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          Instagram taps machine learning to detect online bullying      Cache   Translate Page      
Instagram has revealed new tools that target bullying and other hurtful content, the aim being to reduce negative interactions on the site and improve the experience for everyone. As well, the company has introduced a new camera effect that it says will help “spread kindness” among users, at least when they’re using the Stories feature. Instagram already has the ability … Continue reading
          General Motors Hackathon, Oct 13      Cache   Translate Page      
Come hack at the ML@B/General Motors Hackathon this weekend!

The hackathon challenge will be a unique machine learning challenge related to General Motor’s infotainment system in all of their cars.

GM also has tech talks planned during the hackathon and will have 9 engineers present to work with you and answer any of your questions.

There will be cool prizes for the winners including an internship at GM for the first place winning team.

Four meals will be provided for everyone. Swag will be provided as well.

Please sign up on https://tinyurl.com/mlabhackathon if you are interested!
          Machine Learning/AI Engineer - Groom & Associates - Montréal, QC      Cache   Translate Page      
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, 06 Sep 2018 08:57:48 GMT - View all Montréal, QC jobs
           Instagram using AI to scan for bullying in photos and captions       Cache   Translate Page      
Instagram is taking action against trolls and bullies. The social media giant on Tuesday rolled out new machine learning tools that are able to 'proactively detect' bullying in photos and captions.
          Comment on Your First Machine Learning Project in Python Step-By-Step by Jason Brownlee      Cache   Translate Page      
I expect the code will require some modification before it can be applied to new problems. I recommend that you follow this process: https://machinelearningmastery.com/start-here/#process Perhaps some of these tutorials will help: https://machinelearningmastery.com/start-here/#python
          Comment on Multi-step Time Series Forecasting with Machine Learning for Household Electricity Consumption by Jason Brownlee      Cache   Translate Page      
You can choose a model and configuration, train a final model and start using it to make forecasts. Perhaps I don't follow, what problem are you having exactly?
          Comment on A Gentle Introduction to Applied Machine Learning as a Search Problem by Jason Brownlee      Cache   Translate Page      
The math can add depth, but is not required. I do not need to know how to design or even repair combustion engines in order to drive my car.
          Morgan Stanley brokers, bankers to get new tech-friendly offices      Cache   Translate Page      

Morgan Stanley is remodeling.

About 1.2 million square feet of office space will get an overhaul in the next 15 months to put technology experts closer to brokers, traders and bankers, the firm’s head of technology, Rob Rooney, said in an interview. After changes to wealth-management operations, trading floors, investment-banking offices and space tied to asset management will all get a remake.

“The workplace needed to be designed around a much more dynamic, millennial kind of workforce,” said Rooney, 51, who stepped into the technology role this year. “We’re trying to attract the next generation of the best and brightest.”

Demolition work in lower Manhattan has already created open floor plans that give more employees views of the Statue of Liberty and Hudson River, a perk previously reserved for senior executives cloistered in their wood-walled offices. Now, glass partitions and interactive whiteboards abound, and the dress code is decidedly more casual.

The first phase represents about 9,000 seats around the world, though the project may expand, Rooney said.

Morgan Stanley’s past technology investments helped make it the biggest stock-trading firm in the world, and Chief Executive Officer James Gorman has said it’s a major priority to replicate that success in bond markets. The bank, which also has a $2.4 trillion wealth-management division, is spending $4 billion annually on the effort, including the building of what it calls “Centers of Excellence” to focus on blockchain, automation and other technologies.

With 18 million transactions a day on the firm’s electronic-trading platform for equities, pushing the efficiency envelope “is kind of challenging the speed of light,” Rooney said. “If you’re an engineer, these are real problems you’re trying to solve.”

In wealth management, where a lot of the initial office changes will roll out, the bank built algorithms and is using machine learning to help more than 15,000 brokers make trade suggestions for clients and handle more routine tasks.

The overhaul is one of Wall Street’s biggest. WeWork Cos. last year began helping UBS Group AG update wealth-management offices in Weehawken, New Jersey. Also in 2017, Goldman Sachs Group Inc. unveiled the largest revamp of its trading hub since 2009, when about 500 asset managers were moved into an open floor plan.

Modernization isn’t optional for a firm like Morgan Stanley, said Ekene Ezulike, global head of corporate services. “The question is how quickly we do it, versus whether we should do it,” he said.

As little as 60% of Morgan Stanley’s work space is occupied at any given time, according to Ezulike, who said the changes will push that rate as high as 90% as options such as desk sharing let more people use fewer seats.

Despite the less stuffy dress code and other updates, Morgan Stanley shouldn’t be confused with a Silicon Valley startup, Rooney said.

“We’re not a technology firm, we’re a bank,” Rooney said. “We don’t sell technology, we sell advice.”

While there’s no kombucha on tap as there is at Goldman Sachs’s revamped San Francisco offices, there are common dining rooms, and the firm hired its first-ever community manager, Fiona Thomas. She helps plan office get-togethers and is overseeing a meditation event that was oversubscribed.

Morgan Stanley’s executives approved the project, called “Workplace Evolution,” in March and some spaces were fully revamped in months, with help from WeWork and the architectural firm Gensler. The first set of changes included offices in New York, Houston, Frankfurt, Chicago, Glasgow, Budapest, London, Mumbai and Bangalore.

The firm’s headquarters in Times Square—which is about 1.3 million square feet—will also see changes, Rooney said. The capital-markets division for the wealth-management unit will be revamped this year. The technology division and back-office functions tied to finance are also being renovated.

“Our traders need to be with our techies,” Rooney said. “You’ll see a very different trading floor in five years time than you see today.”


          Avaya: un "social network" per i chatbot      Cache   Translate Page      
I chatbot, sistemi completamente automatici per l'interazione con l'utenza oggi estesamente utilizzati per la gestione della customer care, sono sempre più avanzati grazie al massiccio ricorso agli algoritmi per il Machine Learning. Non sorprende ...
          Introducing the DailyFaceoff Betting Section      Cache   Translate Page      

First we teamed up with our friends at Corsica Hockey to add Player Ratings to our Line Combination pages and now we have added their Betting Tools to DailyFaceoff.  Corsica uses “sophisticated machine learning algorithms” to generate their betting predictions. Corsica has set out to find an edge against the Money Line, the Spread and…

The post Introducing the DailyFaceoff Betting Section appeared first on Daily Faceoff.


          IBM’s Machine Learning Accelerator at VLSI 2018      Cache   Translate Page      

IBM presented a neural network accelerator at VLSI 2018 showcasing a variety of architectural techniques for machine learning, including a regular 2D array of small processing elements optimized for dataflow computation, reduced precision arithmetic, and explicitly addressed memories.

(Visited 16 times, 16 visits today)

The post IBM’s Machine Learning Accelerator at VLSI 2018 appeared first on Real World Tech.


          Machine Learning to Accellerate Drug Research      Cache   Translate Page      

The European Research Council (ERC) is awarding a "proof-of-concept grant" to scientists from the German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) and the Heidelberg University for the first time to enable them to develop new drugs more cheaply and quickly using image-based analyses.

Copyright: M. Boutros, Deutsches Krebsforschungszentrum

Copyright: M. Boutros, Deutsches Krebsforschungszentrum


          With the CBAC, East Meets West in Search of Adoption and Innovation      Cache   Translate Page      
CBAC

The noise that surrounds economic relations between the United States and China is amping up exponentially. You can thank the latest trade wars for that, as fresh tensions boil over between the two nations who are currently trading new tariffs on imports, with no shortage of ill will underpinning the moves.

But the Chinese government’s ire is not just outward-facing. As a country where ICOs are currently not allowed, exchanges have had their bank accounts frozen, and internet and mobile access to cryptocurrency trading information has been banned, China is taking an equally hard line on a wide range of crypto-centric activities within its own borders. All this despite a stark dichotomy, wherein over 50 percent of the worldwide mining population resided within its borders in 2017, and cryptocurrency adoption is outpacing most other countries.

While trade war bullets may be flying thick and fast between these two mega-economies, the key to a better Chinese blockchain sector just may be unlocked by deploying cooperative forces in the United States, as seen by the recent launch of a New York City office for the China Blockchain Application Center (CBAC). The CBAC NY was founded with the hope of paving the way for rapid blockchain adoption in China, in part by picking up regulatory best practices from the United States, all while fostering blockchain and crypto collaboration between the two nations.

An early-stage, non-governmental organization (NGO) established in 2015, the CBAC collaborates with regulatory bodies to develop comprehensive regulations, encourage the application of blockchain technology in traditional industries, and connect Chinese practitioners with peers around the world. By helping to develop increased regulations, blockchain industry applications and international connections, members of the CBAC are hoping to elevate blockchain’s role in China’s $12 trillion economy.

Crypto Challenges in China

One of the speakers at a well-attended August launch party in NYC’s financial district was Stewie Zhu, founder and CEO of the distributed banking public blockchain Distributed Credit Chain (DCC), and a standing committee member of the CBAC. While ICO scams and other bad actors have significantly hindered progress in his home country, Zhu sees plenty of near-term potential for crypto and blockchain technology there.

“While China has prohibited the sale of new cryptocurrencies through ICOs since early last September, there is still a big appetite for the application of blockchain technology,” Zhu told Bitcoin Magazine. “In fact, a [recent] Chinese Supreme Court ruling has stated that blockchain technology can be used to authenticate evidence in legal contexts. The trading of cryptocurrencies is possible, but the government is trying to create financial stability to minimize any illegal activity. The Chinese government is eager to make considerable strides on the technology front, and while stringent, they are trying to ensure that cryptocurrency trading is done responsibly.

“There are challenges behind blockchain technology as it relates to banking,” Zhu continued, “because it requires a reconstruction of long-standing relationships in the current market, as well as time for citizens to understand the mechanisms behind using blockchain. Companies may need to make significant changes to their daily operations to incorporate blockchain, not to mention the time and resources needed for pre-application research.”

Zhu pointed out that it also takes time for private citizens to fully understand and trust cryptocurrency. Between price swings and security vulnerabilities, they may be leery of entering the market, he believes.

“Given the volatility in the crypto market and the negative news about problematic ICOs, individual customers can be cautious of tokens,” he said. “Security is also an issue. Blockchain technology is not perfect. We still need more R&D to develop ways to prevent potential threats such as the 51% attack, where an organization controlling the majority of network mining power can prevent transactions by others and allow its own coin to be spent twice (double spending). So long as these threats exist, many companies may not see blockchain as a practical tool.”

Part of a Bigger Picture

A successful push by the Chinese government to instill crypto confidence goes beyond better banking and protecting consumers, however.

“The Chinese government is trying to shift the economy from manufacturing-based to a more value-added, services-based, to move from being the factory of the world to being the service provider of the world, which is a natural economic evolution that you would expect from any country as they try to level-up,” Zennon Kapron observed in an interview with Bitcoin Magazine. Kapron is the founder of Kapronasia, a Singapore-based firm focused on providing insights into Asia's financial industry.

“China has always tried to stay ahead and it’s used technology as a way of leveraging that with things like AI, machine learning, and some of the camera/surveillance technology as a way for the country to differentiate itself. From an economic perspective, it’s a very challenging transition to make because you're trying to shift demands from import/export to domestic consumption, while you've got a banking system that's relatively new that has a lot of challenges internally. So the government wants to avoid risk to the financial system, as well as the economy as a whole, and largely a lot of the pushback that we've seen from the government on cryptocurrencies is because of that.”

While Kapron is guarded on the immediate impact of blockchain on banking within China, he sees a real near-future need for applying distributed ledger to other fields.

“If we look at health records and food provenance in China, in certain ways [blockchain] would allow China to become coordinated and move much quicker, so the government is very open to the idea of launching technology and seems to be very supportive of it. That support is coming from the idea that, first of all, there are challenges that can be solved and, second of all, if they establish a leading edge around blockchain that could be a competitive advantage for them going forward.”

For Zhu, however, the most tantalizing possibilities for blockchain, in China and elsewhere, lay firmly within the financial realm.

“Blockchain can provide a comprehensive solution to multiple problems in the current financial industry,” he stated. “First off, it provides a decentralized structure, which will break the data monopoly of big, traditional banks and allows individual customers to control their own information. Second, the information on the chain cannot be changed or tampered with, which helps to enhance data security, one of the most important aspects in credit and banking. Third, while enhancing data security, blockchain also helps improve transparency, because every action is recorded on a smart contract and is always trackable.

“In my opinion, the philosophy behind blockchain is security and sharing,” added Zhu. “This technology connects people around the world, allowing them to access reliable information in a more efficient manner.”

Zhu has applied this outlook to his twin goals of growing his company, DCC, while also improving the CBAC’s prospects. For the industry to succeed, he believes it needs to embrace the same cooperation that blockchain facilitates by design.

“Collaboration is very important for companies in the tech sector, especially in an emerging industry like blockchain,” he said. “By joining the CBAC, I’d like to create a connection between the Chinese government authorities, as well as unite projects from both geographies and accelerate development processes. We would like to help our peers and DCC connect with additional experts in the space so that we can grow together at a much faster rate.

“Any new industry, at its birth, will experience volatility before the period of stable, healthy growth. I’d like to work as an active member of the CBAC on the development of industrial regulations, so that the blockchain industry will become more organized, allowing individual companies to fully unleash their potential.”

Coming to America

With the launch of a New York City presence, the CBAC is looking to foster collaboration not just between companies but between countries. Despite the many stumbles of Wall Street and the SEC in their approaches to crypto, Zhu maintains that both entities represent a “gold standard” of regulation, and the CBAC NY is in place specifically to model their best practices.

“As many countries are exploring and experimenting regulations for the blockchain industry, the U.S. is at the forefront and is doing a good job of placing certain safeguards and protection for investors stepping into the ICO world,” he said. “For example, the SEC is taking steps to create regulatory standards with the ‘Howey Test’ which determines whether cryptocurrencies are securities and thus subject to federal securities laws. Also, the United States Securities and Exchange Commission’s Office of Investor Education and Advocacy (OIEA) has published a report that seeks to warn investors of the potential ‘risks associated with self-directed Individual Retirement Accounts (self-directed IRAs)’ in which ICOs and cryptocurrencies are highlighted.”

Backed by the Beijing Municipal Bureau of Financial Work, the CBAC NY’s mission is to arrange meetings with U.S. regulators and lawyers to gain insight into how the U.S. is setting such standards. From there, the organization will work to push those standards to the top of regulatory authorities in China to help create change. Ultimately, China’s Ministry of Industry and Information Technology develops policies for the blockchain industry.

Seizing Momentum

A range of attendees present at the CBAC NY launch, including blockchain projects, entrepreneurs, regulators, academics, investment firms and exchanges across China and the U.S., indicated the high stakes and hopes for the organization’s success.

“The promise of decentralization and the new platforms and applications currently in development is to lead towards a much more connected world that will make financial value transfer faster, easier, and cheaper,” David Namdar, co-head of trading for the crypto asset merchant bank Galaxy Digital, told Bitcoin Magazine. “As the regulatory landscape develops globally, the CBAC can be instrumental in helping China to regulate blockchain in a way that leads toward greater involvement in an improved global financial ecosystem.

“I have been spending more and more time in Asia this year as we’ve expanded Galaxy Digital to Hong Kong and Tokyo,” Namdar continues, “and have been blown away by the amount of activity and enthusiasm around cryptocurrencies and blockchain applications. In China, I believe there is a lot of momentum as more and more people become educated about the space and understand the technology and it’s potential. However, it continues to be an important challenge to promote innovation around actual uses while curbing speculation.”

While classifying the record of non-profit organizations in influencing Chinese government policy as “hit or miss,” Kapronasia’s Zennon Kapron sees how an improved blockchain ecosystem could translate into major gains for the country with the world’s largest economy in terms of purchasing power parity.

“Certainly, China has become an epicenter of blockchain development,” he stated, “and China moves at China speed: [For example] we did a study a couple of years ago and we looked at cash usage in China. You think about China being a very cash-driven society. 60 percent of all retail transactions in 2010 were done with cash, and we expect that to drop in half to 30 percent by 2020. That shift, considering the population and considering the amount of money that that represents is massive. China can move very quickly on something like this, and so when we look at blockchain development in general and then regulation around blockchain, China could very well be a leader in this space as we go forward.”

“In China, the blockchain industry is growing quickly,” Zhu affirmed. “According to CoinTelegraph, in 2017, the most patent filings for blockchain technology to the World Intellectual Property Organization (WIPO) came from China, so we anticipate further applications of blockchain in China in the form of innovation in technology and banking/Internet finance. As I mentioned earlier, China is already using blockchain in a legal context and this type of growth will only continue to increase.”

With its massive footprint, rich resources, deep talent and influencers like the CBAC at work, China looms as a tempting frontier for outside operators in search of the ultimate blockchain destination.

“One of our core missions is to help grow the entire industry globally through technological development, investment and sensible regulation,” says Namdar. “Groups like the CBAC play an important role in education and cross-collaboration efforts with wide-reaching global impact.”

Still, in the eyes of educated observers like Kapron, it’s too early to say definitively if China, crypto and blockchain technology are bound for a copacetic outcome.

“I think we've seen very positive output from them in terms of their opinion and the way they think it could go, but that could change rapidly if there is a risk to the financial system,” he said.

“We're at the very early days right now, and, for most investors or either funds or individuals overseas that are looking at investing in Chinese blockchain projects, it's critical to understand the ecosystem. There are potentially outsized returns in China from some of these platforms, but it still remains to be seen how successful they are and how much the government allows things to move forward.”


This article originally appeared on Bitcoin Magazine.


          HPCC machine learning project      Cache   Translate Page      
This is HPCC based machine learning project which includes NB, RF and LR. (Budget: $250 - $750 USD, Jobs: C Programming, Electrical Engineering, Engineering, Machine Learning, Matlab and Mathematica)
          Google presenta el Pixel 3 y Pixel 3 XL: la magia vuelve a estar en el software      Cache   Translate Page      
Google presenta el Pixel 3 y Pixel 3 XL: la magia vuelve a estar en el software#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000\

No hubo sorpresa. Los nuevos Google Pixel 3 y Pixel 3 XL, presentados hoy en la ciudad de Nueva York, son justo lo que esperábamos ver: pequeñas mejoras internas, sutiles cambios estéticos y una cámara tan prometedora como la de sus predecesores

El lanzamiento de los nuevos Google Pixel 3, albergado en la ciudad de Nueva York y replicado en otras ‘urbes’ europeas, ha sido, probablemente, el más insípido de los últimos años. El río de filtraciones vivido durante las últimas semanas han dejado sin sorpresas a uno de los teléfonos más importantes -y, probablemente, de los mejores- de este curso tecnológico.

Pese a ello, el interés en torno a los nuevos teléfonos de Google parece no haberse diluído en absoluto. Y la razón es bastante sencilla: la magia de los teléfonos de Google no está en el diseño ni en las especificaciones técnicas; está en el software y en la experiencia de uso. Y eso, sumado al gran sabor de boca que dejó su predecesor, es un poderoso atractivo.

Cumpliendo con los estándares de la gama alta

Tanto el nuevo Pixel 3 como el Pixel 3 XL montan un microprocesador Snapdragon 845, 4 GB de memoria RAM y, dependiendo de la versión, un almacenamiento de 64 o 128 GB. Esta combinación de componentes, ya validada en otros teléfonos de la competencia, ofrece una experiencia solvente bajo cualquier situación. Tan solo Apple, con su recién estrenado A12 Bionic, puede presumir de una potencia bruta superior.

Las pantallas de ambos equipos han crecido en dimensiones. La variante estándar alcanza ahora las 5,5 pulgadas de tamaño, mientras que el modelo XL llega hasta las 6,3 pulgadas. En ambos casos se emplea la tecnología OLED, aunque, como ya se observó el pasado año, no todos los OLED son sinónimo de calidad. Habrá que evaluar su desempeño final en el futuro análisis.

La zona posterior de ambos productos ha sido ligeramente rediseñada para habilitar la carga inalámbrica mediante inducción. Ahora está construída en cristal, un material que sí permite la transmisión de energía a las baterías, cuyas capacidades son 2.915 mAh (Pixel 3) y 3.430 mAh (Pixel 3 XL).

En cuanto a conectividad, ambos productos presumen de Wi-Fi, 4G LTE, NFC, Bluetooth 5.0, A-GPS, GLONASS, etc. También presentan un conector USB-C y prescinden del puerto de auriculares -algo cada vez más habitual en los segmentos altos del mercado-.

Las cámaras de ambos teléfonos no presentan grandes mejoras en el ámbito del hardware -el tamaño del sensor y las lentes permanecen intactas respecto a los encontrados en el Pixel 2 XL-. Sin embargo, el nuevo ISP del Snapdragon 845, combinado con el Pixel Visual Core y los mejorados algoritmos de procesamiento de imágenes, prometen un rendimiento superior al de sus predecesores.

Además, del rendimiento bruto, Google ha integrado nuevos modos de disparo que, aprovechando la inteligencia artificial, permiten:

  • Realizar zoom sin pérdida.
  • Evitar imágenes fallidas con Top Shot.
  • Recoger más luz en condiciones de baja luminosidad con el modo *Night Shift.
  • Editar el desenfoque del modo retrato.

La interfaz de la cámara ha sido completamente rediseñada, facilitando el intercambio de modos y la operatividad de los controles. La disposición de los elementos se asemeja ahora a la mostrada por iOS y algunos productos de Huawei.

La cámara frontal ahora está compuesta por dos sensores y objetivos diferentes. Uno de ellos ofrece un mayor angular que posibilita mejores selfies grupales y un rango de visión mayor.

Pequeños detalles

  • Los altavoces, como en el modelo previo, se sitúan en la región frontal de los teléfonos, y ofrecen sonido estéreo durante la reproducción de contenidos audiovisuales. En conjunto, ofrecen un sonido un 40% más elevado.
  • Aprovecha todas las novedades de Android 9.0 Pie, la versión más reciente del sistema operativo de Google. Es veloz, opera apropiadamente con el notch y, haciendo uso de los diferentes módulos del Snapdragon 845, introduce la inteligencia artificial y el machine learning a lo largo del sistema operativo.
  • Para invocar a Google Assistant, puedes estrujar los laterales, como en el modelo previo. Esta comienza a ser una seña de identidad de los teléfonos Pixel.

Precios y disponibilidad

Google lanzará el Pixel 3 XL por 799 dólares en Estados Unidos. La empresa también lo pondrá a la venta en España comenzando en los 849 euros. No hay disponibilidad o fechas de salida en Latinoamérica.


          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
From Lincoln Financial Group - Wed, 26 Sep 2018 21:21:38 GMT - View all Boston, MA jobs
          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page      
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
          Machine Learning Researcher - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page      
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 - Sun, 26 Aug 2018 12:12:32 GMT - View all Palo Alto, CA jobs
          Interactive Machine Learning Researcher - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page      
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 - Sun, 26 Aug 2018 12:12:32 GMT - View all Palo Alto, CA jobs
          Designing for Change in Elastic Machine Learning      Cache   Translate Page      

As everyone working on software knows, the only thing constant is change. When trying to identify anomalies in time series data, one of the major factors that needs to be addressed is how to design the baseline model to effectively respond to changes as new data enters the system. To discover anomalies in dynamic data, we are constantly working on methods that will make Elastic machine learning more effective.

Fundamentally, the systems that we are monitoring are not static. As new users, new features, new pages are added to an application, the baseline model will change. Sometimes these changes are gradual and other times they can be sudden. We want to continually provide the best modeling and are always looking for better techniques for handling multiple types of change in the systems we are monitoring.

Starting with version 6.4 of the Elastic Stack, we have added a significant improvement in the way our machine learning modelling adapts to changes in system behaviour. Before discussing the details of the enhancements we’ve made, it is useful to review modelling in the context of changing systems in general and how we have historically approached this problem. If you want to skip straight to what’s new in 6.4 skip ahead to the ‘What’s new” section.

Types of Change

In a more general setting, such changes are referred to as concept drift: what was true for a (data set, model) pair historically ceases to be true over time. This is a reasonably well studied problem in machine learning because it is critically important to deal with if one wants to use a model indefinitely. If you don’t handle it, a model which was very accurate initially can start to exhibit significant biases over time and become effectively useless. In working on this problem, people typically classify the types of change a system can undergo as recurring changes, gradual changes or sudden changes (or shocks). These are mostly self explanatory, but it is useful to think about them in terms of some concrete examples for time series data.

A recurring change is one which happens repeatedly over a long time span. For example, memory used by a long running process can be subject to recurring drops due to garbage collection. These are usually not completely predictable ahead of time and so should be distinguished from something that is, like seasonal behaviour. The reason for this uncertainty is that the trigger for garbage collection would be the actual memory used by the process. This is subject to a random forcing function, namely, the operations the process performs which will depend on how an external system interacts with it. Equally, these are not unique events: when they occur, they have similar characteristics and depend in predictable ways on the current state of the process.

Gradual changes correspond to a slow drift in the process which generates the time series values. Let’s imagine the total traffic to a website over time. This will have common periodic patterns, which amount to the traffic changing as a function of hour of day or day of week. However, you would expect over time the popularity of the website to increase (or decrease). This could correspond to the traffic being modulated (or multiplied) by some time varying value. For example, the traffic might increase by between 5% and 15% month-on-month and do so subject to some stable (probability) distribution. An important property of such systems is they typically change smoothly. This means the increase in the traffic one observed last week, as compared to a month ago, will be a better predictor for the increase in the week to come.

Finally, sudden changes correspond to an unpredictable and discontinuous change in the process generating the time series values. By way of an example, consider the rate of data indexed into an Elasticsearch cluster. If one is ingesting application log files this will be some smooth function of time. However, when someone adds a new data source the rate will change in a discontinuous fashion at this time. In particular, it will experience a step increase whose magnitude is equal to the rate of data from this new source.

Modelling the Types of Change

How one deals with different types of change from a modelling standpoint is quite dependent on the type of model one is using. It is also important to realise that different types of change are best dealt with in different ways, and this is independent of the choice of model. In practice, people do not always adopt the best approach for their data characteristics because other constraints make this difficult. For example, a simple, widely used approach is to periodically retrain on a sliding time window of historical data. This is attractive because it is easy to apply to any model. However, for a gradually changing system it has some significant disadvantages. It makes poor use of computational resource, typically processing the same piece of data many times during the train step and can lead to instabilities when the model is retrained.

Instead, the basic modelling techniques we use are all online. We incorporate each new piece of data into our model and then discard that piece of data, and such formulations naturally pick up ways to allow the model parameters to change smoothly over time. We also have a natural way of learning the rate at which to do this from the data. In particular, we use a simple feedback controller to adjust the rate of change of the model parameters as a function of the error in the distribution we predict for the next value we’ll see. This fits our use case well since we first detect a change in the system, flag an anomaly, but then adapt to it at an accelerated rate.

A useful way of thinking about this approach is that our models are continuously learning a horizon of relevant historical data given the predictive power of the model with respect to the data set at hand. This horizon is a function of the ability of the model to capture recent behaviour of the time series. One finds that a more expressive model, or more precisely, a model which is better able to capture the particular behaviour of the time series, is able to use more historic data without suffering from significant error in the distribution it predicts for the next value.

In this context, it is important to strike a balance between how quickly the model is able to change and whether it fits anomalous behaviour. Ideally, anomalous time intervals should be entirely ignored by the model. But there is a problem, because one doesn’t know in advance how the future will play out and whether the values constitute an anomaly or some permanent system change. For our model, which continually learns, this amounts to how quickly it adapts its parameters to new behaviour. Aside from the rate at which we allow the model parameters to change, we automatically back off how much it learns from new values based on how unlikely they are given its current expectations. The more unlikely the values, the less notice the model takes of them. Formally, this provides a continuous parameterisation of classes of robust estimating functions for the components of our model. It is worth noting that this also means that the more history our model is able to use, the more confident it can be that values are unusual and so the less notice it’ll take of anomalous intervals.

To summarise, for gradually changing systems, our modelling prior to 6.4 performed well, was stable and computationally efficient. However, starting with version 6.4, we wanted to improve our behaviour with respect to sudden changes, and to do this whilst maintaining or even improving our handling of gradual change.

What’s New in 6.4

In Elastic Stack 6.4, we introduced a change which assumes parametric forms for sudden changes to time series. As a result, we are able to use standard model selection techniques to decide if the system has undergone one of these changes. This approach is complementary to our existing approach to adapt to gradual changes; it simply functions side-by-side with it. Importantly, in systems which undergo sudden changes, it means we can use more history whilst avoiding errors in our predicted distribution. It, therefore, has an incidental advantage that, for these systems, our models are more robust to anomalous intervals, because they remember more history and so take less notice of them.

It might seem that assuming simple parametric descriptions for types of change is overly restrictive, but it has some big practical advantages. There is good reason to have inductive bias in this process, because, in practice, one often sees similar types of sudden changes. For example, periodic data undergoes some linear scaling, an otherwise continuous time series undergoes a step change and a periodic pattern experiences a shift in time.

To understand how linear scaling can commonly arise, consider any system which uses load balancing and experiences a constant external seasonal forcing function, such as traffic to a website. It will exhibit linear scaling for individual metrics during failover or if compute resources are added or removed. Level shifts often occur in otherwise smooth metrics at any point when resources are added or recovered, such as when a file is copied or garbage collection occurs. This is because these processes typically act over a relatively short time scale.

Having simple parametric descriptions of possible changes means we can identify changes and learn the new model parameters quickly, both of which are important to minimize the “down time” for anomaly detection, i.e. when prediction error is high. Finally, we can detect these classes of change with high accuracy, which is important to avoid fitting anomalous data.

Sudden Change Example

We’ll finish up by reviewing a couple of examples of the way the model adapts to sudden changes as a result of the enhancements in version 6.4. The first example is a prototypical linear scaling. This data set comprises the total count of 404 statuses generated by visitors to a website. A code change resulted in a sudden increase in the number of 404s around 28th February 2017. We expect a linear scaling to occur at this point because the chance of a 404 increased while the seasonal traffic to the website remained more or less unchanged.

Figures 1, 2 and 3 show the model bounds which can be viewed in the Single Metric Viewer tab. Figure 1 shows the traffic pattern and Figures 2 and 3 highlight the difference in our modeling technique before and after version 6.4. It should be clear from the second two figures that we largely ignore the values associated with the scaling initially. This is because, as discussed above, we reweight highly unusual values when updating the model.

You will notice the benefits of of 6.4 modeling enhancements In Figure 3, just before midnight on the 28th February, we create a new mode for the values we’ve seen for the preceding 6 hours. Around 6 hours later, we detect the scaling event and start using the scaled model. Note that because we maintain a copy of the prediction error distribution updated conditionally on there having been a scaling event, the distribution narrows significantly at this point.

Figure 1. Sudden increase of website 404 status messages

Figure 2. Prior to 6.4 the 95th percentile bounds react to the sudden change slowly

Figure 3. Post 6.4 the model bounds respond to the scaling event quickly

Seasonality and Step Change Example

The next example shows the model adapting to a step discontinuity in the values. In fact, this is complicated by the fact that the data also becomes seasonal, with weekly period, after the change point. Figures 4, 5 and 6 show the model bounds, log of the predictive distribution and snapshots of the predictive distribution for daily hits on a webpage page. It is visually very clear that around 14th August 2016 something changes significantly in this process. The model learns the new level and trend fairly quickly. It then takes around 5 repeats to learn the new seasonality and reassess the prediction error distribution in this context. At this point it is able to predict the values accurately and so it picks up the unusually low values, given the new behaviour, at the beginning of November.

Figure 4. Model 95th percentile bounds before and after a step change

Figure 5. Contour plot of the log of our model predictive distribution for the same time period as Figure 4

Figure 6. Time snapshots (at the crosses in Figure 5) of the predictive distribution. Note that the range of the X- and Y- axes varies between the snapshots

Summary

As you can see there is a lot of work that happens behind the scenes to make Elastic machine learning modeling more effective at identifying anomalies. With time series data, the model needs to be continually updated to respond to dynamic data. Starting in 6.4, we have improved the way our modeling adapts to changes in system behaviour to make sure that you get better results.

Try it out for yourself on your data, download the Elastic Stack and enable the 30-day trial license, or start a free trial on Elastic Cloud.


          Program Manager, Services - CloudMoyo - Bellevue, WA      Cache   Translate Page      
CloudMoyo’s proven track record includes developing impactful solutions using big data, machine learning, predictive analytics and visual story-telling for...
From CloudMoyo - Fri, 05 Oct 2018 23:54:14 GMT - View all Bellevue, WA jobs
          Lead/Sr. Functional Consultant, Services - CloudMoyo - Bellevue, WA      Cache   Translate Page      
CloudMoyo’s proven track record includes developing impactful solutions using big data, machine learning, predictive analytics and visual story-telling for...
From CloudMoyo - Fri, 05 Oct 2018 23:54:13 GMT - View all Bellevue, WA jobs
          Architect / BI & Analytics, Services - CloudMoyo - Bellevue, WA      Cache   Translate Page      
CloudMoyo’s proven track record includes developing impactful solutions using big data, machine learning, predictive analytics and visual story-telling for...
From CloudMoyo - Fri, 05 Oct 2018 23:54:13 GMT - View all Bellevue, WA jobs
          Approximate Homomorphic Encryption over the Conjugate-invariant Ring, by Duhyeong Kim and Yongsoo Song      Cache   Translate Page      
The Ring Learning with Errors (RLWE) problem over a cyclotomic ring has been the most widely used hardness assumption for the construction of practical homomorphic encryption schemes. However, this restricted choice of a base ring may cause a waste in terms of plaintext space usage. For example, the approximate homomorphic encryption scheme of Cheon et al. (ASIACRYPT'17) is able to store a complex number in each of the plaintext slots since its canonical embedding of a cyclotomic field has a complex image. The imaginary part of a plaintext is not underutilized at all when the computation is performed over the real numbers, which is required in most of the real-world applications such as machine learning. In this paper, we propose a new approximate homomorphic encryption scheme which is optimized in the computation over real numbers. Our scheme is based on RLWE over a special subring of a cyclotomic ring, which is no easier than a standard lattice problem over ideal lattices by the reduction of Peikert et al. (STOC'17). Our scheme allows real numbers to be packed in a ciphertext without any waste of a plaintext space and consequently we can encrypt twice as many plaintext slots as the previous scheme while maintaining the same security level, storage, and computational costs.
          Data Analyst, International - Bandwidth - Raleigh, NC      Cache   Translate Page      
Machine learning/AI techniques, features, and classifiers. Simply changing the way people communicate, connect and do business....
From Bandwidth - Thu, 04 Oct 2018 16:32:34 GMT - View all Raleigh, NC jobs
          Data Engineer 2 - IMO - Intelligent Medical Objects, Inc. - Northbrook, IL      Cache   Translate Page      
Familiarity with machine learning methods, such as clustering analysis and neural networks. Downtown commuters will enjoy free shuttle service to IMO’s...
From IMO - Intelligent Medical Objects, Inc. - Mon, 24 Sep 2018 17:52:43 GMT - View all Northbrook, IL jobs
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page      
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
          Sr. Data Scientist - Life Sciences - Health Catalyst - Salt Lake City, UT      Cache   Translate Page      
Machine learning experience required. The role has great potential for a successful candidate as they will form the initial seed of a new business unit with...
From Health Catalyst - Tue, 11 Sep 2018 02:31:12 GMT - View all Salt Lake City, UT jobs
          Senior Machine Learning Engineer - Hired - San Francisco, CA      Cache   Translate Page      
Have a strong understanding of the product, business, key KPIs across product and business. Hired is looking for a Senior Machine Learning Engineer to join our...
From Hired - Thu, 23 Aug 2018 06:17:36 GMT - View all San Francisco, CA jobs
          Engineering Manager, Search - Hired - San Francisco, CA      Cache   Translate Page      
The company is backed by Lumia Capital, Sierra Ventures, and other leading investors. The desire to learn about how Data & Machine Learning can influence Search...
From Hired - Tue, 07 Aug 2018 18:31:17 GMT - View all San Francisco, CA jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - Seattle, WA      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Tue, 02 Oct 2018 15:21:24 GMT - View all Seattle, WA jobs
          Sr. Associate, AI in Management Analytics Consultant - KPMG - McLean, VA      Cache   Translate Page      
Ability to apply statistical, machine learnings, and artificial intelligence techniques to achieve concrete business goals and work with the business to...
From KPMG LLP - Sat, 29 Sep 2018 15:21:53 GMT - View all McLean, VA jobs
          PA Headline      Cache   Translate Page      
The photo sharing app’s new boss hopes machine learning will limit bullying and spread kindness.
          Data Scientist - Deloitte - Springfield, VA      Cache   Translate Page      
Demonstrated knowledge of machine learning techniques and algorithms. We believe that business has the power to inspire and transform....
From Deloitte - Fri, 10 Aug 2018 06:29:44 GMT - View all Springfield, VA jobs
          Database Administrator - Radiant Solutions - Springfield, VA      Cache   Translate Page      
Machine learning, data mining, and knowledge discovery. Work is non-routine and very complex, involving the application of advanced technical and business...
From Radiant Solutions - Wed, 04 Jul 2018 01:36:29 GMT - View all Springfield, VA jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - Dallas, TX      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Fri, 07 Sep 2018 02:02:14 GMT - View all Dallas, TX jobs
          Associate, Machine Learning AI Consultant - KPMG - Dallas, TX      Cache   Translate Page      
Broad, versatile knowledge of analytics and data science landscape, combined with strong business consulting acumen, enabling the identification, design and...
From KPMG LLP - Fri, 07 Sep 2018 02:02:14 GMT - View all Dallas, TX jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - Philadelphia, PA      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Fri, 14 Sep 2018 08:38:34 GMT - View all Philadelphia, PA jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - New York, NY      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Tue, 02 Oct 2018 15:21:24 GMT - View all New York, NY jobs
          Associate, Machine Learning AI Consultant - KPMG - New York, NY      Cache   Translate Page      
Broad, versatile knowledge of analytics and data science landscape, combined with strong business consulting acumen, enabling the identification, design and...
From KPMG LLP - Fri, 14 Sep 2018 08:38:34 GMT - View all New York, NY jobs
          Threat Finance Subject Matter Expert - People, Technology & Processes - Fort Bragg, NC      Cache   Translate Page      
The TF SME contractor shall have proficiency and experience with applied data processing and scientific analysis of large datasets and machine learning....
From People, Technology & Processes - Tue, 17 Jul 2018 03:09:42 GMT - View all Fort Bragg, NC jobs
          Google prezanton Google Pixel 3 dhe Pixel XL      Cache   Translate Page      

Më shumë shpirt se trup. Google fokusohet tek Inteligjenca Artificiale dhe jo tek paraqitja, që e përkthyer në fushën e smartphone-ve do të thotë computer vision. Telefoni i ri i familjes Pixel, i prezantuar sot në Londër, synon të dallojë nga konkurrentët si Samsung, Huawei dhe Apple, duke integruar gjithnjë e më shumë algoritme dhe machine learning në përpunimin e imazheve. Dy modelet që u prezantuan sot janë Pixel 3 dhe Pixel 3 XL me çmime që nisin nga 899 […]

The post Google prezanton Google Pixel 3 dhe Pixel XL appeared first on Revista Monitor.


          Riding the Waves of Data Modernization      Cache   Translate Page      
In this contributed article, Jeff Healey, Senior Director for Vertica at Micro Focus, discusses how there’s still time for leaders from traditional industries to modernize their aged data warehouses in becoming more data-driven. However, they face a serious threat of extinction from data-hungry disruptors born in the 21st century that base their decision-making on cloud-optimized analytical platforms with machine learning capabilities at scale.
          BrandPost: Successful data-driven companies must balance human and machine roles      Cache   Translate Page      

The latest Future of Jobs report from the World Economic Forum speaks to how technological advancements like artificial intelligence, machine learning, and big data could affect jobs worldwide in the next five years. The anticipated redistribution of work between humans and machines may displace 75 million jobs, but it’s likely to create as many as 133 million new ones, too, according to the report.

This major shift in jobs may not reassure those of you who believe technology is a threat to your role. But the reality is that smarter technologies provide an amazing opportunity to focus on the ways that we create the most value for our organizations. Creativity and strategic thinking remain distinctly human advantages. When paired with the increased processing capacity of machines, there is plenty of room to be optimistic about the future.

To read this article in full, please click here


          IC Resources Ltd: Senior Research Scientist       Cache   Translate Page      
£65000.00 - £110000.00 per annum + Depending on experience: IC Resources Ltd: This Senior Research Scientist vacancy will allow you to apply your Computer Vision and / or Machine Learning Research expertise, working for a global London
          Security Attacks: Analysis of Machine Learning Models      Cache   Translate Page      

Have you wondered what would it be like to have your machine learning (ML) models under security attack? In other words, have you given much thought to what would happen if your machine learning models were hacked? And, have you thought through how to monitor such security attacks on your AI models? As a data scientist and machine learning researcher, it would be good to know some of the scenarios related to attacks on ML models.

In this post, we will address the following aspects related to security attacks (hacking) on machine learning models.


          Code of Ethics in AI: Key Traits      Cache   Translate Page      

Do you know that organizations have started paying attention to whether AI/Machine Learning (ML) models are doing unbiased, safe, and trustable predictions based on ethical principles? Have you thought through the consequences if the AI/Machine Learning (ML) models you created for your clients make predictions, which are biased towards a class of customer, thus, hurting other customers? Have you imagined scenarios in which customers blame your organization of benefitting a section of customers (preferably their competitors), thus, filing a case against your organization and bringing bad names and loss to your business? Have you imagined the scenarios when ML models start making incorrect predictions, which could result in loss of business?

If the above has not started haunting you, its time that you started thinking about it. And, this is where you need to think about implementing the code of ethics in implementing Artificial Intelligence (AI) practices/principles. This post represents the definition of ethical AI, key traits, and why businesses need to start paying attention to rolling out ethical AI practices in their organization. The following topics will be discussed:


          Databricks Launches First Open Source Framework for Machine Learning      Cache   Translate Page      
databricks_logor_stacked_rgb_1200px

Databricks recently announced a new release of MLflow, an open source, multi-cloud framework for the machine learning lifecycle, now with R integration.

The post Databricks Launches First Open Source Framework for Machine Learning appeared first on RTInsights.


          Senior Machine Learning Engineer - SAM Inc. - Edmonton, AB      Cache   Translate Page      
The chosen candidate will work with the SAM team to build and maintain our SAM AI Engine. SAM prides itself on being a global leader in the application of... $80,000 - $110,000 a year
From Indeed - Tue, 09 Oct 2018 15:36:18 GMT - View all Edmonton, AB jobs
          How to Develop LSTM Models for Multi-Step Time Series Forecasting of Household Power Consumption      Cache   Translate Page      

Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables that in turn could be used to model and even forecast future electricity consumption. Unlike other machine learning […]

The post How to Develop LSTM Models for Multi-Step Time Series Forecasting of Household Power Consumption appeared first on Machine Learning Mastery.


          Fusion Informatics      Cache   Translate Page      

India 916361054076

Fusion Informatics is a Leading AI, Mobility, IOT, Blockchain & Cloud solution provider, IT outsourcing company with a focus on implementing high-level technology solutions & services. We transform businesses through our extensive specialist knowledge and the status of the design Imaginative. We are 17 years old Software Development Company having global services in India (Bangalore, Ahmedabad), UAE, Europe, and the USA. An ISO 9001:2008 Certified & Quality Mark Award Winning Company, founded in 2000 and worked on 5000+ companies with 1000+ Mobile Apps. Our services are working on Artificial Intelligence, Machine Learning, Blockchain, Data Science, Bots, Cognitive services, IOT and Mobile App Development. We have developed Various Solutions using AI, ML, Cloud-like RoboAdvisor0y, LogiGo, Intellinsight etc. We served our work with a lot of startups, SME and big enterprises such as Bosch, Lenovo, Bharat Petroleum, Reliance, Othaim, WorkerAppz, Aditya Birla, Dainik Bhaskar, Tardebulls etc. We are producing high-quality software development and advising services.we have achieved valuable expertise in an extended array of business areas and cutting-edge technologies. Our solutions include a whole software product lifecycle from idea invention to software development, testing, combination, and support. Fusion Informatics has obtained a worthy background in a wide variety of technologies and applications. Recognition to a high skill of our builders, we are similarly qualified to operate with non-mainstream technologies and perform science-intensive projects. Key points:- We allow clients to achieve more value and extension by optimizing the development method and concentrating on core projects An extremely proficient development company, our developers essentially consist of Senior and Middle-level professionals who have special technical knowledge usually with a Programming degree A designers\' engineers have a strong, experienced and concentrated on development and non-mainstream technologies. We are Specialized in all verticals and domains across the globe using the latest technologies & solutions We understand the requirements of the market and develop software, web or mobile solutions for both domestic and international markets. Fusion Informatics is a technologically concentrated business aimed at providing customers with cutting-edge technology solutions in many areas.
          Instagram using AI to scan for bullying in photos and captions      Cache   Translate Page      
Instagram using AI to scan for bullying in photos and captions#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000 Instagram is taking action against trolls and bullies. The social media giant on Tuesday rolled out new machine learning tools that are able to 'proactively detect' bullying in photos and captions. Reported by MailOnline 4 hours ago.
          Senior NLP / Machine Learning Developer      Cache   Translate Page      
NC-Charlotte, SENIOR NLP / MACHINE LEARNING EXPERT Enterprise Select has partnered with an industry leading financial services firm to help staff their progressive Natural Language Processing initiatives. We are specifically looking for a Senior NLP / Machine Learning expert to join their exceptional Analytics team. WHAT YOU’LL DO: Design and develop reports and dashboards that drive daily business decisions Co
          Les Google Pixel 3 et Pixel 3XL sont officiels… Et ils sortiront bien en France !      Cache   Translate Page      
Il est rare dans le monde du smartphone d’être vraiment surpris par l’annonce d’un nouveau téléphone, tant les fuites en amont sont devenues la norme. Mais dans le cas des Pixel 3, tous les records ont été battus. Depuis plusieurs semaines, voire mois, le nouveau smartphone de Google est en vente sur plusieurs marchés noirs, que ce soit en Europe de l’Est ou à Hong Kong. Design, fiche technique, appareil photo, on savait déjà tout des nouveaux porte-étendards de Google. Mais qu’importe : ils sont désormais officiels et surtout ils vont enfin avoir droit à une commercialisation en France. Un Google Pixel 3 « old school », mais doté d'une fiche technique solide On commence donc avec le Google Pixel 3. Pas question d’encoche sur l’écran ici, mais d’un affichage « old school » au format 18:9 d’une diagonale de 5,5 pouces. Comme l’année dernière, il s’agit d’un écran AMOLED, que l’on espère beaucoup mieux calibré que ceux de l’année dernière. Question design, Google ne change pas une formule déjà convaincante. On retrouve donc un mélange de verre (sur la partie supérieure) et de métal sur la coque, un lecteur d’empreinte et… un unique capteur photo. Un choix étrange de la part de Google alors que les autres constructeurs n’hésitent plus à multiplier les capteurs de leur bloc photo dorsaux. Comme l’année dernière également, le Pixel 3, comme le Pixel 3 XL, n’embarque pas de port jack, ni de port microSD (comme toujours). Il faudra faire avec l’adaptateur USB Type-C. Enfin trois coloris sont prévus : Simplement Noir, Résolument Blanc, Subtilement Rose. La fiche technique enfin est sans aucune surprise. Le Pixel 3 embarque un Snapdragon 845 associé à 4 Go de RAM, un Pixel Visual Core, un module Titan M Security, et 64 ou 128 Go d’espace de stockage. Il dispose d’un écran Full HD+ (2160 x 1080 pixels) et d’une batterie de 2 915 mAh. Google oblige, il est installé sous la dernière version d’Android : Android 9.0 Pie avec un Pixel launcher, légèrement tweaké de façon à intégrer le maximum de fonctionnalités de Google. On le disait un peu plus haut, la plus grande surprise concernant ce téléphone provient de son bloc photo. Il n’embarque en effet qu’un unique capteur de 12,2 MP ƒ/1.8 au dos et Google promet des merveilles en basse luminosité. Ce capteur est d’ailleurs être soutenu par une application photo dotée à l’IA. Trois modes de prise de vue se démarquent : Top Shot, où une IA dopée au machine learning sera capable de sélectionner la meilleure photo prise lors d’une rafale, Photobooth, qui est l’équivalent de Top Shot, mais pour les selfies (et qui devrait prendre en compte le double capteur photo présent à l’avant de l’appareil) et enfin Super Res Zoom, qui a pour but d’améliorer la qualité des photos prises avec le zoom numérique. En façade on retrouve un 8 MP ƒ/2.2 grand angle (97°). Ses dimensions sont de 68.2 x 145.6 x 7.9 mm pour 148g. Il faudra tester tout cela en conditions réelles pour savoir ce que valent les promesses de Google. Le Google Pixel 3 XL, plus grand et avec une encoche Toutes ces fonctionnalités et cette fiche technique devraient se retrouver sur le Google Pixel 3 XL. Ce dernier se différencie de son petit frère par trois traits : il est plus grand (6,3 pouces) et son écran 18,5:9 dispose d’une encoche et passe en QHD+ (2960 x 1440), alors que sa batterie à une capacité de 3430 mAh. Et quelle encoche. Peu esthétique, cette dernière est plutôt large et descend assez bas dans l’écran. On s’attendait à mieux de ce point de vue de la part de Google. Ses dimensions sont de 76.7 x 158.0 x 7.9 mm pour 184g. La recharge sans fil est bien là En option, Google propose le Pixel Stand, notre nouveau chargeur sans fil compatible Qi. Lorsque vous mettez votre téléphone à charger sur le Pixel Stand, votre appareil se transforme en écran intelligent, grâce à l’Assistant Google intégré. Il sera disponible au prix de 79 euros. Les Google Pixel 3 enfin disponibles en France (mais pas donnés) La bonne nouvelle, c’est que les Google Pixel 3 vont enfin être commercialisés en France. Ce n’était pas le cas des deux premiers exemplaires. Ils ne seront toutefois pas donnés. Le Google Pixel 3 sera en effet vendu 859 euros en version 64 Go et 959 euros en version 128 Go. Pour le Google Pixel 3 XL comptez 959 euros en version 64 Go et 1059 euros pour la version 128 Go. Le message est clair : il s’agit de smartphones haut de gamme et Google compte les aligner face aux flagships de Samsung ou Huawei.  Ils seront en prévente dès demain, le 10 octobre, puis en vente à partir du 2 novembre, chez Orange, Fnac-Darty, Boulanger, SFR, Bouygues Telecom et sur le Google Store. https://www.youtube.com/watch?v=vKSA_idPZkc&feature=youtu.be

          Data Engineer - Amazon.com - Seattle, WA      Cache   Translate Page      
Experience working with large data sets in order to extract business insights or build predictive models (data mining, machine learning, regression analysis)....
From Amazon.com - Mon, 13 Aug 2018 19:25:19 GMT - View all Seattle, WA jobs
          Data Scientist / Operations Research Engineer - 67712 - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page      
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      
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      
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
          Sales Engineer - Hitachi Vantara - New York, NY      Cache   Translate Page      
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
          AI Solutions Architect - Industrial - Petuum - Sunnyvale, CA      Cache   Translate Page      
Machine learning or IIoT preferred. PaaS, SaaS, IaaS and business intelligence/analytics implementation experience are a plus....
From Petuum - Fri, 07 Sep 2018 20:07:58 GMT - View all Sunnyvale, CA jobs
          TPL, Discovery, and CMDB ah-ha moment (Application Lookup)      Cache   Translate Page      

Hi, Everyone!

 

I got to meet Doug Mueller... Engage 2016 gives you access, (BMC CMDB Architect) and Antonio Vargas (BMC Discovery Product Manager). in person recently.  Here is my recent ah-ha moment with CMDB, and Discovery.  I will start with BMC discovery (ADDM) then move to the CMDB topic of service concept.  There is lots confusion out there on the difference of technical and business service should be within the CMDB.  Some of you might say, I knew that years ago!  But, It took me awhile to grabs the concepts even though we used, implemented, and develop the various BMC products.  I am a visual and tactical learner.  I am writing this blog for those type of students.  Diagram 1 explains The Pattern Language (TPL) and how things are discovery

 

Picture 1 explains the discovery concepts and how things are the development by looking for a pattern within a process(s) running on a device.  Once you find that process you write a pattern or use discovery to find that process via TPL. By using the discovered process, you can now create a software instance(s) by the group the process into the software(s).

 

Screen Shot 2017-06-17 at 8.01.21 AM.png

 

Diagram 2 shows the structure of the TPL base on Diagram 1.  Notice the trigger is on a node of node kind base on the condition.  You now see the relationship between the pattern and the TPL.  Once you define the software instances into business application instance(s).  Once BAI is moved into the CMDB CI called BMC.CORE:BMC_Application.  You have to make a logical entry for BMC.CORE:BMC_ApplicationSystem using non-insteance names.  (The Instance name is production, development, and QA environment coming form BMC Discovery.). Base on your application model you create using the pattern that is consumed by CMDB common data model in different ways.  You also need to know that TPL's foundation is in Python.  Those of you are interested in the pattern, machine learning, and Artificial intelligence.  That's another discussion/blog.

 

Let's look at BAI and SI from the discovery with SAAM and predefine SI that becomes part of a larger model like BSM.

 

SAAM's Business Application Instances are consumed by these forms:

  • BMC.CORE:BMC_Application
  • BMC.CORE:BMC_ApplicationSystem

 

Let's look at the CDM for CMDB forms BMC.CORE:BMC_ApplicaitionSystem and BMC.CORE:BMC_Applicaiton.  You have to understand that parent class is BMC.CORE_ApplicationSystem.  The subclasses are BMC.CORE:BMC_Application, BMC.CORE:BMC_ApplicationInfrastructure, BMC.CORE:BMC_SoftwareServer.  (Basics)

 

CI Name
CI ClassDescription
BMC Atrium Discovery and Dependency Mapping Active Directory Proxy 10.1 identified as Active Directory on %hostname%Parent: BMC.CORE:BMC_ApplicationSystemChild:   BMC.CORE:BMC:SoftwareServerThe BMC_SoftwareServer class represents a single piece of software directly running (or otherwise deployed) on a single computer.
manager module on Apache Tomcat Application Server 7.0 listening on 8005, 8080, 8009 on %hostname%Parent:  BMC.CORE:BMC_SystemServiceChild:    BMC.CORE:BMC_ApplicaitonServiceClass that stores information about services that represent low-level modules of an application, for example, the components deployed within an application server. This class has no corresponding DMTF CIM class.
BSM (Business Service Managment is define pattern via TKU of software instance)Parent:  BMC.CORE:BMC_SystemChild: BMC.CORE:BMC_ApplicationSystemChild:   BMC.CORE:BMC_ApplicationThe BMC_Application class represents an instance of an end-user application that supports a particular business function and that can be managed as an independent unit.

 

By understanding the above and what's documented by discovery leaves ITSM team a decision to make between BMC_SoftwareServer or BMC_ApplicationSystem. Why do you have to make a decision is that BMC discovery sync with both of these CI? (BMC did not make the decision for you.) To understand why, let's review and understand model: FACTS:

  • ApplicationSystem is parent CI.
  • SoftwareServer is child CI of ApplicationSystem.
  • BMC Sync the Business Application instance into BMC_Application CI which is Child CI for ApplicationSystem out of the box.  (OOTB)

 

To be continue.... It is not comsume by the follwing forms:

  • BMC.CORE:BMC_SystemSoftware
  • BMC.CORE:BMC_ApplicationInfrastructure
  • BMC.CORE:BMC_SystemService

 

The way I'd understood @Doug Muller:  There is no direct relationship between business and technical services that relate(s) into BMC.CORE:BMC_ApplicationSystem.  These definitions can be defined by how your business generate revenue with a business service. (If your company makes cars.  Any system that supports selling cars is tied to business services.). Technical services are defined supporting of business service(s).  You can define the technical services without a business service(s).  These are logoical break down of your services based on your organization. 

 

The confusion comes from the type of business your company is providing to its customers and way BMC represents examples of technical vs business service(s). BMC is a company that sells software so a lot of the business services sounds like technical services but, it is not a technical service(s).  Becuase those services help generate revenue for BMC software. 

 

Let's review Why CMDB & Discovery project fail.

 

CMDBDiscovery
Suggestions
Project ScopeThe scope of these projects starts out has let's map the services but, the reality is that there are lots of scope creeps.  The value creation is loose scoped based on my experience.  The value creation for CMDB needs to understand and measure for each ORG.Discovery covers the automation of discovering IT infrastructure at the data center level but, does not cover end to end communications at the network level.  Mapping of BAI isn't scoped right.  BMC has recognized this issue by adding manage service to map application in CMDB.To realize and reduce the education need to use the CMDB.  We need a quick application lookup solution until the whole CMDB and discovery project in completed in scope.
Project ConstraintsHuman Resource, Knowledge Base, Wisdom Base, and Where to start the value creation for an ORG.There isn't a good way to resolving and track Access issue release in a large enterprise environment.

 

Draft thoughts: Service Modeling brain Dump Service Modeling Best Practices comparable CDM fieldsIf you want to learn discovery in detail and how you can answer debated question.  Please start here:  ADDM Support Guide When you create an application mapping in discovery.  You have to create dev, qa, and production instance that sync's into CMDB.  Those instance has to be grouped into relationships and parent class.  The Parent CI is ApplicationSystem use impact relationship to BMC.CORE:BMC_ConcreteCollection CI is used for tore a generic and instantiable collection, such as a pool of hosts available for running jobs. This class is defined as a concrete subclass of BMC_Collection and was added rather than changing BMC_Collection from an abstract class to a concrete class. I'd often get questions about how does discovery provided value to application owners vs management.   Here are some key thoughts about the value the discovery delivers. System Administrator & IT Architecture Value

  • Ability to produce a DR plan with discovery data
  • Ability to understand the impact of shared applications and Infrastructure
  • Provided a common understanding of the Business Application Instances for the company
  • Ability to produce up to date diagram of your application
  • Reduce work production current infrastructure diagrams and inventory for management

Management & C-Suite Value

  • Ability to the audit process, people, data, and tools
    • For Example:  If plan datacenter has 50 hosts to be create but, discovery find 100.
      • Management can ask a question about how the other 50 was created and who is paying for them
  • Understand Share Impact and risk management of applications
  • Ability to the budget datacenter or cloud moves

          McAfee anuncia novos produtos      Cache   Translate Page      
McAfee anuncia novos produtos

A McAfee, a empresa de ciber segurança de dispositivos na nuvem, revelou na passada semana à comunicação social, e onde a Wintech esteve presente, a última versão da sua gama de produtos de segurança pensados para o consumidor, centrada no rendimento do sistema, na eficácia e na proteção das vidas digitais das pessoas. Num mundo cada vez mais ligado, esta nova linha oferece benefícios que abordam as necessidades do consumidor, independentemente da sua idade. Com a incorporação da McAfee Safe Family na McAfee Total Protection e McAfee LiveSafe, e juntamente com o PC Boost em toda a gama, incluindo o McAfee AntiVirus, McAfee AntiVirus Plus e McAfee Internet Security, a McAfee foi mais além do antivírus tradicional para proteger o que mais importa aos seus clientes.

"A segurança é imensamente pessoal e a proteção contra as ameaças tradicionais em PC e telemóveis já não é suficiente. As pessoas agora exigem uma segurança que aborde o panorama de ameaças em constante evolução, ao mesmo tempo que protege as suas identidades e a sua família", comenta Gary Davis, chefe de evangelização de segurança do consumidor na McAfee. "A nossa linha, este ano, reflete uma carteira ampliada que permite aos nossos clientes proteger o que mais lhes importa, ao mesmo tempo que oferece um melhor rendimento e uma melhor segurança.”

"Os utilizadores esperam muito mais da segurança endpoint do que a simples deteção do malware; trata-se de estar “seguro” e fazer com que os endpoints funcionem de maneira ótima, especialmente se pretende prolongar a vida útil dos antigos PC. O foco da McAfee na proteção da atividade digital da família, ao mesmo tempo que melhora o rendimento do sistema, é totalmente correto", afirma Frank Dickson, vice-presidente de investigação de produtos de segurança da IDC.

Um melhor rendimento

Ao carregar a análise de malware para a nuvem da McAfee Global Threat Intelligence (GTI), a linha principal de produtos da McAfee requer menos recursos do sistema que, combinado com as novas aplicações e capacidades de melhoria do navegador, ajuda os PC a funcionar a velocidades ótimas. A McAfee obteve a classificação mais alta nas provas de comparativa de impacto de rendimento da AV desde outubro de 2016 pela sua capacidade para detetar e otimizar o rendimento. Para melhorar ainda mais a experiência do utilizador, a programação deste ano inclui as seguintes melhorias-chave para PC e dispositivos móveis:

  • McAfee App Boost :  Ajuda as aplicações que consomem mais recursos a completar as tarefas de forma mais rápida atribuindo automaticamente mais recursos às aplicações que o cliente está a utilizar ativamente.
  • McAfee Web Boost: evita os downloads não desejados ou não solicitados e a atividade do sistema causada pelos vídeos de reprodução automática, que reduz a largura de banda e o consumo de recursos.
  • Melhorias móveis
  • McAfee Mobile Security para Android: agora inclui capacidades de aprendizagem automática dentro do motor AV móvel, que proporciona uma examinação mais eficiente e uma deteção de malware mais rápida
  • McAfee Mobile Security para iOS: a nova examinação de ameaças Wi-Fi mostra o estado de segurança da rede Wi-Fi ligada e alerta os utilizadores se a rede Wi-Fi a que estão ligados está em risco

 

Proteção contra malware mais eficaz

Segundo investigações recentes dos McAfee Labs, o ransomware continua a aumentar com um crescimento de 62% nos últimos quatro trimestres, até alcançar os 14,8 milhões de amostras. Tendo em conta a importância de se proteger num panorama de ameaças em constante expansão, no ano passado, a McAfee anunciou um motor antivírus com a Real Protect baseado em machine learning para proteger os utilizadores das ameaças atuais. Desde então, o antivírus conseguiu continuamente níveis de deteção superiores de AV-Test.

A McAfee continua a investir em tecnologia de aprendizagem automática para proteger melhor os clientes contra o malware, ao mesmo tempo que agrega características que apoiam o seu compromisso de ajudar os clientes na hora de se sentirem seguros. A empresa também traduziu o seu compromisso de proteção contra vírus para mais seis idiomas. Se um vírus atacar um cliente e tiver a proteção ativada, a equipa de atendimento ao cliente eliminá-lo-á, ou o cliente receberá um reembolso. Aplicam termos adicionais. 

 

Salvaguardar as vidas digitais das pessoas

Com a nova gama de produtos deste ano, a McAfee aumentou o seu foco mais além dos antivírus tradicionais para enfrentar as novas ameaças que são importantes para as pessoas à medida que passam mais tempo online. A nova linha inclui agora características que fazem com que seja mais fácil do que nunca proteger o mais importante.

  • McAfee Safe Family: dá aos pais a visibilidade e o controlo necessário para manter a segurança online dos seus filhos quando usam os seus PC, smartphones e tablets.
  • As principais características e benefícios incluem: relatórios de atividade, aplicações e bloqueio de websites, controlos de tempo em frente ao ecrã, seguimento de localização ou tempos de descanso, entre outros. A McAfee Safe Family Premium está incluída nas subscrições da McAfee Total Protection 10 e McAfee LiveSafe.
  • McAfee Mobile Security: completamente redesenhado para oferecer uma experiência de utilizador mais intuitiva e atrativa

A gama de produtos McAfee para a área de consumo está disponível no mercado português em várias versões : McAfee Antivirus 10 (39,95€);  Internet Security 3 Devices (59,95€); Internet Security 10 Devices (69,95€); Total Protection 5 Devices (69,95€); Total Protection 10 Devices (79,95€); e McAfee Livesafe (89,00€). Estes produtos podem ser adquiridos online ou através dos principais retalhistas localizados em Portugal.

Mais informações podem ser encontradas em https://www.mcafee.com/pt-pt/index.html .


          Instagram Says It Can Now Detect Cyberbullying in Videos      Cache   Translate Page      
Instagram is now using machine learning to detect cyberbullying in images and videos.
          ‫انتشار ML.NET 0.6 (یادگیری ماشین تحت NET.)      Cache   Translate Page      
ML.NET is a cross-platform, open source machine learning framework for .NET developers. We want to enable every .NET developer to train and use machine learning models in their applications and services.

          Research Scientist – Machine Learning Algorithms      Cache   Translate Page      
CA-Palo Alto, Job Summary: Ford Motor Company is moving into a new phase of its 100+ year history, one in which openness is at the heart of its future. The automobile is being redefined as a networked computing platform upon which an ever-evolving set of applications is being designed, allowing us to create exciting new consumer experiences. We are a research lab within Ford that is investigating computing plat
          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
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          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page      
Database architecture, Big Data, Machine Learning, Business Intelligence, Advanced Analytics, Data Mining, ETL. Internal teammate application guidelines:....
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          Nlyte Software Gets FedRAMP Certification for Machine Learning Tool      Cache   Translate Page      
Nlyte Software has received a Federal Risk and Authorization Management Program certification for its cognitive data center infrastructure management platform. Nlyte Machine Learning leverages the artificial intelligence capabilities of the IBM Watson IoT platform to help agencies meet the federal government’s security requirements and comply with the Data Center Optimization Initiative, Nlyte said Thursday. “With this […]
          Driving identity security in banking using biometric identification      Cache   Translate Page      

Combining biometric identification with artificial intelligence (AI) enables banks to take a new approach to verifying the digital identity of their prospects and customers. Biometrics is the process by which a person’s unique physical and personal traits are detected and recorded by an electronic device or system as a means of confirm identity. Biometric identifiers are unique to individuals, so they are more reliable in confirming identity than token and knowledge-based methods, such as identity cards and passwords. Biometric identifiers are often categorized as physiological identifiers that are related to a person’s physicality and include fingerprint recognition, hand geometry, odor/scent, iris scans, DNA, palmprint, and facial recognition.

image

But how do you ensure the effectiveness of identifying a customer when they are not physically in the presence of the bank employee? As the world of banking continues to go digital, our identity is becoming the key to accessing these services. Regulators require banks to verify that users are who they say they are, not bad actors like fraudsters or known money launderers. And verifying identities online without seeing the person face to face is one of the biggest challenges online and mobile services face today.

It’s problematic because identity documents were created to be verified in person. For example, you can shine an infrared light, you can feel the texture, or you can see if a photo has been stuck on. But with remote verification, you’re just dealing with an image. Without the physical artifact, you only have the human eye to rely on and this makes the task of verification much harder to do quickly, or accurately.

A complicating factor is the online user experience. Users expect a fast, frictionless process in all that they do. If they have to wait, or if the process is too fiddly, they’ll go elsewhere. In the banking sector, almost half of all people who start opening an online bank account drop off due to a bad user experience.

This is where identity assurance is needed for online and mobile onboarding processes. Identity assurance is the ability for the bank to determine, with a high level of certainty, that an electronically provided credential representing a person can be trusted to serve as a proxy for that individual and not someone else. Assurance levels (ALs) are levels of trust associated with a credential as measured by the supporting technology, processes, procedures, policies, and operational practices.

To facilitate the assurance part of the customer onboarding process, the bank must have an innovative identity verification technology (IVT) to ensure that customers provide information that is associated with the identity of a real person. Physical authenticity identity documents like passports, drivers permits, and other documents are used to compare against government or service databases. Fraudsters continually strive to debunk bank processes to perform account takeovers, system infiltrations, and unauthorized transactions. Fraud detection is tough! In many cases it’s easy to alter content, images, and verification digits of common identification resources.

To combat these methods, Microsoft has many partners leveraging our Azure Cognitive Services – Vision API Platform and Azure Machine Learning. One such partner Onfido, provides a multi-factor identity verification service that helps accurately verify online users, uses a cloud-based risk assessment platform that leverages artificial intelligence to automate and scale traditionally human-based fraud expertise to derive identity assurance. The Onfido service validates physical identity documents (document validation), verifies biometric inputs (biometric identity verification), and analyzes information an end user provides about themselves (ID validation). These techniques give companies a measurable assurance that the person is who they say they are.

Biometric identity verification

Onfido verifies identities through two factor verification:

  • Something you have, such as a government issued ID (driver’s license, passport or ID card).
    • Document validation answers this question. Is it authentic?
  • Something you are, such as your facial biometrics.
    • Feature and attribute validation answers these questions. Is there a match, and are they alive?
  • Biometric identity is quite robust, an identity document is the most legally binding proof of identity, while remaining user friendly. The face is the easiest biometric to capture using mobile devices.
    • In figure below, the first step is to verify that a document is genuine. Onfido has several different algorithms to test for different fraud techniques.
    • The second step is to match the photo on the document with the selfie taken by the customer. Rather than take a static selfie, customers can also choose a video option which asks the user to perform randomised movements such as turning their head and voice commands. This prevents using deceitful practices, impersonation, or spoofing attempts. 

image

Figure 2

Harnessing the power of Microsoft Azure's Cognitive Services, Onfido helps clients adhere to their due diligence requirements via an effective, compliant and robust digital verification experience.

Together, Microsoft and Onfido deliver an easy onboarding experience for users through a scalable and automated process. The solution addresses compliance needs and reduces fraud costs associated with identity theft. This helps our clients to build trust and integrity within their community.

Want to learn more about combating online and mobile fraud? First, read the Detecting Online and Mobile Fraud with AI use case providing actionable recommendations and solutions. This will provide information on solutions and guidance for you to get started, as well as information on many other partners that also provide identity verification solutions.

Make sure you also check out more Azure partners on the Azure Marketplace. Then, engage with the author on this topic by reaching out to me via LinkedIn and Twitter.


          Making HIPAA and HITRUST compliance easier      Cache   Translate Page      

Many healthcare organizations are starting to adopt artificial intelligence (AI) systems to gain deeper insight into operations, patient care, diagnostic imaging, cost savings and so on. However, it can sometimes be daunting to even know where to get started. Many times, you need a clear lighted path to start your journey and embrace AI and machine learning (ML) capabilities rapidly.

image

One method is using an Azure Healthcare AI blueprint. It’s a shortcut to using Microsoft Azure at low cost and without deep knowledge of cloud computing. Blueprints include resources such as example code, test data, security, and compliance support. The largest advantage of using a blueprint is explicit advice and clear instructions on keeping your solution in compliance. We’re trying to eliminate the mystery, so you don’t have to research it yourself.

Three core areas where the blueprint can help with compliance are cloud provider and client responsibilities, security threats, and regulatory compliance. These three areas can get overlooked at the beginning of any technology project, yet they are important parts of creating healthcare systems. Applying formal discipline to these areas is made easier by using the blueprint to create an AI/ML experiment installation.

Helpful artifacts

The blueprint includes a script to create an AI/ML system, complete with a sample experiment. It also includes several documents to help system implementers keep their installations secure and compliant. These include worksheets, whitepapers, and spreadsheets that will help you ensure system compliance with healthcare regulations and certifications. The artifacts are easily re-purposed for other healthcare-based systems implemented on Azure.

Clarifying responsibilities

When creating any system on a cloud platform, there are two possible owners for any part of the solution, the cloud provider and the customer. It is important to know who is responsible for specific actions, services, and other operational details. Without a clear understanding of this delineation, customers or vendors may find themselves in a difficult situation if an issue arises, like service outages or security breaches. Therefore, it is in everyone’s interest to be clear about the responsibilities of design and operations.

Preventing misunderstandings and setting clear expectations of responsibilities is the goal of the Shared Responsibilities for Cloud Computing document. If you are trying to meet HITRUST certification standards, the HITRUST Customer Responsibilities Matrix spreadsheet identifies exactly what Microsoft and the customer are respectively responsible for managing.

Planning for security threats

Before creating complex systems, it is always advisable to perform a threat assessment. It is a best practice to create a threat assessment model. It helps you to visualize the system and find the points of vulnerability in the proposed architecture. This leads to conversations about where the system may be improved and hardened against attacks.

Microsoft provides a Threat Model Tool enabling architects to identify and mitigate potential security issues early, when they are relatively easy and cost-effective to resolve. The blueprint includes a model to be used with the tool. This comprehensive threat model provides insights into the potential risks of the architecture and how they may be mitigated.

A standard approach to security threat analysis involves identifying the surface area of your system, creating a model of that surface area, identifying potential threats, mitigating them and validating each mitigation, updating the threat model as you proceed. The following diagram highlights the major phases this process.

The figure below shows four stages: diagram, identify, mitigate, and validate.

image

Figure 1: Security cycle

This process flow provides an iterative and collaborative approach to threat analysis that ultimately helps create a more robust and secure system architecture.

Regulatory compliance

Healthcare systems need to meet regulatory compliance standards. At installation, the blueprint complies with HIPAA and HITRUST requirements. Whitepapers are included to help you understand how to continue to meet these requirements. Let’s examine the whitepapers and other provided artifacts to see how they might help.

HITRUST certification

The Common Security Framework (CSF) from HITRUST is a security standard for healthcare systems. The HITRUST compliance review whitepaper was published to aid in ensuring the healthcare blueprint meets CSF regulations. The whitepaper states:

“This whitepaper constitutes a review of the Blueprint architecture and functionality with respect to HITRUST-certified customer environments, examining how specifically it can satisfy HITRUST CSF security requirements.”

The whitepaper helps organizations plan their cloud implementation and understand how to meet HITRUST CSF compliance.

HIPAA compliance built into the blueprint

Compliance with HIPAA standards is fundamental to any healthcare organization. The blueprint was created with HIPAA in mind, and includes a whitepaper covering the topic in detail.

The HIPAA compliance review whitepaper is similar to the HITRUST whitepaper in its intent, to help organizations reach regulatory compliance. This document guides readers through the architecture, a shared responsibility model and deployment considerations for your solution. Protected healthcare information (PHI), a fundamental practice in well-designed system architectures, is also included in the whitepaper.

Recommended next steps

Use the supporting collateral below to prepare for your installation of the blueprint. The artifacts demonstrate how responsibilities, compliance, and security are established and how you can maintain them going forward.

Prepare for installation and ongoing maintenance with the following documents.

Collaboration

What other artifacts or considerations do you think would be helpful when putting healthcare systems into production? Your comments and recommendations are welcome below. I regularly post on technology in healthcare topics. Reach out and connect with me on LinkedIn or Twitter.


          Data Analyst, International - Bandwidth - Raleigh, NC      Cache   Translate Page      
Machine learning/AI techniques, features, and classifiers. Simply changing the way people communicate, connect and do business....
From Bandwidth - Thu, 04 Oct 2018 16:32:34 GMT - View all Raleigh, NC jobs
          Data Engineer 2 - IMO - Intelligent Medical Objects, Inc. - Northbrook, IL      Cache   Translate Page      
Familiarity with machine learning methods, such as clustering analysis and neural networks. Downtown commuters will enjoy free shuttle service to IMO’s...
From IMO - Intelligent Medical Objects, Inc. - Mon, 24 Sep 2018 17:52:43 GMT - View all Northbrook, IL jobs
          Machine Learning / Algorithim Developer - TECHNICA CORPORATION - Dulles, VA      Cache   Translate Page      
Job Description: We are seeking a highly creative software engineer experienced in artificial intelligence and deep learning techniques to design, develop,...
From Technica Corporation - Fri, 05 Oct 2018 10:31:19 GMT - View all Dulles, VA jobs
          Machine Learning Engineer - TECHNICA CORPORATION - Dulles, VA      Cache   Translate Page      
Technica Corporation is seeking a Machine Learning. Engineer to support our internal Innovation, Research....
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          Seminar 217, Risk Management: Robust Learning: Information Theory and Algorithms, Oct 9      Cache   Translate Page      
This talk will provide an overview of recent results in high-dimensional robust estimation. The key question is the following: given a dataset, some fraction of which consists of arbitrary outliers, what can be learned about the non-outlying points? This is a classical question going back at least to Tukey (1960). However, this question has recently received renewed interest for a combination of reasons. First, many of the older results do not give meaningful error bounds in high dimensions (for instance, the error often includes an implicit sqrt(d)-factor in d dimensions). Second, recent connections have been established between robust estimation and other problems such as clustering and learning of stochastic block models. Currently, the best known results for clustering mixtures of Gaussians are via these robust estimation techniques. Finally, high-dimensional biological datasets with structured outliers such as batch effects, together with security concerns for machine learning systems, motivate the study of robustness to worst-case outliers from an applied direction.

The talk will cover both information-theoretic and algorithmic techniques in robust estimation, aiming to give an accessible introduction. We will start by reviewing the 1-dimensional case, and show that many natural estimators break down in higher dimensions. Then we will give a simple argument that robust estimation is information-theoretically possible. Finally, we will show that under stronger assumptions we can perform robust estimation efficiently, via a "dual coupling" inequality that is reminiscent of matrix concentration inequalities.
          Principal Program Manager - Microsoft - Redmond, WA      Cache   Translate Page      
Our internal customers use machine learning models to analyze multi-exabyte datasets. The Big Data team builds solutions that enable customers to tackle...
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          Software Development Manager - Core Video Delivery Technologies, Prime Video - Amazon.com - Seattle, WA      Cache   Translate Page      
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 - Chicago, IL      Cache   Translate Page      
DevOps, Big Data, Machine Learning, Serverless computing etc. High level of comfort communicating effectively across internal and external organizations....
From Amazon.com - Fri, 13 Jul 2018 07:54:23 GMT - View all Chicago, IL jobs
          Solutions Architect - Amazon Web Services - Amazon.com - San Francisco, CA      Cache   Translate Page      
DevOps, Big Data, Machine Learning, Serverless computing etc. High level of comfort communicating effectively across internal and external organizations....
From Amazon.com - Sun, 02 Sep 2018 07:30:50 GMT - View all San Francisco, CA jobs
          Machine Learning Software Developer - Huawei Canada - Montréal, QC      Cache   Translate Page      
We thank all applicants for their interest in career opportunities with Huawei. ML Software developer....
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          MACHINE LEARNING HARDWARE RESEARCHER OR DEVELOPER - Huawei Canada - Montréal, QC      Cache   Translate Page      
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Hardware Researcher or Developer....
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          Interne en apprentissage automatique / Machine Learning Intern - Huawei Canada - Montréal, QC      Cache   Translate Page      
Situés à Hong Kong, Shenzhen, Péking, Londres, Paris, Montréal, Toronto et Edmonton, les laboratoires Noah’s Ark forment le laboratoire de recherche phare de...
From Huawei Canada - Tue, 18 Sep 2018 17:46:31 GMT - View all Montréal, QC jobs
          MACHINE LEARNING ENGINEER FOR SPEECH RELATED APPLICATIONS - Huawei Canada - Montréal, QC      Cache   Translate Page      
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      
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Intern for Speech related Applications....
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          5 Ways Banks Can Combat Phishing       Cache   Translate Page      
5 Ways Banks Can Combat Phishing phil.goldstein_6191 Tue, 10/09/2018 - 08:21

More than a third — 35.7 percent — of the 107 million attempts to visit phishing pages halted by Kaspersky Lab technologies in the second quarter of 2018 were related to financial services, the company recently reported.

Customers were targeted primarily via fraudulent banking or payment pages, the report states. Those insights point to the need for consumers to exercise extreme caution when browsing online banking sites. Attacks on financial organization customers, including banks, payment systems and e-commerce transactions, remain a continuing trend in cybercrime. Typically, those crimes result in theft of money as well as personal data

“The permanence of attacks targeting financial organizations reflects the fact that more and more people use electronic money,” observes Nadezda Demidova, lead web content analyst at Kaspersky Lab. “Still, not all of them are sufficiently aware of the possible risks, so intruders are actively trying to steal sensitive information through phishing.”

While there’s a lot of good information available to help customers improve their security awareness, what can banks do to better protect themselves from ever-increasing phishing threats? 

“Phishing techniques are similar across all industries, but it’s clear that some organizations — like banks — can experience more immediate and severe repercussions from a successful attack, just based on the nature of their business and the sensitivity of the customers they support,” says Gretel Egan, security awareness and training strategist for Wombat Security, a division of Proofpoint

In speaking to Egan and Steven D’Alfonso, a research director at IDC Financial Insights, we developed the following list of five tips for banks and credit unions:

MORE FROM BIZTECH: Discover why advanced video surveillance tech is a solid investment for banks! 

1. Identify Staff Who Have Opportunity and Access

Banks should identify staff members beyond the C-suite and management teams who have access to customer information and other business-critical data and systems, because cybercriminals certainly have, Egan says. Attackers mine social media and public websites, among other sources, to identify key individuals, such as loan officers, before targeting them directly. 

2. Get Serious About Security Awareness Training

Employees who transfer funds regularly, manage sensitive data or participate in important business functions need additional training on how to spot and avoid more sophisticated phishing traps, Egan says. Cybercriminals frequently exploit employees’ fear and anxiety to solicit a quick (or unsafe) responses via email targeting. It’s a good idea for users to ask themselves these questions about any email they receive: Was I expecting this message? Does this email make sense? Am I being pushed to act hastily or out of fear? Does this seem too good to be true? What if this is a phishing email?

3. Focus on Public-facing Information

Bank and credit union technology teams should communicate with marketing and C-suite teams about the potential hazards of sharing company details on public channels such as public-facing websites or social media, Egan advises. That can be a double-edged sword in the banking world, where organizations strive to make it easy for customers to contact them, while still offering protection from cybercriminals and social engineers. Egan cautions that if information such as general email aliases, phone numbers, or lists of bank staff and their roles are visible publicly, cybercriminals will use the information to launch phishing attacks. IT staff should monitor all inbound email channels (even aliases) and train personnel who respond to inbound communications to recognize and avoid malicious messages.

4. Deploy Products that Analyze Malwarelike Behavior

Banks should consider deploying tools such as IBM’s Trusteer Rapport, which uses advanced analytics and machine learning to analyze suspicious behavior, increasing the chances that the software will detect and remove malware before it can infect a computer or broader network, IDC’s D’Alfonso says. Many banks now offer Trusteer as a free service for users to download before continuing any e-commerce functions. 

5. Consider Continuous Authentication

Behavior biometric products that feature continuous authentication can detect nonauthorized users, such as a fraudster or a bot, D’Alfonso says. Such new tools help users safely authenticate and transfer money or pay bills, while continuous authentication keeps watch during every step of the process. 

Staying on top of phishing requires consistent training and adjustments on the part any organization looking to combat ever-evolving threats. And while new behavioral and analytics tools can help, banks and credit unions can also achieve a great deal simply by keeping better tabs on employees and offering frequent training updates.

Cybersecurity-report_EasyTarget.jpg


          Wireless Wars: When Censors Control the Sensors      Cache   Translate Page      
Op-Ed by Patricia Burke The conveniences of the emerging explosion of artificial intelligence, machine-to-machine learning, and ubiquitous “5G” fifth-generation connectivity of the 4th industrial revolution...
          2018-10-09 Tuesday - Early Access: Model Based Machine Learning (book)      Cache   Translate Page      

http://mbmlbook.com/
by John Winn and Christopher Bishop, with Thomas Diethe, John Guiver and Yordan Zaykov
          Instagram Will Use Machine Learning to Help Tackle Cyberbullying      Cache   Translate Page      
This is notable in light of the platform's rank as the number one social network for cyberbullying, especially among young people.
          Self-Organizing Maps for Selecting Hedge Funds      Cache   Translate Page      
A new paper by Claus Huber, of Rodex Risk Advisers, looks at machine learning for risk analysis, working especially from the “self-organizing maps” associated with Finnish Professor Teuvo Kohonen. A SOM is a low-dimensional representation of input space (thinking of it as two dimensional makes the “map” analogy intuitive, andRead More
          דרוש מהנדס אלגוריתמי Computational Geometry/3D/ (רצוי למידה עמוקה או למידת מכונה)      Cache   Translate Page      
מהנדס אלגוריתמי Computational Geometry/3D (רצוי למידה עמוקה או למידת מכונה).במשרה מלאה, לא מהביתחובה:נסיון בפיתוח אלגוריתמי Computational Geometry/D3/CADC++ או מטלאב או פייתוןM.Sc. או תואר ראשון עם המון נסיון בפיתוח אלגוריתמים כנ"ל.רצוי:Computer VisionDeep Learning or Machine Learning
          Catalysing Growth: SME Interview Series with Validus Capital      Cache   Translate Page      
Validus is a successful, Singapore-based startup that specialises in crowdfunded business loans. So far, the platform has generated S$132,824,551 in business loans for Singapore’s SMEs. Validus leverages its expertise in data analytics, machine learning and artificial intelligence (AI) in order to minimise fees and logistical hurdles for both borrowers and investors. We reached out to […]
          Consultant MSBI Senior (H/F) – Montréal – Perm – Jusqu’à 100k CAD - Elitesoft - Montréal, QC      Cache   Translate Page      
Canada USA US Montréal Québec Azure Machine Learning Azure HD insights MSBI Microsoft BI Business Intelligence SSIS SSRS SSAS Power BI Cubes OLAP... $60,000 - $100,000 a year
From Indeed - Fri, 28 Sep 2018 14:24:37 GMT - View all Montréal, QC jobs
          Machine Learning Software Developer - Huawei Canada - Montréal, QC      Cache   Translate Page      
We thank all applicants for their interest in career opportunities with Huawei. ML Software developer....
From Huawei Canada - Fri, 05 Oct 2018 05:47:02 GMT - View all Montréal, QC jobs
          MACHINE LEARNING HARDWARE RESEARCHER OR DEVELOPER - Huawei Canada - Montréal, QC      Cache   Translate Page      
We thank all applicants for their interest in career opportunities with Huawei. Machine Learning Hardware Researcher or Developer....
From Huawei Canada - Fri, 05 Oct 2018 05:47:02 GMT - View all Montréal, QC jobs
          Interne en apprentissage automatique / Machine Learning Intern - Huawei Canada - Montréal, QC      Cache   Translate Page      
Situés à Hong Kong, Shenzhen, Péking, Londres, Paris, Montréal, Toronto et Edmonton, les laboratoires Noah’s Ark forment le laboratoire de recherche phare de...
From Huawei Canada - Tue, 18 Sep 2018 17:46:31 GMT - View all Montréal, QC jobs
          MACHINE LEARNING ENGINEER FOR SPEECH RELATED APPLICATIONS - Huawei Canada - Montréal, QC      Cache   Translate Page      
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      
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
          Data Analyst, International - Bandwidth - Raleigh, NC      Cache   Translate Page      
Machine learning/AI techniques, features, and classifiers. Simply changing the way people communicate, connect and do business....
From Bandwidth - Thu, 04 Oct 2018 16:32:34 GMT - View all Raleigh, NC jobs
          Data Engineer 2 - IMO - Intelligent Medical Objects, Inc. - Northbrook, IL      Cache   Translate Page      
Familiarity with machine learning methods, such as clustering analysis and neural networks. Downtown commuters will enjoy free shuttle service to IMO’s...
From IMO - Intelligent Medical Objects, Inc. - Mon, 24 Sep 2018 17:52:43 GMT - View all Northbrook, IL jobs
          Senior Software Development Engineer - Distributed Computing Services (Hex) - Amazon.com - Seattle, WA      Cache   Translate Page      
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      
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      
Assemble large, complex data sets that meet business requirements and power machine learning algorithms. EC2, Lambda, ECS, S3.... $30 - $40 an hour
From Indeed - Tue, 18 Sep 2018 14:44:55 GMT - View all Radnor, PA jobs
          Executive Director- Machine Learning & Big Data - JP Morgan Chase - Jersey City, NJ      Cache   Translate Page      
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 C/C++ Software Engineer/Machine Learning - Mobica - Warszawa, mazowieckie      Cache   Translate Page      
I hereby agree for processing of personal data by Mobica Limited with headquarter in Crown House, Manchester Road, Wilmslow, UK, SK9 1BH whose representative is...
Od Mobica - Fri, 14 Sep 2018 21:05:32 GMT - Pokaż wszystkie Warszawa, mazowieckie oferty pracy
          Senior C/C++ Software Engineer/Machine Learning - Mobica - Warszawa, mazowieckie      Cache   Translate Page      
I hereby agree for processing of personal data by Mobica Limited with headquarter in Crown House, Manchester Road, Wilmslow, UK, SK9 1BH whose representative is...
Od Mobica - Fri, 14 Sep 2018 21:05:32 GMT - Pokaż wszystkie Warszawa, mazowieckie oferty pracy
          HomeAdvisor Partners With Grid4C      Cache   Translate Page      
Golden, Colorado-based home services marketplace HomeAdvisor has linked up with a developer of predictive analytics and machine learning software for the utility industry.... (more)
          Edifecs Experts to Discuss Machine Learning and AI for Payer, Provider and Government Organizations      Cache   Translate Page      
...2018] Edifecs Experts to Discuss Machine Learning and AI for Payer, Provider and Government Organizations Edifecs, Inc. , a global health information technology solutions company, is hosting an online and in-person session with the Massachusetts Health Data Consortium on October 10 ...

          How AI Could Affect Recruiting in the Next Few Years      Cache   Translate Page      
If you used a search engine to find this post, you used AI. Artificial intelligence (and its close cousin, machine learning) is everywhere in our daily lives. For example, we use it to avoid traffic snarls during the morning rush, filter out spam from our inboxes, deposit checks without stopping by the bank … … and […]
          Artificial Intelligence (AI) Robots Market Technology Insights, Overview, Sales, and Forecast 2018 to 2025      Cache   Translate Page      

Latest research study and detailed insights on "Global Artificial Intelligence (AI) Robots Market Insights, Forecast to 2025."

Albany, NY -- (SBWIRE) -- 10/09/2018 -- The global artificial intelligence (AI) robots market report, offers in-depth insights, revenue details, and other vital information regarding the global artificial intelligence (AI) robots market and the various trends, drivers, restraints, opportunities, and threats in the target market till 2025. The report also offers insightful and detailed information regarding the various key players operating in the global artificial intelligence (AI) robots market, and their financials, apart from strategies, acquisitions & mergers, and market footprint. The global artificial intelligence (AI) robots market is segmented on the basis of type, component, application area, and region.

The Artificial Intelligence (AI) Robots market was valued at Million US$ in 2017 and is projected to reach Million US$ by 2025, at a CAGR of during the forecast period. In this study, 2017 has been considered as the base year and 2018 to 2025 as the forecast period to estimate the market size for Artificial Intelligence (AI) Robots.

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This report presents the worldwide Artificial Intelligence (AI) Robots market size (value, production and consumption), splits the breakdown (data status 2013-2018 and forecast to 2025), by manufacturers, region, type and application.

This study also analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, distributors and Porter's Five Forces Analysis.

The following manufacturers are covered in this report:
ABB
Alphabet
Amazon
Asustek Computer
Blue Frog Robotics
Bsh Hausgerte
Fanuc
Hanson Robotics
Harman International Industries
IBM
Intel
Jibo
Kuka
LG
Mayfield Robotics
Microsoft
Neurala
Nvidia
Promobot
Softbank
Xilinx

Artificial Intelligence (AI) Robots Breakdown Data by Type
by Robot Type
Service
Industria
by Offering
GPU
MPU
by Technology
Machine Learning
Computer Vision

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Artificial Intelligence (AI) Robots Breakdown Data by Application
Public Relations
Stock Management

Artificial Intelligence (AI) Robots Production by Region
United States
Europe
China
Japan
Other Regions

Artificial Intelligence (AI) Robots Consumption by Region
North America
United States
Canada
Mexico
Asia-Pacific
China
India
Japan
South Korea
Australia
Indonesia
Malaysia
Philippines
Thailand
Vietnam
Europe
Germany
France
UK
Italy
Russia
Rest of Europe
Central & South America
Brazil
Rest of South America
Middle East & Africa
GCC Countries
Turkey
Egypt
South Africa
Rest of Middle East & Africa

The study objectives are:
To analyze and research the global Artificial Intelligence (AI) Robots status and future forecastinvolving, production, revenue, consumption, historical and forecast.
To present the key Artificial Intelligence (AI) Robots manufacturers, production, revenue, market share, and recent development.
To split the breakdown data by regions, type, manufacturers and applications.
To analyze the global and key regions market potential and advantage, opportunity and challenge, restraints and risks.
To identify significant trends, drivers, influence factors in global and regions.
To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market.

In this study, the years considered to estimate the market size of Artificial Intelligence (AI) Robots :
History Year: 2013 - 2017
Base Year: 2017
Estimated Year: 2018
Forecast Year: 2018 - 2025

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This report includes the estimation of market size for value (million USD) and volume (K Units). Both top-down and bottom-up approaches have been used to estimate and validate the market size of Artificial Intelligence (AI) Robots market, to estimate the size of various other dependent submarkets in the overall market. Key players in the market have been identified through secondary research, and their market shares have been determined through primary and secondary research. All percentage shares, splits, and breakdowns have been determined using secondary sources and verified primary sources.

For the data information by region, company, type and application, 2017 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

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          The Origins and History of Machine Learning      Cache   Translate Page      

Before this article gets on to the details of machine learning, it will enlighten the reader about the definition and concepts of machine learning. Some of the researchers and studies suggest that machine learning is a technique of artificial intelligence where the system automatically learns from information and data. IT professionals define machine learning as […]

The post The Origins and History of Machine Learning appeared first on Imarticus.


          Leading Market Players Are Planning to Dominate the Global and Regional Markets Through New Strategic Tie-Ups and Innovations      Cache   Translate Page      

Recent trends in North America include convergence of new and advanced technologies with existing technologies to enhance operations in different verticals such as automobiles and aerospace & defence.

Valley Cottage, NY -- (SBWIRE) -- 10/09/2018 -- A recent market research report by Future Market Insights presents a thorough overview of the global market for neuromorphic chip. The report is titled "Neuromorphic Chip Market: Global Industry Analysis and Opportunity Assessment, 2016-2026." The report states that the market will exhibit a promising CAGR of 20.7% from 2016 to 2026, rising from a valuation of US$1,420 mn in 2015 to a revenue opportunity of US$10,810 mn by the end of 2026.

Promise of a significant leap of improvement in terms of operational prowess, speed of processing, and better suitability to artificial intelligence applications are pushing ahead developments in the field of neuromorphic chips. The vast rise in the scope of applications of these chips across industries such as automotive, defense and military, aerospace, and machine learning is the key factor expected to drive the global neuromorphic chip market in the next few years.

Request for sample copy of report @: https://www.futuremarketinsights.com/reports/sample/rep-gb-1289

In terms of incremental opportunity across key regional markets covered in the report, the market in North America is expected to emerge as the most profitable over the report's forecast period. Owing to the thriving automotive, aerospace, and consumer electronics industries, growth opportunities are vast in the region. Moreover, the region is also home to some of the world's leading chipmakers and technology companies, making it the hub for technological developments in the area of neuromorphic chips.

The report states that the North America neuromorphic chip market will exhibit a promising 19.7% CAGR over the report's forecast period, representing an incremental opportunity of US$2,300 mn from 2016 to 2026. If the numbers hold true, the market will rise from a valuation of US$400 mn in 2015 to US$2,700 mn by the end of 2026. Several industries in the region are promoting the convergence advanced, new technologies such as neuromorphic chips with existing technologies so as to improve productivity and efficiency of operations. One of the most innovative usages of neuromorphic chips is being witnessed in the field of unmanned drones. Neuromorphic chips in drones are being tested for their ability in enabling drones to recognize any defined space visited earlier by storing and processing signal patterns emerging from surroundings of that space.

In terms of application, the report segments the global neuromorphic chip market into image recognition, data mining, and signal recognition. The key end-use industries of neuromorphic chips examined in the report are aerospace and defense, automotive, healthcare, consumer electronics, and industrial. Demand is expected to be promising across all these end-use sectors in the next few years, with the automotive and defense and aerospace sectors expected to remain at the forefront in terms of adoption of neuromorphic chips in the next few years. In the automotive industry, neuromorphic chips, in conjunction with technologies such as signal processing, could help drivers in understanding their immediate surroundings better thus helping them take better decisions.

Request to view Table of Content @: https://www.futuremarketinsights.com/askus/rep-gb-1289

Other industries are also increasingly representing growth opportunities for neuromorphic chips and are expected to help the market expand at a promising pace over the report's forecast period. The vendor landscape has started becoming crowded and companies, with the aim of strengthening their hold on the market and making the most of available opportunities, have started focusing on strategic collaborations. Some of the leading companies presently operating in the market are IBM Corporation, Hewlett Packard Labs, HRL Laboratories, LLC, General Vision, and Intel Corporation.

About Future Market Insights
Future Market Insights is the premier provider of market intelligence and consulting services, serving clients in over 150 countries. FMI is headquartered in London, the global financial capital, and has delivery centres in the U.S. and India.

FMI's research and consulting services help businesses around the globe navigate the challenges in a rapidly evolving marketplace with confidence and clarity. Our customised and syndicated market research reports deliver actionable insights that drive sustainable growth. We continuously track emerging trends and events in a broad range of end industries to ensure our clients prepare for the evolving needs of their consumers.

For more information on this press release visit: http://www.sbwire.com/press-releases/leading-market-players-are-planning-to-dominate-the-global-and-regional-markets-through-new-strategic-tie-ups-and-innovations-1061022.htm

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Abhishek Budholiya
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          Machine Learning/AI Engineer - Groom & Associates - Montréal, QC      Cache   Translate Page      
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, 06 Sep 2018 08:57:48 GMT - View all Montréal, QC jobs
          Data Scientists / AI & Machine Learning Engineer - IVADO Labs - Montréal, QC      Cache   Translate Page      
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, 11 Aug 2018 03:14:21 GMT - View all Montréal, QC jobs
          Artificial Intelligence/Machine Learning Lead - Dutech Systems Inc - Round Rock, TX      Cache   Translate Page      
Requirements - Bachelor of Science in Information Technology or equivalent with 10+ years of overall IT experience - 2-5 years AI work experience - Experience...
From Indeed - Mon, 08 Oct 2018 18:49:42 GMT - View all Round Rock, TX jobs
          Business Analytics Masters Intern (Summer 2019) - DELL - Round Rock, TX      Cache   Translate Page      
Life at Dell. Ability to use machine learning techniques. The business analytics intern will be working on a problem which would require detailed analytics and...
From Dell - Fri, 05 Oct 2018 11:17:11 GMT - View all Round Rock, TX jobs
          Compliance Consultant - DELL - Round Rock, TX      Cache   Translate Page      
Apply a broad range of techniques and theories from business intelligence, data analysis, statistics, predictive analytics and machine learning to deliver...
From Dell - Fri, 07 Sep 2018 11:18:03 GMT - View all Round Rock, TX jobs
          Director, Software Engineering - DELL - Austin, TX      Cache   Translate Page      
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      
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
          Desenvolvedor Java para projetos de IA e Machine Learning - Hop - Belo Horizonte, MG      Cache   Translate Page      
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
          Del Google Pixel 2 al Google Pixel 3: todo lo que ha cambiado      Cache   Translate Page      

Del Google Pixel 2 al Google Pixel 3: todo lo que ha cambiado#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Los Pixel no se presentan en solitario. Es algo que Google nos enseñó hace ya dos generaciones y que permanece imborrable en nuestro recuerdo. Como también es imborrable que tenemos cita con el gigante de Mountain View en el mes de octubre para conocer su nueva propuesta. Hoy, la tercera generación de sus dispositivos, los que reemplazaron a los Nexus y que hoy se plasman en los nuevos Google Pixel 3 y Google Pixel 3 XL.

Como ocurrió el pasado año, la renovación de los Pixel se ha basado en una iteración bastante común, actualizando los componentes principales, aunque también hay novedades. Como el hecho de que la cámara frontal se duplica, algo que curiosamente no ocurre con la trasera. Pero vayamos paso a paso y veamos qué ha ocurrido en este salto generación, la evolución de los Pixel 2 a los Pixel 3. ¿Nos acompañas?

Móviles de 2018, cerebros de 2018

Snapdragon 845#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

El salto lógico en estos nuevos Google Pixel 3 y Pixel 3 XL es el de la actualización de sus procesadores. En la pasada generación, los Pixel 2 se vistieron con el Snapdragon 835, el chip predominante en el catálogo de Qualcomm en aquel momento, pese a que un par de meses después afloraría su sustituto, el Snapdragon 845 que se presenta en esta generación.

El nuevo cerebro supone mantenerse en los 10 nanómetros, pero ganar en potencia bruta y, sobre todo, en capacidad de procesamiento de código de inteligencia artificial. Un chip mejor preparado para ejecutar códigos de machine learning y deep learning, favoritos de Google desde hace tiempo para sus Pixel y para otras apps de la casa, como Google Photos o Google Assistant.

Tampoco hay salto en el equipo de memorias, con 4GB de modelo básico con 64GB y 128GB en el almacenamiento. Como en la generación pasada, aquí tampoco hay ranura para expandir la tarjeta microSD, por lo que será mejor tener claro qué capacidad necesitaremos para no andar después lamentándonos de no poder ampliarla. Como vemos, un líder de catálogo de 2018 con todas las de ley, aunque la RAM tal vez sea algo corta. Pero estando Google detrás, optimizando el sistema, puede no ser un factor diferencial.

Las pantallas crecen y se estiran

Pantalla#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Si hay un detalle estético que va a marcar la diferencia entre los Pixel 3 y los Pixel 2, ése va a ser el del recorte en la pantalla. El archiconocido 'notch', también llamado ceja y que aparece en esta renovación, aunque únicamente en el modelo XL. El pequeño se mantiene rectangular, aunque también estira al adoptar el aspecto 18:9, algo que ya debió ocurrir en el pasado Google Pixel 2.

El Pixel 3 estira hasta los 18:9 y el Pixel 3 XL va más allá, añadiendo el notch a su diseño de pantalla

Así pues, el Google Pixel 3 pasa de tener 5 pulgadas a 5,5 pulgadas, y estira la resolución FullHD hasta el FullHD+ pero manteniendo la densidad. Eso lo logra con el nuevo aspecto 18:9, que le permite llevar la resolución hasta los 2.160 x 1.080 píxeles, respetando los 460 píxeles por pulgada. Como en ocasiones anteriores, tendremos Gorilla Glass 5 protegiendo la pantalla, AMOLED, contra golpes y arañazos.

En cuanto al Pixel 3 XL, su cambio es el más sensible. El pasado año ya teníamos una pantalla 18:9, y ahora tenemos una 18,5:9. ¿Cómo? Pues añadiendo el ya citado 'notch'. Así que las 6 pulgadas del Pixel 2 XL se convierten ahora en 6,3 pulgadas, y la resolución QHD+ se estira algo más y alcanza los 2.960 x 1.440 píxeles. Más larga, más píxeles, una densidad semejante. Y claro, también tenemos Gorilla Glass 5 sobre este panel P-OLED.

Cámaras únicas en la espalda, y dobles en el frontal

Camaras#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

En un primer momento, resultó extraño que Google apostase únicamente por un sensor trasero para sus teléfonos, pues estaban diseñados para competir contra los mejores, y ya emergían con fuerza las cámaras duales con desenfoque selectivo. Pero Google ofreció este desenfoque con un único ojo, y volvió a hacerlo una generación después. También lo hace en esta tercera pero no en el frontal, pues sobre la pantalla se coloca una tercera lente.

Así, el salto en cámaras traseras se mantiene con una sencilla actualización de sensores pero con características casi gemelas. Los Pixel 3 y Pixel 3 XL repiten con sus 12,2 megapíxeles, con lentes de 27 milímetros y apertura f/1.8, estabilizadas ópticamente, y sus sensores son Dual Pixel y tienen píxeles de 1,4 micrómetros. Eso significa que tendremos vídeo 4K con cámara lenta, y el resto de funciones vendrá del apartado de software del teléfono.

Pero en el frontal la cosa se complica, y se duplica. De 8 megapíxeles en los Pixel 2 hemos pasado a 8 y 8 megapíxeles en los Pixel 3. Dos ojos sobre pantalla en el Pixel 3, y dentro de ella en el Pixel 3 XL por su notch. Dos sensores de ocho megapíxeles con lentes f/2.2 y apertura de 27 milímetros, con píxeles de 1,4 micrones y una gran diferencia, que aquí no hablamos de Dual Pixel. Eso sí, habrá desenfoque frontal, tendremos bokeh para los selfies. Y vídeo FullHD, claro está.

2018 es Android Pie, también los Pixel 3

Android Pie#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Al menos en el ecosistema propio de Google y no dependiente de él. Si los Pixel 2 llegaron con Oreo corriendo por sus venas, los nuevos Pixel 3 y Pixel 3 XL aterrizan con la nueva versión de su sistema, Android 9.0 Pie, lo que hace que los teléfonos cuenten con todos los avances del sistema, como el modo de bienestar digital que pretende que seamos conscientes de cuánto tiempo dedicamos al smartphone cada día.

También tendremos gestos para navegar por el teléfono, pudiendo realizar transiciones entre aplicaciones deslizando el dedo sobre la pantalla. También se estrenan las llamadas Slices, o porciones de información extra que el sistema extrae de las propias aplicaciones para poder lanzarlas desde el propio buscador del sistema. Por supuesto, no estarán ausentes los avances en gestión de batería, con el brillo inteligente que debe reducir el consumo tanto en primer como en segundo plano.

Algunas características inmutables, o casi

A pesar de que ha habido un salto generacional entre los Pixel 2 y los Pixel 3, algunas características han resistido el salto sin apenas cambiar. Como el marco táctil heredado de HTC, el Active Sense que se perpetúa en esta tercera generación de Pixel. O como el hecho de contar con lector de huellas en la espalda. O la conectividad, con WiFi 5, Bluetooth 5.0, GPS o USB tipo C. Incluso el jack de auriculares, que vuelve a estar ausente.

Los nuevos Pixel 3 y Pixel 3 XL mantienen la resistencia al agua IP67, después del IP53 de su primera generación, y sí hay pequeñas variaciones en las baterías. El Pixel 3 cuenta con una pila interna de 2.915 mAh y el 3 XL sube hasta los 3.430 mAh, parecidas a las de los Pixel 2. Ambas con carga rápida y con carga inalámbrica.

Google Pixel 2 vs Google Pixel 3, las características comparadas

Google Pixel 3 Xl#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Google Pixel 2

Google Pixel 3

Google Pixel 2 XL

Google Pixel 3 XL

Pantalla

5,0" FHD (1920 x 1080)
AMOLED 16:9

5,5" FHD+ (2160 x 1080)
AMOLED 18:9

6,0" QHD+ (2880 x 1440)
P-OLED 18:9

6,3" QHD+ (2960 x 1440)
P-OLED 18.5:9

Procesador

Snapdragon 835

Snapdragon 845

Snapdragon 835

Snapdragon 845

RAM

4 GB

4 GB

4 GB

4 GB

Almacenamiento

64 / 128 GB

64 / 128 GB

64 / 128 GB

64 / 128 GB

Cámara trasera

12.2 megapíxeles f/1.8
Píxeles de 1.4µm
OIS y EIS
Pixel Core
Vídeo 4K/30fps

12.2 megapíxeles f/1.8
Píxeles de 1.4µm
OIS y EIS
Pixel Core
Vídeo 4K/30fps

12.2 megapíxeles f/1.8
Píxeles de 1.4µm
OIS y EIS
Pixel Core
Vídeo 4K/30fps

12.2 megapíxeles f/1.8
Píxeles de 1.4µm
OIS y EIS
Pixel Core
Vídeo 4K/30fps

Cámara frontal

8 megapíxeles f/2.2
Píxeles de 1.4µm
Vídeo 1080p

8 megapíxeles f/2.2
8 megapíxeles f/1.8
Píxeles de 1.4µm
Vídeo 1080p

8 megapíxeles f/2.2
Píxeles de 1.4µm
Vídeo 1080p

8 megapíxeles f/2.2
8 megapíxeles f/1.8
Píxeles de 1.4µm
Vídeo 1080p

Batería

2.700 mAh
Carga rápida

2.915 mAh
Carga rápida
Carga inalámbrica

3.520 mAh
Carga rápida

3.450 mAh
Carga rápida
Carga inalámbrica

Dimensiones y peso

145.7 x 69.7 x 7.8 mm
143 gramos

145.6 x 68.2 x 7.9 mm
148 gramos

157.9 x 76.7 x 7.9 mm
175 gramos

-

Software

Android 8 Oreo (actualizado a Pie)

Android 9 Pie

Android 8 Oreo (actualizado a Pie)

Android 9 Pie

Otros

Sin jack
NFC
Lector de huellas trasero
Bluetooth 5.0
GPS
USB tipo C
Active Edge
WiFi 5 (ac)
Gorilla Glass 5
Protección IP67

Sin jack
NFC
Lector de huellas trasero
Bluetooth 5.0
GPS
USB tipo C
Active Edge
WiFi 5 (ac)
Gorilla Glass 5
Protección IP68

Sin jack
NFC
Lector de huellas trasero
Bluetooth 5.0
GPS
USB tipo C
Active Edge
WiFi 5 (ac)
Gorilla Glass 5
Protección IP67

Sin jack
NFC
Lector de huellas trasero
Bluetooth 5.0
GPS
USB tipo C
Active Edge
WiFi 5 (ac)
Gorilla Glass 5
Protección IP68

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La noticia Del Google Pixel 2 al Google Pixel 3: todo lo que ha cambiado fue publicada originalmente en Xataka Móvil por Samuel Fernández .


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          Hack / Protect / Predict SQL Server (Slides for SQL Saturday Orlando 801)      Cache   Translate Page      

Hi, I’m excited to share with you that I’ll be presenting this Saturday, October 6, 2018 at SQL Saturday Orlando. The most fun SQL Saturday event I’ve attended.It’s going to be my first time announcing this content on SQL Server 2019 (currently ctp 2) and SSMS 18 preview 4 . Plus, maybe playing SQL Family Feud.

I tend to pack a lot of content into my presentations so the audience can take the most home. I am sharing my slides here, prior to the presentation, so we can cover all the demos at the session and have ample time for Q&A.

Please join me at Room 1 (means To Be Announced) at Seminole State College (Sanford/Lake Mary Campus) building UP at 11 am for SQL Saturday Orlando 801 .

Slides

Title:Hack / Protect / Predict SQL Server Come learn them.

Speaker: Fleitas , Hiram

Duration: 60 minutes

Track: Application & Database Development

Level: Advanced

https://sqlsaturday.com/801/Sessions/Details.aspx?sid=83672

Abstract:

In this session, I’ll show you how to hack SQL Server using a simple C# console application and other tools. Most importantly, I’ll show you how to protect vectors that perhaps you’re trying to use to safeguard sensitive data for GDPR compliance.

Tabular Data Stream (TDS) Protocol Dynamic Data Masking Row Level Security (Yep…) Database Source Control

Perhaps, you’ve seen these exploits before but do you really know how to reproduce them? Or, how to even protect yourself against them? No worries, I’ll show you the way along with a load test.

Finally, I am very excited to share with you how to analyze text using pre-trained Machine Learning models to predict a sentiment, on-prem with SQL Server 2017.

SQL ML / AI A deep dive to predict the sentiment

Looking forward to meeting you all.

Bio:

Hiram Fleitasis the Principal Database Architect at Universal Property and Casualty Insurance Company and leads the company’s intelligent edge using Microsoft’s data platform. He currently is developing database applications using Machine Learning models trained on claims, policy, and social-media data to predict business opportunities for customer satisfaction and loyalty in real-time.

He has worked with SQL Server for 20 years, from version 6.0 to 2019 with some of the largest companies in the world. He’s spoken on SQL Server at User Groups, South Florida Code Camp, PASS SQL Saturdays and corporate business events, often presenting talks on security, performance, devops, machine learning, and business intelligence. He coded his first program in BASIC when he was 13 years old as a school project and developed a passion in computers ever since.

Hiram is also a code contributor to several opensource projects and serves as an IS Flotilla Staff Officer for the United States Coast Guard Auxiliary. On his time off he mostly enjoys spending time with his wife Christina and two kids, Ocean and Skylar Fleitas. He also wakeboards, wakesurfs, snowboards and does endurance training events by GORUCK’s Cadre-led decorated combat veterans of Special Operations.

https://linkedin.com/in/hiramfleitas/

https://fleitasarts.com

Date & Time:

Saturday Oct 6, 2018

11:00 am 12:00 pm Presentation

Location:

Seminole State College

Sanford/Lake Mary Campus

100 Weldon Blvd Building UP, Sanford, Florida, 32773

Room #: R1 (TBA)

Follow #SQLSatOrlando on Twitter

Follow @HiramFleitas on Twitter


          Google Pixel 3 y Pixel 3 XL, primeras impresiones: la pelea por la mejor cámara es más software que hardware      Cache   Translate Page      

Google Pixel 3 y Pixel 3 XL, primeras impresiones: la pelea por la mejor cámara es más software que hardware#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Hace unos días se cumplió un año desde la presentación del dúo Pixel 2 y hemos asistido a su renovación. Tras un imparable ataque de filtraciones por tierra, mar y aire, hoy por fin hemos podido conocer en persona a los Pixel 3 y 3 XL, una pareja de smartphones que mantiene la fórmula de los dos tamaños, el más grande, la versión XL, con un diseño de marcos más reducidos que este año trae una novedad un tanto polémica: un enorme notch que se lleva todo el protagonismo del frontal. Sin embargo, a diferencia del año pasado, esta vez sí tendremos los dos modelos en España, por lo que será posible huir del diseño con notch si optamos por el Pixel 3 más compacto.

La estrategia también se mantiene en la fotografía, el pilar sobre el que se sustenta la gama Pixel; ni el diseño ni la pantalla o el hardware, la cámara. Con los Pixel 2, Google consiguió la que para muchos (me incluyo) ha sido la mejor cámara móvil de 2018; todo con una sola lente acompañada de un software que hace maravillas. Los Pixel 3 siguen el mismo camino en lo que a la cámara principal se refiere, pero introduce la tan de moda dualidad en la cámara delantera. La guinda del pastel la pone el software, una experiencia Google sin igual y sus funciones exclusivas. Así es la apuesta de Google para seguir conservando su puesto en el podio del panorama móvil.

Diseño: el patito feo que se hizo cisne

Pixel 3 #source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000 Pixel 3 XL (izquierda) y Pixel 2 XL (derecha).

Empecemos con el Pixel 3 XL, el más grande y también el más polémico. Por detrás, el terminal es prácticamente igual que el modelo anterior. Amplio, con esquinas ligeramente redondeadas y esa combinación de dos acabados, brillante arriba y mate abajo. Pero ojo porque aunque a simple vista parezcan casi iguales, al sujetarlos en mano nos damos cuenta de un cambio clave: la trasera ya no es de metal sino cristal. Google ha conseguido esos dos acabados en el mismo material, con una zona mate muy suave y agradable al tacto que consigue matar dos pájaros de un tiro: no atrae tanto las huellas y posibilita la carga inalámbrica.

Cuesta entender el motivo que ha llevado a Google a colocar un notch tan enorme teniendo esa 'barbilla' en la parte inferior. El resultado no es estético, pero sobre todo no tiene sentido.

Al darle la vuelta nos encontramos con el cambio más llamativo de su diseño, y no precisamente para bien. Cuesta entender el motivo que ha llevado a Google a colocar un notch tan enorme en su nuevo buque insignia, sobre todo teniendo en cuenta que el marco inferior sigue siendo igual de ancho que antes. Vale, tenemos cámara doble en el frontal, pero cabía perfectamente en un marco como el del Pixel 2 XL. El resultado no es estético, pero sobre todo no tiene sentido.

En cuanto al tamaño, a pesar de que la pantalla suma 0,3 pulgadas con respecto al modelo anterior, las dimensiones totales se mantienen igual. Sólo sube de peso, pero es un aumento tan discreto que apenas se nota. Tenemos más pantalla en el mismo tamaño, pero el Pixel 2 XL ya era un terminal muy grande, por lo que si te costaba manejar el primero con una sola mano, el segundo no mejora este apartado.

Pixel 3 #source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Por su parte, el Pixel 3 también mantiene el estilo de diseño, pero ahora estrena una pantalla con formato 18:9 que crece hasta las 5,5 pulgadas y apuesta por las esquinas redondeadas que ya se han hecho habituales. Como decía antes, este modelo se escapa de ese notch tan poco favorecedor y además tiene un tamaño mucho más manejable. Curioso que el año pasado fuera el Pixel 2 el que resultaba menos agraciado y este año se alza como la opción más acertada en términos de diseño. El patito feo se ha hecho cisne.

El Pixel 2 resultaba menos agraciado y este año el Pixel 3 se alza como la opción más acertada en términos de diseño. El patito feo se ha hecho cisne.

Para cerrar, con respecto a los colores, tanto el Pixel 3 como el Pixel 3 XL llegan en tres colores: blanco con el botón de encendido en azul turquesa, todo negro o rosa claro con el botón en naranja. Personalmente creo que el rosa claro es el más bonito de los tres. También hemos podido ver una buena colección de fundas protectoras con acabado de tela en nuevos colores.

Pantallas: más tamaño y sobre todo calidad

Pixel 3 #source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Si la cámara del Pixel 2 XL ha sido una de las mejor valoradas, la pantalla se ganó muchas críticas por su mala calibración y unos ángulos de visión muy deficientes. Este año, el Pixel 3 XL vuelve a montar un panel P-OLED que crece hasta las 6,3 pulgadas, pero en estas primeras impresiones se ve claramente que ha habido una mejora importante en cuanto a calidad.

La calibración del panel es mucho mejor, con colores vivos que están muy lejos de los tonos apagados de la generación anterior, pero sin llegar a saturar.

La calibración del panel es mucho mejor, con colores vivos que están muy lejos de los tonos apagados de la generación anterior, pero sin llegar a saturar. La pantalla no azulea en cuanto la giramos un poco y en general los blancos son más puros y agradables a la vista. He podido compararlo con mi Pixel 2 XL y la diferencia es más que notable.

El Pixel 3 monta la misma pantalla P-OLED pero con una diagonal de 5,5 pulgadas. Las buenas sensaciones se mantienen con el hermano pequeño de la gama; buena nitidez, brillo elevado y colores acertados. Además, destacar que este modelo se suma al formato 18:9 (el Pixel 2 era 16:9), lo que lo hace todavía más manejable.

Cámaras: la dualidad llega, aunque a medias

Pixel 3#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Las filtraciones no se equivocaban y este año Google sigue apostando por una única cámara principal, alejándose de las tendencias que duplican e incluso triplican este elemento. Ambos modelos tienen un sensor trasero de 12,2 megapíxeles con píxeles de 1,4 um, acompañado por un estabilizador óptico, lente de apertura f/1.8 y capaz e grabar en 4K. Sobre el papel, no parece que haya demasiados cambios, aunque en Google nos han asegurado que el hardware se ha renovado totalmente, pero tampoco han querido entrar en demasiados detalles, está claro que quieren poner el foco en el software. Tendremos que esperar al 'teardown' de turno para saber más acerca del tipo de sensor.

Desde Google nos han asegurado que el hardware se ha renovado totalmente, pero tampoco han querido entrar en detalles. Está claro que quieren poner el foco en el software, su arma secreta.

En el evento apenas he podido tomar un par de fotos, por lo que no puedo emitir un juicio sobre la cámara, pero la primera impresión ha sido buena, al nivel del Pixel 2 como mínimo. Habrá que esperar al análisis a fondo para ver si consigue superarlo y por cuánto. Eso sí, un punto que me ha gustado mucho es el rediseño de la app de la cámara. No es muy original que digamos (se parece mucho a la estructura del iPhone), pero es mucho más cómodo movernos entre modos que con el sistema anterior.

Pixel 3 #source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000 El modo Top Shot dispara muchas fotos y elige la mejor.

Una vez más, Google pone el foco en el software como arma para diferenciarse, aunque aquí tengo dudas ya que las funciones anunciadas no son precisamente revolucionarias. Un ejemplo es Top Shot, un modo que toma varias fotos y selecciona la mejor toma por nosotros mediante machine learning. Está pensado para evitar la típica foto en la que alguien sale con los ojos cerrados o se mueve y aparece desenfocado.

Otra de las novedades es Night Sight, un modo nocturno que al menos en las pruebas que nos han mostrado apunta a mejorar considerablemente el rendimiento con poca luz. El modo retrato también se ha mejorado y ahora permite elegir la profundidad de campo una vez hecha la foto, pero sigue sin dejarnos ver si el desenfoque se aplica en el momento de hacer la toma.

Por último, pero no menos importante, toca hablar de la cámara delantera, la verdadera novedad fotográfica de este Pixel. Google sí introduce un sensor doble en la cámara selfie, y lo hace apostando por una lente angular con la que conseguir selfies con un mayor ángulo, perfecto para tomar selfies de grupo. Es una función interesante, pero resulta un poco decepcionante que la dualidad llegue de este modo. Podrían haber incluido dos cámaras detrás y ofrecer un zoom real, una característica mucho más práctica y en la que la competencia le lleva bastante ventaja (¿Alguien dijo Huawei?). Google lo sabe y lo soluciona con Super Zoom, una función que reduce el ruido al ampliar, de nuevo mediante software y he de decir que en la demo el resultado ha sido bastante bueno.

Un hardware conservador con un software único

Pixel 3 #source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Durante el evento, Google ha destacado sobre todo sus funciones exclusivas, dejando a un lado el hardware, que para eso son una compañía de software. Los Pixel 3 cuentan con especificaciones a la altura de la gama alta, aunque tampoco apuestan por lo más potente. Tenemos un chip Snapdragon 845, el más avanzado de Qualcomm, pero en la memoria se quedan con 4 GB de RAM. No es la configuración más puntera, pero la fluidez del sistema es más que evidente, y de nuevo aquí el software es gran responsable. A falta de probarlos más a fondo, la experiencia Google brilla como sólo se puede esperar en un Pixel.

Hablando de software, Google incluye bastantes funciones que, al menos de momento, serán exclusivas de los Pixel 3. Un ejemplo es el nuevo Google Lens. Ahora se integra completamente en la app de la cámara y resulta mucho más intuitiva para, por ejemplo, detectar números de teléfono o correos electrónicos en una tarjeta de visita (sólo hay que apuntar la cámara y hacer tap en el elemento en cuestión).

También cuenta con una novedad llamada Call Screen con la que podemos hacer que Assistant responda una llamada por nosotros cuando no podemos coger el móvil. Una locución comunica a la persona que llama que no podemos atender su llamada y le invita a describir el motivo de contactarnos, después Call Screen nos muestra una transcripción en la pantalla, todo sin descolgar. De momento parece que se quedará en Estados Unidos pero sin duda es una novedad de lo más curiosa.

La comunión entre hardware y software también está presente en la carga inalámbrica con la base Pixel Stand. Cuando colocamos el terminal en la base, podemos configurarlo para que entre automáticamente en modo no molestar y también cuenta con un despertador nuevo que sólo funcionará cuando esté cargándose.

Conclusiones: la experiencia Google es inigualable, pero puede no ser suficiente

Pixel 3#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

Los Google Pixel son una gama joven, tan sólo llevan tres generaciones desde que Google anunciara los primeros en 2016, pero en poco tiempo han conseguido hacerse un hueco entre los gama alta más consagrados. La cámara es sin duda su punto fuerte y esta nueva entrega tiene muchas papeletas para seguir siendo una de las mejores a pesar de mantener una única lente trasera.

El software es su principal baza para diferenciarse, no sólo a nivel fotográfico, sino de experiencia de uso en general. El hardware pasa a un segundo plano para dar paso a funciones exclusivas enfocadas a un objetivo clave: ofrecer una experiencia inigualable. Aquí es donde los Pixel son fuertes, pero la competencia es feroz y no pueden descuidarse.

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Lavadora con función vapor: qué es y cómo debe utilizarse

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La noticia Google Pixel 3 y Pixel 3 XL, primeras impresiones: la pelea por la mejor cámara es más software que hardware fue publicada originalmente en Xataka por Amparo Babiloni .


          Data Engineer - Amazon.com - Seattle, WA      Cache   Translate Page      
Experience working with large data sets in order to extract business insights or build predictive models (data mining, machine learning, regression analysis)....
From Amazon.com - Mon, 13 Aug 2018 19:25:19 GMT - View all Seattle, WA jobs
          Data Scientist / Operations Research Engineer - 67712 - Advanced Micro Devices, Inc. - Austin, TX      Cache   Translate Page      
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      
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      
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
          Data Scientist - Yamaha - Cypress, CA      Cache   Translate Page      
Develop statistical models, machine learning-based tools or processes to measure and manage business performance....
From Yamaha - Wed, 22 Aug 2018 00:54:18 GMT - View all Cypress, CA jobs
          Senior Software Development Engineer - Distributed Computing Services (Hex) - Amazon.com - Seattle, WA      Cache   Translate Page      
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      
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      
Assemble large, complex data sets that meet business requirements and power machine learning algorithms. EC2, Lambda, ECS, S3.... $30 - $40 an hour
From Indeed - Tue, 18 Sep 2018 14:44:55 GMT - View all Radnor, PA jobs
          Executive Director- Machine Learning & Big Data - JP Morgan Chase - Jersey City, NJ      Cache   Translate Page      
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
          General Motors Hackathon, Oct 13      Cache   Translate Page      
Come hack at the ML@B/General Motors Hackathon this weekend!

The hackathon challenge will be a unique machine learning challenge related to General Motor’s infotainment system in all of their cars.

GM also has tech talks planned during the hackathon and will have 9 engineers present to work with you and answer any of your questions.

There will be cool prizes for the winners including an internship at GM for the first place winning team.

Four meals will be provided for everyone. Swag will be provided as well.

Please sign up on https://tinyurl.com/mlabhackathon if you are interested!
          Prozess-Visualisierung per Software: Was genau steckt hinter Process Mining?      Cache   Translate Page      
Process Mining verspricht einen Überblick über alle Abläufe. Lesen Sie, wie diese Technologie entstanden ist, wie sie funktioniert und wie KI und Machine Learning neue Möglichkeiten eröffnen.
          Working student in Data Science (m/f/div) (Berlin) // caresyntax      Cache   Translate Page      

Support in building up a Machine Learning team in digital health IT Berlin, Germany Our technologies have raised the performance bar in more than 6,000 operating rooms worldwide, and are supporting the next generation of data-enabled OR teams in over 10 million procedures per year. Headquartered in Berlin and Boston, with over 90 employees from...

Check out all open positions at http://BerlinStartupJobs.com


          Machine Learning SME - JM Group - Montréal, QC      Cache   Translate Page      
Hands on Client experts who has knowledge of SPARK, can use Machine Learning Libraries and has knowledge of Big Data. The person need to be hands on and has...
From JM GROUP - Sat, 06 Oct 2018 03:20:02 GMT - View all Montréal, QC jobs
          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
From Lincoln Financial Group - Wed, 26 Sep 2018 21:21:38 GMT - View all Boston, MA jobs
          Analytics Consultant - PRA Health Sciences - Phoenix, AZ      Cache   Translate Page      
Stat / Mathematical / Business Analytics background. Have studied or done machine learning projects. Incorporate business information and data from a variety of...
From PRA Health Sciences - Thu, 04 Oct 2018 14:33:18 GMT - View all Phoenix, AZ jobs
          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page      
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
          5 Ways to Turbocharge Your Digital Marketing Campaign in 2019      Cache   Translate Page      
Read this article to know how the landscape of Digital Marketing has changed drastically. How the latest technologies like Machine Learning and AI have made in roads in digital marketing and companies are utilizing their power to deliver the best quality of services not available before.
          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
From Lincoln Financial Group - Wed, 26 Sep 2018 21:21:38 GMT - View all Boston, MA jobs
          Analytics Consultant - PRA Health Sciences - Phoenix, AZ      Cache   Translate Page      
Stat / Mathematical / Business Analytics background. Have studied or done machine learning projects. Incorporate business information and data from a variety of...
From PRA Health Sciences - Thu, 04 Oct 2018 14:33:18 GMT - View all Phoenix, AZ jobs
          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page      
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
          Machine Learning Researcher - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page      
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 - Sun, 26 Aug 2018 12:12:32 GMT - View all Palo Alto, CA jobs
          Interactive Machine Learning Researcher - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page      
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 - Sun, 26 Aug 2018 12:12:32 GMT - View all Palo Alto, CA jobs
          Instagram is claiming to detect and combat bullying on their platform (4 replies)      Cache   Translate Page      
[www.ksl.com] Instagram is continuing its efforts to combat bullying on the platform. On Tuesday, the company said it is starting to use new machine learning technology to proactively...

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

          Microsoft open-sources Infer.Net machine learning      Cache   Translate Page      

Microsoft has released through open source its Infer.Net cross-platform framework for model-based machine learning.

Infer.Net will become part of the ML.Net machine learning framework for .Net developers, with Infer.Net extending ML.Net for statistical modeling and online learning. Several steps toward integration already have been taken, including the setting up of a repo under the .Net Foundation.

Microsoft cited the applicability of Infer.Net to three use cases:

To read this article in full, please click here


          Project Consultant / Expert - Blockchain at SAP IBSO - Cloud Service Center - SAP - Sankt Leon-Rot      Cache   Translate Page      
We make innovation real by using the latest technologies around the Internet of Things, blockchain, artificial intelligence / machine learning, and big data and...
Gefunden bei SAP - Thu, 04 Oct 2018 17:33:36 GMT - Zeige alle Sankt Leon-Rot Jobs
          Working Student: SAP Innovative Business Solutions - SAP - Sankt Leon-Rot      Cache   Translate Page      
We make innovation real by using the latest technologies around the Internet of Things, blockchain, artificial intelligence / machine learning, and big data and...
Gefunden bei SAP - Wed, 03 Oct 2018 05:30:12 GMT - Zeige alle Sankt Leon-Rot Jobs
          Vice President, Data Science - Machine Learning - Wunderman - Dallas, TX      Cache   Translate Page      
Goldman Sachs, Microsoft, Citibank, Coca-Cola, Ford, Pfizer, Adidas, United Airlines and leading regional brands are among our clients....
From Wunderman - Sat, 25 Aug 2018 05:00:40 GMT - View all Dallas, TX jobs
          Consultant MSBI Senior (H/F) – Montréal – Perm – Jusqu’à 100k CAD - Elitesoft - Montréal, QC      Cache   Translate Page      
Canada USA US Montréal Québec Azure Machine Learning Azure HD insights MSBI Microsoft BI Business Intelligence SSIS SSRS SSAS Power BI Cubes OLAP... $60,000 - $100,000 a year
From Indeed - Fri, 28 Sep 2018 14:24:37 GMT - View all Montréal, QC jobs
          UR - Corporate Engineering - Precision Systems Engineer (Maplewood, MN) - 3M - Maplewood, MN      Cache   Translate Page      
Proactively collaborate with business partners to connect and extend process data management solutions with complimentary machine learning and analytics efforts...
From 3M - Wed, 05 Sep 2018 17:09:45 GMT - View all Maplewood, MN jobs
          Sr. Product Marketing Manager - Automation Anywhere - San Jose, CA      Cache   Translate Page      
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      
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
          Senior C++ /.NET Developer      Cache   Translate Page      
Voor een eindklant in Utrecht zijn wij zoekende naar een .NET developer. Je gaat bij deze klant aan de slag met innovatieve ontwikkelings- en implementatieprojecten waarbij je veel vrijheid krijgt en je kennis snel kan laten groeien. Het is belangrijk dat je geïnteresseerd bent in datastructuren, machine learning, algoritmes en grafieken...
          Abschlussarbeit (m/w) Mathematiker/Ingenieur/Physiker Bereich Machine Learning - Raum Siegen      Cache   Translate Page      
Jobangebot: Mehr Infos und bewerben unter: https://www.campusjaeger.de/jobs/8459?s=18101178+ Was erwartet dich?Als Masterand oder Bachelorand (m/w) arbeiten Sie in einem hochmotivierten Team an den Technologien der Zukunft – was wir gemeinsam entwickeln, gibt es noch nicht auf dem Markt – Sie können dadurch Ihren Teil zur Verwirklichung innovativer Systeme und Lösungen beitragen und sich langfristig als Experte in diesem speziellen Feld etablieren. Erleben Sie den gesamten Life-Cycle von Systemen aus dem Bereich künstlicher Intelligenz - vom Design bis hin zum Betrieb. Werden Sie ein Teil des Teams – und machen Sie Ihre ... 0 Kommentare, 65 mal gelesen.
          Services Operations Manager - Highspot - Seattle, WA      Cache   Translate Page      
Equipped with new Apple products. We employ advanced technologies, included patented machine learning algorithms to:....
From Highspot - Mon, 01 Oct 2018 20:07:27 GMT - View all Seattle, WA jobs
          Mobile Engineer React Native - Xevo - Bellevue, WA      Cache   Translate Page      
Technical and non-technical, internal and external. Do you dream about artificial intelligence and machine learning?...
From Xevo - Tue, 02 Oct 2018 02:28:59 GMT - View all Bellevue, WA jobs
          Sr Software Engineer, Applied Machine Learning - Apple - Austin, TX      Cache   Translate Page      
We work on many high-impact projects that serve various Apple lines of business. Understanding of JVM internals and garbage collection....
From Apple - Sun, 30 Sep 2018 01:50:35 GMT - View all Austin, TX jobs
          Instructor (Data Science, Artificial Intelligence, Machine Learning) - Cortechma Inc. - Thornhill, ON      Cache   Translate Page      
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
          Instagram To Use Machine Learning To Spot Bullying In Photos      Cache   Translate Page      
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          The Pixel 3 Uses A.I. Better Than Any Smartphone Yet      Cache   Translate Page      

There are few smartphone annoyances worse than your device not doing what you expect it to. When you tap the shutter button, but the camera fires a split second too late. When you’re in the middle of an important meeting, but your phone is blowing up with notifications. In its new Pixel 3 smartphone, which debuted Tuesday, Google has added a number of subtle A.I.-powered features that make the device react not just more speedily but in the way you thought your phone should work all along.

The camera is perhaps the most dramatic place you can see Google’s A.I. at work. Reviewers have consistently lauded the Pixel for having one of the best smartphone cameras available, but Google introduced several new features that further anticipate your needs. The first is Top Shot, a tool that takes a burst of photos and selects the best image from that series. It appears that, like Apple’s Live Photos, it starts capturing shots shortly before you hit the shutter, as well as shortly after. (TechCrunch called Top Shot “Live Photo but useful.”) It then uses machine learning to analyze the images, screen out blurry shots or ones with blinking eyes or weird faces, and surface the best of that bunch. It does what you hope your camera would do: capture a great photo even if you hit the shutter a split second too late.

Another camera feature called Motion Auto Focus uses object recognition to keep a specific person, animal, or moving object in focus as it zooms around the frame. It aims to eliminate blurry action shots or the need to shoot a dozen burst photos. And then there’s Night Sight, which uses machine learning to determine the right colors for a scene based on the content in the image. The result: photos shot in darker hours that are significantly brighter than otherwise—the kind of photos you’d hope your phone would be able to take, rather than the noisy, pixelated result you often get in those conditions.

Google has also honed its Duplex technology, which uses an incredibly lifelike-sounding A.I. to make or answer calls on your behalf. In the Pixel 3’s call screening feature, it leverages Duplex so that you never have to answer a robocall or telemarketer call again. After tapping a button on the device’s screen, the A.I. takes over, answering on your behalf and asking why the person is calling. For transparency, the phone transcribes the entire conversation onto your screen, should you be curious about what’s happening. It sounds like having a secretary that you don’t need to feel guilty about fielding annoying phone calls on your behalf.

Another new feature called Flip to Shhh lets you silence notifications by flipping your phone face down onto a desk or tabletop rather than diving into your phone’s settings to enable Do Not Disturb mode. Google also made some clever changes for when the device is on its wireless charging dock: Voice-based communication is now the default, and notifications are now easier to see or dismiss from a distance. When it’s charging on this base and not in use, the phone also uses photos from Google Photos to transform into a photo frame (much like the newly announced Google Home Hub).

Individually, each of these features brings the device up to par or exceeds those of other leading smartphones. Taken together, they demonstrate that Google is trying to make its smartphone better anticipate and accommodate user needs through A.I. and machine learning in subtle, methodical ways. None of these A.I.-based updates is jarring or screams “privacy violation.” Google is taking its time to get consumers used to the idea that a smartphone is controlling some experiences. The company likely has a grand vision for mobile devices that’s far more reliant on A.I., but it’s learned from its missteps with products like Google Glass. With the Pixel 3, it’s demonstrating that when it succeeds, it will make your phone a little bit smarter and easier to use.


          Editing the Internet’s Second Screen      Cache   Translate Page      

The great social media cleanup of the past two years hasn’t seemed to leave it much cleaner. As some of the most powerful companies in the world have struggled to ferret out viral false news, harassers, conspiracy theories, and foreign agents, Congress and the American public have begun to lose faith in internet platforms like Facebook, YouTube, Google, and Twitter. But one social website—the web’s fifth-largest site, by some metrics—has dodged the brunt of the ire: Wikipedia.

One might think Wikipedia would be a top target for those peddling conspiracy theories and counterfactual narratives. Yet somehow, the massive online encyclopedia has managed to retain its reputation for reliability, at least generally speaking. And that’s thanks to a sprawling online network of editors who work for free to pull fact from fiction in crafting the articles that provide the answers that float to the top of Google search queries.

Of course, Wikipedia has its problems. The most glaring may be that the editor community is overwhelmingly male, and likely white too. And that leads to erasures and omissions that reflect the worldview and concerns of the editor community. For instance, when Canadian physicist Donna Strickland won the Nobel Prize, it turned out Wikipedia didn’t have an entry about her. One had apparently been submitted prior to her winning the award, but the edit community apparently didn’t consider her sufficiently noteworthy to warrant an entry.

To talk more about how Wikipedia, a volunteer-run project, manages to be a sort of second screen for the entire internet, and to delve into some of the problems the community is facing, we spoke with the Wikimedia Foundation’s executive director, Katherine Maher, for Slate’s tech podcast If Then. An expert on technology policy across the globe, Maher discussed the role Wikipedia plays in the current debates about healthy online platforms and what the community is doing to diversify its contributors.

Read or listen to our conversation below, or get the show via Apple PodcastsOvercast, SpotifyStitcher, or Google Play.

April Glaser: Wikipedia’s the fifth-most-popular website on the internet, according to Alexa Ratings from earlier this year. And that popularity is in part due to the fact that the most popular website on the internet, Google, regularly directs people to Wikipedia at the top of its search pages. It’s a symbiotic relationship where people search Google for answers and Wikipedia is the answer that they get. More recently, YouTube and Google have begun linking to Wikipedia to provide info on topics that tend to attract false news and conspiratorial theories in their efforts to be a more reliable source of information. According to Wikipedia itself, there are over 5.7 million articles on the English version of the site thanks to nearly 35 million users, of whom fewer than 200,000 are considered active editors. That means about 200,000 people make at least one edit a month. There are 300 active Wikipedias in different languages across the world, 48 million articles written worldwide. And this whole project is made possible thanks to volunteers who write the entries and thanks to grants and donations from the readers and the editors who use the site. I want to start by discussing the phenomenon that is Wikipedia, and that it actually seems to be largely correct. Is that correct? Am I correct about that?

Katherine Maher: Yeah. There have been numerous different studies that have shown that Wikipedia is on average as correct as any traditional encyclopedia would be, in part simply because of the volume of articles that we have—that when you do have inaccuracies they tend to be very few and far between. But also they tend to get corrected really quickly, and so as you take a look across the sites, the majority of content is correct at any given time.

Glaser: So if I were to go on there and change the birthday of President Obama, that would get corrected really quickly?

You wouldn’t be able to change the birthday of President Obama because you probably do not have enough of an edit-contribution history to be able to touch an article that is as highly scrutinized as something like Obama. So anytime we have articles that are either of top interest to folks at any given time or are in the news in any given moment, our editors take them very seriously and will protect them to make sure they don’t go ahead and get vandalized. So it would be tough to change his birthday.

Glaser: Who is editing Wikipedia? It seems that everyone wants to use it, but not everyone wants to edit it. And my understanding is that something like 90 percent of the editors are male. I don’t know the racial background of editors, but I think it’s safe to say that most are probably white or come from some kind of white-collar background. So in addition to who’s editing Wikipedia, I’d like to know also the consequences of homogeneity in the edit community.

We don’t actually know much of the background of Wikipedia editors either. We have pretty strict privacy policies. In fact, you don’t need to give us really any information to edit Wikipedia. You don’t even need an account—you can just do it anonymously. And over the years, we’ve tried different ways of surveying and sampling editors to get a better sense. I think our best-case scenario is about 20 percent of the editors identify as female, but worst case would be closer to about 10. And then in terms of ethnic and racial makeup, obviously that really depends based on what Wikipedia we’re talking about. Our Indic-language Wikipedias are primarily going to be edited by people from probably South Asia. But it is true that we tend to assume that folks editing Wikipedia have what we think of as disposable time, and disposable time tends to correlate with higher socio-economic status. How does this play out for Wikipedia? It means that we tend to have biases that reflect the composition of our editors, and I will say that those biases also tend to reflect the broader world around us.

So we talk about ourselves as a mirror held up to the world. Wikipedia is a tertiary source that is based on secondary sources, and when we go to create articles on Wikipedia, we’re very reliant on what’s already been published and what exists in the world. And so if there is a dearth of secondary sources about female scientists or African novelists, it’s going to be very hard for us to then create articles that reflect those individuals on Wikipedia itself.

Glaser: When a new public figure comes on the scene, everyone jumps to visit Wikipedia, it becomes a second screen, and a bunch of editors also jump in to get their version of the truth up there. I wrote about this for Wired a couple years ago when Merrick Garland was nominated [for the Supreme Court]. The traffic of his page soared because nobody really knew who he was unless you’re in the court scene. And then behind the scenes, the editors fought over whether to call him a judicial moderate or a strong liberal. And with so many people coming to Wikipedia for information on Garland, these descriptions really matter. How do Wikipedia editors grapple with attempts to insert their own ideological leanings?

I think that this is one of those things where the more people who have an eye on the Wikipedia article, the more accurate and neutral it tends to be. Don’t take my word for it. There’s been lots of research on this subject. The more volume of traffic, the more likely it is that someone’s going to make an edit, the more editors who are involved in the conversation, the more compact and neutral and accurate the content is going to be, the more citations, the less verbose or adjectival a description is going to be. And so it’s likely in the case of Garland—and I’m not familiar with that particular article and how that moment in time affected its composition—but it’s likely in that case, or in the case of anybody who’s under the spotlight, that if they couldn’t decide on how to describe him, they would either say, “Some people describe as ‘citation, citation, citation.’ ‘Others describe as the opposite, citation, citation, citation.’ ” Or they would not make a determination about how that description actually plays out.

And so Wikipedians will tend to present information and ask you instead to draw your conclusions rather than draw their own inferences or conclusions on topics that are difficult to be neutral around.

Will Oremus: And a lot of that discussion happens on what’s called a talk page.

That’s right. It’s almost like the newsroom behind any Wikipedia article. One of the things we like to say is, “If you’re curious about what a talk page is, go to your hometown and look at the fights that people are having about the history of the town, the town hall, local celebrities, things like this.” Because it can give you the best and most immediate understanding of how talk pages actually work. There are places where Wikipedia editors take the conversation, not offline. It all happens in public. It’s just behind the curtain. Anyone can click on the talk tab and take a look at it. Anyone can contribute to that discussion, but it’s where these differences of opinion get hammered out while articles might be paused for editing, or while folks are having robust difficult conversations about how to frame or present something or whether something should be included in an article at all.

Oremus: You talked about how Wikipedia is better when there are more people involved in this editing process. That makes a lot of sense. How is the health of the Wikipedia editor community these days? In what direction is it trending? Is it getting livelier and healthier, or are the ranks thinning out? Is there a crisis of Wikipedia editorship? How’s it doing?

There is no crisis of Wikipedia editorship. Our editors are alive and well. No, I think that there was this interesting moment in time where people were very concerned about the trajectory of editorship, and it happened around, I want to say, 2010, where Wikipedia grew very rapidly in popularity between 2001 and 2010, and then what ended up happening was a lot of that original content was filled out, at least in some of the major languages, and we started to see a decline in casual editing. But what is happening is that our numbers have really stabilized to the point where we have about a quarter-million editors every single month, and about 80,000 of those come back month on month on month and make significant contributions to the site. So overall our editor health is really good. What we would love to see is an increased diversification of that, and we’d love to see some of the languages that are perhaps not as robust as they should be relative to the size of a million speakers and the like. We’d love to see some of that grow.

So for us, it’s about maintaining the health of our current editing community but then also thinking about how do we reach people for whom we’re not there yet in their language, in their geography, or representing their sense of identity.

Glaser: I imagine if you come from a community that is not well represented in the editor community that you may be prone to harassment or feel somewhat ostracized in these tightknit talk pages where a lot of difficult conversations happen. So I’m curious about harassment on Wikipedia. Have we seen coordinated attempts to insert ideological bias or to harass people to the point where they stop maintaining certain pages?

Yeah. Absolutely. These things happen, and happen in places that you wouldn’t necessarily expect. I think that the instances of extreme harassment—the kind that you see on some of the other social platforms—we see a lot less of that because Wikipedia has rules around civility that determine whether you can participate as an editor, and if you violate those rules you will get blocked and banned by our community members. I think the bigger issue for us tends to really focus on tonality, so we’ve done some interesting research around conversational failure, and it turns out that if you start a sentence in dialogue with another editor with the word please, it actually is a really high predictor that that conversation is going to fail. Because it tends to be followed by “Please stop doing that” or “Please don’t do something that you don’t know anything about,” and so please is actually not an indicator of a necessarily positive outcome.

So what we’re trying to understand is in a community and in an ecosystem where harassment and unfriendly spaces look very different than harassment on, say, the comment section of YouTube or in a Twitter channel. What can we do to facilitate more civil and respectful conversations when we can’t necessarily automate to be able to understand because of the use of bad words, for example. And so it’s really about how we create a culture of friendly interaction as opposed to certain instances of harassment, ’cause we just don’t have that problem in quite the same way, which is not to say it doesn’t exist. I do want to be really careful to acknowledge that we have had instances of people who’ve been harassed on Wikipedia. It tends to be somebody gets a bone between their teeth and really goes after an editor or a group of editors. The times that we’ve seen these things happen in a targeted way have tended to be around things that you would expect to be controversial. We were one of the sites to be enveloped in the whole Gamergate controversy, and we absolutely saw people really go head-to-head over what that particular discussion meant, and we had a number of Wikipedia editors on all sides of the conversation who found themselves sanctioned for the way that they participated in those conversations.

Glaser: I write a lot about harassment on social media, and obviously Wikipedia is a social place where people can interact. We do not hear as much about creating a culture where people will be less prone to harass each other. It’s more about moderation, so this is really interesting.

We don’t do moderation in the same way that other social platforms do. We don’t have armies of folks sitting offshore going through content posting trying to determine if it’s harassing language or if it violates our terms of service. Our community, because it is truly a community, engages in that conversation directly, and then they have modes and means of policies to refer conversations for review and sanction as appropriate. I think that harassment is a problem, but for us it is a relatively small problem relative to the challenge of how do you create a truly inclusive space for folks when we come from a certain culture, and we come from a certain demographic background. How do you open that up so that it becomes a place where more people feel welcome?

Oremus: That’s so interesting to hear you say you don’t have moderation. I understand what you mean: You mean that Wikipedia, or the Wikimedia Foundation, isn’t going in and moderating what the editors can say or what people can add to an article. But in another sense, the whole project of Wikipedia is a project of moderation, where people are moderating what each other can say and regulating each other’s speech in various ways. It reminds me, you talked about the social platforms, and it reminds me of the difficulties that the big social networks are having right now with misinformation, conspiracy theories, fake news, all that sort of thing. And they talk about, Well, we can’t be an arbiter of truth. Or maybe in Facebook’s case: We’re trying, but it’s really hard to be an arbiter of truth. Wikipedia is at its core an arbiter of truth—that’s what you guys do. So why do you think they’re having such a tough time with it, and would you have any advice for the people running those platforms?

I think one thing that’s really different from us is, from the beginning, it’s been a community-driven project. We don’t set editorial policies for Wikipedia. The community sets that, and the community has evolved over time with these editorial policies in order to assess information quality and also the standards that they want in their spaces, to tie it back to the conversation around friendly spaces and contribution. But specifically for content moderation there are a couple of really core policies that drive the way that Wikipedia articles are created, and I think the reason that they are effective is that they’re clear, there are only three of them, they’re fairly easy to understand, there are tons of examples for how they work, there are lots of different eyeballs that focus on insuring that those policies are upheld, and it all happens in the open. The polices around accuracy of information, it requires that we site back to what we call reliable sources. It means that people can’t just put out fringe theories based on what their interests are. They have to find citations and information. It has to be peer-reviewed, or published, or have some editorial scrutiny.

These are the policies that have created a sense of accuracy and accountability on Wikipedia, and accountability not just for the editors but accountability to the public who reads this content. And I think that’s just so completely different from the way these other platforms work. Another thing that I point to is we don’t have divergent forking narratives or feeds that you sign up for. When you come to a Wikipedia article, you’re looking at the exact same thing whether you’re sitting on the other side of the continent from me or if you’re sitting in the next office over. That doesn’t afford us the space to shift narratives based on what your interest is or what an algorithm suggests that you might like. We have to be open and publicly accountable for what is published no matter what your perspective or viewpoint actually is.

It’s funny you mentioned, or refer to, Wikipedia as an arbiter of truth. We actually don’t agree with that characterization. What we would say is that Wikipedia reflects knowledge as it exists at any given moment in time. That is, knowledge is constantly being constructed, and it’s constantly being deconstructed. And so edits are made to Wikipedia, content is removed from Wikipedia, knowledge changes dramatically over time, and what Wikipedia offers is just an aggregate understanding of what we know about a topic at any given moment based on what’s been published or what common consensus says. I always use the example of Copernicus or Galileo. However many hundreds of years ago, had they written an article, we’d have some really strong articles about how the sun revolves around the Earth. But we, as hopefully humanity, have learned a lot more about our solar system, and now we know that the Earth revolves around the sun. So knowledge is a living thing, but it’s not necessarily about trying to get to some understanding of truth. It’s more just about representation about what we can all agree on at any point in time.

Where I start to find this really powerful is less on things that are settled, like heliocentricity versus geocentricism, but more about how our history—and understanding of culture, and understanding of politics, and understanding of representation—Is constantly evolving. Wikipedia’s edited 350 times a minute, which essentially means that every minute there are 350 opportunities to challenge what it is that we know, and how it is that it’s been assembled, and who has contributed to that knowledge base, and whose voices are included, and how it is that we might change that over time. So I think of Wikipedia not as an arbiter of truth but really a living contestation for how knowledge is formed and created, which is why we always say, “Don’t trust Wikipedia. Read it with a critical eye. Check the citations. And if you see something, contribute to it.” Because the way that we form knowledge is by contributing to it together and building on what’s come before.

Glaser: It’s true that there are all of these ways to gain social clout and to gain social trust in the Wikipedia community, but sometimes people edit Wikipedia anonymously, or it’s their first time, and sometimes it’s funny. And it’s something that you guys call vandalism. And I want to ask about that, because I saw this meme going around a few years ago—I think it was an actual screenshot from Wikipedia, and it was edited to say that Charlie Sheen was half-man, half-cocaine. And it was changed quickly back, I’m sure, but I think this happens a lot, and I’m curious: How does Wikipedia contend with vandalism, and is there any particularly funny one that comes to mind?

Just the other day, somebody tweeted about the fact that there’s a Wikipedia article that’s a list of fictional states, and nestled in there was Wyoming, a fictional state made up for tourism revenue by Idaho. I thought that was funny. As soon as I retweeted it, it was gone. But I think that often we see these sorts of vandalism. I think that they are funny. I also know that they can be quite annoying for our Wikipedia editors. I like to think of them as ways of demonstrating that Wikipedia is a living project and as a reminder to folks that you can go ahead and get in there and edit. We actually have more Wikipedians than we’d like to admit who first started because of vandalism. They came in to mess with the site, and they realized, “Oh, that vandalism didn’t stay up for very long. I’m curious how that works,” and then get involved in that way. In general, there are different ways that vandalism works. I don’t think this will surprise anyone, but some of the highest volume of vandalism tends to happen in school hours, and it tends to be bad words.

But we have bots that scrub the site from end to end and remove instances of poop that shouldn’t belong in a sentence and the like. The other forms of vandalism tend to get reverted very quickly. I think Congress is notorious for vandalizing Wikipedia, in fact I think it’s blocked this week; you can’t edit with a congressional IP this week because people abuse their privileges. And we’ve seen that happen. Wikipedia editors tend to keep a very close eye on what we call our recent-changes feed, and there are folks who consider themselves just to be vandalism patrollers who are always looking for things that are a little suspicious. We give this a boost by having machine learning systems that are able to identify what is likely a good edit or a bad edit and help editors triage in order to keep pace with the 350 edits a minute. Because it’s a pretty huge volume of activity on the sites at any given time.

Glaser: With Wikipedia now serving as a fact-checker for YouTube’s most polarizing conspiracy-theory videos, is there a fear that people will see these videos about how climate change isn’t real and then click on the Wikipedia link and edit the article to incorporate the counterfactual information they just saw?

This was something that we were concerned about. Obviously any time a major platform turns the worst of the internet against our sites, we worry about what the implications are for our editing community. Our editing community actually took it all in stride. They said, “We’ve got means by which we monitor these pages. We know how to deal with vandalism. We’ve been doing this for 17 years. We’ll let you know when it’s a problem.” And we went back and said pretty much the same thing to YouTube. We said, “We’ll let you know when it’s a problem, and if it does become a problem we’d appreciate some support around this. But overall it seems as though it’s something that is working out.” Our mission overall is to get knowledge out there and to be the correct place for information—to have it be as accurate as possible. And if it is a tool in the arsenal of insuring a more accurate and fact-based internet, then I think we’re probably all for it.

Glaser: It’s fascinating how the community is able to morph and absorb more responsibility as more people start to use the internet and as more large platforms start to rely, and continue to rely, on Wikipedia as a source of information.

I think what I would say there is that because it works in this instance, it doesn’t mean that we’re going to be the catchall for all of the worst bits of the internet. In reality, anytime that there is an intermediary layer between people who are reading Wikipedia content and where the content itself is created, we see that as a risk. Part of the promise of Wikipedia is that anybody anywhere can go in and check where does the information come from, when was it added, what’s the edit history, what’s the discussion on the talk page, where does the citation go, and so whenever there’s an intermediary layer that sits between our readers and our contributors, we view that as a breakdown of the trust and the promise model that Wikipedia offers in terms of accountability and transparency, but we also view it as a risk factor to the sites as a whole because of how Wikipedia works when people stop by to read it. Wikipedia works when a reader’s like, “Oh, I think that information isn’t accurate. That probably needs an update,” or “That probably could use a different citation.”

And so the volume of traffic to our sites is actually the way in which Wikipedia stays up to date and makes sure that our content is constantly expanding, and so if that information is being siphoned off and presented in different ways and in different places, that actually does create a risk for us. So I think there’s a tension between how do we make sure information’s available in the most useful ways, such as the referral back with the YouTube videos, but also making sure that information is not taken completely out of context and presented in a way that ultimately chokes off the way that Wikipedia works.

Oremus: You actually just answered part of the question that I was going to ask, which is: Part of the future of computing right now on the internet seems to be this move toward voice assistance. Whether it’s on a smart speaker, like an Amazon Echo or Google Home, or it’s Siri on your phone, or talking to the Google Assistant on your phone, for a lot of those, when you ask a question—when you ask Siri or Alexa or Google a question—the answer you get will be content from Wikipedia. So that’s a way that people are gonna be using Wikipedia more and more, presumably, but maybe not even know that they are using Wikipedia. Certainly not visit the site and maybe run into a fundraising appeal or get involved in that way. Is that a big concern for you going forward, and are you hopeful that donations from those big platforms … I saw that Amazon recently gave $1 million to the Wikimedia Foundation maybe partly for that reason.

Does that have to become more of your business model now if these platforms are going to be siphoning off your information or siloing your information in that way?

I think I have many thoughts and responses to your question. One thing to note is that there’s the immediate value that these platforms get out of having Wikipedia as a resource from which they can pull answers and information to provide to their users. But the other part that most people aren’t as aware of is that Wikipedia is also this massive computational resource for many different platforms in terms of the way that they’re developing machine learning, the way that they’re training their A.I assistants, in the way that they treat natural language processing. And so we view ourselves as a resource that should be supported by industry as a whole—not just because we create a transactional value to them, because Amazon or Siri or whatever can answer our question, but because we’ve actually created a tremendous resource just in terms of data modeling and support that these companies can go out and train and do advanced computational science around. And there is nobody who is contributing back because of the value we’ve created in that space.

The reason that we think that should engender long-term support is because we are essentially the commons as a resource for the entire internet that entire business models have been created around. And if you don’t actually support that commons, it’s not going to exist at some point, and that’s going to be really problematic for a business model that depends on its existence, particularly as companies are pushing into new and different markets. It’s increasingly the case that they’re looking to sites like Wikipedia, which have content that’s available in those local languages as a baseline to assess the market maturity, and whether people are using the open web, and whether they’re creating content in those languages. So overall supporting Wikipedia so that we’re out there and accessible to more people and accessible to more users in more languages, and so that our content is diverse and reflective of the entire world and not just North America or the male experience, is ultimately a good thing, not just for us, and not just for our readers, but for the internet as a whole.

In terms of what that support looks like, I think that it’s not just about monetary support. We are very proud of the fact that 85 percent of our donations come from small-dollar donations from individual users. The average donation is $15, and the remaining 15 percent tends to be more traditional foundation donations. We don’t want to be entirely beholden to large corporations giving us money, but we do feel as though having some sort of sustainable model of support, whether it is engineering support, or in-kind support, or just thinking about what the product decisions actually mean in terms of their implications for how people can contribute to and access Wikipedia, that’s the conversation that we want to be having with these different platforms. At the end of the day, we create a tremendous amount of value in the world, and we want to make sure that value is being recognized and being supported and sustained, because it’s very easy to make a series of decisions that in aggregate could really damage that value, and I don’t think anybody intentionally wants to hurt Wikipedia.

Glaser: No, Wikipedia has a tremendous amount of value for so many different parties. Can you tell us about efforts to expand Wikipedia in other languages? Also, what’s the second-biggest language after English? And is Wikipedia available in China?

So there are a whole host of Wikipedias that are smaller languages. They tend to be secondary languages within countries, or indigenous languages, or noncolonial languages, and we at the Wikimedia Foundation actually place a great deal of emphasis on supporting communities that are doing work in these languages. We have grants that are available to community members who are organizing and doing events around outreach and growth of smaller-language projects. We call them our emerging community projects. And the whole idea is that we don’t make a decision based on winners and losers on language. The fact that Spanish and German and English and French are enormous doesn’t mean that that is sufficient to cover the entire world. We want people to be able to access content in Zulu and in whatever the indigenous languages and identities are that they’re seeking information.

We know that’s actually critical to the way that cultures continue to live, the way that identities continue to live. There’s a whole example of Welsh Wikipedia where the national government of Wales has a Wikipedian in residence who is just there to make sure that Welsh continues to be a living thriving Wikipedia language in large part because they know that having Wikipedia exist in Welsh is actually a marker as to whether other internet services will index Welsh as a living digital language. And of course we can all understand that if your language goes away or doesn’t exist in a digitized form, your identity starts to go away too. So we’re really supportive of this. We think this is part of our cultural diversity in recognizing that the sum of all knowledge requires all sorts of different understandings of knowledge, all sorts of linguistic bases for knowledge.

Glaser: Is Wikipedia available in China?

Wikipedia is not currently available in China or in Turkey. But of course Wikipedia does continue to be built by Chinese speakers. We have Chinese speakers who are outside of China, like Taiwan and Hong Kong of course, but then other Chinese speakers including folks from mainland China who edit Wikipedia from outside of the country as well as from inside the country using circumvention technologies. So Wikipedia in Chinese is not as large, and we would like to see the relative number of Chinese speakers, but we remain optimistic that it will continue to be built and will be there for when and if China ever decides to unblock us. We would love that.


          Sr. Associate, Machine Learning AI Consultant - KPMG - Seattle, WA      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Tue, 02 Oct 2018 15:21:24 GMT - View all Seattle, WA jobs
          Sr. Associate, AI in Management Analytics Consultant - KPMG - McLean, VA      Cache   Translate Page      
Ability to apply statistical, machine learnings, and artificial intelligence techniques to achieve concrete business goals and work with the business to...
From KPMG LLP - Sat, 29 Sep 2018 15:21:53 GMT - View all McLean, VA jobs
          Data Scientist - Deloitte - Springfield, VA      Cache   Translate Page      
Demonstrated knowledge of machine learning techniques and algorithms. We believe that business has the power to inspire and transform....
From Deloitte - Fri, 10 Aug 2018 06:29:44 GMT - View all Springfield, VA jobs
          Database Administrator - Radiant Solutions - Springfield, VA      Cache   Translate Page      
Machine learning, data mining, and knowledge discovery. Work is non-routine and very complex, involving the application of advanced technical and business...
From Radiant Solutions - Wed, 04 Jul 2018 01:36:29 GMT - View all Springfield, VA jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - Dallas, TX      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Fri, 07 Sep 2018 02:02:14 GMT - View all Dallas, TX jobs
          Associate, Machine Learning AI Consultant - KPMG - Dallas, TX      Cache   Translate Page      
Broad, versatile knowledge of analytics and data science landscape, combined with strong business consulting acumen, enabling the identification, design and...
From KPMG LLP - Fri, 07 Sep 2018 02:02:14 GMT - View all Dallas, TX jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - Philadelphia, PA      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Fri, 14 Sep 2018 08:38:34 GMT - View all Philadelphia, PA jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - New York, NY      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Tue, 02 Oct 2018 15:21:24 GMT - View all New York, NY jobs
          Associate, Machine Learning AI Consultant - KPMG - New York, NY      Cache   Translate Page      
Broad, versatile knowledge of analytics and data science landscape, combined with strong business consulting acumen, enabling the identification, design and...
From KPMG LLP - Fri, 14 Sep 2018 08:38:34 GMT - View all New York, NY jobs
          Threat Finance Subject Matter Expert - People, Technology & Processes - Fort Bragg, NC      Cache   Translate Page      
The TF SME contractor shall have proficiency and experience with applied data processing and scientific analysis of large datasets and machine learning....
From People, Technology & Processes - Tue, 17 Jul 2018 03:09:42 GMT - View all Fort Bragg, NC jobs
          What's new with the cameras of Pixel 3 and Pixel 3 XL?      Cache   Translate Page      
Google just launched the new Pixel 3 and Pixel 3 XL, two promising camera phones! Thanks to the very good reputation of the Pixel 2 series last year.
What's new with the cameras of Pixel 3 and Pixel 3 XL?
Better cameras!

It still has a single 12MP sensor at the back. But, it is now better than ever. It has an 12MP f/1.8 aperture size, Dual Pixel PDAF, OIS, EIS, spectral + flicker sensor, and LED flash.

For selfies, you will find two cameras! It has an 8MP f/1.8 primary shooter with with AF and another 8MP f/2.2 secondary sensor with 97-degree wide-angle lens.

Aside from the hardware camera specs, Google also made its software even better.

Let's have a look!

What's new with the cameras of Pixel 3 and Pixel 3 XL?

Basically, Google added improved zoom, wider-angle camera, smile and blink detection, and bokeh control. YES, even if the new Pixel phones are still equipped with a single rear camera.

It uses a lot of AI and machine learning tricks to help you take better photos.


1. Top Shot -  It is AI based feature that capture multiple shots to pick out the best photo. It can recognize and suggest the best shot you took.
2. Super Res Zoom - To produce sharp digital zoom details.
3. Night Sight - An improved lowlight mode to let your take bright, detailed, and colorful shots at night. This feature will be ready soon.
4. Photobooth - It uses AI to recognize that when you’re smiling or making a funny expression for selfies.

It also has Group Selfies feature which allows you to take shots that's 184 percent wider than the normal mode. You can also check the Playground where you will find stickers like Hulk and Iron Man.


Google also made the stabilization of the Pixel 3 phones even better. It also has a motion autofocus feature to ensure your subjects stay as sharp as possible.

Source: Google

          Microsoft open-sources Infer.Net machine learning      Cache   Translate Page      

Microsoft has released through open source its Infer.Net cross-platform framework for model-based machine learning.

Infer.Net will become part of the ML.Net machine learning framework for .Net developers, with Infer.Net extending ML.Net for statistical modeling and online learning. Several steps toward integration already have been taken, including the setting up of a repo under the .Net Foundation.

Microsoft cited the applicability of Infer.Net to three use cases:

To read this article in full, please click here


          Splunk Dev./Architect W2 Position      Cache   Translate Page      
TX-Houston, Required Qualifications 5 or more years of IT development experience required. 5 or more years of Splunk experience. 3 or more years of Machine Learning experience. 3 or more years of experience in the software development methodology life cycle - including business planning, data analysis, process analysis and design, business and functional requirements analysis, use case analysis, system design
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page      
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
          Sr. Data Scientist - Life Sciences - Health Catalyst - Salt Lake City, UT      Cache   Translate Page      
Machine learning experience required. The role has great potential for a successful candidate as they will form the initial seed of a new business unit with...
From Health Catalyst - Tue, 11 Sep 2018 02:31:12 GMT - View all Salt Lake City, UT jobs
          AI for Asset Management Firms: Better Manage Pipelines & RFPs to Improve ROI      Cache   Translate Page      

What if I told you I have a new member of your sales team that, after an initial round of training, will be able to look at an RFP, decide if it’s a worthy opportunity, manage its response from start to finish… and won’t be on your payroll? This team member would only be paid in data, which it would use to further improve its management of RFPs and pipelines.

 

It’s called AI and machine learning, and it’s the next evolution in asset management that you need to know about.

 

 

The success of many asset management firms depends on the complicated and time-consuming process that is RFPs, RFIs, and general requests. Each one is unique and based on varying degrees of information and effort needed. As it stands, it’s likely that your firm doesn’t respond to every single one due to time and bandwidth constraints, but what if the ones that end up in the slush pile could have been major opportunities for your asset management company?

 

The choice to move forward with an RFP or toss it depends largely on the gut of the person reviewing it. Perhaps the salesperson has worked with the client before or has a good relationship with them, or your asset management firm can easily deliver what the RFP has outlined and your strategies align. There’s a lot of business criteria that comes into play when making these decisions, but there are a lot of human factors as well. This qualitative information is not the best for anyone to use and make business decisions. Plus, once you do decide to pursue a request, developing the response can require hundreds of man hours to research, verify, and complete… all because someone had a gut feeling about winning a client.

 

There’s a better way with AI and machine learning for asset management firms.

Using Data to Educate Your AI

Think of AI as any new team member: it needs information about your clients, processes, and business to get going-- it just doesn’t need as much time as a human team member. Machine learning starts with collecting data from your team entering information, but that doesn’t mean it requires data entry! Sure, the system can read structured data off a spreadsheet, but it can also read unstructured data off of a scanned document or e-mail. It will read that information, look for patterns, and use those patterns to make decisions based on what you’ve told it you believe to be the overall effort for winning that opportunity. With these advantages, your asset management team will immediately begin saving time.

 

Your AI will optimize for win percentage and AUM, telling you which opportunities should have the best close rate or the highest ROI. Over time and as you continue to feed it more information, its richer data set will become better at tracking your wins and losses. It will look at what’s driving a number, help you make smarter decisions, and refine your model.

 

With experience, your AI will empower you to improve the chance of winning a deal by suggesting ways to change the criteria. This will allow you to go back to your consultants with data-driven recommendations, you close more deals, and that consultant is more likely to work with you again in the future.

Proving Performance

AI and machine learning can ingest tremendous amounts of data, learn from it, and make recommendations faster and without the bias others on your sales team would. Yes, you must feed data into the machine. This could be data already sitting in your system, but chances are that someone will need to set up and input more into the system. The more you give it, the more you’ll get out of it in the end.

 

Curious about AI and machine learning for asset management? Learn more about machine learning for asset management firms, or give it a try and put it to the test against your current model. See which is right more often, especially as you give the machine more data and time to learn. You’ll see that as its machine brain grows-- something the human brain can’t do-- it will master what a human with years and years of experience has done in a fraction of the time.

 

Ready to see machine learning and learn more about how it can improve your business? Enhance the capabilities of your team and back up those gut instincts with an AI engine of your own. Talk to the Financial Services experts at AKA Enterprise Solutions today!

 


ABOUT AKA ENTERPRISE SOLUTIONS
AKA specializes in making it easier to do business, simplifying processes and reducing risks. With agility, expertise, and original industry solutions, we embrace projects other technology firms avoid—regardless of their complexity. As a true strategic partner, we help organizations slay the dragons that are keeping them from innovating their way to greatness. Call us at 212-502-3900!

The post AI for Asset Management Firms: Better Manage Pipelines & RFPs to Improve ROI appeared first on CRM Software Blog | Dynamics 365.


          Google lanza los nuevos Pixel 3 y 3XL      Cache   Translate Page      

Día de lanzamiento de hardware para Google que sigue intentando marcar el terreno de cómo deberían ser los Android perfectos aunque usualmente siempre quede un poco por hacer y otro tanto por innovar, en este caso llega el turno de la nueva generación de Pixel.

Con mucho énfasis en las cámaras que son el punto fuerte de sus principales rivales, los iPhone y Galaxys. Por dentro cuentan con lo esperable para esta época del año para un flagship, un Qualcomm Snapdragon 845 con 4GB de RAM (curiosamente no ofrecen ni de 6 ni 8), Bluetooth 5, chipset de seguridad Titan M.

El Pixel 3 cuenta con una pantalla de 5.5" y el 3XL de 6.3", que es más bien ENORME, pero con reducción de bezel y notch para que no se extienda el tamaño del equipo (el 3 no tiene notch, sólo el XL), son cristales P-OLED de 1080 x 2160 para el tres y 2960x1440 para el XL con un ratio 18.5:9, cubiertos con Gorilla Glass 5.

Los parlantes son frontales, la cámara tiene un sensor de 12.2MP, f/1.8, 28mm wide, pixeles de 1.4µm, OIS, dual pixel PDAF y Dual-LED flash, interesante es que no está en la onda de cámaras duales sino que van todavía con una sola salvo en la frontal que cuenta con una doble de 8MP. El hecho de utilizar un sensor simple para la principal es porque lo potente viene del lado del post procesamiento donde con un poco de Machine Learning (dicen IA, pero no es IA, dejen de joder con IA), para lograr imagenes más claras y mejores en poca luz.

Las baterías son de 2915 y 3430mAh respectivamente con carga inalámbrica QI de hasta 10 watts, obviamente como toda la nueva moda, sin conector de 3.5mm para audio que requiere un adaptador para el USB-C o inalámbricos, el sistema operativo es Android 9.0

Detalle interesante desde el software, Google Assistant puede "atajar" llamadas desde números desconocidos para atender spammers molestos, se llama Call Screen y también llegará a los modelos anteriores de Pixel.

Llegarán al mercado a partir del 18 de Octubre con un precio de USD 799 para el Pixel 3 y USD 899 para el 3XL para modelos con 64GB de almacenamiento unos USD 100 extras por el de 128GB, todo esto incluye seis meses de YouTube Music gratis y la suscripción de Google Photo de por vida.


Copyright (C) 2005-2014, Fabio Baccaglioni [Permalink] [Comentarios] [Google]


          Consultant MSBI Senior (H/F) – Montréal – Perm – Jusqu’à 100k CAD - Elitesoft - Montréal, QC      Cache   Translate Page      
Canada USA US Montréal Québec Azure Machine Learning Azure HD insights MSBI Microsoft BI Business Intelligence SSIS SSRS SSAS Power BI Cubes OLAP... $60,000 - $100,000 a year
From Indeed - Fri, 28 Sep 2018 14:24:37 GMT - View all Montréal, QC jobs
          Sitecore, Machine Learning and Personalization      Cache   Translate Page      
none
          Principal Technologist - Machine Learning and Data Science - Blue Origin - Kent, WA      Cache   Translate Page      
While in this role, you will leverage your extensive experience in machine learning and data science to accelerate and innovate across business areas to drive...
From Blue Origin - Thu, 13 Sep 2018 23:30:35 GMT - View all Kent, WA jobs
          Dropbox makes PDFs and images searchable with automatic OCR feature      Cache   Translate Page      
Dropbox is in the process of launching a new feature that will make life easier for people using the cloud storage service to house PDFs and image files: automatic OCR. The optical character recognition is rolling out to paying subscribers, and is an acknowledgement of the fact that a large proportion of files uploaded to Dropbox are photographs of documents. By adding machine learning-powered OCR, Dropbox is making these files searchable. See also: Google pulls out of $10 billion Pentagon cloud contract over AI concerns Plex adds subtitle downloads, kills plugins, Watch Later and Cloud Sync Microsoft launches Azure-based Windows… [Continue Reading]

          Comment on How to Operationalize Machine Learning and Accelerate Business Value with Insight Platforms and In-Memory Computing by GigaSpaces AI and Machine Learning Event in Munich | GigaSpaces Blog      Cache   Translate Page      
[…] that can leverage AI and machine learning at scale, in real-time, will outperform their competitors, transforming the way they deliver […]
          Comment on How IoT Can Impact Your Business in 2017 and Beyond by GigaSpaces AI and Machine Learning Event in Munich | GigaSpaces Blog      Cache   Translate Page      
[…] today’s fast-paced world of “now,” combined with the exponential growth of data and connected devices (IoT), organizations strive to get from data to real-time insights to action at sub-second latency. […]
          Prozess-Visualisierung per Software: Was genau steckt hinter Process Mining?      Cache   Translate Page      
Process Mining verspricht einen Überblick über alle Abläufe. Lesen Sie, wie diese Technologie entstanden ist, wie sie funktioniert und wie KI und Machine Learning neue Möglichkeiten eröffnen.
          Clark Labs TerrSet 18.21 181010      Cache   Translate Page      

Clark Labs TerrSet 18.21 181010
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http://i83.fastpic.ru/big/2016/0924/16/36acd7f71b81b6fee9d35b6f8e9fed16.jpg

Clark Labs TerrSet 18.21 | 197 MB

TerrSet is an integrated geospatial software system for monitoring and modeling the earth system for sustainable development. The TerrSet System incorporates the IDRISI GIS Analysis and IDRISI Image Processing tools along with a constellation of vertical applications. TerrSet offers the most extensive set of geospatial tools in the industry in a single, affordable package. There is no need to buy costly add-ons to extend your research capabilities.
The Full TerrSet Constellation Includes:
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The GIS Analysis tools - a wide range of fundamental analytical tools for GIS analysis, primarily oriented to raster data. Special features of the GIS Analysis tool set include a suite of multi-criteria and multi-objective decision procedures and a broad range of tools for statistical, change and surface analysis. Special graphical modeling environments are also provided for dynamic modeling and decision support. The GIS Analysis tool set also provides a scripting environment and an extremely flexible application programming interface (API) that allows the ability to control TerrSet using languages such as C++, Delphi and Python. Indeed, all TerrSet components make very extensive use of the API.

The Image Processing System - an extensive set of procedures for the restoration, enhancement, transformation and classification of remotely sensed images. The Image Processing System in Terrset contains the broadest set of classification procedures in the industry, including both hard and soft classification procedures based on machine learning (such as neural networks) and statistical characterization.

The Land Change Modeler (LCM) - a vertical application for analyzing land cover change, empirically modeling its relationship to explanatory variables and projecting future changes. LCM also includes special tools for the assessment of REDD (Reducing Emissions from Deforestation and forest Degradation) climate change mitigation strategies.

The Habitat and Biodiversity Modeler (HBM) - a vertical application for habitat assessment, landscape pattern analysis and biodiversity modeling. HBM also contains special tools for species distribution modeling.

GeOSIRIS - a unique tool for national level REDD (Reducing Emissions from Deforestation and forest Degradation) planning, developed in close cooperation with Conservation International. With GeOSIRIS, one can model the impact of various economic strategies on deforestation and carbon emissions reductions.

The Ecosystem Services Modeler (ESM) - a vertical application for assessing the value of various ecosystem services such as water purification, crop pollination, wind and wave energy, and so on. ESM is based closely on the InVEST toolset developed by the Natural Capital Project.

The Earth Trends Modeler (ETM) - a tool for the analysis of time series of earth observation imagery. With ETM, one can discover trends and recurrent patterns in fundamental earth system variables such as sea surface temperature, atmospheric temperature, precipitation, vegetation productivity and the like. ETM is an exceptional tool for the assessment of climate change in the recent past (e.g., the past 30 years).

The Climate Change Adaptation Modeler (CCAM) - a tool for modeling future climate and assessing its impacts on sea level rise, crop suitability and species distributions.

Whats New:
Version 18.2 includes all the previous service updates listed below, including the following:
DigitalGlobe import utility for WorldView2,3, Quickbird and GeoEye1,2. The utility works similar to the Landsat utility and will transform the data to reflectance or radiance on import. The utility automatically reads the .imd file supplied by DigitalGlobe.
NDVI3g import utility to convert the GIMMS AVHRR Global NDVI VI3g data.
Gdal import utility enhancements to the streamlined interface introduced in 18.10.
Geotiff support for large TIFF files and other improvements.
HDFEOS support for HDF5
Atmospheric Correction (AtmosC) updated the LUT for solar spectral irradiance.
Other enhancements to: Thiessen, Crosstab, Macro Modeler, Viewshed, Pansharpen, Extract, CTA, GenericRaster, Concat, Reclass, Enviidrisi, Metaupdate, Interpol.

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          DevOps - Full Stack - Symetra - Bellevue, WA      Cache   Translate Page      
AWS - Lambda, DynamoDB, Cognito, SNS, API Gateway, EC2, CloudFront, S3. Azure - Functions, EventHub, Machine Learning, CosmosDB, Kubernetes Services, NLP, and...
From Symetra - Thu, 04 Oct 2018 23:57:26 GMT - View all Bellevue, WA jobs
          Iron Mountain InSight Unlocks Value from Data, Driving Revenue and Better Decision-making      Cache   Translate Page      

Cloud-based content services platform powered by Google Cloud's artificial intelligence and machine learning enables innovative content analytics and information management Now commercially available BOSTON, Oct. 10, 2018 /PRNewswire/ -- Iron Mountain Incorporated (NYSE: IRM)®,...



          Project Consultant / Expert - Blockchain at SAP IBSO - Cloud Service Center - SAP - Sankt Leon-Rot      Cache   Translate Page      
We make innovation real by using the latest technologies around the Internet of Things, blockchain, artificial intelligence / machine learning, and big data and...
Gefunden bei SAP - Thu, 04 Oct 2018 17:33:36 GMT - Zeige alle Sankt Leon-Rot Jobs
          Working Student: SAP Innovative Business Solutions - SAP - Sankt Leon-Rot      Cache   Translate Page      
We make innovation real by using the latest technologies around the Internet of Things, blockchain, artificial intelligence / machine learning, and big data and...
Gefunden bei SAP - Wed, 03 Oct 2018 05:30:12 GMT - Zeige alle Sankt Leon-Rot Jobs
          HDFC Bank to gain access to over 30,000 start-ups through AEP      Cache   Translate Page      

[India], Oct 10 (BusinessWire-India): HDFC Bank has announced the launch of its Accelerator Engagement Programme (AEP). In this first-of-its-kind programme, HDFC Bank will partner with leading start-up accelerators from across the world to gain early access to over 30,000 fintech ideas and innovative solutions.

Start-up accelerators are organisations that help early-stage companies develop their product and business model, and also connect with investors. The bank will focus on those working in the area of artificial intelligence (AI), machine learning (ML), analytics, and robotic process automation.

In the first phase, the Bank launched the programme in association with the United Kingdom's Department for International Trade (UK DIT) and start-up accelerators 91springboard and IvyCamp.

AEP was launched in Mumbai by Nitin Chugh, Country Head - Digital Banking, HDFC Bank in the presence of Ben Green, First Secretary, Trade, DIT; Lord Mayor of the City of London, Alderman Charles Bowman; Mithun Shetty, Head of Community Building at 91springboard; and Vikram Gupta, Founder and Managing Partner, IvyCap.

"We are excited to take our engagements with fintech start-ups global with the Accelerator Engagement Programme. At HDFC Bank, we are looking for disruptive, global first innovations. This enables us to be in sync with our customers' needs and desires and become part of their lifestyle. We believe that we need to work as a partner in the digital eco-system and encourage the spirit of innovation in the country. This is a win-win for all of us," said Nitin Chugh, Country Head - Digital Banking, HDFC Bank.

"I am delighted to help launch this powerful new initiative and even happier that the delegation I've brought to India is the first to benefit from it. Fintech is an area of immense potential for the UK and India, and I look forward to seeing more collaboration between our two countries in this area," said Lord Mayor of London, Charles Bowman.

91SpringBoard engages with more than 25,000 start-ups and fintechs globally while IvyCamp is engaged with more than 5,000.

AEP is part of HDFC Bank's Centre of Digital Excellence, where the objective is to create a mutually beneficial ecosystem with relevant players in the start-up space. Other initiatives include a Digital Command Centre and 'Industry Academia', which aims at mentoring and hand-holding fintechs and start-ups incubated at country's top technical and B-schools. The Bank also organises Digital Innovation Summit, an annual event to identify path-breaking fintech solutions. (BusinessWire-India)


          Jen Underwood Joins DataRobot to Support Expanding Legion of Business Analyst Users      Cache   Translate Page      

Business Intelligence Expert Leads the Charge to Put AI Directly into the Hands of Analytics Professionals Boston, October 9, 2018 – DataRobot, the creator of the automated machine learning category, announced that it has appointed Jen Underwood to lead the company’s rapidly growing community of citizen data scientists. These highly capable and motivated business analytics...

The post Jen Underwood Joins DataRobot to Support Expanding Legion of Business Analyst Users appeared first on DataRobot.


          Machine Learning      Cache   Translate Page      
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          Top Goals and Challenges for AI in Business      Cache   Translate Page      
Reading Time: 4 minutesThe technology may have a very futuristic feel to it, but in current implementations, it’s clear now that artificial intelligence (AI) and machine learning (ML) have very practical real-world applications that aren’t nearly as scary as some may fear. Thanks to a freshly completed study by TECHnalysis Research on the usage of AI applications in…
          UR - Corporate Engineering - Precision Systems Engineer (Maplewood, MN) - 3M - Maplewood, MN      Cache   Translate Page      
Proactively collaborate with business partners to connect and extend process data management solutions with complimentary machine learning and analytics efforts...
From 3M - Wed, 05 Sep 2018 17:09:45 GMT - View all Maplewood, MN jobs
          Data Science Manager - Micron - Boise, ID      Cache   Translate Page      
Create server based visualization applications that use machine learning and predictive analytic to bring new insights and solution to the business....
From Micron - Wed, 05 Sep 2018 11:18:49 GMT - View all Boise, ID jobs
          Intern - Data Scientist (NAND) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Wed, 29 Aug 2018 20:54:50 GMT - View all Boise, ID jobs
          Intern - Data Scientist (DRAM) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Mon, 20 Aug 2018 20:48:37 GMT - View all Boise, ID jobs
          Comment on A Gentle Introduction to Applied Machine Learning as a Search Problem by JG      Cache   Translate Page      
OK
          Comment on How to Train a Final Machine Learning Model by Jason Brownlee      Cache   Translate Page      
Great question. It is really a design decision. You can try to re-fit on all data, without early stopping by perhaps performing a sensitivity analysis on how many epochs are required on average. You could sacrifice the a new validation set and refit a new final model on train-test. There is no single answer, find an approach that makes the most sense for your project and what you know about your model performance and variance.
          Comment on How to Train a Final Machine Learning Model by Xu Zhang      Cache   Translate Page      
Thank you so much for your great article. I understood that we should use all the data which we have to train our final model. However, when should I stop training when I train my final model with dataset including train+validation+test? Especially for a deep learning model. Let me explain it with an example: I have a CNN model with 100,000 examples. I will do the following procedure: 1. I split this dataset into training data 80,000, validation data 10,000 and test data 10,000. 2. I used my validation dataset to guide my training and hyperparameter tuning. Here I used early stopping to prevent overfitting. 3. Then I got my best performance and hyperparameters. From early stopping setting, I got that when I trained my model 37 epochs, the losses were low and performance using test data to evaluate was good. 4 I will finalize my model, train my final model with all my 100,000 data. Here is a problem. Without validation dataset, how can I know when I should stop training, that is how many epochs I should choose when I train my final models. Will I use the same epochs which are used before finalizing the model? or should I match the loss which I got before? I think for the machine learning models without early stopping training, they are no problems. But like deep learning models, when to stop training is a critical issue. Any advice? Thanks.
          Staff Data Scientist - Intuit - Mountain View, CA      Cache   Translate Page      
At least 5 years’ experience in applying Machine Learning techniques to solve business problems. As a member of the Intuit data science community, present in...
From Intuit - Sat, 18 Aug 2018 15:39:46 GMT - View all Mountain View, CA jobs
          Paramount Recruitment: Computational Biologist - Machine Learning      Cache   Translate Page      
Negotiable: Paramount Recruitment: Computational Biologist - Machine Learning Exciting opportunity for a Computational Biologist to join a cutting edge drug discovery organisation based in Oxford. Oxfordshire, England
          Sr/Principal Consultant, Red Team - Cylance, Inc. - Texas      Cache   Translate Page      
Internal / External / Wireless - Penetration Testing (2+ years REQUIRED). By successfully applying artificial intelligence and machine learning to crack the DNA...
From Cylance, Inc. - Wed, 05 Sep 2018 19:27:50 GMT - View all Texas jobs
          Consultant - DigitalShield - White Plains, NY      Cache   Translate Page      
By successfully applying machine learning and artificial. Detailing technical issues identified and their associated business....
From DigitalShield - Wed, 15 Aug 2018 07:47:55 GMT - View all White Plains, NY jobs
          Red Team Consultant - Cylance, Inc. - North Carolina      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Wed, 05 Sep 2018 01:27:47 GMT - View all North Carolina jobs
          Senior Compliance Analyst - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Thu, 13 Sep 2018 19:27:37 GMT - View all Irvine, CA jobs
          Senior Compliance & Privacy Analyst - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Sat, 08 Sep 2018 01:27:53 GMT - View all Irvine, CA jobs
          Financial Reporting Director - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Mon, 13 Aug 2018 07:27:33 GMT - View all Irvine, CA jobs
          Technical Account Manager - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Fri, 27 Jul 2018 19:28:10 GMT - View all Irvine, CA jobs
          Senior DevOps Engineer - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Sun, 08 Jul 2018 19:27:56 GMT - View all Irvine, CA jobs
          QA Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
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, 07 Oct 2018 06:47:33 GMT - View all New York, NY jobs
          Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Thu, 04 Oct 2018 06:17:29 GMT - View all New York, NY jobs
          Back End Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
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, 02 Oct 2018 06:17:24 GMT - View all New York, NY jobs
          NLP Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Thu, 27 Sep 2018 14:36:48 GMT - View all New York, NY jobs
          Business Development Representative - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 16:31:17 GMT - View all New York, NY jobs
          UI/UX Designer - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 16:18:14 GMT - View all New York, NY jobs
          Enterprise Platform Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Recruiter - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Customer Success Manager - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Technical Integrations Project Manager - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Enterprise Technical Product Manager - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Enterprise Account Manager - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Software Architect - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          UI Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
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, 25 Sep 2018 06:27:49 GMT - View all New York, NY jobs
          Mid-Market Account Executive - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Mon, 24 Sep 2018 16:34:54 GMT - View all New York, NY jobs
          Data Science Lead - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Mon, 24 Sep 2018 16:34:54 GMT - View all New York, NY jobs
          Industrial-Organizational Psychologist - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Wed, 19 Sep 2018 06:17:02 GMT - View all New York, NY jobs
          AI Conversation Designer - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Wed, 19 Sep 2018 06:16:47 GMT - View all New York, NY jobs
          Full Stack Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 19 Sep 2018 06:16:43 GMT - View all New York, NY jobs
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page      
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
          Enterprise Account Executive - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Mon, 10 Sep 2018 14:31:19 GMT - View all New York, NY jobs
          Sr. Data Scientist - Life Sciences - Health Catalyst - Salt Lake City, UT      Cache   Translate Page      
Machine learning experience required. The role has great potential for a successful candidate as they will form the initial seed of a new business unit with...
From Health Catalyst - Tue, 11 Sep 2018 02:31:12 GMT - View all Salt Lake City, UT jobs
          (USA-CA-Palo Alto) Talent Operations Specialist      Cache   Translate Page      
About Instart: At Instart, we are building a world where digital experiences continuously adapt to become increasingly engaging with every interaction. Every major brand today, whether it is online shopping, travel or news, is making huge investments to improve their online experiences to keep up with consumer expectations. As consumers in a digital world, we tend to abandon websites very quickly if the experience is not as good as we expect it to be - fast, engaging and responsive. With over 100+ patents using artificial intelligence and machine learning, Instart’s unique technology continually optimizes the online consumer experience, learning from every interaction across devices and networks. We’re growing at a rapid pace - on the path to increase our revenue and customer base by 4x in the next couple of years! You have an opportunity to be a part of this amazing growth, working on cutting-edge AI/ML cloud technology with some of the greatest minds, to help the most recognizable brands in the world create better digital experiences. Instart is backed by major investors who are all looking to see us become the next Unicorn and more – Andreessen Horowitz, Kleiner Perkins, Greylock, and Sutter Hill to name a few. We take pride in the great experience we offer our employees from our flexible work schedules to free lunches and our super cool, dog-friendly office in Palo Alto. Let’s talk about your next career opportunity at Instart. We’re looking for a highly organized, resourceful, and proactive Talent Operations Specialist to join our HR and Talent team in Palo Alto. This role will support the Talent team, HR and hiring managers with the objective of owning and improving recruiting-related processes, ATS management, and the candidate and interview experience. We’re looking for someone who can partner with the VP of HR and Marketing to improve our employment brand and take a holistic, global approach to improving our interview and candidate experience and recruiting processes. Responsibilities: • ATS management – open and close reqs, manage users and candidate flow, automate processes where possible to increase efficiency, train new users and managers on best practices; maintain hiring metrics and run reports as needed • Job postings - work with managers and recruiters to standardize and review job descriptions and discuss where to post jobs • Employment Brand and Social Media Management – manage our profile on Linkedin, Glassdoor, and other social media websites as it relates to employment brand - keep sites updated with pictures, content, awards, update leadership team as needed, etc. • Candidate experience - coordinate with recruiters and hiring managers to make sure candidates are hosted well for interviews. Bring creative ideas on how to improve the candidate experience; request Glassdoor interview reviews after the interview is done, create and send candidates an interview survey to see how we could do better • Interview processes – coordinate with hiring team and recruiters and schedulers to ensure we always have pre-and post-interview meetings. Pre-interview meetings to ensure the interview team reviews the candidate profile, talks about job specs, and assigns the role of each interviewer so that each interviewer is targeting a different aspect of the role and candidate. Ensure post interview debriefs happen to gather candidate feedback; implement the use of candidate scorecards • Draft offer letters in a timely manner - this might mean the occasional after business hours or weekend and then transition the upcoming hire to HR post offer acceptance • Manage Recruiting Dashboard - work with HR and recruiters as needed to maintain the dashboard with open reqs, closed reqs, req #s, indicate req status, enter comp guidelines, and candidate status • Manage hiring events – responsible for registration, coordination, scheduling, set-up, and advertisement of hiring events and job fairs. You may also be asked to participate in the job fair • Bring a creative element and a systematic approach to managing the employee referral program • Vendor and contract management (recruiters and agencies, Linkedin, Jobscore) • Run background checks once offers are accepted Instart is an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
          (USA-CA-Palo Alto) Employee Experience and Office Coordinator      Cache   Translate Page      
About Instart: At Instart, we are building a world where digital experiences continuously adapt to become increasingly engaging with every interaction. Every major brand today, whether it is online shopping, travel or news, is making huge investments to improve their online experiences to keep up with consumer expectations. As consumers in a digital world, we tend to abandon websites very quickly if the experience is not as good as we expect it to be - fast, engaging and responsive. With over 100+ patents using artificial intelligence and machine learning, Instart’s unique technology continually optimizes the online consumer experience, learning from every interaction across devices and networks. We’re growing at a rapid pace - on the path to increase our revenue and customer base by 4x in the next couple of years! You have an opportunity to be a part of this amazing growth, working on cutting-edge AI/ML cloud technology with some of the greatest minds, to help the most recognizable brands in the world create better digital experiences. Instart is backed by major investors who are all looking to see us become the next Unicorn and more – Andreessen Horowitz, Kleiner Perkins, Greylock, and Sutter Hill to name a few. We take pride in the great experience we offer our employees from our flexible work schedules to free lunches and our super cool, dog-friendly office in Palo Alto. Let’s talk about your next career opportunity at Instart. We’re looking for someone who takes the office coordinator role to another level! We want someone to be the ambassador of the employee experience at Instart. This person will think about how to build a sense of fun and community with our employees globally through internal communications, events, the use of social media or slack – get creative! Here are some of the other responsibilities: Responsibilities: • Be available at the front desk between 9-5pm to answer calls, greet and direct visitors and vendors, receive deliveries and catering. • Bring a creative and fun element to HQ, improving the overall Employee Experience at Instart via events, perks, internal communications, and enhancements to our workspace • Manage the logistics of all events at HQ – calendaring, decorations, catering, and vendor management • Office Management – we want our Employee Experience Coordinator to have a sense of ownership and pride in our physical workspace and the employee experience while at Instart. You will be responsible for general upkeep, cleanliness, ordering lunch, and supply management for the office as well as responsible for getting employees involved in fun events, volunteer activities and tech talks, fireside chats and lunch and learn discussions Qualifications: • Bachelor’s degree or equivalent work experience • Must be reliable and highly organized, and must have impeccable follow-through, an eye for detail, and a high quality of work • The ideal candidate will work well in a team environment, knowing how to get introverted employees involved in activities, but is also self-driven and resourceful enough to get things done alone • Most importantly, we’re looking for the right cultural fit - a customer service oriented​ ​​​​​​person who genuinely enjoys taking care of their team and having fun Instart is an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
          Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.      Cache   Translate Page      
Icon for Silverchair Information Systems Related Articles

Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.

J Am Med Inform Assoc. 2018 Oct 06;:

Authors: Carlin CS, Ho LV, Ledbetter DR, Aczon MD, Wetzel RC

Abstract
Objective: Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode.
Methods: EMR data from 7256 survivor PICU episodes (5632 patients) collected between 2009 and 2017 at Children's Hospital Los Angeles was analyzed. Each episode contained 375 variables representing physiology, labs, interventions, and drugs. Between medical and physical discharge, when clinicians determined the patient was ready for ICU discharge, they were assumed to be in a physiologically acceptable state space (PASS) for discharge. Each patient's heart rate, systolic blood pressure, diastolic blood pressure in the PASS window were measured and compared to age-normal values, regression-quantified PASS predictions, and recurrent neural network (RNN) PASS predictions made 12 hours after PICU admission.
Results: Mean absolute errors (MAEs) between individual PASS values and age-normal values (HR: 21.0 bpm; SBP: 10.8 mm Hg; DBP: 10.6 mm Hg) were greater (p < .05) than regression prediction MAEs (HR: 15.4 bpm; SBP: 9.9 mm Hg; DBP: 8.6 mm Hg). The RNN models best approximated individual PASS values (HR: 12.3 bpm; SBP: 7.6 mm Hg; DBP: 7.0 mm Hg).
Conclusions: The RNN model predictions better approximate patient-specific PASS values than regression and age-normal values.

PMID: 30295770 [PubMed - as supplied by publisher]


          Senior UI Developer      Cache   Translate Page      
CA-Palo Alto, Hi! We are a VC backed, AI/Machine Learning technology company that just received a large round of funding and are in a major growth phase. Our Executive team is well-experienced in creating successful companies that have been sold to large Fortune level institutions. The space we are focused on within Enterprise automation is a multi-billion opportunity and our solutions are getting national reco
          IBM And NVIDIA Collaborate To Expand Open Source Machine Learning Tools For Data Scientists      Cache   Translate Page      

Click to view a price quote on IBM.

Click to research the Computer Software & Services industry.

          Maxar Technologies' DigitalGlobe And Ecopia Tech Corporation Produce First High-precision U.S. Building Footprints Dataset Created With Machine Learning      Cache   Translate Page      

Click to view a price quote on MAXR.

Click to research the Telecommunications industry.

          Convexity and Operational Interpretation of the Quantum Information Bottleneck Function. (arXiv:1810.03644v1 [quant-ph])      Cache   Translate Page      

Authors: Nilanjana Datta, Christoph Hirche, Andreas Winter

In classical information theory, the information bottleneck method (IBM) can be regarded as a method of lossy data compression which focusses on preserving meaningful (or relevant) information. As such it has recently gained a lot of attention, primarily for its applications in machine learning and neural networks. A quantum analogue of the IBM has recently been defined, and an attempt at providing an operational interpretation of the so-called quantum IB function as an optimal rate of an information-theoretic task, has recently been made by Salek et al. However, the interpretation given in that paper has a couple of drawbacks; firstly its proof is based on a conjecture that the quantum IB function is convex, and secondly, the expression for the rate function involves certain entropic quantities which occur explicitly in the very definition of the underlying information-theoretic task, thus making the latter somewhat contrived. We overcome both of these drawbacks by first proving the convexity of the quantum IB function, and then giving an alternative operational interpretation of it as the optimal rate of a bona fide information-theoretic task, namely that of quantum source coding with quantum side information at the decoder, and relate the quantum IB function to the rate region of this task. We similarly show that the related privacy funnel function is convex (both in the classical and quantum case). However, we comment that it is unlikely that the quantum privacy funnel function can characterize the optimal asymptotic rate of an information theoretic task, since even its classical version lacks a certain additivity property which turns out to be essential.


          Find the dimension that counts: Fast dimension estimation and Krylov PCA. (arXiv:1810.03733v1 [cs.NA])      Cache   Translate Page      

Authors: Shashanka Ubaru, Abd-Krim Seghouane, Yousef Saad

High dimensional data and systems with many degrees of freedom are often characterized by covariance matrices. In this paper, we consider the problem of simultaneously estimating the dimension of the principal (dominant) subspace of these covariance matrices and obtaining an approximation to the subspace. This problem arises in the popular principal component analysis (PCA), and in many applications of machine learning, data analysis, signal and image processing, and others. We first present a novel method for estimating the dimension of the principal subspace. We then show how this method can be coupled with a Krylov subspace method to simultaneously estimate the dimension and obtain an approximation to the subspace. The dimension estimation is achieved at no additional cost. The proposed method operates on a model selection framework, where the novel selection criterion is derived based on random matrix perturbation theory ideas. We present theoretical analyses which (a) show that the proposed method achieves strong consistency (i.e., yields optimal solution as the number of data-points $n\rightarrow \infty$), and (b) analyze conditions for exact dimension estimation in the finite $n$ case. Using recent results, we show that our algorithm also yields near optimal PCA. The proposed method avoids forming the sample covariance matrix (associated with the data) explicitly and computing the complete eigen-decomposition. Therefore, the method is inexpensive, which is particularly advantageous in modern data applications where the covariance matrices can be very large. Numerical experiments illustrate the performance of the proposed method in various applications.


          Services Operations Manager - Highspot - Seattle, WA      Cache   Translate Page      
Equipped with new Apple products. We employ advanced technologies, included patented machine learning algorithms to:....
From Highspot - Mon, 01 Oct 2018 20:07:27 GMT - View all Seattle, WA jobs
          Mobile Engineer React Native - Xevo - Bellevue, WA      Cache   Translate Page      
Technical and non-technical, internal and external. Do you dream about artificial intelligence and machine learning?...
From Xevo - Tue, 02 Oct 2018 02:28:59 GMT - View all Bellevue, WA jobs
          Sr Software Engineer, Applied Machine Learning - Apple - Austin, TX      Cache   Translate Page      
We work on many high-impact projects that serve various Apple lines of business. Understanding of JVM internals and garbage collection....
From Apple - Sun, 30 Sep 2018 01:50:35 GMT - View all Austin, TX jobs
          Selective inference for effect modification via the lasso. (arXiv:1705.08020v3 [stat.ME] UPDATED)      Cache   Translate Page      

Authors: Qingyuan Zhao, Dylan S. Small, Ashkan Ertefaie

Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters from the treatment effect of interest and use machine learning algorithms to estimate the nuisance parameters. Next, after plugging in the estimates of the nuisance parameters, we use the Lasso to choose a low-complexity model for effect modification. Compared to a full model consisting of all the covariates, the selected model is much more interpretable. Compared to the univariate subgroup analyses, the selected model greatly reduces the number of false discoveries. We show that the conditional selective inference for the selected model is asymptotically valid given the rate assumptions in classical semiparametric regression. Extensive simulation studies are conducted to verify the asymptotic results and an epidemiological application is used to demonstrate the method.


          How should we (correctly) compare multiple graphs?. (arXiv:1807.03368v3 [cs.DM] UPDATED)      Cache   Translate Page      

Authors: Sam Safavi, Jose Bento

Graphs are used in almost every scientific discipline to express relations among a set of objects. Algorithms that compare graphs, and output a closeness score, or a correspondence among their nodes, are thus extremely important. Despite the large amount of work done, many of the scalable algorithms to compare graphs do not produce closeness scores that satisfy the intuitive properties of metrics. This is problematic since non-metrics are known to degrade the performance of algorithms such as distance-based clustering of graphs (Stratis et al. 2018). On the other hand, the use of metrics increases the performance of several machine learning tasks (Indyk et al. 1999, Clarkson et al. 1999, Angiulli et al. 2002 and Ackermann et al, 2010). In this paper, we introduce a new family of multi-distances (a distance between more than two elements) that satisfies a generalization of the properties of metrics to multiple elements. In the context of comparing graphs, we are the first to show the existence of multi-distances that simultaneously incorporate the useful property of alignment consistency (Nguyen et al. 2011), and a generalized metric property, and that can be computed via convex optimization.


          Software Engineer - Machine Learning - Convoy - Seattle, WA      Cache   Translate Page      
Today, we use machine learning to figure out freight prices, shipment relevance for carriers, auction bidding strategy, and other internal processes....
From Convoy - Mon, 17 Sep 2018 10:12:27 GMT - View all Seattle, WA jobs
          Sr. Manager, Data Science - eBay Inc. - Austin, TX      Cache   Translate Page      
Expertise in machine learning, deployment of models in real time environments, and accompanying big data tools eBay is a Subsidiary of eBay....
From eBay Inc. - Sat, 22 Sep 2018 08:04:41 GMT - View all Austin, TX jobs
          All Levels Marketing Sciences - Hypothesis Group - Los Angeles, CA      Cache   Translate Page      
Understanding of machine learning techniques and algorithms. Active support in research design for new business....
From Hypothesis Group - Tue, 11 Sep 2018 20:07:41 GMT - View all Los Angeles, CA jobs
          Community Manager - Alation Inc. - Redwood City, CA      Cache   Translate Page      
Machine Learning Data Catalogs, 2018. Act as a liaison between Alation’s internal teams and the Community. You’ll work with a strong set of brand-name customers...
From Alation Inc. - Thu, 23 Aug 2018 20:35:18 GMT - View all Redwood City, CA jobs
          导师招生 香港城市大学计算机博士项目      Cache   Translate Page      
导师招生 香港城市大学计算机博士项目 2019 PhD Student Openings in Applied Machine Learning and Bioinformatics at CityU(HK) (http://bioinfo.cs.cityu.edu.hk/) Each Fellowship recipient will receive financial award in the amount of USD169,200 for th ...
          DevOps - Full Stack - Symetra - Bellevue, WA      Cache   Translate Page      
AWS - Lambda, DynamoDB, Cognito, SNS, API Gateway, EC2, CloudFront, S3. Azure - Functions, EventHub, Machine Learning, CosmosDB, Kubernetes Services, NLP, and...
From Symetra - Thu, 04 Oct 2018 23:57:26 GMT - View all Bellevue, WA jobs
          Nvidia launches Rapids to help bring GPU acceleration to data analytics      Cache   Translate Page      
Nvidia, together with partners like IBM, HPE, Oracle, Databricks and others, is launching a new open-source platform for data science and machine learning today. Rapids, as the company is calling it, is all about making it easier for large businesses to use the power of GPUs to quickly analyze massive amounts of data and then […]
          Software Architect - Server, Scala      Cache   Translate Page      
CA-Palo Alto, Hi! We are a VC backed, AI/Machine Learning technology company that just received a large round of funding and are in a major growth phase. Our Executive team is well-experienced in creating successful companies that have been sold to large Fortune level institutions. The space we are focused on within Enterprise automation is a multi-billion opportunity and our solutions are getting national reco
          Principal Technologist - Machine Learning and Data Science - Blue Origin - Kent, WA      Cache   Translate Page      
While in this role, you will leverage your extensive experience in machine learning and data science to accelerate and innovate across business areas to drive...
From Blue Origin - Thu, 13 Sep 2018 23:30:35 GMT - View all Kent, WA jobs
          Machine Learning / Artificial Intelligence Scientist Site Lead      Cache   Translate Page      
CA-Palo Alto, Ford Global Data Insight and Analytics (GDI&A) is looking for a data scientist with an emphasis on Machine Learning and scalable computing. You will have the opportunity to work with some of the brightest global subject matter experts that are transforming the automobile industry. We are seeking an experienced Machine Learning scientist to assist in all phases of project work, including problem fo
          Understanding AI: A Simple Explanation of AI, Machine Learning and Deep Learning      Cache   Translate Page      
Almost every piece of media content covering the latest technology in the world mentions AI at some point. Is there any piece of tech that includes coding not described as AI, Machine Learning or Deep Learning these days? If there is, it’s an exception to the rule. So when did software all become AI? What […]
          Data Elixir - Issue 203      Cache   Translate Page      

In the News

Data Factories

Ben Thompson writes at the intersection of technology and business strategy in his popular Stratechery blog. In this post, he explores how big online advertising businesses are essentially "data factories" and what that means for everyday users, businesses, and regulators.

stratechery.com

Why you should be data-informed and not data-driven

Everyone wants to be "data driven" these days but that's not always the best approach. This article takes a look at the risks of being a data driven culture and offers a well-reasoned alternative.

hackernoon.com

Sponsored Link

Introduction to "Advances in Financial Machine Learning"

Inspired by Marcos Lopez de Prado's book "Advances in Financial Machine Learning," Quantopian explores the various factors to consider when researching investing through the lens of machine learning. For more posts like this, sign up for Quantopian's free platform and become an expert in Quant Finance.

bit.ly

Tools and Techniques

The hacker's guide to uncertainty estimates

Being able to quantify uncertainty is key for really understanding your data. This is a great walk-through of a variety of methods, including bootstrapping, confidence intervals, regression and Monte Carlo methods.

erikbern.com

How to deliver on Machine Learning projects

The process of developing machine learning models is very different than what most engineers are accustomed to. In this post, Emmanuel Ameisen describes the differences and introduces an approach he calls the "ML Engineering Loop." It's an iterative approach that enables rapid discovery and development of the best models.

insightdatascience.com

A Review of the Recent History of Natural Language Processing (NLP)

Sebastian Ruder's latest post offers high-level overviews of recent NLP advancements with a focus on neural network-based methods. This is organized around 8 key milestones and includes lots of linked references.

aylien.com

A Database Diagram Designer Built for Developers and Analysts

dbdiagram.io is a database diagrams designer for analysts & developers. Create and visualize database schemas using just your keyboard.

hackernoon.com

Data Viz

The av Package: Production Quality Video in R

av is a new package for working with audio/video directly from R. It uses the FFmpeg AV libraries and it enables you to easily create and edit videos using FFmpeg's video editing library. Here are the highlights, along with code snippets and embedded video examples.

ropensci.org

Career

An Introduction to the Data Product Management Landscape

As the field of Data Product Management matures, it's dividing into multiple sub-areas. This article takes a look at the evolving role of Data PMs and where things are going.

insightdatascience.com

Jobs & Careers

Hiring?

Post on Data Elixir's Job Board to reach a wide audience of data professionals.

dataelixir.com

Recent Listings:

More data science jobs >>

In Case You Missed It

Be sure to catch the most popular articles from last week's Data Elixir...

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          QA Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
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, 07 Oct 2018 06:47:33 GMT - View all New York, NY jobs
          Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Thu, 04 Oct 2018 06:17:29 GMT - View all New York, NY jobs
          Back End Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
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, 02 Oct 2018 06:17:24 GMT - View all New York, NY jobs
          NLP Data Scientist - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Thu, 27 Sep 2018 14:36:48 GMT - View all New York, NY jobs
          Business Development Representative - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 16:31:17 GMT - View all New York, NY jobs
          UI/UX Designer - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 16:18:14 GMT - View all New York, NY jobs
          Enterprise Platform Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Recruiter - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Customer Success Manager - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Technical Integrations Project Manager - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          6 ways to make machine learning fail      Cache   Translate Page      

The process of learning in general often means making mistakes and taking the wrong paths, and then figuring out how to avoid these pitfalls in the future. Machine learning is no different.

As you implement machine learning in your enterprise, be careful: Some of technology marketing might suggest that the learning is very right very fast, an unrealistic expectation for the technology. But the truth is that there are bound to be mistakes in the machine learning process. And these mistakes can get encoded, at least for a while, in business processes. The result: Those mistakes now happen at scale and often outside immediate human control.

To read this article in full, please click here

(Insider Story)
          Enterprise Technical Product Manager - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Enterprise Account Manager - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          Software Architect - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 26 Sep 2018 14:35:24 GMT - View all New York, NY jobs
          UI Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
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, 25 Sep 2018 06:27:49 GMT - View all New York, NY jobs
          Mid-Market Account Executive - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Mon, 24 Sep 2018 16:34:54 GMT - View all New York, NY jobs
          Data Science Lead - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Mon, 24 Sep 2018 16:34:54 GMT - View all New York, NY jobs
          Industrial-Organizational Psychologist - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Wed, 19 Sep 2018 06:17:02 GMT - View all New York, NY jobs
          AI Conversation Designer - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Wed, 19 Sep 2018 06:16:47 GMT - View all New York, NY jobs
          Full Stack Engineer - Wade & Wendy - New York, NY      Cache   Translate Page      
Our team is backed by Jazz VP, Slack, ffVC, Randstad and other great VCs, as we bring AI and machine learning to the recruiting/HR space - all in order to make...
From Wade & Wendy - Wed, 19 Sep 2018 06:16:43 GMT - View all New York, NY jobs
          Enterprise Account Executive - Wade & Wendy - New York, NY      Cache   Translate Page      
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 - Mon, 10 Sep 2018 14:31:19 GMT - View all New York, NY jobs
          Ingénieur Analyse de Données et Logiciels - Intelligence Manufacturière - Data Analytics and Software Engineer – Manufacturing Intelligence - Alcoa Corp. - Deschambault, QC      Cache   Translate Page      
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      
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
          แอพ K PLUS เวอร์ชันใหม่มาแล้ว โลโก้ใหม่ หน้าตาใหม่ ต่อ Wi-Fi ได้ตลอดเวลา      Cache   Translate Page      

ธนาคารกสิกรไทย เปิดตัวแอพ K PLUS เวอร์ชันใหม่ ตามที่เคยนำบางส่วนมาโชว์ก่อนหน้านี้

การเปลี่ยนแปลงรอบนี้ถือเป็นการเปลี่ยนครั้งใหญ่ในทุกส่วน ตั้งแต่โลโก้ หน้าตาของแอพ ไปจนถึงฟีเจอร์หลายอย่างที่เพิ่มขึ้น เช่น กดเงินสดไม่ใช้บัตร สลิปโอนเงินพร้อม QR ตรวจสอบย้อนกลับได้ว่าเป็นของจริง และการนำ AI เข้ามาช่วยอำนวยความสะดวกให้ผู้ใช้

นายพัชร สมะลาภา กรรมการผู้จัดการ ธนาคารกสิกรไทย บอกว่า K PLUS ตัวเก่าออกแบบมาเพื่อการทำธุรกรรมทางการเงินเพียงอย่างเดียว แต่ K PLUS ตัวใหม่ต้องคิดใหม่ เน้นผู้ใช้เป็นศูนย์กลาง ไม่ได้เป็นการทำธุรกรรมเพียงอย่างเดียว แต่มีบริการอื่นๆ ที่ไปไกลกว่าธนาคาร และไม่จำเป็นต้องมาจากธนาคารกสิกรไทยเพียงอย่างเดียว

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โลโก้ใหม่

การเปลี่ยนแปลงอย่างแรกสุดที่เห็นได้ชัดเจนคือโลโก้ K PLUS ดีไซน์ใหม่ ที่ใช้แทนโลโก้ตัว K ลายพู่กันที่ใช้มานาน 70 ปี (เปลี่ยนเฉพาะโลโก้ของ K PLUS เท่านั้น โลโก้เดิมยังอยู่ในฐานะโลโก้ของธนาคาร)

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หน้าตาใหม่ ออกแบบใหม่หมด

หน้าตาของแอพ K PLUS ยังดีไซน์ใหม่หมด ย้ายปุ่มและเมนูไปอยู่ด้านล่างสุดของหน้าจอตามอย่างแอพยุคใหม่ ประกอบด้วย 5 ปุ่มหลักคือ

  • หน้าแรก
  • K+ Market
  • ธุรกรรม
  • สแกน
  • อื่นๆ

หน้าแรก (Home) ของแอพ K PLUS ตัวใหม่ ประกอบด้วยสองส่วนหลักคือ K+ Today ที่เป็นหน้าจอแจ้งเตือนข้อมูลสำคัญต่างๆ เช่น ธุรกรรมที่เกิดขึ้น และส่วน Favourites ที่เป็นปุ่มลัดสำหรับธุรกรรมที่ใช้บ่อย

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ส่วนหน้าธุรกรรม ซึ่งเป็นเมนูหลักของแอพที่อยู่ตรงกลาง เป็นการรวบรวมคำสั่งเกี่ยวกับการทำธุรกรรมทั้งหมดมาไว้ที่เดียว เช่น การโอนเงิน เติมเงิน จ่ายบิล ฯลฯ แก้โจทย์ปัญหาของแอพเวอร์ชันเดิมที่เมนูกระจัดกระจาย ทำให้ทำงานธุรกรรมต่างๆ ได้ง่ายขึ้น

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อีกเมนูที่น่าสนใจคือ K+ Market เป็นการรวมดีลสินค้าและบริการต่างๆ ที่สามารถกดซื้อได้จากแอพ K PLUS โดยตรง แถมนอกจากจ่ายด้วยเงินในบัญชีแล้ว ยังสามารถใช้แต้มบัตรเครดิตได้ด้วย

No Description

ต่อ Wi-Fi ได้ตลอดเวลาแล้ว

อีกฟีเจอร์เล็กๆ ที่สำคัญเพราะผู้ใช้เรียกร้องกันมานาน คือการเชื่อมต่อ Wi-Fi ได้ตลอดเวลา โดยไม่ต้องมากดใหม่ทุก 90 วันอีกแล้ว แต่ผู้ใช้ยังสามารถเปิดปิดเองได้เมื่อต้องการ หากต้องการความมั่นใจในการเชื่อมต่อผ่านเครือข่ายมือถือ

No Description

e-Slip สลิปโอนเงินมี QR ช่วยเช็คได้ว่าเป็นของจริง

ฟีเจอร์น่าสนใจคือ e-Slip สลิปการโอนเงินแบบใหม่ที่เพิ่ม QR Code เข้ามาในสลิปให้ด้วย ช่วยให้ผู้รับโอนปลายทางสามารถตรวจสอบย้อนกลับได้ว่าเป็นสลิปของจริง ไม่ได้เป็นสลิปที่ผ่านการปลอมแปลงหรือตัดต่อมา

No Description

กดเงินสดไม่ใช้บัตร มาแล้ว

นอกจากนี้ K PLUS ยังรองรับการกดเงินสดจากตู้ ATM แบบไม่ต้องใช้บัตรแล้ว โดยจะเป็นการสแกน QR บนหน้าจอตู้ ATM ด้วยกล้องมือถือร่วมกับการกรอก PIN เพื่อยืนยันความปลอดภัยอีกชั้น

No Description

รวมบัตรสมาชิกไว้ในแอพ

ฟีเจอร์อีกตัวคือ 'บัตรสมาชิก' ซึ่งเป็นการนำบัตรสมาชิก บัตรสะสมแต้มต่างๆ มาอยู่ไว้ในแอพ และสามารถใช้แต้มที่สะสมไว้เพื่อใช้จ่ายในแอพได้ด้วย เบื้องต้นมีบัตรสมาชิกของบางบริษัทเข้าร่วมแล้ว เช่น บัตร Blue Card ของกลุ่ม ปตท.

No Description

KADE พลัง AI ช่วยสร้างบริการใหม่ๆ ให้ผู้ใช้เป็นรายบุคคล

นอกจากการเปลี่ยนแปลงของตัวแอพทั้งหน้าตาและฟีเจอร์ เป้าหมายในระยะยาวของ K PLUS คือการเป็นแพลตฟอร์ม (K PLUS Intelligence Platform) เพื่อสร้างประสบการณ์ที่ตรงใจลูกค้าเป็นรายบุคคล

หัวใจสำคัญของ K PLUS ตัวใหม่คือ “เกด” (KADE: K PLUS AI-Driven Experience) เทคโนโลยีปัญญาประดิษฐ์ที่เคยเปิดตัวไปก่อนหน้านี้ ใช้เทคนิคด้าน machine learning เรียนรู้พฤติกรรมของลูกค้าแต่ละคน และนำเสนอข้อมูลที่ตรงกับความต้องการของลูกค้า

ตัวอย่างการใช้งานคือฟีเจอร์ K+ Today ในหน้าโฮม ผู้ใช้แต่ละคนจะได้รับข้อความแจ้งเตือนที่แตกต่างกัน เช่น หากจะเดินทางไปต่างประเทศก็อาจนำเสนอบริการแลกเงินหรือประกันการเดินทาง นอกจากนี้ยังมี สินเชื่อส่วนบุคคล (K-Personal Loan) และสินเชื่อธุรกิจ ที่ใช้เทคโนโลยีแมชชีน เลนดิ้ง (Machine Lending) มาช่วยประเมินความเสี่ยงด้วย

เป้าหมายระยะยาวของ KADE คือให้เป็นที่ปรึกษาการเงินให้กับผู้ใช้เป็นรายบุคคล (Robo Advisor) สามารถแนะนำการออม การลงทุนให้ลูกค้าเป็นรายบุคคล, เพิ่มความสามารถ Face Recognition ให้จ่ายเงินที่ร้านค้าด้วยใบหน้าได้เลย, รองรับการสั่งงานด้วยเสียงโดยไม่ต้องกดปุ่มบนหน้าจอเลย (เช่น "เติมเงินทางด่วน 300 บาท")

แนวคิดอีกอย่างของ K PLUS เวอร์ชันนี้คือ การออกแบบโครงสร้างเทคโนโลยีให้เป็น Open Platform รองรับการเชื่อมต่อกับช่องทางบริการและพันธมิตรในธุรกิจอื่นๆ ด้วย

แอพ K PLUS ตัวใหม่จะปล่อยอัพเดตทั้งเวอร์ชัน iOS และ Android ในวันนี้ (10 ตุลาคม 2561)


          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
From Lincoln Financial Group - Wed, 26 Sep 2018 21:21:38 GMT - View all Boston, MA jobs
          Analytics Consultant - PRA Health Sciences - Phoenix, AZ      Cache   Translate Page      
Stat / Mathematical / Business Analytics background. Have studied or done machine learning projects. Incorporate business information and data from a variety of...
From PRA Health Sciences - Thu, 04 Oct 2018 14:33:18 GMT - View all Phoenix, AZ jobs
          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page      
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
          10/10/2018: Düsseldorfer Wirtschaft: Innovationstag heute an der Ackerstraße      Cache   Translate Page      
(tb) Der Innovationshub öffnet heute zum vierten Mal seine Türen für den diesjährigen Innovationstag. Die Workshops beschäftigen sich in diesem Jahr unter anderem mit den Themen Blockchain, 3D Druck, Protoytyping, Machine Learning,...
          Senior UI Developer      Cache   Translate Page      
CA-Palo Alto, Hi! We are a VC backed, AI/Machine Learning technology company that just received a large round of funding and are in a major growth phase. Our Executive team is well-experienced in creating successful companies that have been sold to large Fortune level institutions. The space we are focused on within Enterprise automation is a multi-billion opportunity and our solutions are getting national reco
          Real Time Online Training On T-SQL & SQL Server @ SQL School (Mildura)      Cache   Translate Page      
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 ...
          »KI ist die Zukunft, kein Feature«      Cache   Translate Page      
Mit CRN spricht Marc Bamberg, Senior Regional Partner Manager DACH bei Cylance, über die Sicherheitslösungen des Security-Startups und welche Vorteile diese dank KI und Machine Learning gegenüber traditionellen Lösungen bieten.
          Instructor (Data Science, Artificial Intelligence, Machine Learning) - Cortechma Inc. - Thornhill, ON      Cache   Translate Page      
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
          Microsoft open-sources Infer.Net machine learning      Cache   Translate Page      

Microsoft has released through open source its Infer.Net cross-platform framework for model-based machine learning.

Infer.Net will become part of the ML.Net machine learning framework for .Net developers, with Infer.Net extending ML.Net for statistical modeling and online learning. Several steps toward integration already have been taken, including the setting up of a repo under the .Net Foundation.

Microsoft cited the applicability of Infer.Net to three use cases:

To read this article in full, please click here


          (USA-TX-Austin) Data Scientist      Cache   Translate Page      
Job Description Ticom Geomatics, a CACI Company, delivers industry leading Signals Intelligence and Electronic Warfare (SIGINT/EW) products that enable our nation’s tactical war fighters to effectively utilize networked sensors, assets, and platforms to perform a variety of critical national security driven missions. We are looking for talented, passionate Engineers, Scientists, and Developers who are excited about using state of the art technologies to build user-centric products with a profound impact to the US defense and intelligence community. We are seeking to grow our highly capable engineering teams to build the best products in the world. The successful candidate is an individual who is never satisfied with continuing with the status quo just because “it’s the way things have always been done”. What You'll Get to Do: The prime responsibility of the Data Scientist position is to provide support for the design, development, integration, test and maintenance of CACI’s Artificial Intelligence and Machine Learning product portfolio. This position is based in our Austin, TX office. For those outside of the Austin area, relocation assistance is considered on a case by case basis Duties and Responsibilities: - Work within a cross-disciplinary team to develop new machine learning-based software applications. Position is responsible for implementing machine learning algorithms by leveraging open source and custom machine learning tools and techniques - Use critical thinking to assess deficiencies in existing machine learning or expert system-based applications and provide recommendations for improvement - Generate technical documentation to include software description documents, interface control documents (ICDs) and performance analysis reports - Travel to other CONUS locations as required (up to 25%) You’ll Bring These Qualifications: - Degree in Computer Science, Statistics, Mathematics or Electrical & Computer Engineering from an ABET accredited university with a B.S degree and a minimum of 7 years of related experience, or a M.S. degree and 5 years of experience, or a PhD with a minimum of 2 years of academic or industry experience. - In depth knowledge and practical experience using a variety of machine learning techniques including: linear regression, logistic regression, neural networks, state vector machines, anomaly detection, natural language processing and clustering techniques - Expert level knowledge and practical experience with C++, Python, Keras, TensorFlow, PyTorch, Caffe, Docker - Technical experience in the successful design, development, integration, test and deployment of machine learning based applications - Strong written and verbal communication skills - Self-starter that can work with minimum supervision and has good team interaction skills - US citizenship is required along with the ability to obtain a TS/SCI security clearance Desired Qualifications: - Basic understanding and practical experience with digital signal processing techniques - Experience working with big data systems such as Hadoop, Spark, NoSQL and Graph Databases - Experience working within research and development (R&D) environments - Experience working within Agile development teams leveraging DevOps methodology - Experience working within cross-functional teams following a SCRUM/Sprint-based project execution - Experience implementing software within a Continuous Integration, Continuous Deployment environment - Experience delivering software systems for DoD customers What We can Offer You: - We’ve been named a Best Place to Work by the Washington Post. - Our employees value the flexibility at CACI that allows them to balance quality work and their personal lives. - We offer competitive benefits and learning and development opportunities. - We are mission-oriented and ever vigilant in aligning our solutions with the nation’s highest priorities. - For over 55 years, the principles of CACI’s unique, character-based culture have been the driving force behind our success. Ticom Geomatics (TGI) is a subsidiary of CACI International, Inc. in Austin, Texas with ~200 employees.” We’ve recently been named by Austin American Statesman as one of the Top Places to Work in Austin. We are an industry leader in interoperable, mission-ready Time and Frequency Difference of Arrival (T/FDOA) Precision Geolocation systems and produce diverse portfolio of Intelligence, Surveillance and Reconnaissance (ISR) products spanning small lightweight sensors, rack-mounted deployments, and cloud-based solutions which are deployed across the world. The commitment of our employees to "Engineering Results" is the catalyst that has propelled TGI to becoming a leader in software development, R&D, sensor development, and signal processing. Our engineering teams are highly adept at solving complex problems with the application of leading-edge technology solutions. Our work environment is highly focused yet casual with flexible schedules that enable each of our team members to achieve the work life balance that works for them. We provide highly competitive benefits package including a generous 401(k) contribution and Paid Time Off (PTO) policy. See additional positions at: http://careers.caci.com/page/show/TGIJobs Job Location US-Austin-TX-AUSTIN CACI employs a diverse range of talent to create an environment that fuels innovation and fosters continuous improvement and success. At CACI, you will have the opportunity to make an immediate impact by providing information solutions and services in support of national security missions and government transformation for Intelligence, Defense, and Federal Civilian customers. CACI is proud to provide dynamic careers for employees worldwide. CACI is an Equal Opportunity Employer - Females/Minorities/Protected Veterans/Individuals with Disabilities.
          Tech: The big problem with machine learning algorithms      Cache   Translate Page      
From Bloomberg: Machine learning is enabling investors to tap huge data sets such as social media postings in ways that no mere human could. Yet, despite the enormous potential, its record remains mixed. The Eurekahedge AI Hedge Fund Index, which tracks the returns of 13 hedge funds that use machi...
Article link
          Viasat Unveils Cloud-Enabled Solutions for AI and Machine Learning Applications Over Secure SATCOM and LOS Network      Cache   Translate Page      
Viasat Inc. (NASDAQ: VSAT), a global communications company, today announced the availability of secure cloud-enabled artificial intelligence (AI) and machine learning applications over Viasat's global satellite communications (SATCOM) architecture and line of sight (LOS) tactical network technologies for warfighters on the move. By offering a secure, integrated network of cloud-enabled solutions, Viasat can reduce warfighters' cognitive loads in order to make more accurate, informed,
          Instagram estrena herramienta contra el ciberacoso      Cache   Translate Page      

Instagram se ha propuesto ser el adalid de la positividad y el buenrollismo de este siglo. En la red social de fotos de la casa Facebook no caben haters ni acosadores, especialmente aquellos que puedan perjudicar a su público más joven, los adolescentes. Por eso, acaba de presentar una batería de medidas para combatir el bullyng, incluida una inteligencia artificial -machine learning- para escanear fotos e identificar el contenido "problemático".

Seguir leyendo....


          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page      
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
          Sr. Data Scientist - Life Sciences - Health Catalyst - Salt Lake City, UT      Cache   Translate Page      
Machine learning experience required. The role has great potential for a successful candidate as they will form the initial seed of a new business unit with...
From Health Catalyst - Tue, 11 Sep 2018 02:31:12 GMT - View all Salt Lake City, UT jobs
          Instagram To Use Machine Learning To Spot Bullying In Photos      Cache   Translate Page      
Bullying and harassment online is unfortunately more common than we’d like, although Instagram has introduced various tools to help keep such bullying out of the comments section. However what if the bullying was done in a photo where regular filters can’t pick up on it? That’s an area that Instagram plans to address using machine learning. The company plans on using machine learning to try and spot bullying that might […]
          Impact on Revenue Post Technology Automation      Cache   Translate Page      

Advancement of technology like robotics, artificial intelligence, machine learning and other automation techniques have no doubt created a revolution in the way business is conducted. Machines outperform and exceed the human performances in the work activities especially the repetitive tasks. Now, automation technologies along with new and improved algorithms have gathered the knowledge of decision-making processes and works like a human being to a certain level.

The post Impact on Revenue Post Technology Automation appeared first on Technically Easy.


          Interesting Stuff - Week 40      Cache   Translate Page      

Throughout the week, I read a lot of blog-posts, articles, and so forth, that has to do with things that interest me:

data science data in general distributed computing SQL Server transactions (both db as well as non db) and other “stuff”

This blog-post is the “roundup” of the things that have been most interesting to me, for the week just ending.

.NET Update on .NET Core 3.0 and .NET Framework 4.8 . A blog post from the .NET engineering team, where they talk about the future of the .NET Framework and .NET Core. I wonder if this post was prompted by speculations recently about the future of the .NET Framework, where there were questions whether the .NET Framework 4.8 would be the last version, and all development would be concentrated on .NET Core. Azure Enabling real-time data warehousing with Azure SQL Data Warehouse . This post is an announcement how Striim now fully supports SQL Data Warehouse as a target for Striim for Azure. Striim is a system which enables continuous non-intrusive performant ingestion of enterprise data from a variety of sources in real time. Streaming Is Event Streaming the New Big Thing for Finance? . An excellent blog post by Ben Stopford where he discusses the use of event streaming in the financial sector. Troubleshooting KSQL Part 2: What’s Happening Under the Covers? . The second post by Robin Moffat about debugging of KSQL. In this post - Robin, as the title says, goes under the covers to figure out what happens with KSQL queries. 6 things to consider when defining your Apache Flink cluster size . This post discusses how to plan and calculate a Flink cluster size. In other words; how to define the number of resources you need to run a specific Flink job. MS Ignite Syllabuck: Ignite 2018 Conference . A great list of MS Ignite sessions that Buck Woody found interesting! Now I know what to do in my spare time! Data Science Customized regression model for Airbnb dynamic pricing . This post by Adrian is about a white-paper which details the methods that Airbnb use to suggest prices to listing hosts. Cleaning and Preparing Data in python . A post which lists Python methods and functions that helps to clean and prepare data. The Microsoft Infer.NET machine learning framework goes open source . A blog post from Microsoft Research, in which they announce the open-sourcing of Infer.NET . Is anyone else but me somewhat confused about the various data science frameworks that Microsoft has? How to build a Simple Recommender System in Python . A blog post which discusses what a recommender system is and how you can use Python to build one. What Is Niels Doing (WIND)

That is a good question! As you know, I wrote two blog posts about SQL Server 2019:

What is New in SQL Server 2019 Public Preview SQL Server 2019 for linux in Docker on windows

My plan was to relatively quickly follow up those two posts with a third post how to run SQL Server Machine Learning Services on SQL Server 2019 on Linux , and do it inside a Docker container. After having spent some time trying to get it to work, (with no luck), I gave up and contacted a couple of persons in MS asking for help. The response was that, right now in SQL Server 2019 on Linux CTP 2.0 , you cannot do it - bummer! The functionality will be in a future release.

I am now reworking the post I had started on to cover SQL Server Machine Learning Services in an Ubuntu based SQL Server 2019 on Linux . I should be able to publish something within a week or two.

I am also working on the third post in the Install R Packages in SQL Server ML Services series (still). Right now I have no idea when I can publish it - Sorry!

~ Finally

That’s all for this week. I hope you enjoy what I did put together. If you have ideas for what to cover, please comment on this post or ping me.

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          Principal Technologist - Machine Learning and Data Science - Blue Origin - Kent, WA      Cache   Translate Page      
While in this role, you will leverage your extensive experience in machine learning and data science to accelerate and innovate across business areas to drive...
From Blue Origin - Thu, 13 Sep 2018 23:30:35 GMT - View all Kent, WA jobs
          Instructor (Data Science, Artificial Intelligence, Machine Learning) - Cortechma Inc. - Thornhill, ON      Cache   Translate Page      
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
          Machine Learning      Cache   Translate Page      
none
          Consultant, Business Analytics & Data Science - Lincoln Financial - Boston, MA      Cache   Translate Page      
Phoenix, AZ (Arizona). Knowledge and experience on applying statistical and machine learning techniques on real business data....
From Lincoln Financial Group - Wed, 26 Sep 2018 21:21:38 GMT - View all Boston, MA jobs
          AWS Architect - Insight Enterprises, Inc. - Chicago, IL      Cache   Translate Page      
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
          Phoronix: NVIDIA Announces "RAPIDS" Open-Source Data Analytics / Machine Learning Platform      Cache   Translate Page      
NVIDIA has announced RAPIDS as their latest open-source project...
          IBM and NVIDIA Collaborate to Expand Open Source Machine Learning Tools for Data Scientists      Cache   Translate Page      
With IBM's vast portfolio of deep learning and machine learning solutions, it is best positioned to bring this open-source technology to data scientists ...
          IBM and NVIDIA Collaborate to Expand Open Source Machine Learning Tools for Data Scientists      Cache   Translate Page      
With IBM's vast portfolio of deep learning and machine learning solutions, it is best positioned to bring this open-source technology to data scientists ...
          Instagram To Use Machine Learning To Spot Bullying In Photos      Cache   Translate Page      
The company plans on using machine learning to try and spot bullying that might be taking place in photos and its captions. The use of machine ...
          Instagram To Use Machine Learning To Spot Bullying In Photos      Cache   Translate Page      
The company plans on using machine learning to try and spot bullying that might be taking place in photos and its captions. The use of machine ...
          UK: New EPO Guidelines On Mathematical Methods, AI And Machine Learning      Cache   Translate Page      
The updated Guidelines provide an extended section in relation to mathematical methods and, for the first time, a section dedicated to AI and machine ...
          UK: New EPO Guidelines On Mathematical Methods, AI And Machine Learning      Cache   Translate Page      
The updated Guidelines provide an extended section in relation to mathematical methods and, for the first time, a section dedicated to AI and machine ...
          Instagram is fighting bullying and spreading happiness with new tools      Cache   Translate Page      

Instagram this week introduced new features to help fight online bullying in its community of users.
In an announcement yesterday, new man-in-charge Adam Mosseri revealed the anti-bullying tools, saying that “there is no place for bullying on Instagram”.
“That’s why today we’re announcing our latest tools to help combat bullying, including a new way to identify and report bullying in photos,” he explained.
It means that by using machine learning Instagram will now be able to detect harmful content in pictures and captions so that they can be removed accordingly.
“This change will help us identify and remove significantly more bullying — and it’s a crucial [...]

The post Instagram is fighting bullying and spreading happiness with new tools appeared first on Memeburn.


          Architect - Microsoft - Redmond, WA      Cache   Translate Page      
Preferred experience with at least one of the Machine Learning related technologies (SAS, SPSS, RevR, Azure ML, MapR)....
From Microsoft - Thu, 23 Aug 2018 08:25:24 GMT - View all Redmond, WA jobs
          REMOTE - Machine Learning Engineer - Retail Domain      Cache   Translate Page      
IL-Chicago, If you are a REMOTE - Machine Learning Engineer - Retail Domain with experience, please read on! Top Reasons to Work with Us We are a technology consulting firm specializing in delivering High Performing Omni Channel Fulfillment solutions to the retail vertical. We are passionate about building best of breed enterprise applications, keen on bringing top-notch technical insight to solving business
          Amazon Reportedly Killed an AI Recruitment System Because It Couldn't Stop the Tool from Discriminating Against ... - Fortune      Cache   Translate Page      

Fortune

Amazon Reportedly Killed an AI Recruitment System Because It Couldn't Stop the Tool from Discriminating Against ...
Fortune
Machine learning, one of the core techniques in the field of artificial intelligence, involves teaching automated systems to devise new ways of doing things, by feeding them reams of data about the subject at hand. One of the big fears here is that ...
Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against WomenNews18
Amazon scraps recruiting AI that 'taught itself to prefer men over women'The Sun

all 19 news articles »

          Machine Learning Researcher - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page      
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 - Sun, 26 Aug 2018 12:12:32 GMT - View all Palo Alto, CA jobs
          Interactive Machine Learning Researcher - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page      
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 - Sun, 26 Aug 2018 12:12:32 GMT - View all Palo Alto, CA jobs
          From Camera To Price, Everything About Google’s New Pixel 3 & 3XL      Cache   Translate Page      
  Yesterday , the Made By Google event took place in New York city where they’ve launched 5 new products including the most awaited Pixel 3 and Pixel 3XL. Stock Android experience ni pakkana pedithe, Google pixel phones selling point daani fantastic camera, Googles astounding AI & Machine learning process use chesi almost DSLR (...
          So You Want To Learn Data Science With Python Track !!!      Cache   Translate Page      

If You Want A Radical Career Change, Expect To Do It All On Your Own But Don't Burn Your Bridges Immediately.This article is mainly geared towards folks who want to learn more about data science with python on their own.

This post was Originally published here . Why Python for Data Science?

Python is mature, and there's plenty of resources available from books to online courses. It has a significant set of data science libraries one can use. It is a ready-to-use programming language with different packages for loading and playing around with data, visualizing the data, transforming inputs into a numerical matrix, or actual machine learning and assessment.

Critical Python Skills for a Data Scientist
So You Want To Learn Data Science With Python Track !!!
Learn Data Science With Python Track ...

Python has an intuitive coding style, its ease of use and clean syntax have led it to be embraced by beginners and experts alike. We have listed some of the best (and free!!!) available resources in the following sections to help you bootstrap your career in the field of Data Science using Python.

Data Science With Python - Courses

Start with a Course or a book and study all the important topics for doing data science with Python. Our brain is similar to a muscle, Keeping your brain “fit” with deliberate practice almost every day will help you find a sweet spot for Python.

1. Python For Everybody Specialization -University of Michigan 2. IBM Python for Data Science - IBM 3. Introduction to Python for Data Science - Microsoft 4. IBM Data Science Professional Certificate - IBM :sunglasses: 5. Python Programming Track - DataCamp Get Good at Stats and Maths :bar_chart:

It's easy to fall into a state of depression when you don't have the know-how-to of Statistics and Maths when learning Numpy, Pandas or Scikit-learn. We hope that the following resources will help you to start building the Data Science skills required today.

Learn Statistics for Data Science

A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician. As a data scientist student, You can master the core concepts, probability, Bayesian thinking, and even statistical machine learning from best available books or an online course.

Statistics for Data Science Courses

If you need an introduction to Statistics, start with any of the beginner level course listed below. Try and integrate some of these online courses into your schedule while learning python. You'll feel very confident while learning to work with analytical libraries for Python.

1. Introduction to Probability and Data - Duke University 2. Inferential Statistics - University of Amsterdam 3. Bayesian Statistics: From Concept to Data Analysis - University of California 4. Statistics Foundations: Understanding Probability and Distributions - Dmitri Nesteruk 5. MicroMasters Program in Statistics and Data Science - Massachusetts Institute of Technology Learn Math for Data Science

Mathematics is the bedrock of any contemporary discipline of science. It is no surprise then that, almost all the techniques of modern data science (including all of the machine learning) have some deep mathematical underpinning or the other.

Maths for Data Science Courses

You don’t need a math degree to succeed in data science. Yet, if you do have a math background, you’ll definitely get ahead. Here are some best online classes to master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.

1. Introduction to Mathematical Thinking - Stanford University 2. Data Science Math Skills - Duke University 3. Introduction to Algebra - SchoolYourself 4. Algebra I - Khan Academy

Also, If you have little to no background in Maths or need a refresher, we suggest that get a copy of All the Mathematics You Missed: But Need to Know for Graduate School for an overview of mathematics that one should have been exposed to upon reaching Graduate School.

Networking for Nerds

If you are in the right group of people, you'll get the right kind of support. Find people who you could learn from and create some positive reinforcement. Here are some resources to help you get connected and understand your in-group.

Data Science Conferences and Meetup

The whole point of the going to conferences and regularly attending a Meetup is not to be liked but to benefit from the high-impact sessions and find someone who you will like because then they'll like you in return and be help to you if you are seen around repeatedly.

1. PyData 2. Data Science Meetups 3. The Data Science Conference 4. KDNuggets Meetings 5. Machine Learning Meetups

If you don't find any Meetups around your area, write some Python code to find the right Meetup groups around your location. There is a Meetup API client written in Python with all the documentation that has a complete list of available API methods and their descriptions.

Before You Go :beers:

If you liked this article enough, do share it with your friends and If there is anything you feel we should have included? Let us know in the comments below!


          Instagram Employs Machine Learning to Stop Bullying      Cache   Translate Page      

New Head of Instagram Adam Mosseri is only 10 days into the job and already introducing high-tech tools to limit bullying on the social network. “Online bullying is complex,” Mosseri wrote in a […]

The post Instagram Employs Machine Learning to Stop Bullying appeared first on Geek.com.


          MIMO Matlab project      Cache   Translate Page      
communication MIMO machine learning deep learning Telecommunication fading channel (Budget: $30 - $250 USD, Jobs: Electrical Engineering, Electronics, Engineering, Matlab and Mathematica, Telecommunications Engineering)
          Nvidia launches Rapids to help bring GPU acceleration to data analytics      Cache   Translate Page      

Nvidia, together with partners like IBM, HPE, Oracle, Databricks and others, is launching a new open-source platform for data science and machine learning today. Rapids, as the company is calling it, is all about making it easier for large businesses to use the power of GPUs to quickly analyze massive amounts of data and then […]

The post Nvidia launches Rapids to help bring GPU acceleration to data analytics appeared first on RocketNews | Top News Stories From Around the Globe.


          Google lanza los nuevos Pixel 3 y 3XL      Cache   Translate Page      

Día de lanzamiento de hardware para Google que sigue intentando marcar el terreno de cómo deberían ser los Android perfectos aunque usualmente siempre quede un poco por hacer y otro tanto por innovar, en este caso llega el turno de la nueva generación de Pixel.

Con mucho énfasis en las cámaras que son el punto fuerte de sus principales rivales, los iPhone y Galaxys. Por dentro cuentan con lo esperable para esta época del año para un flagship, un Qualcomm Snapdragon 845 con 4GB de RAM (curiosamente no ofrecen ni de 6 ni 8), Bluetooth 5, chipset de seguridad Titan M.

El Pixel 3 cuenta con una pantalla de 5.5" y el 3XL de 6.3", que es más bien ENORME, pero con reducción de bezel y notch para que no se extienda el tamaño del equipo (el 3 no tiene notch, sólo el XL), son cristales P-OLED de 1080 x 2160 para el tres y 2960x1440 para el XL con un ratio 18.5:9, cubiertos con Gorilla Glass 5.

Los parlantes son frontales, la cámara tiene un sensor de 12.2MP, f/1.8, 28mm wide, pixeles de 1.4µm, OIS, dual pixel PDAF y Dual-LED flash, interesante es que no está en la onda de cámaras duales sino que van todavía con una sola salvo en la frontal que cuenta con una doble de 8MP. El hecho de utilizar un sensor simple para la principal es porque lo potente viene del lado del post procesamiento donde con un poco de Machine Learning (dicen IA, pero no es IA, dejen de joder con IA), para lograr imagenes más claras y mejores en poca luz.

Las baterías son de 2915 y 3430mAh respectivamente con carga inalámbrica QI de hasta 10 watts, obviamente como toda la nueva moda, sin conector de 3.5mm para audio que requiere un adaptador para el USB-C o inalámbricos, el sistema operativo es Android 9.0

Detalle interesante desde el software, Google Assistant puede "atajar" llamadas desde números desconocidos para atender spammers molestos, se llama Call Screen y también llegará a los modelos anteriores de Pixel.

Llegarán al mercado a partir del 18 de Octubre con un precio de USD 799 para el Pixel 3 y USD 899 para el 3XL para modelos con 64GB de almacenamiento unos USD 100 extras por el de 128GB, todo esto incluye seis meses de YouTube Music gratis y la suscripción de Google Photo de por vida.


Copyright (C) 2005-2014, Fabio Baccaglioni [Permalink] [Comentarios] [Google]


          Complessità della rete aziendale, la soluzione sta nella SD Wan      Cache   Translate Page      

Gestire in modo efficace la rete aziendale odierna è un mestiere difficile. E lo sarà sempre di più se non si adotta una strategia differente. Strategia che peraltro è ben nota e si chiama SD Wan.

Il dato emerge da una indagine di Oracle, “Enterprise Networks in Transition: Taming the Chaos“, nella quale responsabili IT e telecomunicazioni hanno messo in evidenza che la sicurezza, le frodi e una maggiore complessità, derivanti dalla proliferazione dei canali, sono le principali preoccupazioni.

Lo scorso giugno Oracle ha intervistato 277 responsabili delle decisioni in ambito IT, telco e reti in merito alle proprie prospettive sui trend business, reti e sulle soluzioni per l’economia digitale. Il 66% degli intervistati opera in aziende con almeno 11 sedi e con il 62% delle operazioni gestite su più nazioni.

La tecnologia di rete SD-Wan (Software-Defined Wide Area Networking) è stata elencata come un elemento critico alla base dell’evoluzione delle reti aziendali. Il Nord America è in ritardo rispetto ad altre regioni nella distribuzione di soluzioni di networking software-defined (SDN) (50% contro il 65-78%).

 

Più grande la rete, maggiori i problemi

Il 91% degli intervistati ha classificato la sicurezza tra le tre sfide principali, in particolare in Asia e America Latina, dove l’utilizzo della telefonia mobile è più diffusa.Oltre un terzo degli intervistati ha classificato la sicurezza come la problematica principale in relazione alla pianificazione, distribuzione e gestione delle reti aziendali. Poiché i dispositivi mobili continuano ad espandersi e a ridefinire i confini della rete, la sicurezza continua ad essere una questione di primaria importanza.

Il 76% degli intervistati concorda sulla necessità di nuove soluzioni tecnologiche per migliorare la visibilità e il controllo dell’intera rete. Il 69% ha evidenziato che utilizzerà la biometria per la sicurezza di rete e il 57% in ambito intelligenza artificiale. Parallelamente, l’intelligenza artificiale e machine learning sono stati identificati come tecnologie fondamentali per supportare la qualità del servizio e l’efficienza dei costi (30% ciascuno), 27% degli intervistati identifica la Blockchain come una tecnologia di punta per migliorare il controllo della rete.

Il 76% degli intervistati ha dichiarato che l’ampiezza e la portata della propria rete aziendale è in evoluzione. Gli utenti della rete dispongono di un numero crescente di canali, il che significa che la gestione del tempo e della produttività sta diventando sempre più critica. Mentre voce e posta elettronica sono i canali di comunicazione predominanti, video, chat e altre comunicazioni in-app sono sempre più diffuse. Tutto questo ha un impatto sul modo in cui la rete viene distribuita e gestita e su ciò che definisce il perimetro della rete stessa.

Almeno l’83% degli intervistati ha identificato praticamente tutti i tipi di frode in ambito reti e telecomunicazioni come un problema serio, oltre la metà ha menzionato le frodi sull’identità come principale preoccupazione in relazione alle comunicazioni in tempo reale. Le soluzioni devono adattarsi ed evolvere in previsione delle minacce attuali e in continua evoluzione, in particolare con l’aumento della complessità della rete.

La tecnologia SD-Wan alla base dell’evoluzione della rete 

La maggioranza (71%) degli intervistati concorda sul fatto che la tecnologia SD-Wan è fondamentale per l’evoluzione delle reti aziendali e una percentuale maggiore (88%) degli intervistati di aziende internazionali con 101 o più sedi, concorda con tale affermazione.

Per il 48% delle grandi aziende internazionali che stanno prendendo in considerazione la tecnologia SD-WAN, la convenienza e la facilità di implementazione sono state la motivazione principale, seguite dall’affidabilità (36%) e dalla flessibilità legata al traffico (34%).

L'articolo Complessità della rete aziendale, la soluzione sta nella SD Wan è un contenuto originale di 01net.


          Nvidia launches Rapids to help bring GPU acceleration to data analytics      Cache   Translate Page      
Nvidia, together with partners like IBM, HPE, Oracle, Databricks and others, is launching a new open-source platform for data science and machine learning today. Rapids, as the company is calling it, is all about making it easier for large businesses to use the power of GPUs to quickly analyze massive amounts of data and then […]
          Ingénieur Analyse de Données et Logiciels - Intelligence Manufacturière - Data Analytics and Software Engineer – Manufacturing Intelligence - Alcoa Corp. - Deschambault, QC      Cache   Translate Page      
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      
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
          NVIDIA Introduces RAPIDS Software Platform for Large-Scale Data Analytics and Machine Learning      Cache   Translate Page      
NVIDIA today announced RAPIDS, a GPU-acceleration platform for data science and machine learning, with broad adoption from industry leaders, that enables companies to analyze massive amounts of data and make accurate business predictions fast.
          Gmail passa a ter ferramenta que sugere palavras para composição de e-mails      Cache   Translate Page      

A Google está cheia de novidades nesta terça-feira (9), e um dos novos anúncios da gigante é um recurso chamado Smart Compose, para o Gmail. A ferramenta sugere palavras para o usuário enquanto uma mensagem é criada, e a novidade aqui é que agora o recurso tem total suporte ao português do Brasil, além de espanhol, italiano e francês.

O mecanismo funciona por machine learning, sendo que o mecanismo de inteligência artificial da Google consegue “prever” qual a próxima palavra que o usuário pretende digitar e já oferece uma opção.

Isso quer dizer que o sistema “lê” todos seus e-mails para sugerir palavras? Não. Segundo a Google, o Compose utiliza bilhões de frases comuns em um banco de dados externo. Contudo, ele pode propor uma resposta baseada no contexto da sua conversa, mantendo somente as sugestões para aquele conjunto de e-mails. Assim, a Google garante que o Compose “não consegue identificar o estilo de escrita do usuário”.

Enquanto usuários vai escrevendo, sistema sugere palavras (Imagem: Divulgação/Google)

A proposta é tornar mais ágil a escrita, sobretudo para usuários em plataforma mobile. Ainda, a empresa garante que o sistema utiliza palavras e sugestões baseado em regras gramaticais, ou seja, para ajudar as pessoas a não fugirem da norma culta. O sistema também funciona com o G Suite, conjunto de ferramentas voltado a produtividade empresarial.

O Smart Compose deve ser adicionado ao Gmail automaticamente nas próximas semanas.


          Google anuncia os novos Pixel 3 e Pixel 3 XL com "a melhor câmera possível"      Cache   Translate Page      

As expectativas para o evento Made By Google, apresentado hoje pela empresa, era a de que, entre uma novidade e outra, fossem apresentados os novos smartphones da linha Pixel — o Google Pixel 3 e o Google Pixel 3 XL. Depois de muitos vazamentos (muitos mesmo), a gigante da internet enfim mostrou os dois aparelhos durante a ocasião realizada em Nova York.

Chamando de “o smartphone com a melhor câmera possível”, Rick Osterloh, vice-presidente sênior de hardware da Google, apresentou o Google Pixel e Pixel 3 XL, ambos apresentando displays maiores do que seus predecessores (respectivamente, 5.5 e 6.3 polegadas nos novos flagships) e sistema de câmera dupla posicionado sobre o notch, para as fotos frontais, com uma das lentes voltada às imagens mais abertas, como selfies em grupo. Na traseira, ambos os aparelhos vêm com uma única câmera, resistindo à tendência de outras fabricantes (olhando pra você, LG).

O design do Pixel 3 e Pixel 3 XL mantém o mesmo padrão de seu predecessor, salvo pelo arredondamento dos cantos, onde a carcaça e o vidro da tela se encaixam, tornando o visual mais interligado e fluído. Contudo, no Pixel 3 XL, o notch é consideravelmente maior, ainda que o modelo de display maior “estique” a imagem por toda a borda da tela. Nas configurações internas, os modelos virão com 64GB de armazenamento interno e 4GB de memória RAM, ambos com processador Snapdragon 845, Bluetooth 5.0 e alto-falantes frontais.

Google anunciou em evento os smartphones Pixel 3 e Pixel 3 XL, além do Google Slate (Captura de Imagem: Rafael Arbulu)

Ambos os aparelhos trazem um foco enorme na automatização da experiência de se tirar uma foto, com recursos avançados de autofoco, color pop, mudança de foco para a frente ou fundo da imagem. Uma feature interessante — e exclusiva da linha Pixel — é o “Top Shot”, criado para assegurar que o usuário sempre tenha a foto mais bacana. Combinando aspectos de machine learning e o exclusivo HDR Plus, o Top Shot captura diversos quadros de uma imagem antes e depois de você pressionar o botão de foto para que, no caso de acidentes (alguém sair do quadro antes do obturador bater, uma piscada ou espirro no momento errado), o usuário possa voltar ou avançar quadros para escolher o melhor momento. Uma vez definido, os outros quadros são excluídos automaticamente e, em alguns casos, o próprio sistema faz sugestões de quadros mais interessantes.

Alia-se a isso a função “Night Sight”, um modo fotográfico desenhado para registrar imagens em ambientes noturnos (bares, baladas, shows) sem o uso do flash. Em um jab bem direto ao novo iPhone Xs, a apresentação mostrou uma comparação lado-a-lado entre uma foto noturna pelo smartphone da Apple (supostamente, com flash) e uma pelo Pixel 3 (via Night Sight): a da Apple estava bem obscura. Completando a “zoeira” com a Maçã de Cupertino, as câmeras frontais para selfies em grupo, segundo a apresentação, possuem uma abertura até 183% maior que a do iPhone Xs, sem cortar elementos do quadro.

Comparativo lado-a-lado entre uma foto de fim do dia com a câmera do iPhone Xs (com flash, presumidamente) e com o Pixel 3 (com Night Sight) (Captura de imagem: Rafael Arbulu)
Exemplo da selfie em grupo em um Pixel 3: dupla câmera frontal evita que objetos sejam cortados do quadro (Captura de Imagem: Rafael Arbulu)

Outros dois recursos que completam o pacote — Motion Auto Focus e Super Res Zoom — consistem em 1) clicar na tela para selecionar um ponto de foco que permanecerá em boa qualidade com vídeos de alta movimentação (o exemplo da apresentação era o de um labrador correndo, com o foco acompanhando o cão por todo lugar); e 2) que consegue tirar fotos em zoom sem granulação de imagem ou a necessidade de uma mini lente específica de profundidade. Uma integração direta entre a câmera e o Google Lens permite também que usuários tirem fotos de itens de interesse e, automaticamente, tenham acesso a links de compra e informações de aquisição (quando disponíveis).

Para os entusiastas da fotografia, a Google anunciou na ocasião uma parceria com a renomada fotógrafa Annie Leibovitz, que, pela primeira vez em parceria com uma empresa “de câmeras” (a Google não fabrica câmeras, mas o foco do Pixel 3 é certamente o de registrar grandes imagens), está viajando pelos EUA e tirando fotos exclusivamente com o smartphone para um projeto vindouro.

Pela primeira vez com uma parceria deste tipo, a renomada fotógrafa Annie Leibovitz está registrando imagens com um Pixel 3 (Captura de Imagem: Rafael Arbulu)

Junto dos novos Pixel, a Google também mostrou o Google Stand, uma dock para recarga wireless da bateria dos smartphones, que reconhecem automaticamente quando estão posicionados sobre o acessório, executando o Google Assistant para exibir opções específicas de interação com o usuário.

O sistema operacional, como já era de se esperar, é o Android 9 Pie. Mas com uma novidade: ao contrário dos seus predecessores, o Pixel 3 e o Pixel 3 XL dispensaram botões virtuais, favorecendo a navegação gestual e um forte foco na virtualização interativa, como o Google Assistant. De fábrica, o assistente virtual poderá inclusive monitorar chamadas telefônicas, evitando spams e propagandas, mas sempre com o poder de atender ou ignorar nas mãos do usuário. Isso pode levantar questões de privacidade no futuro, mas deixemos isso de lado por enquanto.

Pixel 3 e Pixel 3 XL saem por US$ 799 e US$ 899, respectivamente, podendo ser encomendados pelas lojas online da Google (Captura de Imagem: Rafael Arbulu)

Como sempre, a Google mantém a parceria com a Verizon Wireless como operadora oficial do Pixel 3 e Pixel 3 XL nos EUA, mas é possível adquirir o aparelho destravado pela loja online da empresaou pelo serviço Project Fi. Os preços sugeridos são de US$ 799 para o Pixel 3 e US$ 899 para o Pixel 3 XL. Com US$ 100 a mais, você leva as versões de 128 GB de armazenamento interno. O Pixel Stand para carregamento wireless sai por US$ 79. Quem comprar qualquer um dos dois smartphones, aliás, terá acesso a seis meses gratuitos do YouTube Music, o serviço de streaming de música da empresa.


          Instagram emprega machine learning para detectar bullying em posts      Cache   Translate Page      

Em sua primeiro publicação desde que assumiu a chefia do Instagram, Adam Mosseri descreveu no blog oficial da rede social os novos métodos da subsidiária do Facebook para combater o bullying. O Instagram criou novas vias para que usuários reportem imagens de potencial ofensivo (e seus respectivos comentários e legendas), além de empregar tecnologias de machine learning detectar automaticamente conteúdo que possa aferir ofensa ou ataque aos outros.

“Ainda que a maior parte das fotos compartilhadas no Instagram sejam positivas e tragam alegria às pessoas, ocasionalmente uma imagem maligna e malquista é publicada”, explicou o executivo.

“Nós agora estamos empregando tecnologias de machine learning para proativamente detectar bullying em imagens e legendas, enviando-as ao nosso time de operações de comunidade para revisão. Esta mudança nos ajudará a identificar e remover mais conteúdo de bullying — e representa um passo crucial já que muitas pessoas que experimentam ou observam situações de bullying não as reportam”.

Exemplo do menu para reportar comentários e imagens ofensivas (Imagem: Reprodução/Instagram Blog)

A nova medida também auxilia na proteção ao público mais jovem do Instagram, ao que Mosseri refere-se como o ente demográfico mais vulnerável à prática de bullying no mundo. A disponibilização dos novos recursos já começou e deve estar disponível a toda a base de usuários do Instagram nas próximas semanas.

Mais além, a ferramenta de denunciar comentários agora também está disponível para transmissões ao vivo feitas pelo app do Instagram. Finalmente, em uma parceria com a cantora, atriz, autora e dançarina Maddie Ziegler, a rede lança um novo filtro de imagens para o Instagram Stories apelidado Spread the Kindness (“Espalhe a Bondade”, na tradução literal). A artista é uma ativista renomada antibullying e quem já a segue terá o filtro disponibilizado automaticamente; não seguidores poderão ver o filtro pelo Story de outra pessoa e clicar na opção “Experimentar”. Ao usar o filtro, o usuário poderá marcar um amigo para quem ele queira dedicar apoio.

Filtro "Spread The Kindness" foi feito em parceria com a ativista antibullying Maddie Ziegler (Imagem: Reprodução/Instagram Blog)

          Computational Linguist      Cache   Translate Page      
Are you looking for the next step in your career as a Computational Linguist? Are you ready to be part of an Artificial Intelligence, Machine Learning, NL working team? Do you want to work for AI software focused on Multimodal and Cross-Channel User Interfaces for Enterprise Customer Service as a Computational Linguist? Then this is the role you need! I am looking for Computational Linguist in various levels (entry level, seniors) for a great company to work at. My client undertake projects delivering Product Development and Projects Implementations in Europe, Middle East and...
          Instagram stringe sul cyberbullismo      Cache   Translate Page      
Usa il 'machine learning' per le foto e i testi
          Apple buys machine learning AR firm specializing in mixed realities      Cache   Translate Page      
Article Image

Apple has reportedly bought a Danish startup called Spektral, which specialized in real-time separation of objects in photos and videos for a "green screen" effect.
          Sitecore legt die Messlatte höher und bietet die weltweit leistungsfähigste Plattform für personalisierte Erlebnisse      Cache   Translate Page      
Sitecore: Orlando, Florida (ots/PRNewswire) - Neue Funktionen für Headless, Machine Learning und Analytics bieten unübertroffene Flexibilität und Benutzerfreundlichkeit für Marketingfachleute und Entwickler Sitecore Symposium 2018 -- Sitecore®, der weltweit ...
          Machine learning makes a cost-effective environmental watchdog      Cache   Translate Page      
Using machine learning could help take financial and personnel burdens off of government regulators, which could help stave off environmental disasters.
          Sitecore legt die Messlatte höher und bietet die weltweit leistungsfähigste Plattform für personalisierte Erlebnisse      Cache   Translate Page      
Sitecore: Neue Funktionen für Headless, Machine Learning und Analytics bieten unübertroffene Flexibilität und Benutzerfreundlichkeit für Marketingfachleute und Entwickler Orlando, Florida (ots/PRNewswire) - Sitecore Symposium 2018 -- Sitecore® ...
          World Factory 2.0: Will China Become Global Artificial Intelligence Trainer?      Cache   Translate Page      
China is becoming a global factory for artificial intelligence (AI). The country has proposed becoming a world leader in this field by 2030. With the introduction of robots and machine learning into production processes, China will be able to advance in the global value chain.
          UR - Corporate Engineering - Precision Systems Engineer (Maplewood, MN) - 3M - Maplewood, MN      Cache   Translate Page      
Proactively collaborate with business partners to connect and extend process data management solutions with complimentary machine learning and analytics efforts...
From 3M - Wed, 05 Sep 2018 17:09:45 GMT - View all Maplewood, MN jobs
          Data Science Manager - Micron - Boise, ID      Cache   Translate Page      
Create server based visualization applications that use machine learning and predictive analytic to bring new insights and solution to the business....
From Micron - Wed, 05 Sep 2018 11:18:49 GMT - View all Boise, ID jobs
          Intern - Data Scientist (NAND) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Wed, 29 Aug 2018 20:54:50 GMT - View all Boise, ID jobs
          Intern - Data Scientist (DRAM) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Mon, 20 Aug 2018 20:48:37 GMT - View all Boise, ID jobs
          DevOps - Full Stack - Symetra - Bellevue, WA      Cache   Translate Page      
AWS - Lambda, DynamoDB, Cognito, SNS, API Gateway, EC2, CloudFront, S3. Azure - Functions, EventHub, Machine Learning, CosmosDB, Kubernetes Services, NLP, and...
From Symetra - Thu, 04 Oct 2018 23:57:26 GMT - View all Bellevue, WA jobs
          (USA-NY-New York) Senior Data Scientist, Economic Graph Analytics      Cache   Translate Page      
Senior Data Scientist, Economic Graph Analytics LinkedIn’s vision is to create economic opportunity for every member of the global workforce. The Economic Graph team mission is to reach that vision by bringing to life data and pilot projects that help leaders address the challenges of the modern workforce and help members outside of LinkedIn’s core navigate their education and employment challenges. The Analytics team is looking for a talented and driven data scientist to join our team to support projects partnering with governments, NGOs, and think tanks. Our projects have ranged from understanding talent migration to computing the relative supply and demand of skills on a local, national, and international level. We are looking for an individual to join our team with a focus on developing internal tools and external products to scale our work more broadly. This role will work closely with various cross-functional teams such as product, engineering, and research to develop these solutions, and requires an adaptable analytics professional that is both passionate about making an impact through rigorous analyses and who also exhibits strong technical acumen. Responsibilities Analyze large datasets to investigate key regional trends, behaviors and metrics Take customized one-off analyses and scale them to represent millions of members Build scalable reports, dashboards, models and tools to help our policy team communicate the value of LinkedIn to external stakeholders Consult and communicate with LinkedIn’s policy team and external policy leaders to showcase Economic Graph insights Assist with developing strategy and managing operational metrics for various initiatives Coordinate cross-functionally with engineering, communications, marketing, product and sales to drive business objectives Develop methods and tooling to improve operational efficiencies for our analytical work, including decreasing time to market and automating repeated work Engage with internal technology partners to prototype and launch products that make our analytical insights more broadly available for consumers Evangelize technical tooling and educate team on best practices for tools and products Required Qualifications: Bachelor’s degree in a quantitative or social sciences field of study such as Computer Science, Engineering, Statistics, Economics, or Public Policy Experience using R, Python, or other programming languages for data manipulation, analysis, machine learning, or application development Experience using SQL to analyze datasets with databases such as Teradata, Hive, and PrestoDB 3+ years of research and/or analytics experience within a relevant industry or organization (Ex. tech, social media, finance, retail, research institutes). Preferred Qualifications: Masters or PhD degree in a quantitative or social sciences field of study Background and experience in statistics, predictive modeling, and machine learning Experience working in the internet industry, on social impact, public policy or consulting Experience with applied social science research or familiarity with publicly-available government data sets such as those provided by the BLS, Census Bureau, and the Department of Labor Experience with Hadoop (Hive, Pig, or MapReduce) Familiarity with data visualization and/or BI tools (Ex. Tableau, PowerBI) Experience in ETL tools and processes Experience using scripting languages such as Unix shell, Python, and PHP Experience working with product or engineering teams to develop and launch products, or experience developing software applications Strong oral and written communication skills. Strong ability to communicate findings clearly to both technical and non-technical audiences. Ability to interact internally and externally to drive business and technical discussions
          Machine Learning SME - JM Group - Montréal, QC      Cache   Translate Page      
Hands on Client experts who has knowledge of SPARK, can use Machine Learning Libraries and has knowledge of Big Data. The person need to be hands on and has...
From JM GROUP - Sat, 06 Oct 2018 03:20:02 GMT - View all Montréal, QC jobs
          Machine Learning Anti-Bullying Systems - A New Instagram Anti-Bullying Feature Stops User Harassment (TrendHunter.com)      Cache   Translate Page      
(TrendHunter.com) A new Instagram anti-bullying feature is being implemented on the photo-sharing app to help further thwart the prevalence of online harassment. Utilizing machine learning, this new system works to...
          Getting Answers Faster: NVIDIA and Open-Source Ecosystem Come Together to Accelerate Data Science      Cache   Translate Page      

No matter the industry, data science has become a universal toolkit for businesses. Data analytics and machine learning give organizations insights and answers that shape their day-to-day actions and future plans. Being data-driven has become essential to lead any industry. While the world’s data doubles each year, CPU computing has hit a brick wall with Read article >

The post Getting Answers Faster: NVIDIA and Open-Source Ecosystem Come Together to Accelerate Data Science appeared first on The Official NVIDIA Blog.


          Machine learning provides new insights into cellular biology      Cache   Translate Page      

artistic rendering showing how molecular

A new, interdisciplinary study at the University of Texas at Austin, funded by the National Science Foundation, has provided insights into how cells respond to their environments. The study used machine learning technology in tandem with next-generation RNA sequencing to reveal the inner workings of cells. 


Full story at https://che.utexas.edu/2018/10/04/machine-learning-provides-new-insights-into-cellular-biology/

Source
University of Texas at Austin


This is an NSF News From the Field item.

          Commercial Video Evaluation via Low-Level Feature Extraction and Selection      Cache   Translate Page      
To discover the influence of the commercial videos’ low-level features on the popularity of the videos, the feature selection method should be used to get the video features influencing the videos’ evaluation mostly after analyzing the source data and the audiences’ evaluations of the videos. After extracting the low-level features of the videos, this paper improved the Correlation-Based Feature Selection (CFS) method which is widely used and proposed an algorithm named CFS-Spearmen which combined the Spearmen correlation coefficient and the classical CFS to select features. The 4 datasets in UCI machine learning database were employed as the experiment data. The experiment results were compared with the results using traditional CFS, Minimum Redundancy and Maximum Relevance (mRMR). The SVM was used to test the method in this paper. Finally, the proposed method was used in commercial videos’ feature selection and the most influential feature set was obtained.
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page      
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
          Sr. Data Scientist - Life Sciences - Health Catalyst - Salt Lake City, UT      Cache   Translate Page      
Machine learning experience required. The role has great potential for a successful candidate as they will form the initial seed of a new business unit with...
From Health Catalyst - Tue, 11 Sep 2018 02:31:12 GMT - View all Salt Lake City, UT jobs
          NVIDIA Introduces RAPIDS Open-Source GPU-Acceleration Platform for Large-Scale Analytics and ML      Cache   Translate Page      

MUNICH, Germany, Oct. 10, 2018—NVIDIA today announced a GPU-acceleration platform for data science and machine learning, with broad adoption from industry leaders, that enables even the largest companies to analyze massive amounts of data and make accurate business predictions at unprecedented speed. RAPIDS open-source software gives data scientists a giant performance boost as they address […]

The post NVIDIA Introduces RAPIDS Open-Source GPU-Acceleration Platform for Large-Scale Analytics and ML appeared first on HPCwire.


          Instagram Employs Machine Learning to Stop Bullying      Cache   Translate Page      

New Head of Instagram Adam Mosseri is only 10 days into the job and already introducing high-tech tools to limit bullying on the social network. “Online bullying is complex,” Mosseri wrote in a […]

The post Instagram Employs Machine Learning to Stop Bullying appeared first on Geek.com.


          Software Developer (Machine Learning) - Lincoln Electric - Cleveland, OH      Cache   Translate Page      
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 - Sat, 18 Aug 2018 08:48:39 GMT - View all Cleveland, OH jobs
          IoT Developer w/Machine Learning Experience - Experis - Euclid, OH      Cache   Translate Page      
Experience with popular languages (C++, C#, Java, Python, and R). Must have 3years - 10 years of experience with Windows, Linux, or Java platforms.... $60,000 - $100,000 a year
From Experis - Wed, 03 Oct 2018 17:43:05 GMT - View all Euclid, OH jobs
          Services Operations Manager - Highspot - Seattle, WA      Cache   Translate Page      
Equipped with new Apple products. We employ advanced technologies, included patented machine learning algorithms to:....
From Highspot - Mon, 01 Oct 2018 20:07:27 GMT - View all Seattle, WA jobs
          Mobile Engineer React Native - Xevo - Bellevue, WA      Cache   Translate Page      
Technical and non-technical, internal and external. Do you dream about artificial intelligence and machine learning?...
From Xevo - Tue, 02 Oct 2018 02:28:59 GMT - View all Bellevue, WA jobs
          Sr Software Engineer, Applied Machine Learning - Apple - Austin, TX      Cache   Translate Page      
We work on many high-impact projects that serve various Apple lines of business. Understanding of JVM internals and garbage collection....
From Apple - Sun, 30 Sep 2018 01:50:35 GMT - View all Austin, TX jobs
          (USA-CT-Trumbull) Digtal Engineer, Skin Cleansing      Cache   Translate Page      
Background & Purpose of the Job In a Digitally connected world, the impact we have is larger than ever\. The Skin Cleansing Category at Unilever is committed to offering superior products that drive growth with purpose\-driven global brands like Dove, Lifebuoy, and Love Beauty & Planet\. Through providing consumers with an enjoyable cleansing experience, our brands also deliver programs around self\-esteem, diversity, health and environmental sustainability among others\. With the increased investment in Digital, the Skin Cleansing team is looking for someone who wants to deliver real changes\. Through Digital, we are unlocking benefits in end\-to\-end product design from ideation to the consumer\. We are creating better products for our consumer and creating a better work environment within R&D\. Our mission is to deliver products to market more quickly, reliably and with better quality\- from lab, through pilot plant and into production\. The team is responsible for providing Digital capabilities to reduce project timelines and provide value and savings\. We need someone with a real passion for modelling, simulationn & data analytics who has the desire to blend theory with practice\. Who You Are & What You’ll Do + Be a key driving force for building Skin Cleansing’s data, database modelling & simulation + Work with complex data sets to create business insights and design better products + Collaborate with Digital R&D, Supply Chain and external partners to deliver the change program + Interpret complex real\-time data sets using statistical software to unlock insights on product design + Work in collaboration with Data Scientists, Subject Matter \(Product, Process and Pack, etc\.\) Experts, and Supply Chain to access and analyze data sets\. + Support the upskilling within the team and deploying modelling & simulation tools across the global community for Skin Cleansing You’re a born leader:You can independently lead projects and influence stakeholders\. You’re a dot connector:You can learn the end\-to\-end process of innovating skin cleansing products and see the big picture in connecting Digital activities\. You love to win, and have fun doing it:You bring passion to your work and take pride in delivering new solutions\. You’re a story teller:You can communicate the benefits of your work and make people understand the value of the Digital program You’re a culture & change champion:You are at the forefront of Digital activities and can champion new ways of working aimed at delivering speed to market and cost savings\. What You’ll Need To Succeed + BS in any Engineering or Applied Science discipline \(including, but not limited to, Data Science, Computer Science, etc\.\) + Recommended 2\-3 years Industry experience \(e\.g\. Consumer Goods, Pharmaceutical, Aerospace, Automotive, Medical\) + Digital analytical skills + Statistical Design knowledge + Database experience + Knowledge of simulation / modelling techniques applied to practical problems + Understanding of statistical tools and methods \(JMP, Principle Component Analysis, machine learning\) + Familiarity with programming \(Python, Javascript, R, SQL\) What We Can Offer You Culture for Growth|Top Notch Employee Health & Well Being Benefits|Every Voice Matters|Global Reach|Life at Unilever|Careers with Purpose|World Class Career Development Programs|Check Out Our Space|Focus On Sustainability Unilever is an organization committed to diversity and inclusion to drive our business results and create a better future every day for our diverse employees, global consumers, partners, and communities\. We believe a diverse workforce allows us to match our growth ambitions and drive inclusion across the business\. Equal Opportunity/Affirmative Action Employer Minorities/Females/Protected Veterans/Persons with Disabilities Applicants and employees are protected from discrimination under Federal law\. For more information, please seeEEO is the Law Employment is subject to verification of pre\-screening tests, which may include drug screening, background check, credit check and DMV check\. **Job:** Research/Development **Primary Location:** United States\-Connecticut\-Trumbull\-Trumbull \-40 Merritt Blvd **Schedule:** Full\-time **Shift:** Day Job **Unposting Date:** Ongoing **Req ID:** 18000EXJ
          Microsoft Joins the Open Invention Network, NVIDIA Announces RAPIDS, Asterisk 16.0.0 Now Available, BlockScout Released and Security Advisory for Debian GNU/Linux 9 "Stretch"      Cache   Translate Page      

News briefs for October 10, 2018.

Microsoft has joined the Open Invention Network (OIN), an open-source patent consortium. According to ZDNet, this means "Microsoft has essentially agreed to grant a royalty-free and unrestricted license to its entire patent portfolio to all other OIN members." OIN's CEO Keith Bergelt says "This is everything Microsoft has, and it covers everything related to older open-source technologies such as Android, the Linux kernel, and OpenStack; newer technologies such as LF Energy and HyperLedger, and their predecessor and successor versions."

NVIDIA has just announced RAPIDS, its open-source data analytics/machine learning platform, Phoronix reports. The project is "intended as an end-to-end solution for data science training pipelines on graphics processors", and NVIDIA "laims that RAPIDS can allow for machine learning training at up to 50x and is built atop CUDA for GPU acceleration".

The Asterisk Development Team announces that Asterisk 16.0.0 is now available. This version includes many security fixes, new features and tons of bug fixes. You can download it from here.

BlockScout, the first full-featured open-source Ethereum block explorer tool, was released yesterday by POA Network. The secure and easy-to-use tool "lets users search and explore transactions, addresses, and balances on the Ethereum, Ethereum Classic, and POA Network blockchains". And, because it's open source, anyone can "contribute to its development and customize the tool to suit their own needs".

Debian has published another security advisory for Debian GNU/Linux 9 "Stretch". According to Softpedia News, CVE-2018-15471 was "discovered by Google Project Zero's Felix Wilhelm in the hash handling of Linux kernel's xen-netback module, which could result in information leaks, privilege escalation, as well as denial of service". The patch also addresses CVE-2018-18021, a privilege escalation flaw. The Debian Project recommends that all users of GNU/Linux 9 "Stretch" update kernel packages to to version 4.9.110-3+deb9u6.


          Apple buys machine learning AR firm specializing in mixed realities: Apple has reportedly bought a Danish startup called Spektral, which specialized in real-time separation of objects in photos and videos for a 'green screen' effect.      Cache   Translate Page      
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          Amazon desecha una IA de reclutamiento por su sesgo contra las mujeres      Cache   Translate Page      

Amazon desecha una IA de reclutamiento por su sesgo contra las mujeres#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

La inteligencia artificial está produciendo avances enormes en muchas ramas a un ritmo hasta ahora desconocido. Sin embargo, no se debe olvidar que bajo determinadas circunstancias y casos, debido a problemas generalmente relacionados con los datos usados en la base de datos de entrenamiento del software, su uso puede llegar a jugar malas pasadas. Amazon lo ha sufrido con una herramienta que emplea inteligencia artificial para reclutamiento laboral, según informa Reuters.

Estos sistemas ofrecen grandes ventajas para seleccionar a candidatos con mayor eficiencia, pero en el caso de la inteligencia artificial de Amazon, los resultados que ofrecía presentaban sesgos contra las mujeres, es decir, que por ejemplo, en reclutamiento de candidatos para trabajos de desarrollo de software y en otras áreas técnicas, el sistema no estaba aplicando principios de igualdad y meritocracia, sino que producía una importante discriminación contra las mujeres.

La inteligencia artificial presenta retos enormes incluso para gigantes como Amazon

El hecho de que una situación así se dé dentro de una empresa como Amazon, referente en el campo de la automatización y el uso de herramientas de aprendizaje automático, puede parecer inexplicable, pero no lo es. La razón detrás del sesgo contra el sexo femenino está en cómo ha funcionado el reclutamiento en Amazon en los últimos 10 años.

La inteligencia artificial debe ser alimentada de datos para generar modelos y encontrar similitudes positivas. Si el sistema de captación de empleados de la compañía en los últimos años generaba mayoritariamente contrataciones masculinas, y esos datos se le ofrecen a la herramienta sin indicaciones de lo que está bien o mal, o de la igualdad de oportunidades que se busca, el software interpreta que lo corriente es lo positivo. Acto seguido, actuará en base a ello.

En este caso, se crearon 500 modelos informáticos con atención a funciones específicas del empleo y localizaciones. Cada uno de esos 500 aprendió a reconocer 50.000 palabras que aparecían en el currículum de los candidatos. El problema es que los hombres, según la fuente de Reuters, tienden a usar términos como realización o conquista más que las mujeres, y el sistema los primó.

Si no se hace nada para frenarlo, la inteligencia artificial puede reproducir y amplificar los sesgos humanos

La simple introducción del término "women’s" en el currículum generaba un agravio contra las mujeres. Las mujeres tituladas en dos universidades femeninas no mixtas también sufrían efectos de degradación para el sistema. Aunque dentro de la empresa esto se vio como algo negativo y se quiso modificar ese comportamiento de la inteligencia artificial para hacerla más neutra, el proyecto se desechó cuando se percibió que no había certeza de que el sesgo no se pudiera reproducir en otro sentido.

El equipo de desarrolladores a cargo fue disuelto a causa de una pérdida de esperanza en el proyecto. La herramienta se llegó a usar, pero no para basarse únicamente en sus recomendaciones.

Un problema con difícil solución

Entrenar a un software de inteligencia artificial para obtener resultados óptimos no es algo banal. Como se ha mencionado, no es lo mismo alimentar a una máquina con datos a discreción, sin aportar más profundidad al análisis de datos, que hacerlo. Lo segundo es mucho más complicado, porque en este caso en Amazon se ha visto mal el sesgo, pero en otros muchos puede no ser así y que la desigualdad se perpetúe e incluso crezca a largo plazo.

Al diseñar estas herramientas las empresas no sólo deben buscar la productividad, sino de alguna forma reconocer qué patrones repetitivos y del todo positivos se pueden estar dando con decisiones por personas, para así generar modelos que tiendan más a la centralidad.

Puedes aprender más sobre inteligencia artificial escuchando nuestro podcast ‘Captcha’, donde debatimos en profundidad sobre todo lo que le rodea.

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La noticia Amazon desecha una IA de reclutamiento por su sesgo contra las mujeres fue publicada originalmente en Genbeta por Antonio Sabán .


          Amazon drops secret AI recruiting tool that showed bias against women      Cache   Translate Page      
Bias in machine learning may be problematic even for tech giants like Amazon who are at the forefront of this field.  A new report from Reuters shows the e-commerce company had to get rid of an internal project used to vet job applications after the recruiting tool displayed an inherent bias against women.
          Sr. Associate, Machine Learning AI Consultant - KPMG - Seattle, WA      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Fri, 07 Sep 2018 02:02:14 GMT - View all Seattle, WA jobs
          Sr. Associate, AI in Management Analytics Consultant - KPMG - McLean, VA      Cache   Translate Page      
Ability to apply statistical, machine learnings, and artificial intelligence techniques to achieve concrete business goals and work with the business to...
From KPMG LLP - Sat, 29 Sep 2018 15:21:53 GMT - View all McLean, VA jobs
          Database Administrator - Radiant Solutions - Springfield, VA      Cache   Translate Page      
Machine learning, data mining, and knowledge discovery. Work is non-routine and very complex, involving the application of advanced technical and business...
From Radiant Solutions - Wed, 04 Jul 2018 01:36:29 GMT - View all Springfield, VA jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - Dallas, TX      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Fri, 07 Sep 2018 02:02:14 GMT - View all Dallas, TX jobs
          Associate, Machine Learning AI Consultant - KPMG - Dallas, TX      Cache   Translate Page      
Broad, versatile knowledge of analytics and data science landscape, combined with strong business consulting acumen, enabling the identification, design and...
From KPMG LLP - Fri, 07 Sep 2018 02:02:14 GMT - View all Dallas, TX jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - Philadelphia, PA      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Fri, 14 Sep 2018 08:38:34 GMT - View all Philadelphia, PA jobs
          Sr. Associate, Machine Learning AI Consultant - KPMG - New York, NY      Cache   Translate Page      
Modeling (regression, machine learning, feature selection, dimension reduction, validation); Strong aptitude for quickly learning business operational, process,...
From KPMG LLP - Fri, 14 Sep 2018 08:38:34 GMT - View all New York, NY jobs
          Associate, Machine Learning AI Consultant - KPMG - New York, NY      Cache   Translate Page      
Broad, versatile knowledge of analytics and data science landscape, combined with strong business consulting acumen, enabling the identification, design and...
From KPMG LLP - Fri, 14 Sep 2018 08:38:34 GMT - View all New York, NY jobs
          Threat Finance Subject Matter Expert - People, Technology & Processes - Fort Bragg, NC      Cache   Translate Page      
The TF SME contractor shall have proficiency and experience with applied data processing and scientific analysis of large datasets and machine learning....
From People, Technology & Processes - Tue, 17 Jul 2018 03:09:42 GMT - View all Fort Bragg, NC jobs
          UR - Corporate Engineering - Precision Systems Engineer (Maplewood, MN) - 3M - Maplewood, MN      Cache   Translate Page      
Proactively collaborate with business partners to connect and extend process data management solutions with complimentary machine learning and analytics efforts...
From 3M - Wed, 05 Sep 2018 17:09:45 GMT - View all Maplewood, MN jobs
          Data Science Manager - Micron - Boise, ID      Cache   Translate Page      
Create server based visualization applications that use machine learning and predictive analytic to bring new insights and solution to the business....
From Micron - Wed, 05 Sep 2018 11:18:49 GMT - View all Boise, ID jobs
          Intern - Data Scientist (NAND) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Wed, 29 Aug 2018 20:54:50 GMT - View all Boise, ID jobs
          Intern - Data Scientist (DRAM) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Mon, 20 Aug 2018 20:48:37 GMT - View all Boise, ID jobs
          CTO XL Galil      Cache   Translate Page      
השתלבות בצוות המוביל של יחידה עסקית אשר מהווה חלק מחברה ותיקה ויציבה.היחידה עוסקת באספקת שירותי ייעוץ מומחים לארגוני R&D משמעותיים בתחומי :Software Architecture, Machine Learning & Cyber Securityמחפשת CTO לטווח ארוך בהיקף משרה מלא .
          Sr/Principal Consultant, Red Team - Cylance, Inc. - Texas      Cache   Translate Page      
Internal / External / Wireless - Penetration Testing (2+ years REQUIRED). By successfully applying artificial intelligence and machine learning to crack the DNA...
From Cylance, Inc. - Wed, 05 Sep 2018 19:27:50 GMT - View all Texas jobs
          Consultant - DigitalShield - White Plains, NY      Cache   Translate Page      
By successfully applying machine learning and artificial. Detailing technical issues identified and their associated business....
From DigitalShield - Wed, 15 Aug 2018 07:47:55 GMT - View all White Plains, NY jobs
          Red Team Consultant - Cylance, Inc. - North Carolina      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Wed, 05 Sep 2018 01:27:47 GMT - View all North Carolina jobs
          Senior Compliance Analyst - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Thu, 13 Sep 2018 19:27:37 GMT - View all Irvine, CA jobs
          Senior Compliance & Privacy Analyst - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Sat, 08 Sep 2018 01:27:53 GMT - View all Irvine, CA jobs
          Financial Reporting Director - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Mon, 13 Aug 2018 07:27:33 GMT - View all Irvine, CA jobs
          Technical Account Manager - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Fri, 27 Jul 2018 19:28:10 GMT - View all Irvine, CA jobs
          Senior DevOps Engineer - Cylance, Inc. - Irvine, CA      Cache   Translate Page      
By successfully applying artificial intelligence and machine learning to crack the DNA of malware, Cylance has redefined the endpoint protection market,...
From Cylance, Inc. - Sun, 08 Jul 2018 19:27:56 GMT - View all Irvine, CA jobs
          Instagram Releases Tools to Combat Bullying      Cache   Translate Page      

Facebook-owned photo sharing platform Instagram on Tuesday announced their latest tools to help combat bullying, including a new way to identify and report bullying in photos. The company is using Machine Learning (ML) technology to proactively detect bullying in photos and their captions. “This change will help us identify and remove significantly more bullying — and it’s a crucial next step since many people who experience or [...]

The post Instagram Releases Tools to Combat Bullying appeared first on NewsGram.


           Prostate Cancer Classification on VERDICT DW-MRI Using Convolutional Neural Networks       Cache   Translate Page      
Chiou, E; Giganti, F; Bonet-Carne, E; Punwani, S; Kokkinos, I; Panagiotaki, E; (2018) Prostate Cancer Classification on VERDICT DW-MRI Using Convolutional Neural Networks. In: Shi, Y and Suk, H-I and Liu, M, (eds.) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science, vol 11046. (pp. pp. 319-327). Springer: Cham.
          Danish startup Spektral sold to Apple for $30 million      Cache   Translate Page      

Copenhagen-based visual effects startup Spektral was acquired by Apple for $30 million at the end of 2017 in a deal that was only disclosed this week. The company focused on applying machine learning techniques to image and video editing. Spektral, formerly known as CloudCutout, had raised some $3 million prior to the acquisition. The main […]

The post Danish startup Spektral sold to Apple for $30 million appeared first on Tech.eu.


          Services Operations Manager - Highspot - Seattle, WA      Cache   Translate Page      
Equipped with new Apple products. We employ advanced technologies, included patented machine learning algorithms to:....
From Highspot - Mon, 01 Oct 2018 20:07:27 GMT - View all Seattle, WA jobs
          Mobile Engineer React Native - Xevo - Bellevue, WA      Cache   Translate Page      
Technical and non-technical, internal and external. Do you dream about artificial intelligence and machine learning?...
From Xevo - Tue, 02 Oct 2018 02:28:59 GMT - View all Bellevue, WA jobs
          Sr Software Engineer, Applied Machine Learning - Apple - Austin, TX      Cache   Translate Page      
We work on many high-impact projects that serve various Apple lines of business. Understanding of JVM internals and garbage collection....
From Apple - Sun, 30 Sep 2018 01:50:35 GMT - View all Austin, TX jobs
          Google AI Resident, 2019 Start (Fixed-Term Employee) - Google - Montréal, QC      Cache   Translate Page      
Or applications of machine learning to NLP, human-computer interaction, computer vision, speech, computer systems, robotics, algorithms, optimization, on-device...
From Google - Wed, 10 Oct 2018 14:23:04 GMT - View all Montréal, QC jobs
          Google AI Resident, 2019 Start (Fixed-Term Employee) - Google - Toronto, ON      Cache   Translate Page      
Or applications of machine learning to NLP, human-computer interaction, computer vision, speech, computer systems, robotics, algorithms, optimization, on-device...
From Google - Wed, 10 Oct 2018 14:22:59 GMT - View all Toronto, ON jobs
          Ingénieur Analyse de Données et Logiciels - Intelligence Manufacturière - Data Analytics and Software Engineer – Manufacturing Intelligence - Alcoa Corp. - Deschambault, QC      Cache   Translate Page      
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      
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
          Style Transfer Model Development      Cache   Translate Page      
Implement similar to (https://medium.com/tensorflow/neural-style-transfer-creating-art-with-deep-learning-using-tf-keras-and-eager-execution-7d541ac31398) for capturing style from multiple sources, storing into embeddings and to then re-apply them at differing scales (i.e... (Budget: $500 USD, Jobs: Machine Learning, Python, Tensorflow)
          Microsoft Joins the Open Invention Network, NVIDIA Announces RAPIDS, Asterisk 16.0.0 Now Available, BlockScout Released and Security Advisory for Debian GNU/Linux 9 "Stretch"      Cache   Translate Page      

News briefs for October 10, 2018.

Microsoft has joined the Open Invention Network (OIN), an open-source patent consortium. According to ZDNet, this means "Microsoft has essentially agreed to grant a royalty-free and unrestricted license to its entire patent portfolio to all other OIN members." OIN's CEO Keith Bergelt says "This is everything Microsoft has, and it covers everything related to older open-source technologies such as Android, the Linux kernel, and OpenStack; newer technologies such as LF Energy and HyperLedger, and their predecessor and successor versions."

NVIDIA has just announced RAPIDS, its open-source data analytics/machine learning platform, Phoronix reports. The project is "intended as an end-to-end solution for data science training pipelines on graphics processors", and NVIDIA "laims that RAPIDS can allow for machine learning training at up to 50x and is built atop CUDA for GPU acceleration".

The Asterisk Development Team announces that Asterisk 16.0.0 is now available. This version includes many security fixes, new features and tons of bug fixes. You can download it from here.

BlockScout, the first full-featured open-source Ethereum block explorer tool, was released yesterday by POA Network. The secure and easy-to-use tool "lets users search and explore transactions, addresses, and balances on the Ethereum, Ethereum Classic, and POA Network blockchains". And, because it's open source, anyone can "contribute to its development and customize the tool to suit their own needs".

Debian has published another security advisory for Debian GNU/Linux 9 "Stretch". According to Softpedia News, CVE-2018-15471 was "discovered by Google Project Zero's Felix Wilhelm in the hash handling of Linux kernel's xen-netback module, which could result in information leaks, privilege escalation, as well as denial of service". The patch also addresses CVE-2018-18021, a privilege escalation flaw. The Debian Project recommends that all users of GNU/Linux 9 "Stretch" update kernel packages to to version 4.9.110-3+deb9u6.


          Sr. Program Manager - Lab126 - Bellevue, WA      Cache   Translate Page      
Lead operations for data collection initiatives on Amazon campus and at an external site to support Machine learning teams....
From Lab126 - Thu, 04 Oct 2018 22:41:07 GMT - View all Bellevue, WA jobs
          Sr. Program Manager - Amazon.com - Bellevue, WA      Cache   Translate Page      
Lead operations for data collection initiatives on Amazon campus and at an external site to support Machine learning teams....
From Amazon.com - Thu, 04 Oct 2018 19:24:05 GMT - View all Bellevue, WA jobs
          Software development manager - Amazon.com - Bellevue, WA      Cache   Translate Page      
Build Products for amazon external facing and internal facing systems. The team uses various content classification and machine learning algorithms for solving...
From Amazon.com - Wed, 18 Jul 2018 19:20:37 GMT - View all Bellevue, WA jobs
          Sr. Product Manager - Amazon.com - Seattle, WA      Cache   Translate Page      
Experience with machine learning and support automation tools. Bachelor’s degree in engineering, business, philosophy, design or related field....
From Amazon.com - Tue, 09 Oct 2018 19:23:59 GMT - View all Seattle, WA jobs
          Principal Solutions Architect, Alexa - Amazon.com - Seattle, WA      Cache   Translate Page      
Are you interested in working with AI, robotics and machine learning experts across Amazon to create compelling, cutting-edge demos and content to help...
From Amazon.com - Tue, 09 Oct 2018 04:01:04 GMT - View all Seattle, WA jobs
          NVIDIA Announces "RAPIDS" Open-Source Data Analytics / Machine Learning Platform      Cache   Translate Page      
NVIDIA has announced RAPIDS as their latest open-source project...
          Software Dev Engineer II - Full Stack - Amazon.com - Seattle, WA      Cache   Translate Page      
We are reinventing our internal tools through metrics, machine learning, automation, and user centered design....
From Amazon.com - Tue, 09 Oct 2018 04:00:58 GMT - View all Seattle, WA jobs
          Anecdotes Program Manager - Amazon.com - Seattle, WA      Cache   Translate Page      
Solid Machine Learning background and familiar with standard speech and machine learning techniques. Business Analytics configuration, design and support....
From Amazon.com - Sat, 06 Oct 2018 01:22:41 GMT - View all Seattle, WA jobs
          Software Development Engineer - Amazon Videos Customer Engagement Channels - Amazon.com - Seattle, WA      Cache   Translate Page      
Experience building machine learning enabled services. Amazon is an Equal Opportunity-Affirmative Action Employer - Minority/Female/Disability/Vet....
From Amazon.com - Fri, 05 Oct 2018 08:45:06 GMT - View all Seattle, WA jobs
          Can Cloud-based AI Boost the Economy?      Cache   Translate Page      
Society of Internet Professionals: Can Cloud-based AI Boost the Economy?
Society of Internet Professionals "Can Cloud-based AI Boost the Economy?"

This article, authored by Cory Popescu was first published on the blog of the Society of Internet Professionals (SIP). SIP is a not-for-profit, Toronto (Canada) based International organization to connect, learn and share. Our Vision is to provide the opportunity to leverage technology to have an inclusive future for everyone. Since 1997, SIP has spearheaded many initiatives, educational programs, and networking events.

Currently Artificial Intelligence (AI) penetrates faster in the tech industry, since it has increased the volume of new products and services in an efficient way and the structure of development, production, and delivery seems suitable to the implementation of this cutting-edge technology. Still plenty of other businesses and industries have yet to take advantage of the advances in this relatively novel application field. In medicine, energy or manufacturing sector, a more intensive and comprehensive application of AI could dramatically transform these fields while boosting the economic productivity.

Not only AI enters a particular sector, high-tech, also within it only a few major organizations make use of it to create expansion through volume and efficiency at unbelievable higher speed. Companies like Google, Amazon, Microsoft, Baidu and some startups engage AI in their matrix since it may be acceptable price-wise, while for the most part and all the rest of our economy, this novel technology proves difficult to implement and extremely costly.

Therefore, companies like Amazon, Microsoft, and Google aim to create cloud-based AI to make the technology cheaper to be implemented and used effortlessly. This leading-edge cloud-based AI is available now and by expanding it to lots of organizations it could trigger more economic development. Therefore, the solution is to bring AI and cloud-based machine-learning tools to large audiences.

In this pursuit, Microsoft has Azure, its own cloud platform and by cooperating with Amazon they are working to offer an open-source machine learning library called Gluon. This is created to engage building neural nets to become a core AI technology to copy and apply the processes of learning exposed by the human brain.

Although at this time, Amazon dominates cloud machine learning with AWS, Google is following suit with an open-source AI library named TensorFlow. This library proves powerful since it can develop and build further machine-learning software. Also, on designers’ table a priority represents the simpler use and implementation of AI, and the recent Google pre-trained system suit Cloud AutoML, promises to do just that. Both organizations start preparing consulting services to cover the shortage of cloud-based AI specialists who can spread the knowledge of the leading-edge AI.

The future will tell who is going to spread more and what quality of AI. It certainly represents a huge business opportunity for those involved. The cloud-based AI technology has great chances to expand and comprehend various sectors untouched so far. We can only realize the true benefits… and the downsides of AI once the cloud-based is ready to roll and found at almost everyone’s fingertips.

Cory Popescu

Your comments are welcomed

Click on the links below to read the other articles by Cory Popescu:
Is It Possible To Get 100% Privacy On The Internet?
Why Incorporate Blockchain in Your Business?
Blockchain: Unbelievable Transactions Blockchain Can Promote!


          Google AI Residency Program 2019: Research Training Role for Graduates in STEM Fields      Cache   Translate Page      
The Google AI Residency Program&nbsp;&mdash; previously known as the Google Brain Residency Program &mdash;&nbsp;is a 12-month research training role designed to jumpstart or advance your career in machine learning research. The goal of the residency is to help residents become productive and successful AI researchers.The Google AI Residency Program was created in 2015 with the goal of training and supporting the next generation of deep learning researchers.&nbsp;Residents will have the opportunity to be mentored by distinguished scientists and engineers from various teams within&nbsp;Google AI, and work on real-world machine learning problems and applications. In addition, they will also have the opportunity to collaborate and partner closely with various research groups across Google and Alphabet.As part of this program, Residents collaborate with distinguished scientists from various&nbsp;Google AI&nbsp;teams working on machine learning applications and problems. Residents have the opportunity to do everything from conducting fundamental research to contributing to products and services used by billions of people. We also encourage our Residents to publish their work externally.&nbsp;Take a look&nbsp;at the impactful research done by earlier cohorts.Residency LocationsBay Area (Mountain View and San Francisco)New York City,&nbsp;Cambridge (Massachusetts)Montreal&nbsp;and&nbsp;Toronto, CanadaSeattle (Washington State)Accra (Ghana)Tel Aviv (Israel)Zurich (Switzerland).Residents are placed based on interest, project fit, location preference and team needs. All are expected to work on site.The Google AI Residency Program will have 3 start dates over the course of 5 months, from June to October 2019. Exact dates are yet to be determined.

Apply at https://ngcareers.com/job/2018-10/google-ai-residency-program-2019-research-training-role-for-graduates-in-stem-fields-639/


          Vice President, Data Science - Machine Learning - Wunderman - Dallas, TX      Cache   Translate Page      
Goldman Sachs, Microsoft, Citibank, Coca-Cola, Ford, Pfizer, Adidas, United Airlines and leading regional brands are among our clients....
From Wunderman - Sat, 25 Aug 2018 05:00:40 GMT - View all Dallas, TX jobs
           Comment on Notes on Gartner’s 2018 Data Science and Machine Learning MQ by Thomas W. Dinsmore       Cache   Translate Page      
Only if (a) 100% of the data you will ever need is already in Teradata; (b) the Teradata software is functionally equivalent to competitive data science and machine learning platforms; and (c) the database administrators have a clue about the needs of data scientists and don't lock down the platform in such a way that data scientists can't use it. In actual practice, (a) is never true; (b) is not true, and (c) is often not true. Teradata has been pitching embedded analytics since 1989. It's a long trail of tears.
           Comment on Notes on Gartner’s 2018 Data Science and Machine Learning MQ by Just Another Funny Opinionist       Cache   Translate Page      
Don´t you see an advantage of having the Data and Analytics instances altogether?
          GigaSpaces AI and Machine Learning Event in Munich      Cache   Translate Page      

In today’s fast-paced world of “now,” combined with the exponential growth of data and connected devices (IoT), organizations strive to get from data to real-time insights to action at sub-second latency. Ingesting, aggregating and analyzing massive amounts of real-time data

The post GigaSpaces AI and Machine Learning Event in Munich appeared first on The GigaSpaces Technologies Blog.


          Support Engineer - Microsoft - Las Colinas, TX      Cache   Translate Page      
Open Source – Linux, Red Hat, etc. Business Division Specific:. HDInsight/Hadoop, Machine Learning, Azure Stream Analytics....
From Microsoft - Thu, 19 Jul 2018 07:31:47 GMT - View all Las Colinas, TX jobs
          Instagram ancora contro il cyberbullismo: lo scoverà nelle foto      Cache   Translate Page      
Prima mossa per Adam Mosseri, nuovo capo dell'app videofotografica: una soluzione di machine learning che individua in automatico immagini, montaggi e didascalie offensive, aggressive e umilianti. E una collaborazione con la teen star Maddie Ziegler
          Why Google can't hack it as a hardware company      Cache   Translate Page      

Google announced a slew of shiny new products this week, including new Pixel smartphones. Unfortunately, a spectre of low sales hovered over all the new stuff. Compared to the iPhone, which has sold tens of millions of units, Google's Pixel phone has dwindled in popularity with the everyday buyer. Sure, the Pixel 2 camera is incredibly impressive, but overall, consumers don't seem care.

This remains true even after this week's big event. Google can launch all the new products it wants, but until it finds a way to truly inspire the average Joe, its ambitions as a consumer hardware company will likely go unfulfilled. The new mediocre Pixel phone line certainly won't do the trick on its own. But there may be a silver lining for Google.

Google's hardware projects are remarkably similar in purpose to Microsoft's Surface line. Both are attempts to showcase the very best of their respective ecosystems. For Google, this is machine learning. The new Pixel 3 phones have a number of features that rely on Google's AI, including a more advanced camera, a way to filter spam calls, and its controversial Duplex feature, which automates calls to make appointments. Similarly, the new Google Home Hub calls on the company's smart home expertise to provide a control center for smart doorbells or thermostats, while also functioning like an alarm clock, or family hub for questions, reminders, and news and weather.

But so far, Microsoft's ecosystem is still more impressive. The Surface Pro line is the standard for Windows hybrids and has sold comparatively well, and high-end halo products like the flashy Surface Studio all-in-one have done a lot to improve Microsoft's reputation, despite low sales.

Meanwhile, the Pixel line of phones seems to have done neither. Sales in 2017 didn't even reach 4 million units. In reviews, customers found the devices were fine — but not great. Last month, after Apple's new iPhone was revealed, The Verge said that while the phone was a solid update, the camera still didn't match the Google Pixel 2. On Twitter, a popular tech commentator known as Kontra found this infuriating, not because it was technically untrue, but because he felt the wild difference in sales between the iPhone and the Pixel made the comparison nonsensical.

The new Pixel 3 and Pixel 3 XL don't seem like they'll do much to change that sentiment, particularly because their design remains uninspiring. Rather than stoking consumer lust, the plain, almost nerdy feel of the Pixel phones seems aimed mostly at choosy techies looking for a pure Android experience rather than the public at large. It's true, trying to establish a new foothold in the now-mature smartphone market is difficult. But Google isn't doing itself any favors by focusing on a few gimmicks rather than producing a desirable product overall.

The one bright spot in Google's hardware lineup has been its Home smart speaker products. Despite the market once being utterly dominated by Amazon's Alexa devices, Google's advantages in search and artificial intelligence have allowed to it gain significant market share. In some ways, these Google products are just better than the others.

If Google truly wants to establish itself as a consumer hardware company, it needs to make all of its products better than the others. Right now, it's hard to see why most mainstream consumers would choose a Pixel phone over a Samsung Galaxy or iPhone. Part of that is marketing, which has thus far lacked, but mostly it comes down to an elusive mix of design, features, and performance that define the very best products.

But it's not all doom and gloom. The good news is that the new Pixel Slate seems very promising. It's a hybrid device, like the iPad Pro or Surface line. The tablet runs ChromeOW and Chromebooks. It has a detachable keyboard with an adjustable angle, and it can adapt between its laptop-like or tablet modes. It is very thin, can be specced to be powerful, and showcases some genuinely useful innovations. In other words, the Pixel Slate is what the Pixel phones are not: slick, different from the competition, and genuinely new. It also seems well thought out: Using ChromeOS rather than Android signals a genuine attempt by Google to take on Windows and iOS.

The Slate was one bright spot for Google in an otherwise unremarkable rollout of new devices. If the company can keep its sights focused on the future of computing, perhaps it can finally become a hardware company and provide some genuinely interesting, drool-worthy products.


          Apple Has Acquired Danish Startup Spektral, Focused on Real-Time 'Green Screen' Technology      Cache   Translate Page      
Apple has acquired Danish computer vision startup Spektral, according to a paywalled report from Danish newspaper Børsen.


Spektral has developed a technology that can intelligently separate people and objects from their original backgrounds in photos and videos, and overlay a new background, resulting in what is called a "cutout." The solution is driven by deep neural networks and spectral graph theory.

The technology can be thought of as real-time "green screen" processing powered by machine learning algorithms:
Our pioneering and unique technology is based on state-of-the-art machine learning and computer vision techniques. Combining deep neural networks and spectral graph theory with the computing power of modern GPUs, our engine can process images and video from the camera in real-time (60 FPS) directly on the device.
The report says Apple acquired Spektral, formerly known as CloudCutout, in late 2017. Spektral co-founders Henrik Paltoft and Toke Jansen, who now lists himself as a manager of computational imaging at Apple, are said to have received 200 million Danish krone, or roughly $30 million as of today's exchange rate.


Spektral's website notes that its solution makes it possible to create unique and immersive mixed reality content. Apple could incorporate the technology into the default Camera app on iPhone, or Messages, or Clips, or use the technology in bigger ways as it continues to push into augmented reality.

Spektral was founded in 2014 and raised $3.3 million in venture capital prior to its acquisition by Apple, according to Crunchbase.


Discuss this article in our forums


          Nvidia launches Rapids to help bring GPU acceleration to data analytics      Cache   Translate Page      
Nvidia, together with partners like IBM, HPE, Oracle, Databricks and others, is launching a new open-source platform for data science and machine learning today. Rapids, as the company is calling it, is all about making it easier for large businesses to use the power of GPUs to quickly analyze massive amounts of data and then […]
          FogHorn Unveils Groundbreaking Lightning Edge Intelligence 2.0      Cache   Translate Page      

According to a recent press release, “FogHorn, a leading developer of edge intelligence software for industrial and commercial Internet of Things (IoT) solutions, announced today the availability of its Lightning™ 2.0 software. The additions to the Lightning portfolio establish new industry benchmarks for edge-based machine learning (EdgeML®), massively scalable edge deployment support, zero-touch sensor configuration, […]

The post FogHorn Unveils Groundbreaking Lightning Edge Intelligence 2.0 appeared first on DATAVERSITY.


          China's Huawei Takes Aim at Qualcomm, Nvidia With New AI Chips      Cache   Translate Page      
Huawei, China's largest telecoms gear and phone maker Huawei unveiled machine-learning capable Ascent series chips • The series can go toe-to-toe with Qualcomm and Nvidia's chips designed for machine learning • The company also announced cloud services and data centers dedicated for autonomous vehicles that will run on its new chips
          Senior Computer Vision Engineer - Brussels - Vivid Resourcing - Brussels      Cache   Translate Page      
Senior Computer Vision Engineer - Brussels Our multinational client based in the Brussels are looking for a senior research engineer to work with the latest machine learning/ computer vision algorithms on projects relating to autonomous driving. Responsibilities: Working with some highly specialised research engineers on the latest machine learning/ computer vision techniques. Identifying problems and propose/ implement solutions. Visits to top research facilities across Europe, Japan and...
          Smart Compose is coming to Gmail mobile starting with Pixel 3      Cache   Translate Page      
Smart Compose is a feature reserved for Gmail users on the web, but Google has decided that it would make a fine addition to the mobile app as well. The Mountain View company announced today that it will bring Smart Compose to Gmail mobile and that the first device to get it will be the Pixel 3.

For those who don't know, Smart Compose helps Gmail users write emails much faster since it uses machine learning to offer suggestions on the fly. Those who plan to buy the Pixel 3/XL will notice that Gmail offers writing suggestions as they type and that they can swipe right to use the suggestions ...
          Java Engineer - Gent - Huxley IT - Ghent      Cache   Translate Page      
Are you open for new opportunities? Are you waiting for very nice and challenging opportunity? Are you willing to work in the area of Gent? Do you want to do more than developing? Are you thrilled by machine learning and artificial intelligence? This company is a worldwide leader in its sector and is re-starting their recruitment activities after a freeze. You will work on digital projects as it is their core business in the latest technologies as Java 8 and Java 10 in some applications....
          Software Developer, Machine Learning - Titus - Ottawa, ON      Cache   Translate Page      
TITUS solutions enable organizations to discover, classify, protect, analyze and confidently share information....
From Titus - Fri, 20 Jul 2018 17:52:29 GMT - View all Ottawa, ON jobs
          Software Developer, Machine Learning - Titus - Ottawa, ON      Cache   Translate Page      
As part of our commitment to diversity we consider all qualified applicants for employment regardless of race, religion, sex, ethnicity, national origin, age,...
From Titus - Fri, 20 Jul 2018 17:52:29 GMT - View all Ottawa, ON jobs
          Project Manager - Autonomy and Machine Learning - Let your imagination fly!      Cache   Translate Page      
CA-San Jose, One of the coolest and most desired start-ups in Silicon Valley is seeking a Project Manager, Autonomy and Machine Learning for a newly created role located in downtown San Jose. If you think autonomous driving vehicles are the technology of the future, then think bigger as you need to check out this exciting opportunity! Give your imagination the flight it deserves! Job description: The Project M
          Software Developer, Machine Learning - Titus - Ottawa, ON      Cache   Translate Page      
Familiarity with text analysis. Undergraduate degree in Computer Science, Engineering or equivalent demonstrable experience....
From Titus - Fri, 20 Jul 2018 17:52:29 GMT - View all Ottawa, ON jobs
          Software Developer, Machine Learning - Titus - Ottawa, ON      Cache   Translate Page      
As part of our commitment to diversity we consider all qualified applicants for employment regardless of race, religion, sex, ethnicity, national origin, age,...
From Titus - Fri, 20 Jul 2018 17:52:29 GMT - View all Ottawa, ON jobs
          Email Marketing in 2018      Cache   Translate Page      

Email marketing is not dead! The tactics marketers must use in order to be effective have just changed. Businesses must be willing to change with the times or fear getting left to rot in the SPAM folder. As a company that has been involved in email marketing for years, IAS has learned many lessons (in some cases, the hard way) through numerous experiments with strategy. Potential clients and subscribers are unlikely to open an email from a business unless there is a clear benefit in doing so. There must be a given initiative such as a 50% off coupon, a promise of knowledge or insight, etc. This needs to be communicated within a captivating subject line or you could severely hinder your open rates. Here are some ways email marketing strategies have shifted, and several examples of how marketers can leverage this knowledge to improve customer outreach: – Tailored Email Campaigns: Today, by analyzing a given customer’s interests, you have the ability to spark their curiosity, leading to more clicks and opens. Marketers can allow each user to customize email settings, which include allowing them to choose frequency and the type of content they receive within those emails. By allowing potential customers to create their own “email experience”, you create an emotional connection with them. – Real-Time Email Outreach: Companies such as Yesmail are already utilizing an early form of this email marketing where a campaign is based on what the consumer is currently reading or searching for in their various social media feeds. For example, if a customer is reading about sports events, the times and option to buy tickets online would arrive in their inbox in real time. The general idea is to leverage awareness about your customer’s situation so you can ignite a real-time dialogue with them via e-mail and achieve greater influence. – Artificial Intelligence and Machine Learning: If you’ve used Google inbox, you’ve already seen how your phone reads your emails and learns to respond. It’s still pretty basic (an algorithm that creates 3 short responses based on the content), but it’s gotten more intelligent in the last couple years and will likely continue. These types of AIs are becoming more common for marketers and consumers alike. The future of email marketing is definitely here, yet far from perfect. In the coming years, businesses and marketers should already be using all the methods available along with being on the cutting edge of marketing strategies to come.

The post Email Marketing in 2018 appeared first on IAS Marketing Services.


          Amazon desecha una IA de reclutamiento por su sesgo contra las mujeres      Cache   Translate Page      

Amazon desecha una IA de reclutamiento por su sesgo contra las mujeres#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

La inteligencia artificial está produciendo avances enormes en muchas ramas a un ritmo hasta ahora desconocido. Sin embargo, no se debe olvidar que bajo determinadas circunstancias y casos, debido a problemas generalmente relacionados con los datos usados en la base de datos de entrenamiento del software, su uso puede llegar a jugar malas pasadas. Amazon lo ha sufrido con una herramienta que emplea inteligencia artificial para reclutamiento laboral, según informa Reuters.

Estos sistemas ofrecen grandes ventajas para seleccionar a candidatos con mayor eficiencia, pero en el caso de la inteligencia artificial de Amazon, los resultados que ofrecía presentaban sesgos contra las mujeres, es decir, que por ejemplo, en reclutamiento de candidatos para trabajos de desarrollo de software y en otras áreas técnicas, el sistema no estaba aplicando principios de igualdad y meritocracia, sino que producía una importante discriminación contra las mujeres.

La inteligencia artificial presenta retos enormes incluso para gigantes como Amazon

El hecho de que una situación así se dé dentro de una empresa como Amazon, referente en el campo de la automatización y el uso de herramientas de aprendizaje automático, puede parecer inexplicable, pero no lo es. La razón detrás del sesgo contra el sexo femenino está en cómo ha funcionado el reclutamiento en Amazon en los últimos 10 años.

La inteligencia artificial debe ser alimentada de datos para generar modelos y encontrar similitudes positivas. Si el sistema de captación de empleados de la compañía en los últimos años generaba mayoritariamente contrataciones masculinas, y esos datos se le ofrecen a la herramienta sin indicaciones de lo que está bien o mal, o de la igualdad de oportunidades que se busca, el software interpreta que lo corriente es lo positivo. Acto seguido, actuará en base a ello.

En este caso, se crearon 500 modelos informáticos con atención a funciones específicas del empleo y localizaciones. Cada uno de esos 500 aprendió a reconocer 50.000 palabras que aparecían en el currículum de los candidatos. El problema es que los hombres, según la fuente de Reuters, tienden a usar términos como realización o conquista más que las mujeres, y el sistema los primó.

Si no se hace nada para frenarlo, la inteligencia artificial puede reproducir y amplificar los sesgos humanos

La simple introducción del término "women’s" en el currículum generaba un agravio contra las mujeres. Las mujeres tituladas en dos universidades femeninas no mixtas también sufrían efectos de degradación para el sistema. Aunque dentro de la empresa esto se vio como algo negativo y se quiso modificar ese comportamiento de la inteligencia artificial para hacerla más neutra, el proyecto se desechó cuando se percibió que no había certeza de que el sesgo no se pudiera reproducir en otro sentido.

El equipo de desarrolladores a cargo fue disuelto a causa de una pérdida de esperanza en el proyecto. La herramienta se llegó a usar, pero no para basarse únicamente en sus recomendaciones.

Un problema con difícil solución

Entrenar a un software de inteligencia artificial para obtener resultados óptimos no es algo banal. Como se ha mencionado, no es lo mismo alimentar a una máquina con datos a discreción, sin aportar más profundidad al análisis de datos, que hacerlo. Lo segundo es mucho más complicado, porque en este caso en Amazon se ha visto mal el sesgo, pero en otros muchos puede no ser así y que la desigualdad se perpetúe e incluso crezca a largo plazo.

Al diseñar estas herramientas las empresas no sólo deben buscar la productividad, sino de alguna forma reconocer qué patrones repetitivos y del todo positivos se pueden estar dando con decisiones por personas, para así generar modelos que tiendan más a la centralidad.

Puedes aprender más sobre inteligencia artificial escuchando nuestro podcast ‘Captcha’, donde debatimos en profundidad sobre todo lo que le rodea.


          Developer      Cache   Translate Page      
PA-Newtown Square, Newtown Square, Pennsylvania Skills : Azure,Engineering,Research Description : Description: Data Scientist / Machine Learning Engineer Lead Primary Responsibilities: • Lead the implementation of research investigations and construction of machine learning models to solve business problems in the health care industry • Verify model effectiveness • Collaborate within cross-disciplinary teams includi
          Apple bought a machine learning green screen startup to focus on AR      Cache   Translate Page      
Apple has quietly bought Spektral, a Danish machine learning startup that specializes in real-time g
          Never Use Your Camera Flash Again With Night Sight on Pixel Phones      Cache   Translate Page      
Google says you’ll never have to use your camera flash again with the Night Sight feature on the Pixel 3. Night Sight uses machine learning to automatically make low-light photos look better.
          3 Things Banks Can Do to Tackle Push Payment Fraud      Cache   Translate Page      

Phone with hacked sign

In August, the UK’s Financial Ombudsman joined the debate on authorised push payment fraud. They argue that banks are not taking enough responsibility for this kind of fraud and say banks should not take the default position that their customer has been negligent. This follows the announcement by the Payment Services Regulator that they intend to require banks to compensate their customers in some cases – the consultation paper on their proposal is due out soon.

The pressure for banks to tackle push payment fraud is mounting – and even if liability isn’t transferred wholesale to the banks, the scope for bad publicity and loss of reputation is significant.

While industry bodies look to redress the balance between bank and customer, it must be remembered that the banks are victims as well. Clever fraudsters have a wide variety of increasingly sophisticated techniques and technology to he