08 Jun 2018 16:35:13 GMT - View all New York, NY jobs
          Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Tue, 05 Jun 2018 16:15:49 GMT - View all New York, NY jobs
          Back End Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sun, 03 Jun 2018 06:21:49 GMT - View all New York, NY jobs
          UI Engineer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sun, 27 May 2018 20:27:03 GMT - View all New York, NY jobs
          AI Conversation Designer - Wade & Wendy - New York, NY      Cache   Translate Page   Web Page Cache   
Our team is backed by Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make the...
From Wade & Wendy - Sat, 14 Apr 2018 06:15:32 GMT - View all New York, NY jobs
          A Where's Wally-Finding Robot Is Here to Steal Your Toddler's Only Job      Cache   Translate Page   Web Page Cache   
Using a camera and machine learning AI, this robot can spot the striped traveller in as little as four-and-a-half seconds.
          Chinese Fintech professionals expect up to 30% pay rise      Cache   Translate Page   Web Page Cache   

The EY-DBS report, The Rise of FinTech in China, attributes the growth of the Fintech sector in the mainland as driven by multiple factors – “the scale of unmet needs being addressed by dominant technology leaders, combined with regulatory facilitation and easy access to capital. Underserved by China’s incumbent banking system, consumers and small-to-medium-sized enterprises (SMEs) are increasingly turning to alternative providers for access to payments, credit, investments, insurance, and even other non-financial service offerings.”

As a consequence of this growth is a rising shortage of Fintech skills.

The China Fintech Employment 2018 Report by specialist recruitment firm, Michael Page China, revealed that 92% of Fintech companies surveyed agreed that China is facing an acute shortage of professional Fintech talent right now. Also, 38% of respondents view the quality of talent as a critical factor to the sustained success in the industry.

Rupert Forster, Managing Director of Michael Page North and East China, says, “Within Fintech, we are observing a growing demand for talent with skills relating to Artificial Intelligence, machine learning and deep learning. These skills are also sought after in sectors outside of Fintech, such as other Chinese Internet companies, creating a wider talent gap in the market.”

For those looking to hire Fintech talent, 85% of surveyed employers expressed difficulty in finding the right people.

Forty-five percent cited shortage of necessary skills as the biggest hurdle. Fintech professionals know they are in demand with 47% stating they had changed jobs in the last 12 months.

What attracts Fintech professionals to switch? Top motivations include strong career path (29%), right company culture fit (24%) and salary (17%). Forty-four percent of those in it for the money say they expect salary increments ranging from 21 – 30% when securing a new job.

Figure 1: What Fintech employees want

What Fintech employees want

Source: Michael Page China Fintech Employment 2018

“The gap between employer demand for skills and the available talent is not a problem exclusive to Fintech. We see this across many sectors which is purely a reflection of the fast-growing, innovative nature of modern China. The most successful companies are those who are able to implement in-house talent development programs,” Forster explained.

Caption: 
Image from iStockPhoto/baona

          做裝修做到200幾億(二)      Cache   Translate Page   Web Page Cache   


Greensky能迅速盈利的關鍵因素之一,是它所針對的人群和產生的高質量資產讓銀行垂涎。願意花幾萬美元翻新裝修,安裝太陽能板節電的,通常是收入穩定,信譽良好的有樓階層。絕對唔係借一兩皮,網上登記,唔敢露面的小額柒頭借貸者。

Greensky的借款人FICO平均分數達到了760,是絕對的Super Prime。銀行朝思暮想拿到這些人的貸款,卻苦於沒有渠道。


Greensky正好提供了這樣一個渠道。以SunTrustFifth Third為代表的14家銀行都和Greensky達成了戰略合作協議,為Greensky提供低息貸款,用於放數或是直接購買Greensky的貸款
SunTrustGreenskycredit line利率低到了什麼程度?3%

Greensky6%-30%的利率放款,還要收判頭6%的服務費,而自己的資金成本只有3%,利潤空間巨大。在2017年,營收已經有將近三億美元,淨利潤六千三百萬美元。明年估計營收要有五億美元了。

David Zalik說,我們並不想和銀行競爭,我們並不想成為一個大耳窿公司,我們是一家Fintech公司。

如果一家FinTech公司所產生的資產是符合銀行風險偏好的,那麼這家公司最好的策略就是把自己定位成生態系統的一部分,通過銀行降低資金成本,和銀行共贏。傳統銀行在錢上真的不很斤斤計較,它們更願意把風險降到最低,至於能收取多高的利息並不太重要,它們不會因為有高利率的補償就願意承擔高風險。這一點和精細地根據風險定價,錙銖必較,願意為高收益承擔高風險的信貸基金完全不同。所以,於金融科技公司來說,和銀行在資金端合作更加有利可圖。

 和銀行的聯姻是有束縛的,這尤其體現在Greensky的信貸模型上。雖然已經有十年歷史,百萬客戶,海量數據,但Greensky的風控模型只使用徵信數據和FICO。不是沒有alternative data,只是為了滿足銀行的合規要求,他們必須嚴格控制數據使用,用最傳統的,簡潔易懂的,被銀行界廣泛接受的方法,以免銀行的監管機構產生異議。

簡單並不意味著最終效果差。也許Greensky的信貸模型如果採用了更多創新的數據和方法可以使風險降低20%,但是和銀行的合作可以保持業內最低的資金成本,從而把利率降到業內最低,吸引到信用最良好的,也是對利率最敏感的借款人。這種正向選擇(positive seleciton)也許可以把風險降低30%。所以,FinTech公司在風控方面務須一味追求大數據,另類數據,Machine LearningAI,要因地制宜,選取對公司整體經營最有利的模式。

David Zalik說,我們的市場不是十億級的,是萬億級的。家裝市場本身自然沒有萬億規模。在家裝市場金融坐穩了行業老大地位的Greensky,開始劍指更多的細分領域。最新的大動作在醫療市場。和判頭一樣,很多醫生,尤其是在整形,不孕不育IVF等醫療保險不涵蓋的科目,都面臨著患者無法一次性支付醫療費用的問題。雖然醫療貸款市場有著更多的競爭對手,Greensky所積累的十年B2B2C銷售經驗,和銀行的深度密切合作,應當會讓它有巨大優勢。

如果複製成功,Greensky的估值應該不止36億美元。進入更多的細分領域,不僅能夠促進利潤持續增長,還能夠使資產組合更加多元化,降低系統性風險。這於任何一家金融科技公司來說,都是非常重要的。讓我們猜測一下,醫療領域之後,Greensky會瞄准哪一個行業?

我們花左兩日,寫咗咁多字,各位同學應該知我地諗乜?




          Update: qplum - AI-driven Robo Advisor (Finance)      Cache   Translate Page   Web Page Cache   

qplum - AI-driven Robo Advisor 10.5.5


Device: iOS iPhone
Category: Finance
Price: Free, Version: 10.5.3 -> 10.5.5 (iTunes)

Description:

Plan and invest automatically using AI in a portfolio of low-cost, low-risk ETFs.

qplum is an online financial advisor which invests automatically using AI, and data. We invest in portfolios of low-cost Vanguard, iShares, Charles Schwab ETFs. We offer free financial plans tailor-made for your income, expenses, assets, etc. We help build and manage wealth. We are the cutting edge of Robo-Advisors.
Free financial planning, wealth management, Traditional, SEP, Roth IRAs, 401(k) rollovers, retirement, individual, joint, and custodial accounts (UTMA/UGMA), savings goals - We got all financial advice covered!
We have low-risk investment portfolios, high-growth high-return investment portfolios, and everything else for your investing needs.
We offer simple, automated online investment - Download the app for free, get started in less than five minutes, open your account, and let our financial advice make your investments grow.

Who are we?
qplum is a fully automated robo-advisor. We manage wealth online. We are a digital investment advisor which invests through algorithms. We invest in ETFs of US and international stocks, US government bonds, international fixed income, and real estate. Our diversified portfolios use AI and HFT. Think of us as a digital hedge fund, which charges 0.5% annual fees.

qplum was founded by Gaurav Chakravorty, one of the earliest high-frequency traders in the world, and Mansi Singhal, a billion dollar portfolio manager with Brevan Howard, one of the most profitable hedge funds.

We offer:
- AI driven chatbot that offers free financial planning to suit your income, expenses, goals, etc. Now that’s Robo-Advising!
- Diversified ETF portfolios of stock, bond, and real estate ETFs to create wealth.
- Traditional IRA, SEP IRA, Roth IRA, Joint Accounts, 401k Rollovers.
- End-to-end automated trading to save time and effort!
- Account transfer from your existing broker to qplum.
- Monthly auto-deposits to invest every month.
- No additional fee for trading.
- Paperless, online investment experience!

Our strengths:
- AI and data science- We invest through algorithms based on AI and machine learning.
- Stellar portfolio managers and data scientists with proven track record in hedge funds.
- End-to-end automation - Robo-advisor with no human trading.
- Alpha - for returns independent of the stock market.
- Risk management to save you from market crashes.
- Algorithmic execution to buy and sell your products at a better price.
tax loss harvesting to save taxes on investments.
- Daily rebalancing optimized to reduce costs and improve returns.

With this app you can:
- Talk to AI-powered chatbot and get a free financial plan.
- Invest in a blend of our top portfolios.
- Open Traditional, ROTH, SEP IRA accounts.
- Rollover your existing IRA/ 401k into qplum.
- Track the performance of your investments in your ETF portfolio.

Regulated and trusted
- QPLUM LLC is registered with the U.S. Securities and Exchange Commission (SEC) as an Investment Advisor and with the National Futures Association (NFA) as a Commodity Trading Advisor (CTA).
- All Brokerage and Clearing services are provided by, and securities are offered through Apex Clearing Corporation and/or Interactive Brokers, both members of FINRA/SIPC.
- qplum accounts are SIPC protected up to $500,000, including a maximum of $250,000 for cash per customer, against losses resulting from the failure of a broker-dealer. An explanatory brochure is available at www.sipc.org.

The future is free. The future is fair. The future is qplum.

Investment Advisory Services offered through QPLUM LLC. All investments carry risk. This material is for informational purposes and should not be considered specific investment advice or recommendation to any person or organization.Past performance is not indicative of future performance. Read more on our disclaimer and terms of use at www.qplum.co/privacy-terms

What's New

With this update:
- Open TD Ameritrade and Interactive Brokers linked investment accounts
- Support to make transfers above $50K ACH limit with wire transfers
- More detailed Monthly Auto-Deposits summary for invested accounts
- Invest one-time or monthly when opening a new account
- Better context for in-progress events on your invested accounts

qplum - AI-driven Robo Advisor


          Machine Learning/AI Engineer - Groom & Associates - Montréal, QC      Cache   Translate Page   Web Page Cache   
Expérience avec tensorflow ou d'autres backends, keras ou autres frameworks, scikit-learn, OpenCV, Pandas. Experience with tensorflow or other backends, keras...
From Groom & Associates - Thu, 07 Jun 2018 14:58:16 GMT - View all Montréal, QC jobs
          Data Scientists / AI & Machine Learning Engineer - IVADO Labs - Montréal, QC      Cache   Translate Page   Web Page Cache   
Experience implementing AI/data science algorithms using one or more of the modern programming languages/frameworks (e.g., Python, Pandas, Scikit-learn,...
From IVADO Labs - Sat, 05 May 2018 03:10:45 GMT - View all Montréal, QC jobs
          Platform Developer, Machine Learning - Kinaxis - Ottawa, ON      Cache   Translate Page   Web Page Cache   
Experience with Machine Learning projects, familiarity with platforms or languages such as scikit-learn, Pandas, NumPy, SciPy, R, TensorFlow....
From Kinaxis - Wed, 08 Aug 2018 20:38:15 GMT - View all Ottawa, ON jobs
          Machine Learning Developer - Kinaxis - Ottawa, ON      Cache   Translate Page   Web Page Cache   
Experience with ML platforms and languages including scikit-learn, Pandas, NumPy, SciPy, Python, R woult be an asset....
From Kinaxis - Wed, 08 Aug 2018 20:38:15 GMT - View all Ottawa, ON jobs
          REMOTE Analytics Engineer - Hadoop Platform - Colorado      Cache   Translate Page   Web Page Cache   
CO-Denver, We have several locations internationally but we are making this role REMOTE. We are a global type of company that specializes in customizing software for over 400 clients. Due to growth and demand for our services, we are in need of hiring for a Analytics Engineer that possesses strong experience with Machine Learning, Hadoop platform, and some sort of programming language (JS, Python, Scala) If
          Machine Learning avec Scikit-Learn. Mise en oeuvre et cas concrets, un livre de Aurélien Géron, critique par Thibaut Cuvelier      Cache   Translate Page   Web Page Cache   
Cet ouvrage, conçu pour tous ceux qui souhaitent s'initier au Machine Learning (apprentissage automatique) est la traduction de la première partie du best-seller américain Hands-On Machine Learning with Scikit-Learn & TensorFlow.

Il ne requiert que peu de connaissances en mathématiques et présente les fondamentaux du Machine Learning d'une façon très pratique à l'aide de Scikit-Learn qui est l'un des frameworks de ML les plus utilisés actuellement.

Des exercices corrigés...
          DataRobot Announces Automated Time Series Solution that Allows Frontline Business People to Predict the Future      Cache   Translate Page   Web Page Cache   

New Offering Solves Time-Dependent Business Problems with Nutonian’s Eureqa Forecasting Technology and DataRobot’s Automated Machine Learning Breakthroughs Boston, August 9, 2018 — DataRobot, the pioneering architects of automated machine learning, today announced the general availability of DataRobot Time Series. Following an extensive collaboration with more than 75 customers and world-class data scientists, this latest breakthrough...

The post DataRobot Announces Automated Time Series Solution that Allows Frontline Business People to Predict the Future appeared first on DataRobot.


          Bell Labs - Integrated Photonics Researcher - NOKIA - Holmdel, NJ      Cache   Translate Page   Web Page Cache   
Nokia is a global leader in the technologies that connect people and things. Investigate and implement machine learning based optimization to control large...
From Nokia - Mon, 18 Jun 2018 15:55:57 GMT - View all Holmdel, NJ jobs
          AA Chief SW Architect - NOKIA - San Jose, CA      Cache   Translate Page   Web Page Cache   
Analytics, AI, and machine learning. Presenting to customers, industry forums, analysts and internal audiences....
From Nokia - Mon, 18 Jun 2018 15:51:40 GMT - View all San Jose, CA jobs
          Data Scientist / Operations Research Engineer - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page   Web Page Cache   
Work closely with the business units to identify Machine Learning applications, define the strategic and tactical needs and drive the appropriate business...
From Advanced Micro Devices, Inc. - Thu, 12 Jul 2018 07:32:54 GMT - View all Austin, TX jobs
          ISV Technology Director - AI and ML - 67511 - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page   Web Page Cache   
AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. What you do at AMD changes everything....
From Advanced Micro Devices, Inc. - Sat, 07 Jul 2018 01:32:18 GMT - View all Austin, TX jobs
          ISV Technology Director - AI and ML - 67453 - Advanced Micro Devices, Inc. - Santa Clara, CA      Cache   Translate Page   Web Page Cache   
AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. What you do at AMD changes everything....
From Advanced Micro Devices, Inc. - Sat, 07 Jul 2018 01:32:16 GMT - View all Santa Clara, CA jobs
          iOS Developer - PGS SOFTWARE - Rzeszów, podkarpackie      Cache   Translate Page   Web Page Cache   
Augmented Reality, Machine Learning, iBeacons, Top Level Security. Elastyczne godziny pracy....
Od PGS SOFTWARE - Wed, 08 Aug 2018 14:51:19 GMT - Pokaż wszystkie Rzeszów, podkarpackie oferty pracy
          Artificial Intellegence / Machine Learning Developer - SafetyTek Software Ltd. - Saskatoon, SK      Cache   Translate Page   Web Page Cache   
Safety forms and resources are accessed on phones, tablets, and computers and required tasks are completed through the app. AI / ML Developer &amp;mdash;... $75,000 - $85,000 a year
From Indeed - Thu, 19 Jul 2018 20:55:34 GMT - View all Saskatoon, SK jobs
          Technical Trainer, Infrastructure, Big data and Machine Learning, Google Cloud - Google - Bogotá, Cundinamarca      Cache   Translate Page   Web Page Cache   
We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship,...
De Google - Thu, 09 Aug 2018 14:39:20 GMT - Ver todos: empleos en Bogotá, Cundinamarca
          Wheelchair Users Call for More Innovative Mobility Devices      Cache   Translate Page   Web Page Cache   

A new survey is prompting wheelchair users to call for more innovation to help them fulfill their potential in the workplace.

The survey, commissioned by the Toyota Mobility Foundation, showed that 92% of respondents have had problems working or finding a job as a direct result of their wheelchair and 36% have been unable to work at all for the same reason. The foundation is calling on engineers and designers, as well as software and data science experts, to find better solutions for people with lower-limb paralysis.

“The challenge is to start thinking beyond the status quo,” August de los Reyes, a director of user experience on the Search, Assistant, and News Ecosystem at Google, Inc., told Design News. “There’s a great opportunity to innovate in this space, whether it’s at the component level, the device level, or in the entire system of transportation.”

The survey painted a stark picture of the work-related problems facing wheelchair users. It noted that wheelchairs and other types of so-called “mobility devices” limited the number of jobs for which users could apply. Approximately a quarter of respondents said they had to become self-employed and 26% said they had to work at home as a result of their wheelchair use.

The Toyota Mobility Foundation wants to change that—not only through the survey, but also by launching a $4 million global challenge for engineers and designers. Known as the Mobility Unlimited Challenge, it will award $500,000 to five finalists to take their concepts to the prototype stage. It will also award $1 million to help bring the winning product to market. The organization has called for entries to be submitted by August 15, 2018 and plans to unveil the winning concept in Tokyo in 2020.

de los Reyes, who serves as a global ambassador for the challenge, said that the goal is to develop better solutions and to raise greater awareness of the need for innovation. The need to innovate is critically important, he said, not only for the 65 million people worldwide who use wheelchairs, but also for the societies that can benefit by making wheelchair users more productive. “The talent pool of people with disabilities is largely untapped,” he said. “Part of the reason it is untapped is because today’s assistive technologies don’t provide, and sometimes actually prevent, people in wheelchairs from accessing opportunities.”

Beyond the ‘Chair on Wheels’

de los Reyes, who is a wheelchair user, compared the state of innovation in wheelchairs to that of the “horseless carriage” market of the early 1900s. Early automobiles were limited by the vocabulary and mental models surrounding them, he said. Similarly, today’s mobility devices have been limited by the idea that all solutions must involve a “chair on wheels.”

August de los Reyes of Google: “There’s a great opportunity to innovate in this space, whether it’s at the component level, the device level, or in the entire system of transportation.” (Image source: August de los Reyes)

The Mobility Unlimited Challenge will encourage engineers and designers to go beyond such limitations. As such, de los Reyes said, developers are urged to think creatively in the areas of material science, electronics, data science, machine learning, and artificial intelligence, among others.

“I would challenge them to think outside the box and beyond the chair,” he said. “Even beyond the exoskeleton.”

As potential innovation examples, de los Reyes cited the use of sensors to locate potholes and unexpected curbs on sidewalks, thereby enabling users to steer around those hazards. Also, the development of lighter weight portable ramps could enable wheelchair users to more easily traverse stairways. And better batteries could help cut the weight of wheelchairs. Today, he said, portable ramps weigh upwards of 100 lbs and powered wheelchairs can be 500 lbs.

He encouraged engineers to employ the best tools at their disposal to conjure up newer and better ideas. “Introducing artificial intelligence and machine learning within a system of actuators and motors could provide new opportunities for mobility,” de los Reyes said.

Often, such innovative new ideas end up benefiting everyone, not just those in wheelchairs, de los Reyes said. “If you look at the curb-cuts in cities, people walk their bikes and baby strollers up them,” he said. “Everyone uses them, even though their original intent was for people in wheelchairs.” Similarly, he noted, delivery drivers and countless commuters make wide use of automatic door openers that were originally intended for wheelchair users.

de los Reyes hopes that a new crop of innovative solutions will have a similarly broad benefit. “This is an urgent call for innovation, not only to help the people who can’t access their opportunities, but also to help economic productivity worldwide,” he said.

Senior technical editor Chuck Murray has been writing about technology for 34 years. He joined Design News in 1987, and has covered electronics, automation, fluid power, and auto.

 

ESC, Embedded Systems ConferenceToday's Insights. Tomorrow's Technologies.
ESC returns to Minneapolis, Oct. 31-Nov. 1, 2018, with a fresh, in-depth, two-day educational program designed specifically for the needs of today's embedded systems professionals. With four comprehensive tracks, new technical tutorials, and a host of top engineering talent on stage, you'll get the specialized training you need to create competitive embedded products. Get hands-on in the classroom and speak directly to the engineers and developers who can help you work faster, cheaper, and smarter. Click here to register today!

          Oracle data visualization (DVD/DVCS) implementation for advanced analytics and machine learning      Cache   Translate Page   Web Page Cache   
Oracle DVD is a Tableau like interactive tool which helps to create analysis on the fly, using any type data from any platform, be it on premise or cloud. Read on to know more about the benefits of Oracle data visualization (DVD/DVCS).
          Ingénieur Analyse de Données et Logiciels - Intelligence Manufacturière - Data Analytics and Software Engineer – Manufacturing Intelligence - Alcoa Corp. - Deschambault, QC      Cache   Translate Page   Web Page Cache   
IoT, Connected Worker, Machine Learning, Cloud, Robotics, Augmented Reality. Ce poste peut être basé à l'une ou l'autre des Alumineries d'Alcoa dans le monde/...
From Alcoa Corp. - Fri, 29 Jun 2018 03:08:28 GMT - View all Deschambault, QC jobs
          Ingénieur Analyse de Données et Logiciels - Intelligence Manufacturière - Data Analytics and Software Engineer – Manufacturing Intelligence - Alcoa Corporation - Deschambault, QC      Cache   Translate Page   Web Page Cache   
IoT, Connected Worker, Machine Learning, Cloud, Robotics, Augmented Reality. Description du poste....
From Alcoa Corporation - Thu, 28 Jun 2018 15:45:09 GMT - View all Deschambault, QC jobs
          Protecting the protector: Hardening machine learning defenses against adversarial attacks      Cache   Translate Page   Web Page Cache   
Harnessing the power of machine learning and artificial intelligence has enabled Windows Defender Advanced Threat Protection (Windows Defender ATP) next-generation protection to stop new malware attacks before they can get started often within milliseconds. These predictive technologies are central to scaling protection and delivering effective threat prevention in the face of unrelenting attacker activity.

Read more


          Machine Learning SW Engineer      Cache   Translate Page   Web Page Cache   
MD-Rockville, Our client now has an open Machine Learning SW Engineer opening. It is a SW Engineering role with Machine Learning. If the right candidate had Deep Learning concepts (Algorithm using Neural Networks) that would be a plus but at least Machine Learning. Mission: There is a NEW Data Strategy for our client to enable to solve DATA problems more efficiently. DAY to DAY: Will be working with Product Tea
          More Performance At The Edge      Cache   Translate Page   Web Page Cache   

Shrinking features has been a relatively inexpensive way to improve performance and, at least for the past few decades, to lower power. While device scaling will continue all the way to 3nm and maybe even further, it will happen at a slower pace. Alongside of that scaling, though, there are different approaches on tap to ratchet up performance even with chips developed at older nodes.

This is particularly important for edge devices, which will be called on to do pre-processing of an explosion of data. Performance improvements there will come from a combination of more precise design, less accurate processing for some applications, and better layout using a multitude of general-purpose and specialized processors. There also will be different packaging options available, which will help with physical layouts to shorten the distance between processors and both memory and I/O. And there will be improvements in memory to move data back and forth faster using less power.

The fundamental equation at the edge is less circuitry for signals to travel through, a reduction of bottlenecks for those signals at various junctions in a system, and much better interaction between software and hardware. Hardware-software co-design has been an on-again, off-again topic of discussion since mainframe days, when the real challenge was to get applications to work consistently without rebooting an entire machine. Intel and Microsoft improved on this with windows on an x86 processor, particularly with the introduction of Windows NT, where applications could be written to an application programming interface and not crash the operating system. That was a major step forward, but it led to increasingly bloated applications, and the cheapest solution was a process shrink for processors and DRAM rather than focusing on a better way to write software.

That problem is only now starting to be addressed. A first step in that direction software-defined hardware. But even with better alignment between hardware design and the software that runs on it, the real performance killer is an endless series of security patches and feature updates.

What’s needed, particularly in the age of machine learning and AI, is hardware-software co-design, where the algorithms are much more transparent and flexible, and where the hardware can adapt to changes easily without massive amounts of margin or a fully programmable solution. Software is good for flexibility, but it’s slow compared to hardware, and it tends to grow over time as it amasses more patches. Hardware is much faster, but it’s fixed, and programmable hardware is not nearly efficient. There is room for improvement everywhere, and the best solutions will require collaboration on all sides.

In addition to all of these steps, three things have to happen.

First, margin needs to be reduced in designs. That requires more and better modeling and simulation of everything from process variation to circuit aging, and it requires much tighter integration of the various tools and processes required to develop chips. This can be done more effectively if everything isn’t integrated onto a single planar die, but the goals generally are the same, which is to shorten the distance that signals need to travel and to split up the functionality on different IP―possibly hardened IP. That will minimize contention for resources as well as the number of possible interactions, which in turn requires less circuitry for worst-case scenarios.

Second, security needs to be part of an overall system architecture so that patches don’t bog down functionality of the rest of the system. But all patches, no matter what they address, need to be a zero-sum gain for code, or at least that should be the goal. If OEMs can demand zero defects in hardware, they also should be demanding zero increases in the number of lines of software. Code pruning should be part of update processes. The more code that is added, the greater the chance for interactions that can slow or crash a system, and that in turn has a major effect on the functionality of the hardware, and how long a battery will last between charges.

Third, AI and machine-learning inferencing algorithms need to be written in a way that can be understood by both hardware and software engineers, who need to be able to adjust the weighting based upon probabilities for accurate results. Some of this is available to end users now, particularly with security applications, but it needs to be transparent to systems companies.

Unlike in the past, just increasing transistor density is not going to provide performance improvements of 30% or more gains every couple years. Those improvements increasingly are measured across a system, and that requires changes on every level to both prioritize which parts require more performance and what resources are required to achieve that, as well as the overall architecture for how those parts fit together and interact to handle an increasing volume and diversity of data.


          Malware zero day: l’antivirus WatchGuard si dota di nuove funzioni di machine learning      Cache   Translate Page   Web Page Cache   

malwareL’ultima versione del sistema operativo Fireware di WatchGuard introduce il nuovo servizio IntelligentAV per la rilevazione del malware

Leggi tutto l'articolo Malware zero day: l’antivirus WatchGuard si dota di nuove funzioni di machine learning su LineaEDP


          TensorFlow 501 Week 6      Cache   Translate Page   Web Page Cache   

Hi,

 

I am working my way through the TensorFlow 501 course and have got slightly stuck on Week 6. I'm slightly unsure what the question "If I tell you that the inner-most values come as `W,b` pairs, can you get the *shapes* of the coefficients?" is asking of me. What does it mean by "inner-most"?  Does it mean 'fc6'?

Apart from that I am struggling with how you would add these to the convolutional layer slightly further on in the notebook.

 

I have completed the Machine Learning 501 course and the first 5 weeks of TensorFlow 501 and throughout both of them it references a set of answers. I would like to check what I have done against these but I cannot find them anywhere! Could anyone help me locate them? It would help a lot.

 

Thanks,

Andrew


          企業でAIをより簡単に活用――Dell EMC、「Dell EMC Ready Solutions for AI」をリリース      Cache   Translate Page   Web Page Cache   
Dell EMCは、企業のAI活用を支援する新ソリューション「Dell EMC Ready Solutions for AI」を提供開始した。「Machine Learning with Hadoop」と「Deep Learning with NVIDIA」という2つのパッケージを用意している。
          'There's Waldo' Machine Learning Robot Exists To Ruin Children's Books      Cache   Translate Page   Web Page Cache   
'There's Waldo' Machine Learning Robot Exists To Ruin Children's Books This robot with the fake hand on it has one job; and that job isn't to learn to grasp objects or hold tools. This robot is meant only to find that stripe wearing, child frustrating, bespectacled Waldo from the kids books. Perhaps the scientists will next time devise a robot AI that reads the last chapter of the new novel from your favorite
          09/14/18: Defence of dissertation in the field of Signal Processing Technology, M.Sc. (Tech.) Adriana Chis      Cache   Translate Page   Web Page Cache   
The title of thesis is “Demand response and energy portfolio optimization for smart grid using machine learning and cooperative game theory”.

Opponents: Professor Yih-Fang Huang, University of Notre Dame, USA and Dr. Iiro Harjunkoski, ABB Corporate Research, Germany

Supervisor:  Professor Visa Koivunen, Aalto University School of Electrical Engineering, Department of Signal Processing and Acoustics.


          Nurse Call System Market – Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2018–2023      Cache   Translate Page   Web Page Cache   

Pune, india -- (SBWIRE) -- 08/10/2018 -- Market Scenario:
Nurse call systems are designed to alert the nurses in case of medical emergency or need for care. These systems are even used to track a patient especially in assisted living or old age centres or are used to give an indication of an event probable in case of ambulatory services.
The drivers of the global nurse call system market are the growing complexity of hospital operations, growing assisted living centres and the concurrent growth in elderly and chronic sick population and others. Technological developments such as advanced coverage of network, growing connectivity, falling cost of devices, negligible cost of scalability of communication in case of expansion and others.
The market constraints of the global nurse call system market are high installation and maintenance costs, concerns of privacy and information due to open nature of the network, lack of awareness and others.
Market players of Global Nurse Call System Industry:
Market players of global nurse call system industry are Austco Communication Systems Pty Ltd., Honeywell International, Inc., Hill-Rom Holdings, Inc., Ascom Holding AG, Tyco International PLC, CSINC and others.
Get PDF Sample Copy @ https://www.marketresearchfuture.com/sample_request/5631
Latest Market Trends:
Wired systems will make way for wireless systems, a trend that is prevalent in all fields of communication technology.
Smart wearable's are expected to trump manual systems. The developments in automated sensor technology, internet of things and machine learning and others is expected to be the next wave of products.
Major Segments:
To generate a bird's view, the global nurse call system market is segmented on the basis of instrument, technology, applications and end users.
Based on the type, the market has been segmented as basic buttons alert systems, audio/visual nurse call systems, integrated communication systems, mobile systems, others.
On the basis of technology the market has been segmented as wired systems and wireless systems), application (medical emergency, alarms, workflow management and others.
On the basis of application the market has been classified as medical emergency, alarms, workflow management and others.
On the basis of end user the market has been classified as hospitals and clinics, assisted living centers, ambulatory, and other.
Get Discount@ https://www.marketresearchfuture.com/check-discount/5631
Regional Analysis:
To generate an accurate representation of the differential demand of the market, the report has been segmented into regions of North America, Europe, Asia-Pacific and Middle East and Africa.
North America commands the largest market share due to the presence of well-developed economies of U.S. and Canada. The faster uptake of new technology, presence of large hospitals and high per capita income of the U.S. makes it a dominating force in the global nurse call system market.
Asia Pacific region is anticipated to generate the fastest CAGR growth during the forecast period of 2017-2023 led by Japan, china and India.
The Middle East & African region, is expected to generate moderate growth due to poor social and economic reasons especially in Africa. However the Gulf economies are expected to generate strong spurts of growth due to the faster expansion of healthcare in the region.
Table of Contents:
Chapter 1. Report Prologue
Chapter 2. Market Introduction
2.1 Definition
2.2 Scope of the Study
2.2.1 Research Objective
2.2.2 Assumptions
2.2.3 Limitations
Chapter 3. Research Methodology
3.1 Introduction
3.2 Primary Research
3.3 Secondary Research
3.4 Market Size Estimation
Chapter 4. Market Dynamics
4.1 Drivers
4.2 Restraints
4.3 Opportunities
4.4 Challenges
4.5 Macroeconomic Indicators
4.6 Technology Trends & Assessment
…Continued!
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For more information on this press release visit: http://www.sbwire.com/press-releases/nurse-call-system-market-global-industry-analysis-size-share-growth-trends-and-forecast-2018-2023-1026489.htm

Media Relations Contact

abhishek sawant
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Market Research Future
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          Comment on How to Develop a Skillful Machine Learning Time Series Forecasting Model by Jason Brownlee      Cache   Translate Page   Web Page Cache   
What do you mean exactly?
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Virtual machine switching); Large scale distributed systems, real-time data analysis, machine learning, windows internals (networking stack and other OS...
From Microsoft - Thu, 09 Aug 2018 04:41:50 GMT - View all Redmond, WA jobs
          Economist - Forecasting - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Experience with machine learning applications. We are breaking fresh ground, pioneering in a program that is crucial for future Amazon growth, and our business...
From Amazon.com - Wed, 27 Jun 2018 07:21:23 GMT - View all Seattle, WA jobs
          Principal Market Validation Specialist - PTC - Needham, MA      Cache   Translate Page   Web Page Cache   
Advance knowledge and experience with Machine Learning / Data Science / Analytics. Customer Satisfaction focus, both internal and external, with strong...
From PTC - Wed, 16 May 2018 14:29:21 GMT - View all Needham, MA jobs
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Virtual machine switching); Large scale distributed systems, real-time data analysis, machine learning, windows internals (networking stack and other OS...
From Microsoft - Thu, 09 Aug 2018 04:41:50 GMT - View all Redmond, WA jobs
          Economist - Forecasting - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Experience with machine learning applications. We are breaking fresh ground, pioneering in a program that is crucial for future Amazon growth, and our business...
From Amazon.com - Wed, 27 Jun 2018 07:21:23 GMT - View all Seattle, WA jobs
          Virtual Principle Data Scientist in Louisville      Cache   Translate Page   Web Page Cache   
A healthcare company needs applicants for an opening for a Virtual Principle Data Scientist in Louisville. Individual must be able to fulfill the following responsibilities: Developing and maintaining a suite of predictive analytics to drive consumer engagement and growth Demonstrating technical and theoretical expertise in a variety of machine learning techniques Demonstrating deep technical and theoretical expertise in mathematics and statistical techniques Required Skills: Master’s Degree in Math, Statistics, Engineering, Computer Science or a related field 4+ years statistics and modeling experience Experience using a variety of modeling tools such as SAS, Python, or R Experience building and maintaining analytic data sets Proven ability with data mining and/or predictive modeling techniques that have been applied in a real world setting Proven ability to effectively communicate results of statistical models to broad audience, including senior leadership
          Machine Learning Engineer - Technica Corporation - Dulles, VA      Cache   Translate Page   Web Page Cache   
Technica Corporation is seeking a Machine Learning Engineer to support our internal Innovation, Research and Development (IRD) team....
From Technica Corporation - Wed, 11 Jul 2018 06:07:15 GMT - View all Dulles, VA jobs
          courseraのmachine learningを受講してみた。      Cache   Translate Page   Web Page Cache   
certificate6/12に開始し、8/8に修了いたしました!キツかった…キツかったよぉぉお(´;ω;`)ブワァ私がガッコウというものに向いてないということを改めて思い知らされた…それでも最後まで完走できたのは、修了証が発行される有料コース(¥8,618)を購入してしまったからですww あとはAndrew先生が超 絶 イ ケ メ ンだったからだな(#♡д♡#)ところでcourseraというのは何なのか?と言いますと。「世界最高峰の大学の講義を誰でもオンライン上で受けられ..
          Sr Director, Growth Marketing Technology - eBay Inc. - Bellevue, WA      Cache   Translate Page   Web Page Cache   
Further, the Marketing Tech Leader will apply the latest data analysis and machine learning technologies to innovate applications in both BI analysis and...
From eBay Inc. - Fri, 01 Jun 2018 08:04:49 GMT - View all Bellevue, WA jobs
          Software Engineer - Machine Learning - Convoy - Seattle, WA      Cache   Translate Page   Web Page Cache   
Today, we use machine learning to figure out freight prices, shipment relevance for carriers, auction bidding strategy, and other internal processes....
From Convoy - Sat, 19 May 2018 10:13:22 GMT - View all Seattle, WA jobs
          Data (Machine Learning) Engineer      Cache   Translate Page   Web Page Cache   
Praelexis - Western Cape - . Consequently, we apply advanced machine learning algorithms to a variety of real-world industrial problems on a large scale. We’re currently... applicant's favour: Experience working closely with machine learning or analytics teams Experience with development process such as agile and scrum...
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Virtual machine switching); Large scale distributed systems, real-time data analysis, machine learning, windows internals (networking stack and other OS...
From Microsoft - Thu, 09 Aug 2018 04:41:50 GMT - View all Redmond, WA jobs
          Economist - Forecasting - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Experience with machine learning applications. We are breaking fresh ground, pioneering in a program that is crucial for future Amazon growth, and our business...
From Amazon.com - Wed, 27 Jun 2018 07:21:23 GMT - View all Seattle, WA jobs
          CSIS RIST Relativity Project Coordinator - CSIS Lead Investigator - Citi - Tampa, FL      Cache   Translate Page   Web Page Cache   
Diversity is a key business imperative and a source of strength at Citi. Degree in Computer Science, Machine Learning, Information Retrieval or related field,...
From Citi - Sun, 05 Aug 2018 06:14:57 GMT - View all Tampa, FL jobs
          Machine Learning Researcher - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page   Web Page Cache   
PARC, a Xerox company, is in the Business of Breakthroughs®. We create new business options, accelerate time to market, augment internal capabilities, and...
From PARC, a Xerox company - Sat, 28 Apr 2018 09:57:12 GMT - View all Palo Alto, CA jobs
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Seattle, WA      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:37:13 GMT - View all Seattle, WA jobs
          Mindsphere Principal PreSales Solutions Consultant - South Central, US - Siemens - Dallas, TX      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:37:15 GMT - View all Dallas, TX jobs
          Mindsphere Principal PreSales Solutions Consultant - South Central, US - Siemens - Austin, TX      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:41:34 GMT - View all Austin, TX jobs
          Mindsphere Principal PreSales Solutions Consultant - South Central, US - Siemens - Houston, TX      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:41:13 GMT - View all Houston, TX jobs
          Mindsphere Principal PreSales Solutions Consultant - South Central, US - Siemens - San Antonio, TX      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:41:34 GMT - View all San Antonio, TX jobs
          ISV Technology Director - AI and ML - 67511 - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page   Web Page Cache   
AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. 3+ Years with leading ISV’s leveraging the Machine...
From Advanced Micro Devices, Inc. - Sat, 07 Jul 2018 01:32:18 GMT - View all Austin, TX jobs
          Незамеченное IT      Cache   Translate Page   Web Page Cache   
Замечали неравномерность человеческого внимания к разным достижениям? Мемы о том, что Стив Джобс и Деннис Ритчи ушли почти одновременно, но обществом была замечена только смерть первого, хотя вклад второго в IT гигантский:



Реальность неумолима: как бы ни был талантлив сценарист фильма, актёрам всегда достанется больше внимания, потому что их видно. Как бы ни был профессионален создатель серверной технологии, Марк Цукерберг привлечёт больше внимания, потому что рядовой пользователь взаимодействует с его проектом напрямую. Это нормально: мы все не замечаем чего-то, с чем не сталкивается напрямую.

В этом посте будет небольшой заплыв в специфику организации мероприятий, а также Kotlin, Machine Learning и создателя ОС Фантом. Коротко о посте в одной картинке (да, вы скоро поймете, о чем речь):



Если ты организуешь какое-то событие, начиная от простой встречи разработчиков в собственном офисе, и заканчивая IT-фестивалем на две тысячи человек, скрытая часть происходящего не менее важна чем то, что на виду. Для участника это огромный дополнительный контент, во много раз увеличивающий ценность происходящего. Для организатора — это набор направляющих идей. Осталось сделать так, чтобы не пропускать еще и эту скрытую часть.

Предлагаемая схема: обращать внимание людей на «незамеченные» вещи и объяснять их значимость. Например, сейчас мы запускаем фестиваль TechTrain, и видим несколько историй, о которых пойдет речь.
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          企業でAIをより簡単に活用――Dell EMC、「Dell EMC Ready Solutions for AI」をリリース      Cache   Translate Page   Web Page Cache   
Dell EMCは、企業のAI活用を支援する新ソリューション「Dell EMC Ready Solutions for AI」を提供開始した。「Machine Learning with Hadoop」と「Deep Learning with NVIDIA」という2つのパッケージを用意している。
          Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Director, Data & AI - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Senior Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:23 GMT - View all Montréal, QC jobs
          5 ways machine learning makes life harder for cybersecurity pros      Cache   Translate Page   Web Page Cache   
While many companies are turning to machine learning tools to fight hackers, they may not be as helpful as they seem thanks to a talent shortage and a lack of transparency.
          Could Machine Learning Mean the End of Understanding in Science?      Cache   Translate Page   Web Page Cache   
Much to the chagrin of summer party planners, weather is a notoriously chaotic system. Small changes in precipitation, temperature, humidity, wind speed or direction, etc. can balloon into an entirely new set of conditions within a few days. That’s why weather forecasts become unreliable more than about seven days into the future—and why picnics need […]
          PPC Trends You Can't Ignore in 2018      Cache   Translate Page   Web Page Cache   
Paid search campaigns are surely the best way of improving your online visibility and increasing engagement on your website. The fact that they offer immediate results makes them the most preferred digital marketing technique. But hiring just another agency offering PPC services may not offer you the mileage you are looking for. The agency needs to be well-versed in the latest trends in the world of PPC. So here are some of the trends that you can’t ignore in 2018.

Also Read
Mobile Continues Growing

For the last couple of years, the thrust of PPC has shifted towards mobile device as the majority of websites generate the bulk of their traffic from mobile devices. 2018 would be the year where mobile PPC is expected to grow even further. Mobile PPC poses its own challenges and it is for marketers to create the right balance running ad campaigns for mobile as well as desktop users. 

Machine Learning Is In

Has the company offered you PPC services taken strides towards machine learning? Google has already signaled its intentions towards this by incorporating smart bidding techniques and also updating its ad rotation settings. The onus is now on PPC marketers to adopt these new strategies while planning and executing campaigns.

Ad Personalization

You have already seen how campaigns from the same brand appear differently on different devices. This is nothing but personalization and this isn’t limited to devices alone. Seasoned marketers are running more personalized campaigns based on the location of the user, the time of day and various other factors. Personalization ensures that the ads strike the right chord with the recipients and thus improve the odds of success. 

Predictive Marketing

Have you gathered tons of data about your customers, their shopping habits and how they use your product? It is time to put all this data to good use and run predictive marketing campaigns and take a lead over your rivals. If a certain category of products saw astronomical demands during the last holiday season you better plan a campaign before the next one even starts

Summary – In this write-up, we take a look at some of the trends that would dominate PPC market in 2018 and ones an agency offering PPC services in New Mexico can’t ignore. 

          Scale New Heights with These IOS Development Trends in 2018      Cache   Translate Page   Web Page Cache   
It’s the beginning of a new year and like always the mobile development industry is buzzing with new activities. There are many new ideas that are being explored and new trends being set in motion. After all iOS 11 has set in new possibilities for the developers.  As far as iOS application development in India goes the leading agencies from the country have had a great 2017 where they increased their footprint in the global market and 2018 promises to be even better. You can expect the leading players in iOSapplication development in India to explore the latest trends in app development and help you scale newer heights. So here are some of the top trends that you should explore to stay ahead of competition.

Also Read

6 Facts You Can't Ignore When You Outsource iPad App Development
Mobile App Development Trends That is Likely to Emerge in the Year 2018

  • Native apps for iPad

While there is lot of talk around app development for iPhones, iPads have often been ignored by the developers due to their lesser penetration among the users. But 2018 would prove to be a   year of course correction for the developers as they look to harness this new opportunity with native apps for iPad. 2017 was a great year for iPad sales as Apple saw surge in the sale of tablets after a few muted years. Along with native apps for iPad the demand for apps that are compatible with both iPads and iPhones would increase and this is a great opportunity for businesses to increase downloads and monetization.   

  • Augmented Reality to go big
We have been hearing about AR for a few years now but with the release of iOS 11, Apple has made its intentions clear. The ARKit toolkit has been specifically designed to allow developers to explore new possibilities with Augmented Reality apps, Three Dimensional apps and Virtual Reality apps and 2018 would be the start of next generation app development.AR would serve as a force multiplier for businesses who wish to take the app experience for their users to the next level. 

  • IoT to make giant strides
We all know that our homes and lives are going to get smarter, but how soon? If Apple’s vision is anything to go by our homes may turn smart in 2018. It launched the Apple HomeKitwith much fanfare and applications built using this framework would revolutionize how we live our lives. The framework allows smart devices at home to communicate with each other using Siri. Users would be able to control all devices with simple voice commands. If you are into app development this is surely one trend you shouldn’t ignore in 2018. 

  • Machine Learning will shape app development
There has been lot of talk around Machine Learning and smatter apps since Apple came out with its Core ML framework. There are several components in the iPhones and iPads that are already exploring this framework such as Siri and the Camera. With lesser amount of coding required to achieve great functionality this niche will shape app development in 2018. 

Are you geared up to explore these possibilities? Agencies offering iOS application development in India surely are.  

Summary – In this write-up we take a look at some of trends that leading agencies for iOS application development in Indiawould explore to help you stay ahead of the competition. 




          Machine learning links brain connectivity patterns with psychiatric symptoms      Cache   Translate Page   Web Page Cache   
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          Sales Engineer - Hitachi Vantara - New York, NY      Cache   Translate Page   Web Page Cache   
Account Managers, internal specialists and customers. Understanding of Data Science and Machine Learning....
From Hitachi Vantara - Sat, 04 Aug 2018 04:47:47 GMT - View all New York, NY jobs
          Gone Mobile 73: Machine Learning with Frank Krueger      Cache   Translate Page   Web Page Cache   
Frank is pretty much a machine himself, so we have him teach us the basics of Machine Learning and how he's been using it in his own apps! Listen to see how you can get started in Machine Learning with C# and .NET! Special Guest: Frank Krueger.
          Dr. Eng Lim Goh presents: Prediction – Use Science or History?      Cache   Translate Page   Web Page Cache   

Dr. Eng Lim Goh from HPE gave this keynote talk at PASC18. "Traditionally, scientific laws have been applied deductively - from predicting the performance of a pacemaker before implant, downforce of a Formula 1 car, pricing of derivatives in finance or the motion of planets for a trip to Mars. With Artificial Intelligence, we are starting to also use the data-intensive inductive approach, enabled by the re-emergence of Machine Learning which has been fueled by decades of accumulated data."

The post Dr. Eng Lim Goh presents: Prediction – Use Science or History? appeared first on insideHPC.


           Embedded Computing on the Edge       Cache   Translate Page   Web Page Cache   
Embedded Computing on the Edge

Embedded computing has passed—more or less unscathed—through many technology shifts and marketing fashions. But the most recent—the rise of edge computing—could mean important new possibilities and challenges. So what is edge computing (Figure 1)? The cynic might say it is just a grab for market share by giant cloud companies that have in the past struggled […]

Embedded computing has passed—more or less unscathed—through many technology shifts and marketing fashions. But the most recent—the rise of edge computing—could mean important new possibilities and challenges.

So what is edge computing (Figure 1)? The cynic might say it is just a grab for market share by giant cloud companies that have in the past struggled in the fragmented embedded market, but now see their chance. That theory goes something like this.

Figure 1. Computing at the network edge puts embedded systems in a whole new world.

With the concept of the Internet of Things came a rather naïve new notion of embedded architecture: all the embedded system’s sensors and actuators would be connected directly to the Internet—think smart wall switch and smart lightbulb—and all the computing would be done in the cloud. Naturally, this proved wildly impractical for a number of reasons, so the gurus of the IoT retreated to a more tenable position: some computing had to be local, even though the embedded system was still very much connected to the Internet.

Since the local processing would be done at the extreme periphery of the Internet, where IP connectivity ended and private industrial networks or dedicated connections began, the cloud- and network-centric folks called it edge computing. They saw the opportunity to lever their command of the cloud and network resources to redefine embedded computing as a networking application, with edge computing as its natural extension.

A less cynical and more useful view looks at edge computing as one facet of a new partitioning problem that the concurrence of cloud computing, widespread broadband access, and some innovations in LTE cellular networks have created. Today, embedded systems designers must, from requirements definition on through the design process, remember that there are several very different processing sites available to them (Figure 2). There is the cloud. There is the so-called fog. And there is the edge. Partitioning tasks and data among these sits has become a necessary skill to the success of an embedded design project. If you don’t use the new computing resources wisely, you will be vulnerable to a competitor who does—not only in terms of features, performance, and cost advantages to be gained, but in consideration of the growing value of data that can be collected from embedded systems in operation.

.Figure 2. Edge computing offers the choice of three different kinds of processing sites.

The Joy of Partitioning

Unfortunately, partitioning is not often a skill embedded-system designers cultivate. Traditional embedded designs employ a single processor, or at worst a multi-core SoC with an obvious division of labor amongst the cores.

But edge computing creates a new scale of difficulty. There are several different kinds of processing sites, each with quite distinct characteristics. And the connections between processors are far more complicated than the nearly transparent inter-task communications of shared-memory multicore systems. So, doing edge computing well requires a rather formal partitioning process. It begins with defining the tasks and identifying their computing, storage, bandwidth, and latency requirements. Then the process continues by characterizing the compute resources you have available, and the links between them. Finally, partitioning must map tasks onto processors and inter-task communications onto links so that the system requirements are met. This is often an iterative process that at best refines the architecture and at worst turns into a protracted, multi-party game of Whack-a-Mole. It is helpful, perhaps, to look at each of these issues: tasks, processing and storage sites, and communications links, in more detail.

The Tasks

There are several categories of tasks in a traditional embedded system, and a couple of categories that have recently become important for many designs. Each category has its own characteristic needs in computing, storage, I/O bandwidth, and task latency.

In any embedded design there are supervisory and housekeeping tasks that are necessary, but are not particularly compute- or I/O- intensive, and that have no hard deadlines. This category includes most operating-system services, user interfaces, utilities, system maintenance and update, and data logging.

A second category of tasks with very different characteristics is present in most embedded designs. These tasks directly influence the physical behavior of the system, and they do have hard real-time deadlines, often because they are implementing algorithms within feedback control loops responsible for motion control or dynamic process control. Or they may be signal-processing or signal interpretation tasks that lie on a critical path to a system response, such as object recognition routines behind a camera input.

Often these tasks don’t have complex I/O needs: just a stream or two of data in and one or two out. But today these data rates can be extremely high, as in the case of multiple HD cameras on a robot or digitized radar signals coming off a target-acquisition and tracking radar. Algorithm complexity has traditionally been low, held down by the history of budget-constrained embedded designs in which a microcontroller had to implement the digital transfer function in a control loop. But as control systems adopt more modern techniques, including stochastic state estimation, model-based control, and, recently, insertion of artificial intelligence into control loops, in some designs the complexity of algorithms inside time-critical loops has exploded. As we will see, this explosion scatters shrapnel over a wide area.

The most important issue for all these time-critical tasks is that the overall delay from sensor or control input to actuator response be below a set maximum latency, and often that it lies within a narrow jitter window. That makes partitioning of these tasks particularly interesting, because it forces designers to consider both execution time—fully laden with indeterminacies, memory access and storage access delays—and communications latencies together. The fastest place to execute a complex algorithm may be unacceptably far from the system.

We also need to recognize a third category of tasks. These have appeared fairly recently for many designers, and differ from both supervisory and real-time tasks. They arise from the intrusion of three new areas of concern: machine learning, functional safety, and cyber security. The distinguishing characteristic of these tasks is that, while each can be performed in miniature with very modest demands on the system, each can quickly develop an enormous appetite for computing and memory resources. And, most unfortunately, each can end up inside delay-sensitive control loops, posing very tricky challenges for the design team.

Machine learning is a good case in point. Relatively simply deep-learning programs are already being used as supervisory tasks to, for instance, examine sensor data to detect progressive wear on machinery or signs of impending failure. Such tasks normally run in the cloud without any real-time constraints, which is just as well, as they do best with access to huge volumes of data. At the other extreme, trained networks can be ported to quite compact blocks of code, especially with the use of small hardware accelerators, making it possible to use a neural network inside a smart phone. But a deep-learning inference engine trained to detect, say, excessive vibration in a cutting tool during a cut or the intrusion of an unidentified object into a robot’s planned trajectory—either of which could require immediate intervention—could end up being both computationally intensive and on a time-critical path.

Similarly for functional safety and system security, simple rule-based safety checks or authentication/encryption tasks may present few problems for the system design. But simple often, in these areas, means weak. Systems that must operate in an unfamiliar environment or must actively repel novel intrusion attempts may require very complex algorithms, including machine learning, with very fast response times. Intrusion detection, for instance, is much less valuable as a forensic tool than as a prevention.

Resources

Traditionally, the computing and storage resources available to an embedded system designer were easy to list. There were microcontroller chips, single-board computers based on commercial microprocessors, and in some cases boards or boxes using digital signal processing hardware of one sort or another. Any of these could have external memory, and most could attach, with the aid of an operating system, mass storage ranging from a thumb drive to a RAID disk array. And these resources were all in one place: they were physically part of the system, directly connected to sensors, actuators, and maybe to an industrial network.

But add Internet connectivity, and this simple picture snaps out of focus. The original system is now just the network edge. And in addition to edge computing, there are two new locations where there may be important computing resources: the cloud, and what Cisco and some others are calling the fog.

The edge remains much as it has been, except of course that everything is growing in power. In the shadow of the massive market for smart-phone SoCs, microcontrollers have morphed into low-cost SoCs too, often with multiple 32-bit CPU cores, extensive caches, and dedicated functional IP suited to a particular range of applications. Board-level computers have exploited the monotonically growing power of personal computer CPU chips and the growth in solid-state storage. And the commoditization of servers for the world’s data centers has put even racks of data-center-class servers within the reach of well-funded edge computing sites, if the sites can provide the necessary space, power, and cooling.

Recently, with the advent of more demanding algorithms, hardware accelerators have become important options for edge computing as well. FPGAs have long been used to accelerate signal-processing and numerically intensive transfer functions. Today, with effective high-level design tools they have broadened their use beyond these applications into just about anything that can benefit from massively parallel or, more importantly, deeply pipelined execution. GPUs have applications in massively data-parallel tasks such as vision processing and neural network training. And as soon as an algorithm becomes stable and widely used enough to have good library support—machine vision, location and mapping, security, and deep learning are examples—someone will start work on an ASIC to accelerate it.

The cloud, of course, is a profoundly different environment: a world of essentially infinite numbers of big x86 servers and storage resources. Recently, hardware accelerators from all three races—FPGAs, GPUs, and ASICs—have begun appearing in the cloud as well. All these resources are available for the embedded system end-user to rent on an as-used basis.

The important questions in the cloud are not about how many resources are available—there are more than you need—but about terms and conditions. Will your workload run continuously, and if not, what is the activation latency? What guarantees of performance and availability are there? What will this cost the end user? And what happens if the cloud platform provider—who in specialized application areas is often not a giant data-center owner, but a small company that itself leases or rents the cloud resources—suffers a change in situation? These sorts of questions are generally not familiar to embedded-system developers, nor to their customers.

Recently there has been discussion of yet another possible processing site: the so-called fog. The fog is located somewhere between the edge and the cloud, both physically and in terms of its characteristics.

As network operators and wireless service providers turn from old dedicated switching hardware to software on servers, increasingly, Internet connections from the edge will run not through racks of networking hardware, but through data centers. For edge systems relying on cloud computing, this raises an important question: why send your inter-task communications through one data center just to get it to another one? It may be that the networking data center can provide all the resources your task needs without having to go all the way to a cloud service provider (CSP). Or it may be that a service provider can offer hardware or software packages to allow some processing in your edge-computing system, or in an aggregation node near your system, before having to make the jump to a central facility. At the very least you would have one less vendor to deal with. And you might also have less latency and uncertainly introduced by Internet connections. Thus, you can think of fog computing as a cloud computing service spread across the network and into the edge, with all the advantages and questions we have just discussed.

Connections

When all embedded computing is local, inter-task communications can almost be neglected. There are situations where multiple tasks share a critical resource, like a message-passing utility in an operating system, and on extremely critical timing paths you must be aware of the uncertainly in the delay in getting a message between tasks. But for most situations, how long it takes to trigger a task and get data to it is a secondary concern. Most designs confine real-time tasks to a subset of the system where they have a nearly deterministic environment, and focus their timing analyses there.

But when you partition a system between edge, fog, and cloud resources, the kinds of connections between those three environments, their delay characteristics, and their reliability all become important system issues. They may limit where you can place particular tasks. And they may require—by imposing timing uncertainty and the possibility of non-delivery on inter-task messages—the use of more complex control algorithms that can tolerate such surprises.

So what are the connections? We have to look at two different situations: when the edge hardware is connected to an internet service provider (ISP) through copper or fiber-optics (or a blend of the two), and when the connection is wireless (Figure 3).

Figure 3. Tasks can be categorized by computational complexity and latency needs.

The two situations have one thing in common. Unless your system will have a dedicated leased virtual channel to a cloud or fog service provider, part of the connection will be over the public Internet. That part could be from your ISP’s switch plant to the CSP’s data center, or it could be from a wireless operator’s central office to the CSP’s data center.

That Internet connection has two unfortunate characteristics, from this point of view. First, it is a packet-switching network in which different packets may take very different routes, with very different latencies. So, it is impossible to predict more than statistically what the transmission delay between two points will be. Second, Internet Protocol by itself offers only best-effort, not guaranteed, delivery. So, a system that relies on cloud tasks must tolerate some packets simply vanishing.

An additional point worth considering is that so-called data locality laws—which limit or prohibit transmission of data outside the country of origin—are spreading around the world. Inside the European Union, for instance, it is currently illegal to transmit data containing personal information across the borders of a number of member countries, even to other EU members. And in China, which uses locality rules for both privacy and industrial policy purposes, it is illegal to transmit virtually any sort of data to any destination outside the country. So, designers must ask whether their edge system will be able to exchange data with the cloud legally, given the rapidly evolving country-by-country legislation.

These limitations are one of the potential advantages of the fog computing concept. By not traversing the public network, systems relying on ISP or wireless-carrier computing resources or local edge resources can exploit additional provisions to reduce the uncertainty in connection delays.

But messages still have to get from your edge system to the service provider’s aggregation hardware or data center. For ISPs, that will mean a physical connection, typically using Internet Protocol over fiber or hybrid copper/fiber connections, often arranged in a tree structure. Such connections allow for provisioning of fog computing nodes at points where branches intersect. But as any cable TV viewer can attest, they also allow for congestion at nodes or on branches to create great uncertainties in available bandwidth and latency. Suspension of net neutrality in the US has added a further uncertainty, allowing carriers to offer different levels of service to traffic from different sources, and to charge for quality-of-service guarantees.

If the connection is wireless, as we are assured many will be once 5G is deployed, the uncertainties multiply. A 5G link will connect your edge system through multiple parallel RF channels and multiple antennas to one or more base stations. The base stations may be anything from a small cell with minimal hardware to a large local processing site with, again, the ability to offer fog-computing resources, to a remote radio transceiver that relies on a central data center for all its processing. In at least the first two cases, there will be a separate backhaul network, usually either fiber or microwave, connecting the base station to the service provider’s central data center.

The challenges include, first, that latency will depend on what kind of base stations you are working with—something often completely beyond your control. Second, changes in RF transmission characteristics along the mostly line-of-site paths can be caused by obstacles, multipath shifts, vegetation, and even weather. If the channel deteriorates, retry rates will go up, and at some point the base station and your edge system will negotiate a new data rate, or roll the connection over to a different base station. So even for a fixed client system, the characteristics of the connection may change significantly over time, sometimes quite rapidly.

Partitioning

Connectivity opens a new world for the embedded-system designer, offering amounts of computing power and storage inconceivable in local platforms. But it creates a partitioning problem: an iterative process of locating tasks where they have the resources they need, but with the latencies, predictability, and reliability they require.

For many tasks location is obvious. Big-data analyses that comb terabytes of data to predict maintenance needs or extract valuable conclusions about the user can go in the cloud. So, can compute-intensive real-time tasks when acceptable latency is long, and the occasional lost message is survivable or handled in a higher-level networking protocol. A smart speaker in your kitchen can always reply “Let me think on that a moment,” or “Sorry, what?”

Critical, high-frequency control loops must stay at or very near the edge. Conventional control algorithms can’t tolerate the delay and uncertainty of any other choice.

But what if there is a conflict: a task too big for the edge resources, but too time-sensitive to be located across the Internet? Fog computing may solve some of these dilemmas. Others may require you to place more resources in your system.

Just how far today’s technology has enriched the choices was illustrated recently by a series of Microsoft announcements. Primarily involved in edge computing as a CSP, Microsoft has for some time offered the Azure Stack—essentially, an instance of their Azure cloud platform—to run on servers on the customer premises. Just recently, the company enriched this offering with two new options: FPGA acceleration, including the Microsoft’s Project Brainwave machine-learning acceleration, for Azure Stack installations, and Azure Sphere, a way of encapsulating Azure’s security provisions in an approved microcontroller, secure operating system, and coordinated cloud service for use at the edge. Similarly, Intel recently announced the OpenVINO™ toolkit, a platform for implementing vision-processing and machine intelligence algorithms at the edge, relying on CPUs with optional support from FPGAs or vision-processing ASICs. Such fog-oriented provisions could allow embedded-system designers to simply incorporate cloud-oriented tasks into hardware within the confines of their own systems, eliminating the communications considerations and making ideas like deep-learning networks within control loops far more feasible.

In other cases, designers may simply have to refactor critical tasks into time-critical and time-tolerant portions. Or they may have to replace tried and true control algorithms with far more complex approaches that can tolerate the delay and uncertainty of communications links. For example, a complex model-based control algorithm could be moved to the cloud, and used to monitor and adjust a much simpler control loop that is running locally at the edge.

Life at the edge, then, is full of opportunities and complexities. It offers a range of computing and storage resources, and hence of algorithms, never before available to most embedded systems. But it demands a new level of analysis and partitioning, and it beckons the system designer into realms of advanced system control that go far beyond traditional PID control loops. Competitive pressures will force many embedded systems into this new territory, so it is best to get ahead of the curve.

 

 

 

 


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          CSIS RIST Relativity Project Coordinator - CSIS Lead Investigator - Citi - Tampa, FL      Cache   Translate Page   Web Page Cache   
Diversity is a key business imperative and a source of strength at Citi. Degree in Computer Science, Machine Learning, Information Retrieval or related field,...
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          Machine Learning Researcher - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page   Web Page Cache   
PARC, a Xerox company, is in the Business of Breakthroughs®. We create new business options, accelerate time to market, augment internal capabilities, and...
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Technica Corporation is seeking a Machine Learning Engineer to support our internal Innovation, Research and Development (IRD) team....
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AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. 3+ Years with leading ISV’s leveraging the Machine...
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          Bill Ward / AdminTome: Data Pipeline: Send logs from Kafka to Cassandra      Cache   Translate Page   Web Page Cache   

Bill Ward / AdminTome: Data Pipeline: Send logs from Kafka to Cassandra

In this post, I will outline how I created a big data pipeline for my web server logs using Apache Kafka, python, and Apache Cassandra.

In past articles I described how to install and configureApache Kafka andApache Cassandra. I assume that you already have a Kafka broker running with a topic of www_logs and a production ready Cassandra cluster running. If you don’t then please follow the articles mentioned in order to follow along with this tutorial.

In this post, we will tie them together to create a big data pipeline that will take web server logs and push them to an Apache Cassandra based data sink.

This will give us the opportunity to go through our logs using SQL statements and possible other benefits like applying machine learning to predict if there is an issue with our site.

Here is the basic diagram of what we are going to configure:


Bill Ward / AdminTome: Data Pipeline: Send logs from Kafka to Cassandra

Lets see how we start the pipeline by pushing log data to our Kafka topic.

Pushing logs to our data pipeline

Apache Web Server logs to /var/logs/apache. For this tutorial, we will work with the Apache access logs which show requests to the web server. Here is an example:

108.162.245.143 - - [08/Aug/2018:17:44:40 +0000] "GET /blog/terraform-taint-tip/ HTTP/1.0" 200 31281 "-" "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)"

Log files are simply text files where each line is a entry in the log file.

In order to easily read our logs from a Python application that we will write later, we will want to convert these log lines into JSON data and add a few more fields.

Here is what our JSON will look like:

{
"log": {
"source": "",
"type": "",
"datetime": "",
"log": ""
}
}

The source field is going to be the hostname of our web server. The type field is going to let us know what type of logs we are sending. In this case it will be ‘www_access’ since we are going to send Apache access logs. The datetime field will hold the timestamp value of when the log was created. Finally, the log field will contain the entire line of text representing the log entry.

I created a sample python application that takes these logs and forwards them to kafka. You can find it on GitHub at admintome/logs2kafka . Let’s look at the forwarder.py file in more detail:

import time
import datetime
import socket
import json
from mykafka import MyKafka
def parse_log_line(line):
strptime = datetime.datetime.strptime
hostname = socket.gethostname()
time = line.split(' ')[3][1::]
entry = {}
entry['datetime'] = strptime(
time, "%d/%b/%Y:%H:%M:%S").strftime("%Y-%m-%d %H:%M")
entry['source'] = "{}".format(hostname)
entry['type'] = "www_access"
entry['log'] = "'{}'".format(line.rstrip())
return entry
def show_entry(entry):
temp = ",".join([
entry['datetime'],
entry['source'],
entry['type'],
entry['log']
])
log_entry = {'log': entry}
temp = json.dumps(log_entry)
print("{}".format(temp))
return temp
def follow(syslog_file):
syslog_file.seek(0, 2)
pubsub = MyKafka(["mslave2.admintome.lab:31000"])
while True:
line = syslog_file.readline()
if not line:
time.sleep(0.1)
continue
else:
entry = parse_log_line(line)
if not entry:
continue
json_entry = show_entry(entry)
pubsub.send_page_data(json_entry, 'www_logs')
f = open("/var/log/apache2/access.log", "rt")
follow(f)

The first thing we do is open the log file /var/log/apache2/access.log for reading. We then pass that file to our follow () function where our application will follow the log file much like tail -f /var/log/apache2/access.log would.

If the follow function detects that a new line exists in the log it converts it to JSON using the parse_log_line () function. It then uses the send_page_data() function of MyKafka to push the JSON message to the www_logs topic.

Here is the MyKafka.py python file:

from kafka import KafkaProducer
import json
class MyKafka(object):
def __init__(self, kafka_brokers):
self.producer = KafkaProducer(
value_serializer=lambda v: json.dumps(v).encode('utf-8'),
bootstrap_servers=kafka_brokers
)
def send_page_data(self, json_data, topic):
result = self.producer.send(topic, key=b'log', value=json_data)
print("kafka send result: {}".format(result.get()))

This simply calls KafkaProducer to send our JSON as a key/value pair where the key is the string ‘log’ and the value is our JSON.

Now that we have our log data being pushed to Kafka we need to write a consumer in python to pull messages off the topic and save them as a row in a Cassandra table.

But first we should prepare Cassandra by creating a Keyspace and a table to hold our log data.

Preparing Cassandra

In order to save our data to Cassandra we need to first create a Keyspace in our Cassandra cluster. Remember that a keyspace is how we tell Cassandra a replication strategy for any tables attached to our keyspace.

Let’s start up CQLSH.

$ bin/cqlsh cass1.admintome.lab
Connected to AdminTome Cluster at cass1.admintome.lab:9042.
[cqlsh 5.0.1 | Cassandra 3.11.3 | CQL spec 3.4.4 | Native protocol v4]
Use HELP for help.
cqlsh>

Now run the following query to create our keyspace.

CREATE KEYSPACE admintome WITH replication = {'class': 'SimpleStrategy', 'replication_factor': '3'} AND durable_writes = true;

Now run this query to create our logs table.

CREATE TABLE admintome.logs (
log_source text,
log_type text,
log_id timeuuid,
log text,
log_datetime text,
PRIMARY KEY ((log_source, log_type), log_id)
) WITH CLUSTERING ORDER BY (log_id DESC)

Essentially, we are storing time series data which represents our log file information.

You can see that we have a column for source, type, datetime, and log that match our JSON from the previous section.

We also have another row called log_id that is of the type timeuuid. This creates a unique UUID from the current timestamp when we insert a record into this table.

Cassandra stores one row per partition. A partition in Cassandra is identified by the PRIMARY KEY. In this example, our PK is a COMPOSITE PRIMARY KEY where we use both the log_source and the log_type values as a primary key.

So for our example, we are going to create a single partition in Cassandra consisting of the primary key (‘www2’,’www_access). The hostname of my web server is www2 so that is what log_source is set to.

We also set the Clustering Key to log_id . These are guaranteed unique keys so we will be able to have multiple rows in our partition.

If I lost you there don’t worry, it took me a couple of days and many headaches to understand it fully. I will be writing another article soon detailing why the data is modeled in this fashion for Cassandra.

Now that we have our Cassandra keyspace and table ready to go, we need to write our Python consumer to pull the JSON data from our Kafka topic and insert that data into our table as a new row.

Python Consumer Application I have posted the source code to the
          Create text classification with TensorFlow      Cache   Translate Page   Web Page Cache   
Looking for someone to build NLP models using Keras and Tensorflow (CNN/RNNs) There will be many tasks and putting one task price here, which we can discuss. (Budget: €250 - €750 EUR, Jobs: Machine Learning, Python, Tensorflow)
          Sales Engineer - Hitachi Vantara - New York, NY      Cache   Translate Page   Web Page Cache   
Account Managers, internal specialists and customers. Understanding of Data Science and Machine Learning....
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          Principal Solutions Architect, Database & Analytics - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Experience with Statistics, Machine Learning and Predictive Modelling. The successful candidate will become a trusted advisor to our customers and will partner...
From Amazon.com - Fri, 20 Jul 2018 01:20:16 GMT - View all Seattle, WA jobs
          Manager - Payments Business Operations - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Experience with statistics or Machine Learning methodologies a plus. Experience in finance, accounting, business intelligence, operations or systems analysis....
From Amazon.com - Wed, 18 Jul 2018 01:20:26 GMT - View all Seattle, WA jobs
          Telecommute Python for Robotics and AI Adjunct Faculty Member      Cache   Translate Page   Web Page Cache   
An educational institution is searching for a person to fill their position for a Telecommute Python for Robotics and AI Adjunct Faculty Member. Individual must be able to fulfill the following responsibilities: Developing and delivering the course according to company's teaching standards Facilitating online discussions and providing feedback on student work Reporting grades and discussing student issues with staff Qualifications Include: Master's degree Current active employment in the Robotics Software Engineering field, or related industry 5 - 10+ years developing Python in AI field Solid understanding of machine learning concepts and experience development AI capability in a real world setting Solid understanding of popular programming languages (C++, Python)
          Data Science Manager      Cache   Translate Page   Web Page Cache   
Data Science Manager London £80,000 THE COMPANY Harnham are working with one of the most exciting and disruptive media agencies (also one of the top globally), who are looking to add a Data Science Manager into their data science product team. As a key player in the Data Science team you will have the opportunity to manage projects for some of the biggest brands worldwide. It will be your job to liaise with clients, take the brief and work with multi-disciplined teams of analysts to deliver solutions to a wide array of business problems spanning several different sectors. This really is a great opportunity, particularly for those looking to enter a 'work hard-play hard' environment - with regular office socials and plenty of great perks for joining!! THE ROLE You will be: Analysing and optimising digital marketing to deliver significant return on investments for clients Working closely with the clients to explore new opportunities within their business Buiilding a variety of different machine learning and data science products, making the most of the latest technologies out there YOUR SKILLS AND EXPERIENCE You must have: An MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Artificial Intelligence or Mathematics An expert working knowledge of at least one programming language Experience of working with large volumes of data Experience in a client facing role is a bonus THE BENEFITS £80,000 + benefits HOW TO APPLY Please register your interest by sending your CV via the Apply link on this page. KEYWORDS R, Python, SQL, Hive, Hadoop, Spark, Machine Learning, Statistics, Mathematics, AWS, Research, Marketing, Online, Data Scientist, Data Science, Predictive modelling, Forecasting, Digital Marketing, Content, Agency, Tech.
          The Top 5 Machine Learning Libraries in Python      Cache   Translate Page   Web Page Cache   
The Top 5 Machine Learning Libraries in Python
The Top 5 Machine Learning Libraries in Python
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | Lec: 33 | 167 MB
Genre: eLearning | Language: English



          Sr Director, Growth Marketing Technology - eBay Inc. - Bellevue, WA      Cache   Translate Page   Web Page Cache   
Further, the Marketing Tech Leader will apply the latest data analysis and machine learning technologies to innovate applications in both BI analysis and...
From eBay Inc. - Fri, 01 Jun 2018 08:04:49 GMT - View all Bellevue, WA jobs
          Software Engineer - Machine Learning - Convoy - Seattle, WA      Cache   Translate Page   Web Page Cache   
Today, we use machine learning to figure out freight prices, shipment relevance for carriers, auction bidding strategy, and other internal processes....
From Convoy - Sat, 19 May 2018 10:13:22 GMT - View all Seattle, WA jobs
          "Machine Learning and the Library or: How I Learned to Stop Worrying and Love My Robot Overlords"      Cache   Translate Page   Web Page Cache   
Charlie Harper has published "Machine Learning and the Library or: How I Learned to Stop Worrying and Love My Robot Overlords" in he Code4Lib Journal. Here's an excerpt: Machine learning algorithms and technologies are becoming a regular part of daily life – including life in the libraries. Through this article, I hope to: * To […]

          Software Developer (Machine Learning) - Lincoln Electric - Cleveland, OH      Cache   Translate Page   Web Page Cache   
Experience with popular languages (C++, C#, Java, Python, and R). 3 - 10 years of experience with Windows, Linux, or Java platforms....
From The Lincoln Electric Company - Thu, 19 Apr 2018 18:30:28 GMT - View all Cleveland, OH jobs
          Innovation Developer - TeamSoft - Sun Prairie, WI      Cache   Translate Page   Web Page Cache   
Are you interested in topics like machine learning, IoT, Big data, data science, data analysis, satellite imagery or mobile telematics?...
From Dice - Thu, 19 Jul 2018 08:35:55 GMT - View all Sun Prairie, WI jobs
          iOS Developer - PGS SOFTWARE - Rzeszów, podkarpackie      Cache   Translate Page   Web Page Cache   
Augmented Reality, Machine Learning, iBeacons, Top Level Security. Elastyczne godziny pracy....
Od PGS SOFTWARE - Wed, 08 Aug 2018 14:51:19 GMT - Pokaż wszystkie Rzeszów, podkarpackie oferty pracy
          Machine Learning Can Identify the Authors of Anonymous Code      Cache   Translate Page   Web Page Cache   
Researchers have repeatedly shown that writing samples, even those in artificial languages, contain a unique fingerprint that's hard to hide.
          Незамеченное IT      Cache   Translate Page   Web Page Cache   
Замечали неравномерность человеческого внимания к разным достижениям? Мемы о том, что Стив Джобс и Деннис Ритчи ушли почти одновременно, но обществом была замечена только смерть первого, хотя вклад второго в IT гигантский:


Реальность неумолима: как бы ни был талантлив сценарист фильма, актёрам всегда достанется больше внимания, потому что их видно. Как бы ни был профессионален создатель серверной технологии, Марк Цукерберг привлечёт больше внимания, потому что рядовой пользователь взаимодействует с его проектом напрямую. Это нормально: мы все не замечаем чего-то, с чем не сталкиваемся напрямую.

В этом посте будет небольшой заплыв в специфику организации мероприятий, а также Kotlin, Machine Learning и создателя ОС Фантом. Коротко о посте в одной картинке (да, вы скоро поймете, о чем речь):


Если ты организуешь какое-то событие, начиная от простой встречи разработчиков в собственном офисе, и заканчивая IT-фестивалем на две тысячи человек, скрытая часть происходящего не менее важна чем то, что на виду. Для участника это огромный дополнительный контент, во много раз увеличивающий ценность происходящего. Для организатора — это набор направляющих идей. Осталось сделать так, чтобы не пропускать еще и эту скрытую часть.

Предлагаемая схема: обращать внимание людей на «незамеченные» вещи и объяснять их значимость. Например, сейчас мы запускаем фестиваль TechTrain, и видим несколько историй, о которых пойдет речь.
Читать дальше →
          Pre-sales Specialist      Cache   Translate Page   Web Page Cache   
CA-San Francisco, Appen is the premier provider of high quality training data for machine learning, enhanced by human interaction, serving the most advance clients within the industries of Technology, Automotive, Government, Retail, Healthcare and Financial Services. Currently, Appen is trusted by 8 of the top 10 technology companies in the world and is growing at a phenomenal pace. Appen has an opportunity for a v
          Software Data Engineer - NLP, Hadoop, SPARK      Cache   Translate Page   Web Page Cache   
CA-San Francisco, If you are a Software Data Engineer with experience, this is an exciting opportunity to join a company that is combining machine learning with medical data to make our world an even better place. We are taking one of the most cutting edge technologies and applying it to one of the most important and archaic industries. Top Reasons to Work with Us 1. Excellent compensation structure and benefits 2.
          Full-Stack Software Engineer - JavaScript, ReactJs, Redux      Cache   Translate Page   Web Page Cache   
CA-San Francisco, If you are a Full-Stack Software Engineer with experience, this is an exciting opportunity to help build the framework for this Machine Learning company in the medical space. You will be taking one of the most cutting edge technologies and applying it to one of the most important and archaic industries. Top Reasons to Work with Us 1. Up and coming Company in the San Francisco area that just receiv
          Infrastructure Software Engineer - SQL, Python, Google Cloud      Cache   Translate Page   Web Page Cache   
CA-San Francisco, If you are a Infrastructure Software Engineer with experience, this is an exciting opportunity to help build the framework for this Machine Learning Company in the medical space. This company is backed by world class investors from some of the major players in Silicon Valley. Top Reasons to Work with Us 1. Up and coming Company in the San Francisco area that just received funding 2. Lots of room f
          Ingénieur Analyse de Données et Logiciels - Intelligence Manufacturière - Data Analytics and Software Engineer – Manufacturing Intelligence - Alcoa Corp. - Deschambault, QC      Cache   Translate Page   Web Page Cache   
IoT, Connected Worker, Machine Learning, Cloud, Robotics, Augmented Reality. Ce poste peut être basé à l'une ou l'autre des Alumineries d'Alcoa dans le monde/...
From Alcoa Corp. - Fri, 29 Jun 2018 03:08:28 GMT - View all Deschambault, QC jobs
          Ingénieur Analyse de Données et Logiciels - Intelligence Manufacturière - Data Analytics and Software Engineer – Manufacturing Intelligence - Alcoa Corporation - Deschambault, QC      Cache   Translate Page   Web Page Cache   
IoT, Connected Worker, Machine Learning, Cloud, Robotics, Augmented Reality. Description du poste....
From Alcoa Corporation - Thu, 28 Jun 2018 15:45:09 GMT - View all Deschambault, QC jobs
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Seattle, WA      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
From Siemens - Tue, 31 Jul 2018 13:37:13 GMT - View all Seattle, WA jobs
          Data Architect - Remote West coast - Insight Enterprises, Inc. - Dallas, TX      Cache   Translate Page   Web Page Cache   
R, Azure Machine Learning. 2017 Arizona’s Most Admired Companies (AZ Business Magazine), 2016 Best Places to Work (Phoenix Business Journal)....
From Insight - Mon, 14 May 2018 23:57:10 GMT - View all Dallas, TX jobs
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Portland, OR      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
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          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page   Web Page Cache   
Database architecture, Big Data, Machine Learning, Business Intelligence, Advanced Analytics, Data Mining, ETL. Internal teammate application guidelines:....
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          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - San Francisco, CA      Cache   Translate Page   Web Page Cache   
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          Machine Learning Engineer - Technica Corporation - Dulles, VA      Cache   Translate Page   Web Page Cache   
Technica Corporation is seeking a Machine Learning Engineer to support our internal Innovation, Research and Development (IRD) team....
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          The Essential Guide to Training Data for Machine Learning      Cache   Translate Page   Web Page Cache   
Download Figure Eight's new ebook, The Essential Guide to Training Data, and you'll learn about the advantages of using more data, the differences ...
          The Essential Guide to Training Data for Machine Learning      Cache   Translate Page   Web Page Cache   
Download Figure Eight's new ebook, The Essential Guide to Training Data, and you'll learn about the advantages of using more data, the differences ...
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Download Figure Eight's new ebook, The Essential Guide to Training Data, and you'll learn about the advantages of using more data, the differences ...
          Show and Tell – Making Interactive Machine Learning Explorers using Dash + Scikit-learn      Cache   Translate Page   Web Page Cache   
Hey all! I've recently completed two apps that lets you explore different parameters for very popular learning algorithms, notably Least-Square ...
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Hey all! I've recently completed two apps that lets you explore different parameters for very popular learning algorithms, notably Least-Square ...
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Much to the chagrin of summer party planners, weather is a notoriously chaotic system. Small changes in precipitation, temperature, humidity, wind speed or direction, etc. can balloon into an entirely new set of conditions within a few days. That’s why weather forecasts become unreliable more than about seven days into the future—and why picnics need […]
          Product Lead      Cache   Translate Page   Web Page Cache   
Product Lead London £80,000-£100,000 Our client is looking for a Product Lead to be a key member in an innovative team where the primary motivation is in driving change. You will be heading up the whole data science function and more specifically the product analytics team. As a Product Lead in this team you focus on championing a data-driven approach to solving the company's toughest problems, such as personalising customer experience and in-depth customer behaviour pieces. THE COMPANY: This global company are investing a lot into finding a top talent to lead a team that analyses products, focusing on acquiring customers. You will be joining a unique culture that drives pioneering ideas in product analytics. With a clear vision and business plan, this company emphasises on a future where customer experience is made simpler. THE ROLE: Day to day you would be analysing large amounts of customer data in Python or R, using machine learning techniques to produce valuable insights for the business around their products. Providing internal consultancy for the data science team across all business areas and liaising with teams across the country. Exciting side projects include working with new data sets and natural language processing techniques. Managing the whole data science function YOUR SKILLS AND EXPERIENCE: Extensive knowledge and use of Python and SQL Proven commercial experience applying machine learning techniques to large, messy data sets using Python The successful Product Lead will have industrial experience analysing product data in Python, focusing on creating solutions to acquire customers The ideal candidate will have the ability to effectively communicate heavily technical concepts to non-technical stakeholders and management You will have managed a team of at least 4 people THE BENEFITS: £80,000-£100,000 1 month off every year, specifically for travelling Working from home Flexible working HOW TO APPLY: Please register your interest by sending your CV to Kian Dixon via the Apply link on this page. For more information about similar roles, please get in touch!
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AI and machine learning-based tools are being increasingly used in dermatology to analyze skin conditions ranging from acne to STDs. (Source: bizjournals.com Health Care:Biotechnology headlines)
          Artificial Intelligence (AI) In Healthcare Market Outlook to 2023: Emerging Trends and Will Generate New Growth Opportunities Status | IBM, Microsoft, Google, Apple, Amazon, Medtronic Inc.      Cache   Translate Page   Web Page Cache   
Artificial Intelligence (AI) In Healthcare Market Outlook to 2023: Emerging Trends and Will Generate New Growth Opportunities Status | IBM, Microsoft, Google, Apple, Amazon, Medtronic Inc. Artificial Intelligence (AI) refers to the creation of intelligent systems that are able to perform tasks without human interventions and instructions. It is the constellation of different technologies such as natural language processing, machine learning, perception, and reasoning. AI is

          Machine Learning Training : Tonex Training      Cache   Translate Page   Web Page Cache   
Added: Aug 10, 2018
By: ecommerceexpertonlin
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Artificial Intelligence (AI) is the more extensive idea of machines having the capacity to complete errands in a way that people would consider "savvy." Machine Learning is a present utilization of AI based around the possibility that we should simply have the capacity to give machines access to information and let them learn for themselves. Machine Learning is a field of software engineering that utilizations factual procedures to enable PC frameworks to "learn" (i.e., continuously enhance execution on a particular errand) with information, without being expressly modified. Learn more about Machine Learning Training from Tonex experts. Visit links below. https://www.tonex.com/machine-learning-training/

          The Failed State of Digital Advertising      Cache   Translate Page   Web Page Cache   

My digital advertising customer experience sucks. Big time. 

I'm speaking at a conference soon, and wanted to know what the official hotel is for the event. I Googled the event. Now all I see are ads for this event. Every. Where. I. Go. I signed up for a software service, because a friend had recommended it. Now all I see are ads for this software service. Every. Where. I. Go. I grabbed a link for a book that I had read on Amazon to share with a friend. Now all I see are ads for this book on Amazon. Every. Where. I. Go. I was at a kid's birthday party at one of those trampoline places. Now all I see are ads for this sporting venue. Every. Where. I. Go. In fact, in looking through my Facebook feed, there is nothing in there that is relevant to me anymore - at all... and the brands that are advertising to me are, without a doubt, paying a premium because of what some algorithm has defined as valuable intent. There is no intent.

This is where digital advertising falls down. Hard.

Feel free to retarget a consumer who has abandoned a shopping cart. There are a myriad of other ways to define true intent. A search? Search may well be one of the worst ways to align your retargeting or personalization marketing strategies. It's expensive and it's wasteful, if you really don't dig in and define some significant metrics that must happen AFTER the search that should trigger an ad. Regardless of how the brand behaves in their media spend, the biggest criminals in this ordeal are the platforms. Think about the depth, data and understanding that these platforms have on their users. Think about the power of their click tracking, user behavior, the underlying machine learning and artificial intelligence technology that they have developed and this is the output? Couple all of that together, and it's somewhat criminal that the vast majority of digital ads that consumers are being exposed to are things that they have already closed the purchase (or research) cycle on. And, with that, why bother wasting a brand impression if that consumer has already (and obviously) been exposed to the brand and, in many cases, is already a consumer? It's the old-school mindset of impression repetition coupled on to this new technology that can personalize and retarget. It makes no sense to mix those two ideologies together.

It's true... is it not?

The worst of the worst (and the most common digital marketing advertising infraction) comes in the continuous flow of ads for a brand that the consumer has just purchased from. And, to make it worse, it never ends. There are countless brands that I have purchased - months later - that are still retargeting me with offers on the exact product or service that I have already purchased. It's a bad brand experience. It doesn't say "thank you for being my customer." It does say: "we have no idea that you're now a customer, so here are some ads for something that you have already purchased from us." It's not a warm and fuzzy feeling.

Isn't that Customer Experience 101? 

Digital marketing and advertising is not easy. Digital marketing and advertising is hard, complex and fast moving. Because of that, I struggle to understand why brands make it even harder on themselves (they are paying a premium for this kind of advertising) and for their consumers (it's hard to feel like a valued consumer, when every brand interaction tells them that you're not a customer). Perhaps it's time for brands to take their foot off of the digital marketing customer journey gas pedal, and re-evaluate where their spend is going, and what the logic tree is that displays an ad in front of consumer. It will cut down on waste. It will build a better brand experience. It will put their media agency on notice that they need to be sharper. It will reduce ad spend. It will increase viewability. It will increase positive recall. It will increase brand value.

How is that not one of the biggest wins a brand could have in 2018?

Tags: advertising ai algorithm amazon artificial intelligence brand brand experience brand impression brand value business business blog business strategy click tracking creativity ctrl alt delete customer experience customer journey digital advertising digital marketing digital marketing blog disruption facebook facebook feed google innovation leadership machine learning management management thinking marketing marketing blog marketing strategy media media agency media company media platform media spend mitch joel mitchjoel ml personalization platform programmatic purchase intent retargeting search search engine six pixels group six pixels of separation social media software targeting technology user behavior viewability


          AI4ALL participants tell all—summer camps get girls involved in AI and tech      Cache   Translate Page   Web Page Cache   

AI4ALL, a nonprofit working to increase diversity and inclusion in artificial intelligence, believes that all students should have the opportunity to learn about AI and explore its applications. We share the same belief, and have gotten more kids involved in computer science and technology by donating to organizations like Code.org, building programs like Made with Code and CS First, and most recently helping AI4ALL expand learning resources for underrepresented youth. With a $1 million grant from Google.org, AI4ALL can scale their nationwide summer camps that spark student interest in AI and help them build foundational technical skills. The Google.org grant will also create a new digital curriculum that will introduce students to fundamental AI concepts.

To learn more about AI4ALL’s impact, we caught up with Tess Posner, CEO of AI4ALL, as well as two program alums: Ananya Karthik, who recently led an AI and art workshop in Oakland for Bay Area middle and high school girls, and 15-year old Ekanem Okeke, who participated in the AI4ALL Stanford camp this summer. Hear from Tess and Ananya in this video, and read on for an interview with Ekanem.

Supporting diversity and inclusion in AI with AI4ALL
43292512671_5ce27985c7_o.jpg

Here's Ekanem at the AI4ALL Stanford camp

Ekanem Okeke participated in the AI4ALL Stanford camp this summer. We chatted with her about her experience:

Tell us a bit about yourself and your background.
My name is Ekanem Okeke. I am 15 years old. I was born in Ottawa, Canada, but for the past three years I’ve been living in Michigan with my family. I have two sisters and one brother. Having lived in Canada for the majority of my life so far, I am fluent in French, but I would really love to learn Korean and Japanese. I also enjoy reading, drawing, playing soccer and basketball, and watching anime. Lastly, I am fascinated with astronomy, biology and science in general.

What was your experience with computer science and AI before camp?
Prior to attending AI4ALL, I hadn’t seriously coded. I’d heard of the development of autonomous cars using AI, but other than that, I didn’t know all that much of the applications of AI. Of course, I was curious about AI and programming before attending AI4ALL, but I hadn’t acted on that curiosity yet. Nevertheless, I am in a very CS-heavy family as both my parents are engineers and my sister is also on her way to becoming one.

What was your favorite part of camp and who were your fellow participants?
I don’t think that I could pick a favorite! I really enjoyed listening to guest speakers, like Professor Jeanette Bohg’s talk comparing computer vision to human vision. Prof. Dan Jurafsky's talk on Natural Language Processing (NLP) was also very fascinating as he discussed using NLP to evaluate police bias. Along with these talks, we watched technical demonstrations and even managed to fit in field trips to Google’s headquarters and the beach!

My fellow participants were really cool and helpful—bonding with them was a high point of the experience. We also had a pretty diverse class, with people from nine states and nine different countries. I even met a fellow Canadian!

What did you think about your field trip to Google?
I definitely think that the most enriching part of the whole experience was the panel we attended at Google. I found that all the panelists had something interesting that they were working on and something unique about their history with AI. It displayed the interdisciplinary nature of AI, as the panelists had very different jobs that all still related to AI, such as health research with machine learning.

What was the subject of your team project and what did you learn while working on it?
While at camp, I was in the robotics group that focused on autonomous vehicles. In our group, we attempted to model the navigation system that would be implemented in an autonomous vehicle. To accomplish this, we used proportional–integral–derivative controller (PIDs) and Dijkstra’s algorithm. The PID controllers worked to enable our robots to follow the lines on our map, while Dijkstra’s algorithm enabled the robots to plan efficient routes. By combining these two algorithms, the robots were able to navigate themselves from one destination to the next.

Leaving camp, has your perspective on AI changed? How?
I’ve learned how AI can solve problems. Before camp, I saw AI as somewhat of a super tool, a technology that could be used to change the world. However, I didn’t really understand what AI actually does. After the camp, I’ve come to understand AI in a more realistic sense. I now understand how to utilize AI as an actual concrete piece of technology.

What excites you the most about AI?
I think the most exciting thing about AI is that it is very much a blank canvas. The broad scope of how interdisciplinary AI is makes it such an interesting and curious field. Although AI is not some kind of all-powerful tool, it is a new technology that can improve one’s daily life. AI’s usefulness is really just limited to our own imagination, and there’s many more possibilities available beyond an autonomous car.

As you look to the future (no pressure!), do you have a sense for what you might be interested in pursuing?
Through this program, I was exposed to many things, which allowed me to picture my own future in any career. I’ve really come to understand that there are a lot of amazing specialized careers that I haven’t heard of before. Attending AI4ALL really encouraged me to follow my passions and turn my passions into a career. As a result, I feel like it would be a waste for me to decide what I want to do right now when there’s so much out there and so much to come.


          "Machine Learning and the Library or: How I Learned to Stop Worrying and Love My Robot Overlords"      Cache   Translate Page   Web Page Cache   
Charlie Harper has published "Machine Learning and the Library or: How I Learned to Stop Worrying and Love My Robot Overlords" in he Code4Lib Journal. Here's an excerpt: Machine learning algorithms and technologies are becoming a regular part of daily life – including life in the libraries. Through this article, I hope to: * To […]

          ISV Technology Director - AI and ML - 67511 - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page   Web Page Cache   
AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. 3+ Years with leading ISV’s leveraging the Machine...
From Advanced Micro Devices, Inc. - Sat, 07 Jul 2018 01:32:18 GMT - View all Austin, TX jobs
          Sr Software Engineer ( Big Data, NoSQL, distributed systems ) - Stride Search - Los Altos, CA      Cache   Translate Page   Web Page Cache   
Experience with text search platforms, machine learning platforms. Mastery over Linux system internals, ability to troubleshoot performance problems using tools...
From Stride Search - Tue, 03 Jul 2018 06:48:29 GMT - View all Los Altos, CA jobs
          ISV Technology Director - AI and ML - 67511 - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page   Web Page Cache   
AMD’s Machine Learning team work on many high-impact projects that serve AMD’s various lines of business. 3+ Years with leading ISV’s leveraging the Machine...
From Advanced Micro Devices, Inc. - Sat, 07 Jul 2018 01:32:18 GMT - View all Austin, TX jobs
          Statistical Overview of Intelligent Process Automation Market Growing at CAGR of +40% by 2025: Know about Influencing Factors by Focusing on Top Companies like Blue Prism, UiPath, Crowd Computing Systems, Accelirate, EMC, OpenText, SAP, Pegasystems      Cache   Translate Page   Web Page Cache   
Statistical Overview of Intelligent Process Automation Market Growing at CAGR of +40% by 2025: Know about Influencing Factors by Focusing on Top Companies like Blue Prism, UiPath, Crowd Computing Systems, Accelirate, EMC, OpenText, SAP, Pegasystems Our Market Analysts Project the Intelligent Process Automation Market to Grow Steadily at a CAGR Of Greater Than 40% By 2025. Intelligent process automation (IPA) is the application of artificial intelligence (AI), computer vision, cognitive automation and machine learning to robotic

          OSS Leftovers      Cache   Translate Page   Web Page Cache   
  • Open source Kaa IoT middleware to take on enterprise IoT

    To benefit from IoT, businesses need a way to network, manage and secure all of their connected devices. While there are proprietary IoT middleware platforms available to do this for the home and heavy industries like manufacturing, the Kaa IoT platform is one of the few open source options on the market today that is business-ready.

  • bzip.org changes hands

    The bzip2 compression algorithm has been slowly falling out of favor, but is still used heavily across the net. A search for "bzip2 source" returns bzip.org as the first three results. But it would seem that the owner of this domain has let it go, and it is now parked and running ads. So we no longer have an official home for bzip2.

  • Three Capabilities Banks Need to Work On While Adopting Open Source

    As banks are now willing to experiment and adopt new age technologies such as artificial intelligence and blockchain, the next big step of its digital disruption has to do with open source banking.

    With the adoption of open source, banks are likely to open their APIs and share customer data with third-party players to develop innovative products and offer customized real-time bespoke services to customers.

    Industry experts consider it to be the best time to embrace open banking as customer buying patterns are changing.

    In a previous interaction with Entrepreneur India, Rajeev Ahuja, Executive Director, RBL Bank accredited this change to “the emergence of nontraditional competition such as fintech startups, growing domination of technologies like blockchain, artificial intelligences, machine learning, etc and lastly, the initiatives taken by the Reserve Bank Of India to regulated the payments banks, peer to peer lending platforms, linking of Aadhar, and e-kyc.”

  • Free and open-source software con returns to International House

    FOSSCon, a free and open-source software conference, will be held Aug. 25 at the International House Philadelphia. Lectures and workshops will teach participants about free software and new ways to use it.

    Unlike most software, which is only available under restrictive licensing, free and open-source software is available under licenses that let people distribute, run and modify the software for their own purposes. It includes well-known projects like the Firefox browser or the Linux kernel. Those who talk about “free software” emphasize the way copyright law restricts users’ freedom, while those who talk about “open source” emphasize the economic and technical benefits of shared development.

    However, most of the scheduled events are far from philosophical, focusing on technical subjects like the use of domain name systems or the filesystem ZFS. The speakers range from professional programmers to enthusiasts. Most famous on the list is Eric S. Raymond, one of the thinkers behind “open source,” who will speak about the history of the C programming language and what might replace it. Of particular local interest is a talk by Eric O’Callaghan, a systems administrator at Thomas Jefferson University, on how to use public data from Indego Bike Share.


          Python Engineer (NLP Machine Learning)      Cache   Translate Page   Web Page Cache   
NC-Morrisville, PYTHON ENGINEER (NLP + MACHINE LEARNING) - CONTRACT - MORRISVILLE, NC Python Engineer (NLP + Machine Learning) Responsibilities: * Participate in cutting edge research using tools such as Machine Learning and Natural Language Processing amongst other capabilities. * Enhance the automated translation engine developed in house * Help develop software and algorithms and build new cognitive computing
          Approximate Computing      Cache   Translate Page   Web Page Cache   
This special issue of IEEE Micro explores exciting, new ideas in the vast design space of approximate computing. We present articles that range from programming languages to circuits and cover important application domains such as machine learning and the Internet of Things.
          Data Scientist - Paradigma Digital - Madrid, España      Cache   Translate Page   Web Page Cache   
Buscamos incorporar un Data Scientist con 3-5 años de experiencia en la implementación de modelos analíticos sobre entorno AWS. Funciones a desempeñar: Identificación, interpretación y combinación de fuentes de datos. Análisis dinámicos y continuados de aspectos estratégicos. Identificación y descubrimiento de patrones de comportamiento según los datos analizados. Definición y aplicación de algoritmos. Aplicación de machine Learning (clasificación, regresión, similitudes,...
          Analista Machine Learning-Scikitlearn - Gfi Informática - Madrid, España      Cache   Translate Page   Web Page Cache   
En Gfi Informática queremos incorporar un Analista Machine Learning para importante entidad bancaria. Requisitos: Experiencia demostrable de al menos 5 años como desarrollador machine learning con Scikitlearn. Recomendable Catboost o Tensorflow. Experiencia en despliegue de modelos para su consumo en producción. Experiencia en captura de requisitos con áreas de negocio. Funciones: Tratamiento de datos internos que apoyen la estrategia de las áreas auxiliares de la Compañía. ...
          SiMul: An Algorithm-Driven Approximate Multiplier Design for Machine Learning      Cache   Translate Page   Web Page Cache   
The need to support various machine learning (ML) algorithms on energy-constrained computing devices has steadily grown. In this article, we propose an approximate multiplier, which is a key hardware component in various ML accelerators. Dubbed SiMul, our approximate multiplier features user-controlled precision that exploits the common characteristics of ML algorithms. SiMul supports a tradeoff between compute precision and energy consumption at runtime, reducing the energy consumption of the accelerator while satisfying a desired inference accuracy requirement. Compared with a precise multiplier, SiMul improves the energy efficiency of multiplication by 11.6x to 3.2x while achieving 81.7-percent to 98.5-percent precision for individual multiplication operations (96.0-, 97.8-, and 97.7-percent inference accuracy for three distinct applications, respectively, compared to the baseline inference accuracy of 98.3, 99.0, and 97.7 percent using precise multipliers). A neural accelerator implemented with our multiplier can provide 1.7x (up to 2.1x) higher energy efficiency over one implemented with the precise multiplier with a negligible impact on the accuracy of the output for various applications.
          IDG Contributor Network: Machine learning: It’s all about the use cases [baby]!      Cache   Translate Page   Web Page Cache   

There’s no question that we’re poised at the dawn of a very exciting time as it relates to the application of machine learning within the context of IT security. That said, without enabling end users to focus these capabilities on concrete use cases, the overall impact of this revolution may be compromised.

Whether or not that concession represents more an issue of perception, versus an impact against the underlying value proposition of this technology is open to debate. However, with machine learning – and, of course, AI – currently infecting the marketing language of so many technology pundits and providers, we’re already awash in the hype cycle.

To read this article in full, please click here


          Dell EMC Targets AI Workloads With Integrated Systems      Cache   Translate Page   Web Page Cache   
The company is rolling out two new Ready Solutions for machine learning with Hadoop and deep learning with GPU accelerators from Nvidia.
          Jask's Chiron Brings AI Threat Detection to Home Networks      Cache   Translate Page   Web Page Cache   
At Black Hat USA 2018, security researchers from Jask are set to demonstrate an all-in-one virtual machine for machine learning powered threat detection
          Instructor (Data Science, Artificial Intelligence, Machine Learning) - Cortechma Inc. - Thornhill, ON      Cache   Translate Page   Web Page Cache   
Cortechma Academy team is looking for professors, instructors and engineers with both academically and professionally strong background specializing in one of...
From Indeed - Wed, 01 Aug 2018 16:56:17 GMT - View all Thornhill, ON jobs
          Desenvolvedor Java para projetos de IA e Machine Learning - Hop - Belo Horizonte, MG      Cache   Translate Page   Web Page Cache   
Nós somos a Hop, uma empresa que nasceu para dar vida às ideias inovadoras! Unimos metodologias de Design com Inteligência Artificial e Computação Cognitiva...
De Hop - Tue, 24 Jul 2018 13:51:19 GMT - Visualizar todas as empregos: Belo Horizonte, MG
          Julia 1.0 release Opens the Doors for a Connected World      Cache   Translate Page   Web Page Cache   

Today Julia Computing announced the Julia 1.0 programming language release. As the first complete, reliable, stable and forward-compatible Julia release, version 1.0 is the fastest, simplest and most productive open-source programming language for scientific, numeric and mathematical computing. "During the last six and a half years, Julia has reached more than 2 million downloads and early adopters have already put Julia into production to power self-driving cars, robots, 3D printers and applications in precision medicine, augmented reality, genomics, energy trading, machine learning, financial risk management and space mission planning."

The post Julia 1.0 release Opens the Doors for a Connected World appeared first on insideHPC.


          Dr. Eng Lim Goh presents: Prediction – Use Science or History?      Cache   Translate Page   Web Page Cache   

Dr. Eng Lim Goh from HPE gave this keynote talk at PASC18. "Traditionally, scientific laws have been applied deductively - from predicting the performance of a pacemaker before implant, downforce of a Formula 1 car, pricing of derivatives in finance or the motion of planets for a trip to Mars. With Artificial Intelligence, we are starting to also use the data-intensive inductive approach, enabled by the re-emergence of Machine Learning which has been fueled by decades of accumulated data."

The post Dr. Eng Lim Goh presents: Prediction – Use Science or History? appeared first on insideHPC.


          Video: New Cascade Lake Xeons to Speed Ai with Intel Deep Learning Boost      Cache   Translate Page   Web Page Cache   

This week at the Data-Centric Innovation Summit, Intel laid out their near-term Xeon roadmap and plans to augment their AVX-512 instruction set to boost machine learning performance. "This dramatic performance improvement and efficiency - up to twice as fast as the current generation - is delivered by using a single instruction to handle INT8 convolutions for deep learning inference workloads which required three separate AVX-512 instructions in previous generation processors."

The post Video: New Cascade Lake Xeons to Speed Ai with Intel Deep Learning Boost appeared first on insideHPC.


          Thierry Pellegrino on What’s New at the Dell HPC Community      Cache   Translate Page   Web Page Cache   

In this video from the Dell EMC HPC Community Meeting, VP Thierry Pellegrino describes how AI and HPC are coming together to foster innovation. "The newly announced Dell EMC Ready Solutions for AI were built to simplify Machine Learning, deliver faster, deeper insights, and leverage Dell EMC’s proven AI expertise. Organizations no longer have to individually source and piece together their own solutions. Instead, they can rely on a Dell EMC-designed and validated set of best-of-breed technologies for software – including AI frameworks and libraries – with compute, networking and storage."

The post Thierry Pellegrino on What’s New at the Dell HPC Community appeared first on insideHPC.


          Data Scientist - Paradigma Digital - Madrid, España      Cache   Translate Page   Web Page Cache   
Buscamos incorporar un Data Scientist con 3-5 años de experiencia en la implementación de modelos analíticos sobre entorno AWS. Funciones a desempeñar: Identificación, interpretación y combinación de fuentes de datos. Análisis dinámicos y continuados de aspectos estratégicos. Identificación y descubrimiento de patrones de comportamiento según los datos analizados. Definición y aplicación de algoritmos. Aplicación de machine Learning (clasificación, regresión, similitudes,...
          Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Director, Data & AI - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:24 GMT - View all Montréal, QC jobs
          Senior Principal Data & AI Developer - Lightspeed - Montréal, QC      Cache   Translate Page   Web Page Cache   
Furthermore, we will apply our advanced data analytics, artificial intelligence/machine learning knowledge and experience in combination with our business...
From LightSpeed - Thu, 12 Jul 2018 14:31:23 GMT - View all Montréal, QC jobs
          08-15-2018 Joint INCOSE/IEEE SMCS Webinar       Cache   Translate Page   Web Page Cache   
Speaker: Thomas McDermott, Jr., Sunil Bharitkar, and Chistopher Nemeth, Stevens Institute of Technology, HP Labs, and Applied Research Associates Talk Title: Bridging the Gulf of Execution Series: INCOSE Speaker Series Abstract: Research results routinely fail to survive into the development phase of Research and Development projects. This gulf of execution that blocks research findings from being realized in the development phase of many projects continues to bog down R and D practice. Concurrent engineering was supposed to be a solution, but was it? Open innovation models were designed to bridge the gap, but have they? What is the gulf, and how did we get here? It might be a matter of professional focus. Research invests in understanding the problem, and Development invests in producing the solution. Innovation happens when these link up around people in a culture that promotes risk-taking. Or is it communication? Innovation happens when people from different disciplines or roles come together with common understanding. The issue spans multiple sectors. Large industries struggle to build an innovation culture when delivery of existing products and services is at the forefront. Universities have an innovation culture, but industry needs to adopt a systems approach to realize value from that culture. Industry-university partnerships are effective when both parties realize relationships across a broad range of university programs, from students to startups, and learn how to couple the university innovation system to the industry innovation enterprise. Three examples from INCOSE and IEEE SMCS members will suggest ways to resolve this enduring issue. Georgia Tech--We view this relationship as a system-of-systems model, where the industry product/service enterprise is coupled to the university innovation enterprise in a larger sociotechnical systems context, and where the relationship promotes all three innovation horizons--sustaining, disruptive, and transformational. Our experience in building such relationships at Georgia Tech indicates both parties can realize success when a range of enablers to industry-university interaction promote a range of innovation opportunities--basic and translational--over a long term partnerships. This systems-of-systems model will be presented as a general context, then generalized examples from Georgia Tech industry partnership efforts will be discussed to illustrate the model. Applied Research Associates--Our team developed a system for DoD over 3 years to support real time decision and communication support among Burn Intensive Care Unit clinicians. This example will describe collaboration among 35 members from military healthcare professionals, to cognitive psychologists and software and machine learning developers. HP Labs--In the Emerging Computer Lab within HP Labs, among other research areas we are involved in the areas of speech analysis and interpretation, audio signal processing in conjunction with machine learning. In this part of the webinar we will explore one research topic in audio processing that we undertook, after identifying the deficiencies on HP devices, and the challenges encountered during development. We also present examples of the solutions to overcome these challenges which have helped contribute towards a scalable deployment of the technology based off of this research. Biography: Thomas A. (Tom) McDermott, Jr is a leader, educator, and innovator in multiple technology fields. He currently serves as Deputy Director of the Systems Engineering Research Center at Stevens Institute of Technology in Hoboken, NJ, as well as a consultant specializing in strategic planning for uncertain environments. He studies systems engineering, systems thinking, organizational dynamics, and the nature of complex human socio-technical systems. He teaches system architecture concepts, systems thinking and decision making, and the composite skills required at the intersection of leadership and engineering. Tom has over 30 years of background and experience in technical and management disciplines, including over 15 years at the Georgia Institute of Technology and 18 years with Lockheed Martin. He is a graduate of the Georgia Institute of Technology, with degrees in Physics and Electrical Engineering. With Lockheed Martin he served as Chief Engineer and Program Manager for the F-22 Raptor Avionics Team, leading the program to avionics first flight. Tom was GTRI Director of Research and interim Director from 2007-2013. During his tenure the impact of GTRI significantly expanded, research awards doubled to over 300M dollars, faculty research positions increased by 60 percent, and the organization was recognized as one of Atlanta's best places to work. He also has a visiting appointment in the Georgia Tech Sam Nunn School of International Affairs. Tom is one of the creators of Georgia Tech's Professional Masters degree in Applied Systems Engineering and lead instructor of the Leading Systems Engineering Teams course. Sunil Bharitkar received his Ph.D. in Electrical Engineering, minor in Mathematics from the University of Southern California in 2004 and is presently the speech-audio research Distinguished Technologist at HP Labs. He is involved in research in array signal processing, speech/audio analysis and processing, biomedical signal processing, and machine learning. From 2011-2016 he was the Director of Audio Technology at Dolby leading-guiding research in audio, signal processing, haptics, machine learning, hearing augmentation, &standardization activities at ITU, SMPTE, AES. He co-founded the company Audyssey Labs in 2002 where he was VP Research responsible for inventing new technologies which were licensed to companies including IMAX, Denon, Audi, Sharp, etc. He also taught in the Department of Electrical Engineering at USC. Sunil has published over 50 technical papers and has over 20 patents in the area of signal processing applied to acoustics, neural networks and pattern recognition, and a textbook, Immersive Audio Signal Processing, from Springer-Verlag. Chris Nemeth is a Principal Scientist with Applied Research Associates, a 1200 member national science and engineering consulting firm. His recent research interests include technical work in complex high stakes settings, research methods in individual and distributed cognition, and understanding how information technology erodes or enhances system resilience. He has served as a committee member of the National Academy of Sciences, is widely published in technical journals. Dr. Nemeth earned his PhD in human factors and ergonomics from the Union Institute and University in 2003, and an MS in product design from the Institute of Design at Illinois Institute of Technology in 1984. His design and human factors consulting practice and his corporate career have encompassed a variety of application areas, including health care, transportation and manufacturing. As a consultant, he has performed human factors analysis and product development, and served as an expert witness in litigation related to human performance. His 26-year academic career has included seven years in the Department of Anesthesia and Critical Care at the University of Chicago Medical Center, and adjunct positions with the Northwestern University McCormick College of Engineering and Applied Sciences, and Illinois Institute of Technology. He is a Fellow of the Design Research Society, a Life Senior Member of the Institute of Electrical and Electronic Engineers and has served 8 years on the IEEE Systems, Man and Cybernetics Society Board of Governors. He retired from the Navy in 2001 at the rank of Captain after a 30-year active duty and reserve career. More Info: Event number: 592 564 704, Event password: INCOSE115 Webcast: https://incoseevents.webex.com/incoseevents/onstage/g.php?MTID=ed47a65b08dbf33c5afed11b8656b48aa
          MACHINE LEARNING ENGINEER FOR SPEECH RELATED APPLICATIONS - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Engineer for Speech related Applications (6 months contract)....
From Huawei Canada - Mon, 18 Jun 2018 23:46:16 GMT - View all Montréal, QC jobs
          MACHINE LEARNING INTERN FOR SPEECH RELATED APPLICATIONS - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Intern for Speech related Applications....
From Huawei Canada - Mon, 18 Jun 2018 17:50:57 GMT - View all Montréal, QC jobs
          MACHINE LEARNING HARDWARE RESEARCHER OR DEVELOPER - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Hardware Researcher or Developer....
From Huawei Canada - Wed, 06 Jun 2018 23:47:32 GMT - View all Montréal, QC jobs
          Machine Learning Software Developer - Huawei Canada - Montréal, QC      Cache   Translate Page   Web Page Cache   
We thank all applicants for their interest in career opportunities with Huawei. ML Software developer....
From Huawei Canada - Wed, 06 Jun 2018 23:47:31 GMT - View all Montréal, QC jobs
          Data Scientist with Machine Learning and C++ skills      Cache   Translate Page   Web Page Cache   
Expert Employment Limited - Abingdon, Oxfordshire - skills; C++ Google Analytics APIs for data mining and analysis Data Science application delivering solutions to engineering domains...
          Machine Learning Engineer - Technica Corporation - Dulles, VA      Cache   Translate Page   Web Page Cache   
Technica Corporation is seeking a Machine Learning Engineer to support our internal Innovation, Research and Development (IRD) team....
From Technica Corporation - Wed, 11 Jul 2018 06:07:15 GMT - View all Dulles, VA jobs
          Microsoft To Develop AI To Catch Cheaters On Xbox Live      Cache   Translate Page   Web Page Cache   
The U.S. Patent and Trademark Office published a patent application filed by Microsoft that describes a method of cheat detection for games on a platform level using machine learning. The idea is to bring cheat detection outside the game itself given platforms like Xbox Live and PlayStation ...
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page   Web Page Cache   
Virtual machine switching); Large scale distributed systems, real-time data analysis, machine learning, windows internals (networking stack and other OS...
From Microsoft - Thu, 09 Aug 2018 04:41:50 GMT - View all Redmond, WA jobs
          Economist - Forecasting - Amazon.com - Seattle, WA      Cache   Translate Page   Web Page Cache   
Experience with machine learning applications. We are breaking fresh ground, pioneering in a program that is crucial for future Amazon growth, and our business...
From Amazon.com - Wed, 27 Jun 2018 07:21:23 GMT - View all Seattle, WA jobs
          Sr Director, Growth Marketing Technology - eBay Inc. - Bellevue, WA      Cache   Translate Page   Web Page Cache   
Further, the Marketing Tech Leader will apply the latest data analysis and machine learning technologies to innovate applications in both BI analysis and...
From eBay Inc. - Fri, 01 Jun 2018 08:04:49 GMT - View all Bellevue, WA jobs
          Software Engineer - Machine Learning - Convoy - Seattle, WA      Cache   Translate Page   Web Page Cache   
Today, we use machine learning to figure out freight prices, shipment relevance for carriers, auction bidding strategy, and other internal processes....
From Convoy - Sat, 19 May 2018 10:13:22 GMT - View all Seattle, WA jobs
          Distributed Dialogues: Blockchain’s Better Side      Cache   Translate Page   Web Page Cache   
Distributed Dialogues: Blockchain’s Better Side

The fact that great responsibility accompanies great power has become crystal clear in the blockchain world. While blockchains are most commonly connected with commerce, the potential impact of distributed ledgers is being discovered in fresh sectors daily.

In the most recent episode of the Distributed Dialogues podcast, a collaborative show between the Let’s Talk Bitcoin Network and Distributed Magazine, blockchain’s better side was on display. The show explored three different perspectives on how the technology is being used, not just to raise crypto value, but to help humanity rise up.

Blockchains for Human Rights

Alex Gladstein, chief strategy officer at the Human Rights Foundation (HRF), explained that organization’s optimism about blockchain technology. HRF is a nonpartisan, nonprofit organization that promotes and protects human rights globally, with a focus on closed societies.

According to Gladstein in his interview with the show’s co-host Rick Lewis, about 90 countries, with a total population of about 4 billion people, currently lack the checks and balances that a more open society would have.

Gladstein believes that decentralized models such as blockchains and cryptocurrencies can make a world of difference for this large population whose rights are routinely violated. It’s part of a nascent field he calls “demtech,” short for “democracy tech,” and its development comes with an unexpected bonus.

“Demtech would be getting power back in the hands of the people,” he said. “It’s not really out there yet … but it’s an opportunity, and what’s cool is you can probably make a lot of money in this space. When you talk about decentralized money networks, decentralized VPNs, censorship-resistant money and communications, I think there’s going to be huge demand for that …There’s tremendous opportunity to both impact the planet and make a lot of money, which is kind of a first for the human rights space.”

Brian Behlendorf on Governance

Brian Behlendorf is the executive director of Hyperledger, the umbrella project of open-source blockchains which is striving to support collaborative development for blockchain technology. As a primary developer of the Apache Web server, Behlendorf’s influence has spanned the web for decades.

His role as a founder of the Apache Software Foundation has also established him as a long-time advocate of the open-software community. Behlendorf strikes a balance between the responsibilities that should be designated to machine and to man, in his interview with Distributed Dialogues co-host Dave Hollerith.

“We can’t give up the need to find ways, as humans, to make decisions together,” Behlendorf pointed out. “And so, I think the more of governance, the more of business processes that we can make algorithmic and auditable using blockchain technology, in addition to lots of others, the better off we’ll be, because the more fair, potentially, we’ll have the application of those rules to society.

“But we still need human governance at the end of the day,” he continued, “and even the public blockchain ledgers have that in the form of the leaders of those projects, and the developers and the miners, who collectively make a decision, ‘Let’s bail out the DAO, but let’s not bail out the Parity Wallet hack victims.’ So these things happen, right? These human governance mechanisms happen. We can either embrace that and find ways to do that right or pretend that doesn’t exist and end up with Lord of the Flies.”

Flux

Blake Burris and Kylen McClintock of Flux, a new protocol for facilitating environmental data, spoke with segment host Tatiana Moroz. Flux is a self-described “proof of impact” play which dedicates 10 percent of its allocations to impact projects to scale the protocol.

According to the Flux website, it is deploying a sensor data network targeted at improving marketplaces and supply chains for agriculture, livestock and aquaculture. Its success, or proof of impact, will be measured by its ability to create partnerships that end desertification, stabilize crisis zones, integrate with micro-finance programs and help farmers increase their profitability.

Here, blockchains prove beneficial, courtesy of the Flux token (currently in pre-sale). “The token really comes in to incentivize data contribution,” McClintock explained

“Currently there’s expert growers around the world, or organizations that have specific data in a certain realm like carbon data, methane data, satellite imagery data, but right now there’s not a global standard way to contribute to that and get rewarded for that contribution. [It’s] another way of actually creating a custom perception engine, basically a custom machine learning model to be able to take the relevant data capsules that an organization, or government or academic research needs to find those insights.”

“It’s really about those insights that can be derived from that mass data set, and paying on a pro rata basis back to those who contributed that data,” added Burris.

This article originally appeared on Bitcoin Magazine.


          Company Apro Software Started With Machine Learning Projects      Cache   Translate Page   Web Page Cache   
Innovative software company Apro Software launches machine learning services to benefit companies in the UK, Europe and the US.
          Machine Learning Engineer - Flex A.I. - Vancouver, BC      Cache   Translate Page   Web Page Cache   
Keep in mind our wages will only temporarily be at this level until the company skyrockets in growth in the next year, at which point we will likely provide... $70,000 - $120,000 a year
From Indeed - Sun, 15 Jul 2018 01:01:22 GMT - View all Vancouver, BC jobs
          Mindsphere Principal PreSales Solutions Consultant - West, US - Siemens - Seattle, WA      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
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          Mindsphere Principal PreSales Solutions Consultant - South Central, US - Siemens - Dallas, TX      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
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          Mindsphere Principal PreSales Solutions Consultant - South Central, US - Siemens - Austin, TX      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
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          Mindsphere Principal PreSales Solutions Consultant - South Central, US - Siemens - Houston, TX      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
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          Mindsphere Principal PreSales Solutions Consultant - South Central, US - Siemens - San Antonio, TX      Cache   Translate Page   Web Page Cache   
Business Analytics, Analytics / Machine Learning tools such as R, SAS, Tableau, or scikit-learn. Analytics and machine learning....
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          Linux Foundation: Academy, Wall Street and Surveillance Giants      Cache   Translate Page   Web Page Cache   
  • The Academy teams up with the Linux Foundation for open source tech
  • Academy Software Foundation will let filmmakers use open source creative software

    The Academy of Motion Picture Arts and Sciences and The Linux Foundation today launched the Academy Software Foundation (ASWF) to provide a neutral forum for open source software developers in the motion picture and broader media industries to share resources and collaborate on technologies for image creation, visual effects, animation, and sound.

  • Hollywood Goes Open Source: Academy Teams Up With Linux Foundation to Launch Academy Software Foundation

    Hollywood now has its very own open source organization: The Academy of Motion Picture Arts and Sciences has teamed up with the Linux Foundation to launch the Academy Software Foundation, which is dedicated to advance the use of open source in film making and beyond.

    The association’s founding members include Animal Logic, Autodesk, Blue Sky Studios, Cisco, DNEG, DreamWorks, Epic Games, Foundry, Google Cloud, Intel, SideFX, Walt Disney Studios and Weta Digital. Together, they want to promote open source, help studios and others in Hollywood with open source licensing issues and manage open source projects under the helm of the Software Foundation.

    The cooperation between the Academy and the Linux Foundation began a little over two years ago, when the Academy’s Science and Technology Council began to look into Hollywood’s use of open source software. “It’s the culmination of a couple of years of work,” said Industrial Light & Magic (ILM) head Rob Bredow in an interview with Variety this week.

  • Hollywood gets its own open-source foundation

    Open source is everywhere now, so maybe it’s no surprise that the Academy of Motion Picture Arts and Sciences (yes, the organization behind the Oscars) today announced that it has partnered with the Linux Foundation to launch the Academy Software Foundation, a new open-source foundation for developers in the motion picture and media space.

    The founding members include a number of high-powered media and tech companies, including Animal Logic, Blue Sky Studios, Cisco, DreamWorks, Epic Games, Google, Intel, SideFX, Walt Disney Studios and Weta Digital.

  • The Linux Foundation Announces Keynote Speakers for All New Open FinTech Forum to Explore the Intersection of Financial Services and Open Source
  • The Linux Foundation Announces Keynote Speakers for All New Open FinTech Forum to Explore the Intersection of Financial Services and Open Source

    The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced the keynote speakers for Open FinTech Forum, taking place October 10-11 in New York.

  • LF Deep Learning signs up 5 more members, names AT&T's Gilbert as governing board chair

    The Linux Foundation's LF Deep Learning Foundation announced it has added Ciena, DiDi, Intel, Orange and Red Hat to its membership roster.

    Open source communities truly thrive when there's an array of vendors and service providers adding to the collective brain trust. With the recent additions, Deep Learning now has 15 members since it was first formed earlier this year.

    The addition of Orange was notable, but Deep Learning is still missing some key service provider players, such as Verizon, BT, CenturyLink, Deutsche Telekom and Telefónica, which seem content to pursue machine learning and artificial intelligence on their own.

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          Facebook Wants to Teach Machine Learning      Cache   Translate Page   Web Page Cache   
When you think of technical education about machine learning, Facebook might not be the company that pops into your head. However, the company uses machine learning, and they’ve rolled out a six-part video series that they say “shares best real-world practices and provides practical tips about how...
          Data Wrangling with Pandas for Machine Learning Engineers      Cache   Translate Page   Web Page Cache   
Data Wrangling with Pandas for Machine Learning Engineers
Data Wrangling with Pandas for Machine Learning Engineers
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour | 90 MB
Genre: eLearning | Language: English

Pandas has become the gold standard for data wrangling in applied machine learning. This course will teach you the basics of data wrangling in Python using Pandas, including basic syntax, functions, and dataframe manipulation.


          Bell Labs - Integrated Photonics Researcher - NOKIA - Holmdel, NJ      Cache   Translate Page   Web Page Cache   
Nokia is a global leader in the technologies that connect people and things. Investigate and implement machine learning based optimization to control large...
From Nokia - Mon, 18 Jun 2018 15:55:57 GMT - View all Holmdel, NJ jobs
          AA Chief SW Architect - NOKIA - San Jose, CA      Cache   Translate Page   Web Page Cache   
Analytics, AI, and machine learning. Presenting to customers, industry forums, analysts and internal audiences....
From Nokia - Mon, 18 Jun 2018 15:51:40 GMT - View all San Jose, CA jobs
          Data Wrangling with Pandas for Machine Learning Engineers      Cache   Translate Page   Web Page Cache   
Data Wrangling with Pandas for Machine Learning Engineers
Data Wrangling with Pandas for Machine Learning Engineers
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour | 90 MB
Genre: eLearning | Language: English

Pandas has become the gold standard for data wrangling in applied machine learning. This course will teach you the basics of data wrangling in Python using Pandas, including basic syntax, functions, and dataframe manipulation.


          Java Machine Learning for Computer Vision      Cache   Translate Page   Web Page Cache   
Java Machine Learning for Computer Vision
Java Machine Learning for Computer Vision
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 5 Hours | 800 MB
Genre: eLearning | Language: English

          Spotify might let free users skip any ad, even though it needs all the ad revenue it can get      Cache   Translate Page   Web Page Cache   
Spotify is dependent on the revenue it gets from subscribers and, for all of you moochers who don't pay anything, the ads that the music streaming giant forces you to listen to on the free tier. Which is why it might sound counterintuitive, on first blush, for the company to be trying this as an experiment -- letting you, well, skip any ad you want, which gets you back listening to music faster. That's exactly what Spotify started testing last month, however, a test currently exclusive to Australia and includes letting free tier users skip any audio and video ad that annoys them or that they just flat-out don't want to listen to. Spotify's hope is that the company ends up with a way to better personalize your experience on the service, but - okay, that's the company line. This might also be a way for Spotify to make more per ad for those ads that don't get skipped. It also improves the overall experience of the company's free tier service, which could spur at least some of those users to spring for the premium version. And, of course, paying attention to ads you don't skip will tell Spotify a little more about you. Danielle Lee, global head of partner solutions at Spotify, told AdAge this is about creating a tailored experience for individual users similar to the custom playlists Spotify uses machine learning to generate for subscribers. “Our hypothesis is if we can use this to fuel our streaming intelligence, and deliver a more personalized experience and a more engaging audience to our advertisers, it will improve the outcomes that we can deliver for brands,” Lee said. “Just as we create these personalized experiences like Discover Weekly, and the magic that brings to our consumers, we want to inject that concept into the advertising experience.” Lee goes on to say the company's hope is to ultimately roll this out worldwide. AdAge describes the new feature, called "Active Media," as similar to Google AdWords, in which customers are only charged when an ad gets clicked on. In Spotify's case, advertisers will pay for ads that are watched. One Spotify advertiser, Dollar Shave Club, told the news outlet that it welcomes the new feature. Dollar Shave Club vice president of media and acquisitions Sam Kang said the company had been hiking its ad spend with Spotify, calling it "one of our key pillars," but that "As an advertiser, I love it," referring to the new feature. Spotify, wh