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          Meet the AMD Radeon Instinct MI60 and MI50 accelerators      Cache   Translate Page      

If you haven't been watching AMD's launch of the 7nm Vega based MI60 and MI50 then you can catch up right here.


You won't be gaming with these beasts, but for those working on deep learning, HPC, cloud computing or rendering apps you might want to take a deeper look.  The new PCIe 4.0 cards use HBM2 ECC memory and Infinity Fabric interconnects, offering up to 1 TB/s of memory bandwidth. 

The MI60 features 32GB of HBM2 with 64 Compute Units containing 4096 Stream Processors which translates into 59 TOPS INT8, up to 29.5 TFLOPS FP16, 14.7 TFLOPS FP32 and 7.4 TFLOPS FP64.  AMD claims is currently the fastest double precision  PCIe card on the market, with the 16GB Tesla V100 offering 7 TFLOPS of FP64 performance.


The MI50 is a little less powerful though with 16GB of HBM2, 53.6 TFLOPS of INT8, up to 26.8 TFLOPS FP16, 13.4 TFLOPS FP32 and 6.7 TFLOPS FP64 it is no slouch.


With two Infinity Fabric links per GPU, they can deliver up to 200 GB/s of peer-to-peer bandwidth and you can configure up to four GPUs in a hive ring configuration, made of two hives in eight GPU servers with the help of the new ROCm 2.0 software. 

Expect to see AMD in more HPC servers starting at the beginning of the new year, when they start shipping.


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          ‘How do neural nets learn?’ A step by step explanation using the H2O Deep Learning algorithm.      Cache   Translate Page      
In my last blogpost about Random Forests I introduced the Bootcamp. The next part I published was about Neural Networks and Deep Learning. Every video of our bootcamp will have example code and tasks to promote hands-on learning. While the practical parts of the bootcamp will be using Python, below you will find the English R version of this Neural Nets Practical Example, where I explain how neural nets learn and how the concepts and techniques translate to training neural nets in R with the H2O Deep Learning function. You can find the video on YouTube but as, as before, it is only available in German. Same goes for the slides, which are also currently German only. See the end of this article for the embedded video and slides. Neural Nets and Deep Learning Just like Random Forests, neural nets are a method for machine learning and can be used for supervised, unsupervised and reinforcement learning. The idea behind neural nets has already been developed back in the 1940s as a way to mimic how our human brain learns. That’s way neural nets in machine learning are also called ANNs (Artificial Neural Networks). When we say Deep Learning, we talk about big and complex neural nets, which are able to solve complex tasks, like image or language understanding. Deep Learning has gained traction and success particularly with the recent developments in GPUs and TPUs (Tensor Processing Units), the increase in computing power and data in general, as well as the development of easy-to-use frameworks, like Keras and TensorFlow. We find Deep Learning in our everyday lives, e.g. in voice recognition, computer vision, recommender systems, reinforcement learning and many more. The easiest type of ANN has only node (also called neuron) and is called perceptron. Incoming data flows into this neuron, where a result is calculated, e.g. by summing up all incoming data. Each of the incoming data points is multiplied with a weight; weights can basically be any number and are used to modify the results that are calculated by a neuron: if we change the weight, the result will change also. Optionally, we can add a so called bias to the data points to modify the results even further. But how do neural nets learn? Below, I will show with an example that uses common techniques and principles. Libraries First, we will load all the packages we need: tidyverse for data wrangling and plotting readr for reading in a csv h2o for Deep Learning (h2o.init initializes the cluster) library(tidyverse) library(readr) library(h2o) h2o.init(nthreads = -1) ## Connection successful! ## ## R is connected to the H2O cluster: ## H2O cluster uptime: 3 hours 46 minutes ## H2O cluster timezone: Europe/Berlin ## H2O data parsing timezone: UTC ## H2O cluster version: ## H2O cluster version age: 1 month and 16 days ## H2O cluster name: H2O_started_from_R_shiringlander_jpa775 ## H2O cluster total nodes: 1 ## H2O cluster total memory: 3.16 GB ## H2O cluster total cores: 8 ## H2O cluster allowed cores: 8 ## H2O cluster healthy: TRUE ## H2O Connection ip: localhost ## H2O Connection port: 54321 ## H2O Connection proxy: NA ## H2O Internal Security: FALSE ## H2O API Extensions: XGBoost, Algos, AutoML, Core V3, Core V4 ## R Version: R version 3.5.1 (2018-07-02) Data The dataset used in this example is a customer churn dataset from Kaggle. Each row represents a customer, each column contains customer’s attributes We will load the data from a csv file: telco_data % select_if(is.numeric) %__% gather() %__% ggplot(aes(x = value)) + facet_wrap(~ key, scales = "free", ncol = 4) + geom_density() ## Warning: Removed 11 rows containing non-finite values (stat_density). … and barcharts for categorical variables. telco_data %__% select_if(is.character) %__% select(-customerID) %__% gather() %__% ggplot(aes(x = value)) + facet_wrap(~ key, scales = "free", ncol = 3) + geom_bar() Before we can work with h2o, we need to convert our data into an h2o frame object. Note, that I am also converting character columns to categorical columns, otherwise h2o will ignore them. Moreover, we will need our response variable to be in categorical format in order to perform classification on this data. hf % mutate_if(is.character, as.factor) %__% as.h2o Next, I’ll create a vector of the feature names I want to use for modeling (I am leaving out the customer ID because it doesn’t add useful information about customer churn). hf_X
          12/11 4pm-5pm: Seminar: Tim Nattkemper      Cache   Translate Page      

When: -

Where: MB 2.23

Marine imaging has developed rapidly into a data - rich technique
applied for monitoring and exploration of the oceans. The analysis and
interpretation of the large image collections represents a bottleneck
problem, challenging computer science in general and computer vision
in particular. One specific obstacle is the lack and cost of image
annotations and class labels for the images, making a straight forward
application of modern deep learning architectures difficult. In my
presentation I will give an overview on our past works and show some
successful applications of Computer Vision methods in this special

          Deep Learning Research Engineer Intern - TandemLaunch - Montréal, QC      Cache   Translate Page      
The project is in development phase, where we are implementing academic works and our patent portfolio into our technology....
From TandemLaunch - Thu, 11 Oct 2018 16:08:59 GMT - View all Montréal, QC jobs
          ISL Colloquium presents Estimating the Information Flow in Deep Neural Networks      Cache   Translate Page      

This talk will discuss the flow of information and the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory. The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in which the mutual information I(X;T) between the input X and internal representations T decreases. Several papers observe compression of estimated mutual information on different DNN models, but the true I(X;T) over these networks is provably either constant (discrete X) or infinite (continuous X). We will explain this discrepancy between theory and experiments, and explain what was actually measured by these past works.

To this end, an auxiliary (noisy) DNN framework will be introduced, in which I(X;T) is a meaningful quantity that depends on the network's parameters. We will show that this noisy framework is a good proxy for the original (deterministic) system both in terms of performance and the learned representations. To accurately track I(X;T) over noisy DNNs, a differential entropy estimator tailor to exploit the DNN's layered structure will be developed and theoretical guarantees on the associated minimax risk will be provided. Using this estimator along with a certain analogy to an information-theoretic communication problem, we will elucidate the geometric mechanism that drives compression of I(X;T) in noisy DNNs. Based on these findings, we will circle back to deterministic networks and explain what the past observations of compression were in fact showing. Future research directions inspired by this study aiming to facilitate a comprehensive information-theoretic understanding of deep learning will also be discussed.

          Machine Learning Engineer / Algorithm 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
          Más allá de 2001: Odiseas de la inteligencia #AI #IA      Cache   Translate Page      
El pasado martes 30 de octubre tuvimos el honor de asistir a la inauguración de la exposición en la Fundación Telefónica llamada: “Más allá de 2001: odiseas de la inteligencia”. En ella se hace un recorrido completo sobre la evolución de la inteligencia humana hasta el presente y futuro de la Inteligencia Artificial, usando como hilo conductor la magnífica película de Stanley Kubrick2001: Una Odisea en el Espacio”.

Figura 1: Más allá de 2001: Odiseas de la inteligencia

Y es que no podemos pensar en otra película que exponga mejor los avances y también los temores de la Inteligencia Artificial que esta película del mítico Kubrick. Una maravilla de la Ciencia Ficción que esta exposición utiliza como base para recorrer todos los rincones de la inteligencia humana y artificial.

Figura 2: Cartel de la exposición den la Fundación Telefónica

En ella se muestran todo tipo de técnicas y avances de la Inteligencia Artificial como reconocimiento facial (por ejemplo cuando HAL lee los labios a los astronautas), NLP o procesamiento de lenguajes naturales (la forma de comunicación de HAL es igual que los seres humanos, con la palabra), IoT (HAL controla todos los dispositivos de la nave espacial) e incluso los miedos a cómo reaccionaría una Inteligencia Artificial a órdenes confusas (a HAL se le ordena terminar la misión sobre cualquier otra cosa, incluso las vidas de la tripulación).

Figura 3: Durante la exposición se muestran diferentes secuencias emblemáticas de la película.

La exposición se plantea desde una doble perspectiva: no sólo desde la Inteligencia Artificial, sino también desde una introspección a la inteligencia humana ya que no podemos hablar de IA sin preguntarnos sobre nuestra propia inteligencia, pues no deja de ser la fuente de todos los algoritmos de los que hacemos uso hoy en día.

Figura 4: Euro Press sobre de la exposición

Dentro de esta doble perspectiva y, de una manera análoga a lo que hizo Stanley Kubrick en 2001, la exposición está dividida en tres ejes temáticos: “El despertar de la inteligencia”, “En el universo de la IA” y “El futuro de las inteligencias y más allá del infinito” que intentan responder a tres preguntas:
• ¿De dónde venimos? o ¿cuál es el origen o despertar de nuestra inteligencia? 
• ¿Dónde estamos?, ¿cuál es el estado actual de la Inteligencia Artificial, cómo interactuamos con ella y cómo esto está afectando a nuestras vidas? 
• ¿Hacia dónde vamos? Esto es: ¿cuál es el futuro de nuestra inteligencia y cómo podría ser esa posible simbiosis entre nuestra inteligencia y la Inteligencia Artificial?
El primero de ellos, el despertar de la inteligencia humana, nos habla del origen de todo, la inteligencia del ser humano. Podemos ver varios dibujos originales de neuronas realizados por Santiago Ramón y Cajal, el guion original de la película 2001 y algo que no puedes perderte, un número de la revista “Ten Story Fantasy” donde aparece el relato “The Sentinel” (El Centinela), la novela corta de Arthur C. Clarke en la cual se basa 2001.

Figura 5: Original de la revista “Ten Story Fantasy” donde aparece la historia corta “Sentinel of Eternity”

Una recreación del interior de la nave espacial “Discovery” nos introduce en la segunda parte de la exposición, “En el universo de la IA”. Aquí podemos ver la máquina “Ajedrecista” (1912) de Leonardo Torres de Quevedo, la primera máquina analítica del mundo de carácter autónomo y funcional con tecnología electromecánica.

Figura 6: “El Ajedrecista” la máquina para jugar al ajedrez fabricada por Leonardo Torres de Quevedo.

Para los más mitómanos (como nosotros), podemos ver la carta original en la que Kubrick le pide a Arthur C. Clarke colaborar en la película. También podemos ver la instalación DATA ergo sum, de 3dinteractivo, la cual permite explorar un cuerpo humano y sacar todo tipo de información (como humor, altura, peso, etc…) y que podréis probar allí mismo.

Figura 7: Data Ergo Sum

Finalmente, la tercera parte “El futuro de las inteligencias y más allá del infinito” se acerca las investigaciones futuras y sobre todo, el impacto sobre la humanidad, como por ejemplo una posible simbiosis de cerebro-ordenador para incrementar las capacidades del ser humano para convertirlo en todo un ciborg. Destacar en esta parte la instalación “The Mutual Wave Machine” que utiliza algoritmos neurocientíficos para explorar las interconexiones de la actividad cerebral entre dos personas y que se puede probar también en la exposición.

Figura 8: Futurista diseño de la máquina “The Mutual Wave Machine” para leer ondas cerebrales.

En esta parte de la exposición también os encontrareis con una sección donde aparecen algunos de los asistentes digitales más conocidos hoy día. Con ellos nos podemos comunicar y dar órdenes usando simplemente el lenguaje natural tal y como lo hacían los astronautas de la Discovery en la película. Aquí no podía faltar Movistar Home con Aura, el dispositivo de Telefónica que acaba de salir al mercado dirigido a ayudar y a gestionar nuestra vida digital basado en inteligencia cognitiva.

Figura 9: Aura y Movistar Home no podían faltar en esta exposición sobre la Inteligencia Artificial.

También mencionar que nuestro equipo de Ideas Locas aparece en los créditos de la exposición ya que hemos el honor de colaborar con ellos en varias ocasiones como asesores, mostrándoles a su vez algunos de nuestros trabajos realizados en el campo de la Inteligencia Artificial. Por ejemplo, nuestra serie “Deep Learning vs Atari: entrena tu IA para dominar videojuegos clásicos” o también “Ciencia Ficción vs Inteligencia Artificial”, ambos publicados en el blog de LUCA. Si queréis ir con los deberes hechos a la exposición, os animamos a leer estos artículos para entender algunos de los conceptos básicos que se plantean en la exposición.

Figura 10: Vídeo donde se muestra cómo un agente entrenado
con DeepMind domina el videojuego de Atari Breakout.

Ha sido un placer colaborar con el fantástico y profesional equipo de la Fundación Telefónica en este proyecto y toda una satisfacción ver el fantástico resultado final de la exposición. No tienes que ser solamente un fan de la Ciencia Ficción o de Kubrick/Clarke para visitarla ya que esta es en general, una visión de la evolución de la inteligencia humana donde nos muestran el irremediable futuro donde esta tendrá que convivir con otro tipo de inteligencia: la Inteligencia Artificial ¡No os la perdáis!
Figura 11: Créditos de la exposición donde aparece el equipo Ideas Locas CDO.

Esperamos que saques tiempo para poder ir a verla y disfrutes tanto como hemos disfrutado nosotros. Un fuerte saludo del equipo de Ideas Locas. Los datos de la exposición son:
Web: Más allá de 2001
Espacio Fundación Telefónica
C/Fuencarral, 3 Madrid.
Cuarta Planta.
Todos los días de 10:00 a 20:00 horas.
Equipo Ideas Locas CDO Telefónica.

          Hyundai investit dans la start-up israélienne      Cache   Translate Page      
Hyundai Cradle a misé sur la start-up pour son expertise dans le domaine de la vision par ordinateur en deep learning et en informatique embarquée....
          Toshiba Memory targets deep learning processing      Cache   Translate Page      
Toshiba Memory Corporation has developed a high-speed and high-energy-efficiency algorithm and hardware architecture for deep learning processing with less degradations of recognition accuracy. The company is developing a new processor for deep learning implemented on an FPGA.

In addition, Toshiba Memory is working on a new hardware architecture, called bit-parallel method, which is suitable for MAC operations with different bit precision. This method divides each various bit precision into a bit one by one and can execute 1-bit operation in numerous MAC units in parallel. It significantly improves utilization efficiency of the MAC units in the processor compared to conventional MAC architectures that execute in series.
          Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation      Cache   Translate Page      
TensorFlow has been the most widely adopted Machine/Deep Learning framework. However, little exists in the literature that provides a thorough understanding of the capabilities which TensorFlow offers for the distributed training of large ML/DL models that need computation and communication at scale. Most commonly used distributed training approaches for TF can be categorized as follows: […]
          A Comparative Measurement Study of Deep Learning as a Service Framework      Cache   Translate Page      
Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of digitized data openly accessible, a growing number of DL software frameworks in open source and commercial markets, and a selection of affordable parallel computing hardware devices. However, no single DL framework, to […]
          Micron's bet: Quad-level cell NAND SSDs will finally replace HDDs      Cache   Translate Page      
The company's Micron 5210 ION enterprise SATA SSD is now generally available and aimed at artificial intelligence, machine learning, deep learning and other intensive workloads.
          11/06 Links Pt2: The Future of the Pittsburgh Synagogue Massacre; On Bari Weiss, Franklin Foer and the Values that Sustain Our People; 4,000 college students from 60 nations sing as one in TLV      Cache   Translate Page      
From Ian:

The Future of the Pittsburgh Synagogue Massacre
In America, Jews have always been able to fight back against anti-Semitism freely. Never having received their emancipation as an “award” (which was the case in Europe), Jews have had no fears of losing it. Instead, from the beginning, they made full use of their freedom, especially freedom of speech. As early as 1784, a “Jew Broker,” probably the famed Revolutionary-era Jewish bond dealer, Haym Salomon, responded publicly and forcefully to the anti-Semitic charges of a prominent Quaker lawyer, not hesitating to remind him that his “own religious sectary” could also form ”very proper subjects of criticism and animadversion.” A few years later, Christian missionaries and their supporters faced Jewish polemics no less strident in tone. Where European Jews often prided themselves on their ”forbearance” in the face of attack, Rabbi Isaac Mayer Wise, the great Reform Jewish leader, once boasted that he was a “malicious, biting, pugnacious, challenging, and mocking monster of the pen.” In more recent times, Jewish defense organizations have taken on anyone who maligned Jews, including national heroes like Henry Ford and General George S. Patton, as well as presidents of the United States.

American anti-Semitism has always had to compete with other forms of animus. Racism, nativism, anti-Quakerism, Anglophobia, Islamophobia, anti-Catholicism, anti-Masonry, anti-Mormonism, anti-Orientalism, , anti-Teutonism—these and other waves of hatred have periodically swept over the American landscape, scarring and battering citizens. Americans have long been extraordinarily pluralistic in their hatreds. Precisely because the objects of hatred have been so varied, hatred has generally been diffused. No one outgroup experiences the full brunt of national odium. Furthermore, most Americans retain bitter memories of days past when they or their ancestors were themselves the objects of malevolence. The American strain of anti-Semitism is thus less potent than its European counterpart, and it faces a larger number of natural competitors. To reach epidemic proportions, it must first crowd out a vast number of contending hatreds.

Anti-Semitism is more foreign to American ideals than to European ones. The central documents of the Republic assure Jews of liberty; its first president, in his famous letter to the Jews of Newport, conferred upon them his blessing. The fact that anti-Semitism can properly be branded “un-American,” although no protection in the formal sense—the nation has betrayed its ideals innumerable times including in our own day—still grants Jews a measure of protection. Elsewhere anti-Semites could always claim legitimacy stemming from times past when the Volk ruled and Jews knew their place. Americans could point to nothing even remotely similar to that in their own past.

America’s religious tradition—what has been called “the great tradition of the American churches”—is inhospitable to anti-Semitism. Religious freedom and diversity, church-state separation, denominationalism, and voluntarism, the key components of this tradition, militate against the kinds of us-them dichotomies (“Germans and Jews,” “Poles and Jews,” etc.) so common in Europe. In America, where religious pluralism rules supreme, there has never been a single national church from which Jews stand apart. People speak instead of American Protestants, American Catholics, American Jews, American Muslims, and American Buddhists—implying, at least as an ideal, that all faiths stand equal in the eyes of the law.

American politics resists anti-Semitism. In a two-party system where close elections are the rule, neither party can long afford to alienate any major bloc of voters—another reason why it is so critical that everyone take the time and trouble to actually vote. For the most part, the politics of hatred have been confined to nonvoters like African Americans, until they won the vote, or to nonvoting immigrants, or to noisy third parties like the anti-Catholic Know Nothings in the 19th century, or to single-issue fringe groups. America’s most successful politicians, now and in the past, have more commonly sought support from respectable elements across the political spectrum. Appeals to national unity, even in the era of Donald Trump, win more elections than appeals to narrow provincialism or to bigotry.

Of course, the fact that America has been “exceptional” in relation to Jews should not obscure the sad reality that there has always been anti-Semitism in America, as well as violence directed against other minority faiths. That history, as I read it, gives cause neither for undue celebration nor for undue alarm.

Alan Dershowitz on Trump, Israel, and Antisemitism
I recently spoke with Alan Dershowitz at the ZOA Gala at the Marriott Marquis in Manhattan, where he was set to make a prominent speech.

Below is an edited transcript of our conversation.

Hannah Grossman: Mr. Dershowitz, can you give us a little preview of what you will be talking about tonight?

Alan Dershowitz: I’m talking about how important it is to have dialogue among people who may not agree. Mort Klein and I don’t agree about a great many things. He’s way to the right of me, but we talk to each other. We dialogue with each other. And I’m here to promote dialogue between the right, the left, [and] the center — not only within the Jewish community, but the more general political community. It’s a tragedy that we now shout at each other, demonize each other, [and] yell slogans instead of having reasoned discourse. We can learn from each other, and I think we ought to.

HG: Where do you think that changed, between the right and left, where the divide became so extreme that it seems that it is impossible to have some dialogue or commonality?

AD: Well, I think there a lot of contributing factors. I think the movement of the left to the hard left in the Democratic Party. I think the movement of the right to the hard right within the Republican Party. I don’t think President Trump has helped with his choice of language, and I don’t think that some of the Democrats have helped with their choice of language. I crave the old days when my friends Ted Kennedy and Orrin Hatch could sit together, and when Senator McCain could sit and work together with Joe Biden. Those days seem long gone, and I want to do everything in my power to bring them back.

HG: Bret Stephens said today at a panel that Donald Trump’s rhetoric has an effect on the culture. Can you describe that; what you think that is?

AD: I think that’s right. Look, I think President Trump has a mixed record. I think he’s done some very good things. Moving the embassy to Jerusalem was a terrific thing. I think his tough negotiating stance toward Iran has been very good. I don’t approve of his policies toward immigration. I certainly don’t approve of separating families the way he did early on, but you know, I’ve never agreed with anything any president did 100 percent.

I disagreed with a great deal of what President Obama did and if Hillary Clinton had been elected, I’m sure I would’ve disagreed with a lot of what she would do. But you don’t demonize, and he’s still the president, and you respect the office of the president. I was appalled at the so-called leaders in Pittsburgh who refused to welcome the president. I think everybody should welcome the president when there’s a tragedy and allow him to serve in his role as a mourner or bereaver-in-chief.
Qanta Ahmed: The ECHR’s ruling on defaming Mohammed is bad news for Muslims
In a monumental irony, the ECHR’s agreement with an Austrian court that offensive comments about the Prophet Mohammed were ‘beyond the permissible limits of an objective debate’ has handed a big victory to both Islamists and Islamophobes – while infantilising believing Muslims everywhere.

The case concerns an unnamed Austrian woman who held a number of seminars during which she portrayed the Prophet as a paedophile. After she was convicted by an Austrian court of ‘disparaging religion’ (and fined nearly €500), she appealed to the ECHR claiming the punishment breached her right to free expression. The court disagreed.

As a practising Muslim, I find this notion – that the Prophet was a paedophile – to be as abhorrent and nasty as they come; not to mention completely false. Yet I could not disagree more with the ECHR’s ruling.

For a start, it implies there is somehow a balance to be struck between people’s freedom of expression and the right of Muslims not to be offended. I just don’t understand this: how can the views of another individual possibly affect my faith or beliefs? Her ignorance – or anyone else’s for that matter – does not equate to my persecution.

Ben Shapiro: Voting for Democrats Isn’t Going to Curb Anti-Semitism
Trump is not the source of the true threats to Jews — anti-Semites are more prominent on the left than the right.

Over the weekend, Bari Weiss, a friend who writes for the New York Times, appeared on Bill Maher’s HBO show. Bari was bat mitzvahed at the Tree of Life Synagogue in Pittsburgh; she knew many of those who were shot and killed by a white supremacist two weeks ago. Her writing on the subject has been beautiful; she’s obviously both raw and real about the situation.

On Maher’s show, she made a number of good points about anti-Semitism more broadly and its rising threat both in the United States and across the world. But then she concluded with this calculation:

One thing that I think was made stark this week is that there are many Jews, including Jews that I know, who have liked many of Trump’s policies regarding Israel and the Middle East . . . but I hope this week that American Jews have woken up to the price of that bargain. They have traded policies that they like for the values that have sustained the Jewish people and frankly, this country, for forever. Welcoming the stranger, dignity for all human beings, equality under the law, respect for dissent, love of truth, these are the things we are losing under this president, and no policy is worth that price.

I have a number of problems with this statement.

To start, the part with which I agree: There’s no justification for Trump’s callous disregard for the truth, which I’ve criticized routinely. Nor is there justification for Trump’s treatment of the alt-Right from 2015 to 2017.

But Bari is wrong in two areas: First, she suggests that Trump presents a unique threat to Jews and Jewish values that outweighs the threat to Jews from other arenas; second, she links Judaism with her preferred immigration policies.
On Bari Weiss, Franklin Foer and the Values that Sustain Our People
So, for Jews who are appalled by Trump’s incendiary rhetoric but who still appreciate his policies on Israel, what should they do? Tell the president not to bother trying to “woo” us with Israel? That he so violates Jewish values that his favorable actions on Israel just aren’t worth it? That after Pittsburgh, we’re no longer willing to pay the price of that bargain?

And how would that work exactly? Weiss didn’t specify, but Franklin Foer, writing in the Atlantic, did have a suggestion to enhance Jewish security after Pittsburgh:

“Any strategy for enhancing the security of American Jewry should involve shunning Trump’s Jewish enablers. Their money should be refused, their presence in synagogues not welcome. They have placed their community in danger.”

Never mind that after Pittsburgh, the President said: “Anti-Semitism represents one of the ugliest and darkest features of human history. Anti-Semitism must be condemned anywhere and everywhere. There must be no tolerance for it.”

According to Foer, however, any Jew who still supports the president must be ostracized and shunned.

I wonder if Foer would be willing to stand outside a synagogue on Saturday morning with a sign repeating his message: “If you support Trump, your presence is not welcome. You have placed your community in danger.”
Amb. Alan Baker: After the Pittsburgh Synagogue Massacre, It’s Time to Adopt an International Convention on the Crime of Anti-Semitism
This proposal comprises the following unique elements, tailored to deal with anti-Semitism:
  • detailed preambular paragraphs documenting the history of anti-Semitism and recalling references to it in international instruments, in statements by senior international figures, and in relevant resolutions of international bodies;
  • an all-embracing definition of the crime of anti-Semitism and its component elements, based upon the various definitions adopted over the past years by various groups and institutions;
  • criminalization of manifestations of anti-Semitism that result in or are intended to result in violence;
  • action by countries to criminalize anti-Semitism in their own domestic law and to prosecute or extradite perpetrators;
  • international cooperation and exchange information on perpetrators and actions taken;
  • establishment by states of appropriate national educational programs to combat anti-Semitism;
  • establishment of an International Anti-Semitism Monitoring Forum for monitoring and coordinating actions by states and international organizations.
With the evident support of the UN Secretary-General and the challenge he placed before the international community, this proposal should be brought before the appropriate UN legal bodies for consideration, with a view to its being studied, amended, and accepted as an international treaty, criminalizing and punishing anti-Semitism.
It’s the antisemitism, stupid
In the aftermath of the Pittsburgh synagogue massacre, two things are irrefutable: There are 11 dead innocent Jews and there is one hateful murderer. All else, as they say, is commentary.

The murderer is a classic antisemite of the most despicable sort. Regardless of the political contours of his warped mind, he believes one thing is certain: Jews have no right to live.

Sadly, this horror has brought forth a spate of finger pointing where people are looking to score political points by somehow attributing the deed to those they politically oppose.

This is harmful and wrong. If you want to look behind the curtain, beyond just the actions of the murderer, then you must conclude that the perpetrator here is antisemitism itself – a hatred that enables its adherents to blame anything and everything on Jews, attributing to them all the paranoid delusions that the irrationally hateful suffer from.

I have spent the last two years immersed in American college campuses helping to make the existential case for Israel. I have encountered not just rejection of Israel’s legitimacy, but the most vile Jew-hatred, over and over again, with little pushback from decent people.

There is a big distinction between free speech and moral conduct. I understand and support the right to voice your opinion, no matter how much I might disagree with it. But I also know that free speech cannot be the rationale for not calling out vile and hateful accusations.
An International View of the Pittsburgh Murders
The killing of 11 Jews at an American synagogue has now inserted itself into a sequence of other murders targeting Jews and their institutions in past decades.

Among the list of murders of Jews outside of Israel, the most lethal tragedy took place in South America in 1984. In the bombing of the AMIA building in Buenos Aires, Argentina, 85 people were killed. The largest terrorist murder aimed at Jews in Africa was the 2002 bombing of the Israeli-owned Paradise Hotel in Mombassa, Kenya, where 13 people were killed. In Europe, two of the most deadly attacks against a Jewish target took place at the Goldenberg restaurant in Paris, where six people were murdered in 1982 — and in a 2012 terrorist attack on a bus transporting Israelis near the Burgas airport in Bulgaria, where another six people were killed.

There is one major difference between the murders of Jews in the US and the three other continents. In Pittsburgh, the murderer was a white supremacist. In the other attacks, the perpetrators were Muslims. Even a superficial look at mega-antisemitism in the world shows that antisemitism coming out of parts of the Muslim world is currently the greatest threat to the Jewish people. Only there does one find heads of state who promote extreme hatred not only against Israel, but also against Jews. Malaysian Prime Minister Mahathir Mohamed, for instance, has a long record of verbally attacking all Jews. There is nothing similar among heads of state in much of the Western world.

Liberalism and democracy by necessity overlap to a substantial extent. But they are not identical. France and Germany have learned from their pasts. Both limit hate speech. In Germany, one can spend a number of years in jail for insulting a part of the population. Many Europeans understand that the principle of free speech doesn’t mean that hate speech should also be tolerated.
Israel Plays an Important Role in Fighting Antisemitism
Today, everybody agrees that combating “cyber-hate,” including antisemitism and anti-Zionism, is a top priority. Israel’s Justice Ministry even has a department dedicated to the fight against online incitement. And the Global Forum Against Anti-Semitism, now under the auspices of the Israeli foreign affairs and diaspora ministries, is tackling Christian theological antisemitism, Holocaust revisionism, Palestinian denial of Jewish history, campus antisemitism, legislative assaults on Jewish practices like ritual slaughter and circumcision, and even antisemitism in sports.

Some experts warn that, unless the rising tide of hate crimes in the US is turned back, American Jewry will have to undergo a process of adopting European-style security measures. Synagogues and Jewish community centers in the United States may need to be protected from neo-Nazis, just as synagogues and Jewish community centers in Europe are protected from radical Islamists.

This means the adding of multilayered defenses to Jewish sites, including security screening with armed guards, surveillance systems, panic rooms, and sterile zones. If this is the unfortunate fate of American Jewish institutional life (I hope not), Israeli security expertise undoubtedly will prove helpful.

In the meantime, Israeli and Diaspora Jews should band together to draw strength from solidarity, jointly combat hate, and raise the flag of unafraid and vibrant Jewish life everywhere. Keep partisan politics out of it.
Democrats’ Deputy Comms Director Laughs About Rising Anti-Semitism in America
The Democratic Congressional Campaign Committee's deputy communications director laughed about charges of anti-Semitism and the growing racism in America that many observers have pinned on a Democratic Party that is increasingly lurching leftwards.

Patrick Burgwinkle‏, a deputy communications director at the DCCC, responded to a tweet decrying the rise of anti-Semitism in America—which has appeared back on the radar in recent days after controversial National Islam Leader Louis Farrakhan took a trip to Iran and led chants of "Death to America"—by responding, "lol," an acronym for "laughing out loud."

The initial tweet, sent by veteran Congressional staffer Matt Wolking, decried the rise of anti-Semitism and included a link to a Slate article claiming, "The story of the midterms is about how Americans will respond to racism, bigotry, and hate."

"So true," Wolking wrote. "The racism endorsed and tolerated by the Farrakhan Democrats, the bigotry the establishment media exhibits against half the country, and the hatred of Christians, conservatives, and men smeared by the radical left as rubes and rapists."

The DCCC's Burgwinkle, in response to that tweet, wrote, "lol," raising eyebrows among some observers.
Pope Francis: A Christian cannot be an anti-Semite
Pope Francis lamented anti-Semitic attacks and remarked that “a Christian cannot be an anti-Semite” during an audience with Jewish emissaries on Monday.

The pope received four first time delegates of the World Congress of Mountain Jews at the Vatican.

Two weeks after the attack on a Pittsburgh synagogue that killed 11 worshipers Francis said in a statement: “Sadly, anti-Semitic attitudes are also present in our own times,” during his meeting with the emissaries of the Mountain Jews, the descendants of Jews who left ancient Persia and settled in the Caucasus.

“A Christian cannot be an anti-Semite; we share the same roots. It would be a contradiction of faith and life. Rather, we are called to commit ourselves to ensure anti-Semitism is banned from the human community,” he said.

The day after the Pittsburgh attack, the head of the Catholic Church said in his weekly prayers that “all of us are wounded by this inhuman act of violence.”
Trump yet to appoint traditional envoys amid antisemitism crisis
In the wake of a mass shooting at a synagogue in Pittsburgh, Pennsylvania, last weekend, White House officials debated who they should send to the grief-stricken town as a representative of the administration.

Their choice was not obvious. The administration has, for two years, declined to appoint traditional envoys to the fight against antisemitism here and around the globe, despite calls from Jewish organizations and a bipartisan group in Congress to do so.

US President Donald Trump has tapped neither a White House Jewish liaison, a post that has been responsible for communicating with the American Jewish community since the 1970s, nor a special envoy at the State Department to monitor and combat antisemitism, a congressionally-mandated position devoted to the fight against antisemitism overseas.

Administration officials instead chose to send Jason Greenblatt, the president's chief envoy for international negotiations, who– while an observant Orthodox Jew and an adviser on Jewish world issues to Trump during the campaign– has neither studied the issue nor spent his time at the White House engaging in outreach with the wider diaspora community.

White House officials tell The Jerusalem Post that in lieu of a formal liaison the president relies on decades-old relationships with Greenblatt and other close advisers, such as his son-in-law, Jared Kushner, and US Ambassador to Israel David Friedman, for guidance on Israel policy and Jewish world concerns.
Why Linda Sarsour’s Denunciation of Pittsburgh Shooter is Meaningless
On the last Saturday in October, Robert Bowers shot and killed eleven members of the Pittsburgh Jewish community, physically injured six other people, including law enforcement officers, and created distress and turmoil for the entire American Jewish community. He’s been indicted in federal court on 44 counts, and may face the death penalty.

Bowers killed eleven Jews solely due to his annihilationist anti-Semitism, which was of the neo-Nazi variety. He provided a political cover – he wrote that he didn’t like the political positions held by some other Jews (not the ones he killed), who support certain immigration policies in this country. But as Alana Newhouse noted in Tablet Magazine, the truth was simply that “he had reached the end point of a brain-eating disease called anti-Semitism.”

One day after these brutal murders, Linda Sarsour once again put herself in the spotlight. She did so while attempting to portray herself as a defender of Jews.

While Sarsour denounced Bowers, she has, in the past, embraced a different unrepentant killer of Jews, Rasmeah Odeh.

Many of us, by now, may have forgotten about Odeh. However, just over a year ago she was deported from the United States, after admitting that she lied on her citizenship application. Odeh also killed two Jews, Edward Joffe and Leon Kanner, who were students at Israel’s Hebrew University in 1969. She was convicted in a trial that was observed and deemed fair by the International Committee of the Red Cross.

Shmuley Boteach: No Holds Barred: Will Clinton, Schumer, Booker finally condemn Farrakhan?
Throughout history, antisemites of all stripes have sought to cast the Jews as something utterly worthless yet massively destructive. It was a way of making the act of killing a Jew something that was not only necessary but meaningless. Hitler’s SS troops, therefore, were able to exterminate tens of thousands of Jews in a day. You see, they were killing an infestation, not people.

It may be extreme to compare Farrakhan to Hitler. But he did it himself. During a radio interview, Farrakhan acknowledged the fact. “[T]he Jews don’t like Farrakhan, so they call me Hitler.” Instead of rebuffing the comparison, he embraced it. “That’s a good name,” he said, “Hitler was a very great man.” He then reinforced it. “[Hitler] raised Germany up from nothing,” he explained, adding, “In a sense, you could say there’s a similarity in that we are raising our people up from nothing.” He’s also used the Holocaust as a metaphor to describe what awaited the Jews in hell. My late friend Christopher Hitchens personally heard Farrakhan punctuate a tirade against Jews with this: “And don’t you forget, when it’s God who puts you in the ovens, it’s forever!”

The only question that remains is this: how could leading political figures like Bill Clinton have agreed to legitimize such genocidal hate by recently appearing on the same stage as Farrakhan at Aretha Franklin’s funeral? And given the unfortunate association, why didn’t Clinton immediately condemn Farrakhan’s genocidal Jewish reference?

Just days before his “termites” slur, Farrakhan announced the release of a new music album made in collaboration with some of the most powerful men in music, including Stevie Wonder, Rick Ross, Snoop Dogg and Common. At least seven members of Congress — including Maxine Waters, Barbara Lee, Danny Davis, Andre Carson, Gregory Meeks, Al Green and most famously, DNC whip Keith Ellison, have all sat down for personal meetings with Farrakhan while representing the American people in Congress. Farrakhan even attended a 2005 meeting of the Black Congressional Caucus. At that meeting, former president Barack Obama even smiled for a photograph with Farrakhan just three years before becoming president.

American Jews need to draw a line.

It is not only neo-Nazis that need to be condemned by Republicans. It is Farrakhan and his ilk who should be repudiated utterly by Democrats. Never again must mean exactly that: Never Again.
‘Ocasio-Cortez in a Scarf’
He [Denver Riggleman] views this as a big advantage over Cockburn, a former journalist who has treated Virginia's 5th more like a research project, telling The Intercept she spent three months investigating the district before announcing her run.

"I'm Virginian to the core," he said. "She lives in Georgetown and doesn't have a clue what's happening in the 5th district."

He thinks the media have left a lot of ground uncovered on his opponent.

"Her books are used by Stormfront and white anarchist groups to explain why the Jewish people are trying to take over," he said, referencing the anti-Semitic book Cockburn wrote with her British-born husband Andrew Cockburn. "That got less attention than jokes about Big Foot."

Riggleman read her book and said it was shocking. "She really believes that Israel has orchestrated a cabal to control U.S. foreign policy."

He described Cockburn as "Alexandria Ocasio-Cortez in a scarf," saying she's run a far-left campaign calling for "Medicare for all, open borders, forgiveness of student debt, and free college."
PreOccupiedTerritory: J Street Vows Investigation After Endorsed Candidate Makes Pro-Israel Statement (satire)
A progressive political advocacy group calling itself “pro-Israel, pro-peace” promised today to get to the bottom of an emerging scandal, in which a politician the group supports in this week’s elections was recorded agreeing that the Jewish State has an unequivocal right to exist, defend itself, and determine its own character, organization sources reported today.

J Street executive director Jeremy Ben-Ami issued a statement to the organization’s donors and employees Tuesday acknowledging that Mike Rotch, a Democratic Congressional candidate running for office in California, had signed a petition circulating in the aftermath of the Pittsburgh synagogue shootings two weekends ago, that called on American public officials to denounce antisemitism, including delegitimization of Israel.

“It has come to our attention that Mike Rotch may support Israel in ways that do not reflect the values of this organization,” the statement began. “We have made it clear on several occasions that our mission requires paying only lip-service to a secure Israel, while in reality pursuing an outcome that effectively destroys Israel as a Jewish State. If the reports of Mr. Rotch’s remarks are accurate, they call into question his suitability as a candidate for the sixty-ninth Congressional District. J Street will conduct a full investigation of Mike Rotch and then make its recommendations.”
Conspiracy theorist selected as Labour candidate in safe ward
It’s pretty shocking that cases like this no longer even seem surprising. But the views of Liam Moore are so outrageous I thought this latest example of Labour’s failure to deal with antisemitism warranted a post. Moore, who is an evangelical vicar (and part-time Phil Collins tribune act) has been selected as the Labour councillor candidate for Norris Green in Liverpool – close to Luciana Berger’s constituency. In 2014 he tweeted that “Rothschilds Zionist run Israel and world governments” and earlier this year compared Zionists – or simply those worried about antisemitism – to Judas:

‘We are seeing a very English right wing Zionist coup mate and sadly the Labour Party is infiltrated by sellouts who would sacrifice a labour government for their 30 pieces of silver.’

Sometimes, when challenged over antisemitism, Labour supporters point out that the number of cases identified reflects only a tiny proportion of the membership. But the real problem isn’t that a few members hold such vile views, but that they are able to spout them without being reported and dealt with by others, and are even, as in this case, thought suitable for elected office.

University investigates professor who claims Israel was behind 9/11 Twin Towers attacks 'with help from Zionists in US government'
Sussex University is investigating a professor after he claimed Israel were behind the September 11 attacks 'with help from Zionists in the US government'.

Professor Kees van der Pijl, the former head of its international relations department, used as his source an article entitled '9-11/Israel did it' by conspiracy theory site Wiki Spooks, which also claims Al-Qaeda is a front for Mossad, the Israeli spy service.

The retired academic, who was responding to an article criticising US sanctions on Iran, was immediately inundated with a storm of mocking tweets, including one that joked, 'Yes, and Bruce Lee was the first man on the moon, damn media!'

Sussex University said it is aware of the post and has not yet decided whether to take further action.

Van der Pijl tweet was posted on November 3 and read: 'Not Saudis, Israelis blew up Twin Towers with help from Zionists in US gov.'
Professor: BDS Panel at University of Michigan Was an ‘Unrelentingly Anti-Israel Propaganda Fete’
A professor at the University of Michigan said a recent panel hosted by the school on the Palestinian-led boycott, divestment, and sanctions (BDS) campaign was “a totally one-sided propaganda fete.”

Victor Lieberman, who teaches a popular history course on the Arab-Israeli conflict, said he was one of some 70 people who attended last week’s “Teach-In Town Hall,” which was organized by U-M’s Center for Middle Eastern and North African Studies (CMENAS) after the university sanctioned a BDS-supporting professor who denied a letter of recommendation to a student who sought to study in Israel.

CMENAS director Samer Mahdy Ali said the event, which was backed financially by multiple U-M departments, would include a “decidedly pro-BDS” panel, followed by a discussion.

Lieberman estimated that less than two dozen undergraduates showed up on Monday morning, two days after a gunman killed 11 worshippers at the Tree of Life synagogue in Pittsburgh while shouting antisemitic epithets.
Regev demands Spanish minister intervene in water polo BDS fracas
Culture and Sport Minister Miri Regev demanded from her Spanish counterpart to move a women’s water polo match between Israel and Spain back to its original venue, after Boycott, Divestment and Sanctions activists had forced its relocation.

“The BDS movements are clearly antisemitic in nature and operate to harm the State of Israel and its citizens,” Regev wrote. “It is my expectation that the government of Spain will take all the necessary steps in order to reverse the decision of the Municipality of Molins de Rei.”

Planned demonstrations by Boycott, Divestment and Sanctions activists led the municipality, just outside Barcelona, where the match was scheduled to take place, to move the game to a different venue and bar supporters.

Regev said Israel expected the organizers to “operate in accordance with the International Olympic Charter, [to] enable all sportsmen to compete as equals in the games.”

As of Tuesday noon, it was still unclear where the game would take place, if at all. Some sources reported that the game was canceled, but a PR company working on behalf of the Culture and Sport Ministry said the game was still on, in a new venue, with supporters banned. The ministry was still working to reverse the decision, the company said.

NGO Monitor, a Jerusalem-based watchdog that analyzes and reports on international NGO activity in regards to Israel, said the decision by the Spanish municipality came as no surprise, as funding of anti-Israel groups by the Catalonian government are no rarity.
Sky News Arabia report on Druze elections in Golan is riddled with distortions and falsehoods
The six falsehoods in the report are as follows:
Falsehood #1: “Israeli elections were forced upon the residents.”

Sky reports without challenge the claim from the Syrian government’s official news service (SANA) that the Israeli government forced elections upon residents of the Druze towns. In fact, the impetus for the elections elections was the effort of group of Druze residents of the Golan who fought in Israeli courts for the change of policy. An article in the Israeli “Walla” website elaborates (CAMERA’s translation):

“During the past eighteen months, six Druze petitions were brought before the supreme court, opposing the way the town council members and heads were appointed [prior to 2018, Israel’s Interior Ministry appointed them], and demanding that open municipal elections be held in their communities as well,” lawyer Fahd Safdi from Mas’adeh tells Walla News. “At the same time we launched letters to the Interior Ministry and applied heavy pressure so it would enable us to exercise our right to vote.”

Falsehood #2: “The voter participation rate ranged from between 0% and 1.5%.”

According to official Israeli statistics (in Hebrew), the participation rate in Majdal Shams was 3.3 percent, and 1.3 percent in ‘Ein Qenya. In Mas’adeh and Buq’atha the participation rate was indeed 0 percent, but, as the report itself points out, this was because the election process was cancelled in advance.

Falsehood #3: Israel has been engaged in “Judaization” in the Golan.

“Judaization” (Tahweed in Arabic) is a vague term among Arabic speakers than can signify any increase of either the physical, metaphorical or imagined presence of Jews in a given place; it is often used in the context of Jerusalem. This includes but is not limited to: Scholars searching for archeological findings in the Old City and its surroundings, visitors walking peacefully around the Temple Mount compound, or even a Christian individual trying to set the Al-Aqsa mosque on fire. The question of what “Judaization attempts” the report is alluding to goes unanswered. In 51 years of Israeli control, the construction of Jewish villages in the Golan Heights has rarely faced Druze opposition of any kind. Moreover, how a democratic process initiated by Druze, in which both the voters and the candidates are Druze, can be labeled “Judaization” is a mystery.
French Police Investigating Violent Assaults on Two Young Jews in Paris in Single Week
Two separate assaults on Jewish teenagers in the Paris area during the last week are being investigated by police as hate crimes, French news outlets reported on Monday.

The first incident occurred last Tuesday in Sarcelles — about ten miles north of the capital. According to news magazine Le Point, a young schoolgirl wearing the uniform of the Jewish school that she attends was approached by an older man who spoke to her in Arabic before punching her hard in the back. The girl told police that her assailant had also mimed the action of shooting a gun at her with his fingers.

Traditionally the home of a large North African Jewish community, tensions have increased markedly in Sarcelles over the last decade between Jews and a growing Muslim population. The neighborhood was the site of a full scale antisemitic riot in July 2014, when 300 mainly Muslims youths looted Jewish-owned shops and attacked a synagogue during a solidarity protest with Palestinians in the Gaza Strip.

A separate incident the following day was reported in the 19th arrondissement in northeastern Paris. A young man wearing a yarmulke was assaulted by three youths at a bus stop after he noticed that one of them was trying to pickpocket a laptop computer from his bag. After they spotted the young man’s yarmulke, the youths shouted antisemitic insults and pushed him to the ground, punching and kicking him as he fell.

Meanwhile, doctors, nurses, patients and other visitors to the Rothschild Hospital in Paris — named for the illustrious French-Jewish family — were greeted on Sunday morning with a lengthy antisemitic screed scrawled on one of the entrance walls.
Former Nazi SS concentration camp guard, 94, goes on trial in Germany
A 94-year-old former enlisted SS man went on trial Tuesday in Germany, charged with being an accessory to murder for crimes committed during the years he served as a guard at the Nazis’ Stutthof concentration camp.

Johann Rehbogen is accused of working as a guard at the camp east of Danzig, which is today the Polish city of Gdansk, from June 1942 to about the beginning of September 1944.

There is no evidence linking him to a specific crime, but more than 60,000 people were killed at Stutthof and prosecutors argue that as a guard, he was an accessory to at least hundreds of those deaths.

Stutthof prisoners were killed in a gas chamber, with deadly injections of gasoline or phenol directly to their hearts or shot, starved and even forced outside in winter without clothes until they died of exposure, prosecutor Andreas Brendel said.

The former SS Sturmmann, roughly equivalent to the US Army rank of specialist, does not deny serving in the camp during the war but has told investigators he was not aware of the killings and did not participate in them, Brendel said.
Doll that survived Holocaust goes on display at Yad Vashem
A doll called Inge that survived the looting during Kristallnacht in November 1938 was recently handed over to the Yad Vashem Holocaust memorial and museum in Jerusalem by its owner and is now on display there.

Lore Mayerfield Stern, a Holocaust survivor from Kassel, Germany, was just 2 years old when the anti-Jewish riots known as Kristallnacht, the "Night of Broken Glass," caused massive destruction to Jewish property in her city and across Nazi Germany and Austria. During the pogrom, which took place on Nov. 9-10, 1938, at least 90 Jews were killed, 30,000 Jewish men were rounded up and sent to concentration camps, almost 300 synagogues were destroyed and thousands of Jewish homes and businesses were damaged or destroyed.

Inge, along with personal letters Stern had kept from that era, is now part of Yad Vashem's It Came From Within online exhibition, which marks the 80th anniversary of Kristallnacht.

During Kristallnacht, Stern's father, Markus Stern, was one of the Jewish men arrested and was sent to Buchenwald concentration camp. Young Lore and her mother, Kaetchen, were taken in and hidden by neighbors, who kept them safe during the riots.

"Lore, already in pajamas, hid with her mother at the neighbors' house until the pogrom was over. When they returned home, they found that the place had been torn apart and was not habitable," Yad Vashem states on its website.
Hyundai venture arm invests in Israeli computer vision startup
The corporate venture arm of Hyundai Motor Company has made a strategic investment of an undisclosed amount into Israeli startup, a developer of computer vision technologies based on deep learning, the companies said in a joint statement on Monday.

The partnership with will allow Hyundai to “speed up deployment of AI technology in many business areas,” improving the quality of its products and creating “a safer driving environment,” Hyundai CRADLE and said.

Founded in 2016, offers software that simplifies the process of developing and managing solutions and products, such as autonomous vehicles, drones, security and logistics systems, that are powered by artificial intelligence and deep learning. The software also allows customers to update their devices and their learning capabilities while in use, so every device can continue to improve its operations.

“Deep learning computer vision is one of the core technologies that can be applied to autonomous driving to navigate roads and make quick decisions in real-time – and is clearly an innovation leader in that field,” said Ruby Chen, head of Investment at Hyundai CRADLE Tel Aviv.

“Our investment in is a further step in enhancing our presence in the Israeli market, a global leader of technological innovation in the fields of automation, artificial intelligence and deep learning,” he said. “This is our fifth investment in an Israeli company and our activities will continue to grow the coming year.”
IsraellyCool: Pharrell Williams at FIDF Gala: “You Guys Show An Incredible Resilience”
A few days ago I posted about the FIDF Gala that saw celebrities help raise $60 million for Israel’s IDF. The incredibly talented Pharrell Williams was the musical act, but what I did not know at the time were the touching words he said about the Jewish people, in the wake of the Pittsburgh synagogue shooting.

Providing entertainment was musical guest Pharrell Williams, who took a moment to share a few words about last week’s deadly shooting in a Pittsburgh synagogue.

“Look, what happened in that synagogue was incredibly cruel, it was wrong, and it’s not supposed to be what our nation is,” Williams said. “This group of people have been tested over and over and over again … but you guys show an incredible resilience.”

This makes me happy.
4,000 college students from 60 nations sing as one in TLV
Four thousand college students from 60 countries gathered in Tel Aviv in October for the latest mass singalong sponsored by the social music project, Koolulam.

The event, at Ganei Yehoshua, was attended by GA 2018 participants, graduates of MASA programs, MASA CEO Liran Avisar Ben Horin, Jewish Agency Chairman Yitzchak Herzog, Government Secretary Tzahi Braverman, and Masa Chairman Ilan Cohen.

Koolulam was founded with the goal of strengthening society through mass singing events in which large groups of non-professionals come together to create a collaborative happening.

The organization gathers as many as 12,000 people at a time from a broad spectrum of Israeli life, and then films the joint singing productions to share on social media.

In June this year, Jews, Muslims and Christians joined together at the Jerusalem Tower of David Museum to sing Bob Marley’s “One Love” in three languages as a show of unity from Israel. The video of the event went viral.

After 56 years of searching, IDF finds remains of pilot in Sea of Galilee
The Israeli military recently discovered the remains of a pilot, Lt. Yakir Naveh, who has been missing since his plane crashed into the Sea of Galilee 56 years ago, the army said Tuesday.

The remains were discovered on October 25 on the bottom of the Sea of Galilee, along with pieces of the aircraft.

Once they were found, the remains were sent to a forensic laboratory for identification, the army said.

The military’s Manpower Directorate informed the pilot’s family that his remains had been found, the army said.

His funeral was scheduled for November 13 at 3 p.m. at Tel Aviv’s Kiryat Shaul military cemetery.

On May 6, 1962, Naveh was training a cadet on a Fouga Magister when their plane got too low over the water and the engine cut out. The nose of the plane hit the water, sending them into a fierce spin, wing over wing.

A year later, a search team found the body of the cadet who had been flying the plane, Oded Kouton, but no trace of Naveh.

We have lots of ideas, but we need more resources to be even more effective. Please donate today to help get the message out and to help defend Israel.
          Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning      Cache   Translate Page      

Making the right choice when it comes to buying a GPU is critical. So how do you select the GPU which is right for you? This blog post will delve into that question and will lend you advice which will help you to make choice that is right for you.

The post Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning appeared first on Tim Dettmers.

          Deep Learning-Experte/Expertin - Bosch Gruppe - Vaihingen an der Enz      Cache   Translate Page      
Standort Stuttgart-Vaihingen Arbeitsbereiche Informationstechnologie Einstieg als Berufserfahrene/-r Startdatum Nach Vereinbarung Arbeitszeit Vollzeit und...
Gefunden bei Bosch Gruppe - Fri, 07 Sep 2018 12:02:46 GMT - Zeige alle Vaihingen an der Enz Jobs
          Deep Learning Expert - Bosch Gruppe - Vaihingen an der Enz      Cache   Translate Page      
Standort Stuttgart-Vaihingen Arbeitsbereiche Informationstechnologie Einstieg als Berufserfahrene/-r Startdatum Nach Vereinbarung Arbeitszeit Vollzeit und...
Gefunden bei Bosch Gruppe - Fri, 07 Sep 2018 12:02:45 GMT - Zeige alle Vaihingen an der Enz Jobs
          How to Use the TimeseriesGenerator for Time Series Forecasting in Keras      Cache   Translate Page      

Time series data must be transformed into a structure of samples with input and output components before it can be used to fit a supervised learning model. This can be challenging if you have to perform this transformation manually. The Keras deep learning library provides the TimeseriesGenerator to automatically transform both univariate and multivariate time […]

The post How to Use the TimeseriesGenerator for Time Series Forecasting in Keras appeared first on Machine Learning Mastery.

          Using deep learning to detect malaria in images      Cache   Translate Page      
          도시바 메모리, 고속·고에너지 효율 딥러닝 프로세싱 알고리즘 및 하드웨어 아키텍처 개발      Cache   Translate Page      
메모리 솔루션 분야의 세계적 기업인 도시바 메모리 코퍼레이션(Toshiba Memory Corporation, 이하 ‘도시바 메모리’)이 인식 정확성의 저하 없이 고속, 고에너지 효율 딥러닝(deep learning) 프로세싱 알고리즘 및 하드웨어 아키텍처를 개발했다고 6일 발표했다. FPGA[1] 상에서 수행되는 이 새로운 딥러닝 프로세서는 기존의 제품에 비해 4배나 높은 에너지 효율을 자랑한다. 이번의 기술 ...
          Toshiba Memory Corporation Develops High-Speed and High-Energy-Efficiency Algorithm and Hardware Architecture for Deep Learning Processor      Cache   Translate Page      
Toshiba Memory Corporation, the world leader in memory solutions, today announced the development of a high-speed and high-energy-efficiency algorithm and hardware architecture for deep learning processing with less degradations of recognition accuracy. The new processor for deep learning implemented on an FPGA [1] achieves 4 times energy efficiency compared to conventional ones. The advance was announced at IEEE Asian Solid-State Circuits Conference 2018 (A-SSCC 2018) in Taiwan on November 6. ...
          Micron’s bet: Quad-level cell NAND SSDs will finally replace HDDs      Cache   Translate Page      
Analytics and big data;; AI data lakes;; Machine and deep learning data lakes;; Large block stories and active archives;; SQL databases and business ...
          Hyundai Invests in Deep Learning Israeli Start-Up      Cache   Translate Page      
Hyundai CRADLE, Hyundai Motor Company's corporate venture and open innovation business announced its strategic investment in, ...
          WhizAI Joins NVIDIA Inception Program      Cache   Translate Page      
The NVIDIA Inception program will benefit WhizAI by providing leading-edge customizable resources in artificial intelligence, deep learning and data ...
          AWS, Veritone: Machine Learning, AI Can Help Monetize Ads, Content      Cache   Translate Page      
Its new drone initiative is “driven by deep learning and machine learning capabilities” also, he said, noting the company has “thousands of ...
          AI May Detect Alzheimer’s Years Before Diagnosis      Cache   Translate Page      
"Deep learning" may "assist in addressing the increasing complexity and volume of imaging data, as well as the varying expertise of trained imaging ...
          AMD unveils Radeon Instinct MI60 and MI50 accelerators for AI and HPC      Cache   Translate Page      
AMD on Tuesday unveiled the Radeon Instinct MI60 and MI50, a pair of accelerators designed for next-generation deep learning, HPC, cloud ...
          Artificial intelligence may fall short when analyzing data across multiple health systems      Cache   Translate Page      
Researchers found that the difficulty of using deep learning models in medicine is that they use a massive number of parameters, making it ...
          AI tool predicts Alzheimer’s more than 6 years ahead of diagnosis      Cache   Translate Page      
A deep learning algorithm developed using imaging data from more than 1,000 Alzheimer's disease (AD) patients can accurately predict the presence ...
          DeOldify – A deep learning based project for colorizing and restoring old images      Cache   Translate Page      
Simply put, the mission of this project is to colorize and restore old images. I’ll get into the details in a bit, but first let’s get to the pictures! BTW – most of these source images originally came from the r/TheWayWeWere subreddit, so credit to them for finding such great photos.
          (PR) AMD announces Radeon Instinct MI60 and Instinct MI50      Cache   Translate Page      
AMD Unveils World’s First 7nm Datacenter GPUs -- Powering the Next Era of Artificial Intelligence, Cloud Computing and High Performance Computing (HPC) AMD Radeon Instinct™ MI60 and MI50 accelerators with supercharged compute performance, high-speed connectivity, fast memory bandwidth and updated ROCm open software platform power the most demanding deep learning, HPC, cloud and rendering applications SAN FRANCISCO, Nov. 06, 2018 (GLOBE NEWSWIRE) -- AMD(NASDAQ: AMD) today announced the AMD Radeon Instinct™ MI60 and MI50 accelerators, the world’s first 7nm datacenter GPUs, designed to deliver the compute performance required for next-generation deep learning, HPC, cloud computing and rendering applications. Researchers, scientists and...

Keep on reading: (PR) AMD announces Radeon Instinct MI60 and Instinct MI50
          Optimized Wishart Network for an Efficient Classification of Multifrequency PolSAR Data      Cache   Translate Page      
High-resolution wide-area images are required in the diverse field of research ranging from urban planning and disaster prediction to agriculture and geology. Sometimes the image is taken under harsh weather conditions or at night time. Current optical remote sensing technology does not have the capability to acquire images in such conditions. Synthetic aperture radar (SAR) uses microwave signal which has a long-range propagation characteristic that allows us to capture images in difficult weather conditions. In addition to this, some polarimetric SAR (PolSAR) systems are also capable of capturing images using multifrequency bands simultaneously resulting into a multitude of information in comparison to the optical images. In this letter, we propose a single-hidden layer optimized Wishart network (OWN) and extended OWN for classification of the single-frequency and multifrequancy PolSAR data, respectively. Performance evaluation is conducted on a single-frequency as well as multifrequency SAR data obtained by Airborne Synthetic Aperture Radar. We observed that for combining multiple band information, proposed single-hidden layer network outperforms deep learning-based architecture involving multiple hidden layers.
          Deep Self-Paced Residual Network for Multispectral Images Classification Based on Feature-Level Fusion      Cache   Translate Page      
The classification methods based on fusion techniques of multisource multispectral (MS) images have been studied for a long time. However, it may be difficult to classify these data based on a feature level while avoiding the inconsistency of data caused by multisource and multiple regions or cities. In this letter, we propose a deep learning structure called 2-branch SPL-ResNet which combines the self-paced learning with deep residual network to classify multisource MS data based on the feature-level fusion. First, a 2-D discrete wavelet is used to obtain the multiscale features and sparse representation of MS data. Then, a 2-branch SPL-ResNet is established to extract respective characteristics of the two satellites. Finally, we implement the feature-level fusion by cascading the two feature vectors and then classify the integrated feature vector. We conduct the experiments on Landsat_8 and Sentinel_2 MS images. Compared with the commonly used classification methods such as support vector machine and convolutional neural networks, our proposed 2-branch SPL-ResNet framework has higher accuracy and more robustness.
          Hyperspectral Unmixing via Deep Convolutional Neural Networks      Cache   Translate Page      
Hyperspectral unmixing (HU) is a method used to estimate the fractional abundances corresponding to endmembers in each of the mixed pixels in the hyperspectral remote sensing image. In recent times, deep learning has been recognized as an effective technique for hyperspectral image classification. In this letter, an end-to-end HU method is proposed based on the convolutional neural network (CNN). The proposed method uses a CNN architecture that consists of two stages: the first stage extracts features and the second stage performs the mapping from the extracted features to obtain the abundance percentages. Furthermore, a pixel-based CNN and cube-based CNN, which can improve the accuracy of HU, are presented in this letter. More importantly, we also use dropout to avoid overfitting. The evaluation of the complete performance is carried out on two hyperspectral data sets: Jasper Ridge and Urban. Compared with that of the existing method, our results show significantly higher accuracy.
          Fastest Analytics Using Hybrid Architectures with Machine Learning      Cache   Translate Page      

Click to learn more about author Henry Bequet. Every year scientists and researchers gather at a conference called Super Computing, or SC, to exchange their views, solutions and problems in computational science. At SC17, there were no fewer than 22 presentations and keynotes involving Machine Learning and Deep Learning (DL). There were actually many more presentations about […]

The post Fastest Analytics Using Hybrid Architectures with Machine Learning appeared first on DATAVERSITY.

          VoiceBase Extends Deep Learning Neural Network Compute to Verne Global      Cache   Translate Page      

A recent press release reports, “Verne Global, a provider of advanced data center solutions for high performance computing (HPC), today announced that VoiceBase, the leading provider of speech analytics for the cloud, is utilizing its HPC-optimized bare-metal infrastructure – hpcDIRECT – to accelerate the development of new artificial intelligence (AI) powered voice analytics services. California-based […]

The post VoiceBase Extends Deep Learning Neural Network Compute to Verne Global appeared first on DATAVERSITY.

          Codeless and ML-Based Automation vs. Traditional Test Automation      Cache   Translate Page      
There’s no doubt that the test automation space is undergoing transformation. Machine Learning (ML), Deep Learning and Artificial Intelligence (AI) are being leveraged more and more as part of the test authoring and test analysis. While the space is still growing from a maturity stand-point, it is a great time for practitioners (developers and test ...
          ONNX and Intel nGraph API Deliver AI Framework Flexibility – Intel Chip Chat – Episode 611      Cache   Translate Page      
In this Intel Chip Chat audio podcast with Allyson Klein: Prasanth Pulavarthi, Principal Program Manager for AI Infrastructure at Microsoft, and Padma Apparao, Principal Engineer and Lead Technical Architect for AI at Intel, discuss a collaboration that enables developers to switch from one deep learning operating environment to another regardless of software stack or hardware [...]
          AMD Radeon Instinct MI50 and MI60 bring 7-nm GPUs to the data center      Cache   Translate Page      

Alongside a preview of its first 7-nm Epyc CPUs built with the Zen 2 microarchitecture, AMD debuted its first 7-nm data-center graphics-processing units today. The Radeon Instinct MI50 and Radeon Instinct MI60 take advantage of a new 7-nm GPU built with the Vega architecture to crunch through tomorrow's high-performance computing, deep learning, cloud computing, and virtualized desktop applications.



          11月6日(火)のつぶやき その2      Cache   Translate Page      


#meigen #名言

— 吉本隆明の名言@名言ナビ (@yoshimoto__bot) 2018年11月6日 - 08:53

現在のDeep Learningの先に原理的に本質的な読解はない、という意見です。人間を超えたかどうか、は、東ロボが既に偏差値50を超えていますので、それだけ見ると、ある意味人間……

— 新井紀子/ Noriko Arai (@noricoco) 2018年11月6日 - 09:11


— 柄谷行人 (@karatani_kojin) 2018年11月6日 - 08:46


— 裕木奈江 NAE YUUKI (@nae_auth) 2018年11月6日 - 09:14


— ひとり配当金生活-さいもん (@hitori_haitou) 2018年11月6日 - 08:13



— 湾岸暮らしの独り言。 (@wangan_goto) 2018年11月6日 - 08:32

読書週間は10/27〜11/9までです。1969年、アメリカの読書週間のポスター。 #読書ポスター #読書週間

— 愛書家日誌 (@aishokyo) 2018年11月6日 - 08:30


— ミスターK (@arapanman) 2018年11月6日 - 09:22

【私の名盤】Jimi Hendrix Experience / Are You Experienced?

— 678 (@678mpth) 2018年11月6日 - 09:29


— 町田奈桜 nao machida (@now2000) 2018年11月5日 - 10:07


— 早見雄二郎(株式評論家) (@hayamiy) 2018年11月5日 - 07:38

リーベックス(Revex) の 簡単デジタルタイマー PT70DW を Amazon でチェック!

— zionadchat (@zionadchat) 2018年11月6日 - 09:28




— 動物の習性図鑑 (@dobutsu_syu) 2018年11月5日 - 21:53


— ふろむだ🍀新著Amazon1位🍀5章分無料公開中 (@fromdusktildawn) 2018年11月6日 - 08:45

新井紀子氏がシンプルなことを言っていてイイ。形式論理外では人間の方が優位よ、と読まずに解釈してみましたw 墓穴は掘るほどパワーなるからなあ~

— 大江昇 (@TKDOMO) 2018年11月6日 - 09:51


— 大江昇 (@TKDOMO) 2018年11月6日 - 10:02

顆粒球が細菌を内部に抱え込んで身体各所に運ぶ…というエビデンスは? 妊娠中に母体が腸内細菌を胎児へ運ぶというのはNHKの生命のスペシャルでやってましたが…。その時の細菌の運搬役は何が担ってるのか? 腸の粘膜層まで細菌を取り込むのは抗体だと紹介されてますが、その後に運ぶのは…?

— 大江昇 (@TKDOMO) 2018年11月6日 - 10:14


— ひとり配当金生活-さいもん (@hitori_haitou) 2018年11月6日 - 10:48


— 高橋大樹 (@Yoshimotoshimao) 2018年11月6日 - 11:03

八王子スーパー射殺事件で新情報 吸い殻13本でDNA3人分 | 2018/7/17 - 共同通信… 「吸った人物が特定できていない13本のたばこの吸い殻が残されていたことを明らかにした……

— 大江昇 (@TKDOMO) 2018年11月6日 - 11:21

グアテマラ事件 亡くなったのは木本結梨香さん… 「死亡したのは、神奈川県茅ヶ崎市出身のフルート奏者、木本結梨香さん(26)であることが家族への取材でわかりました」…海外のニュースでは布教に来てたとか。ご冥福を。

— 大江昇 (@TKDOMO) 2018年11月6日 - 11:23

インフルエンザ患者 異常行動 厚労省が注意呼びかけ… 「子どもを中心に、インフルエンザの患者が突然走り出したり暴れたりする異常行動が95件確認された…」…以前タミフル原因説とともにイン……

— 大江昇 (@TKDOMO) 2018年11月6日 - 11:28

お誕生日おめでとう🎉 弊妻よく頑張りました👏

— ✨↓✨→←✨+✨ー✨ (@ochyai) 2018年11月6日 - 12:43


— 古谷経衡@テレビ朝日『ワイドスクランブル』隔週木曜日出演 (@aniotahosyu) 2018年11月6日 - 11:46


— konso (@oiroppa) 2018年11月6日 - 12:53


— 古谷経衡@テレビ朝日『ワイドスクランブル』隔週木曜日出演 (@aniotahosyu) 2018年11月6日 - 11:18

Union is strength !!


— Konno Mamoru (@funkykong555) 2018年11月6日 - 07:44


— ブルームバーグニュース日本語版 (@BloombergJapan) 2018年11月6日 - 12:45

タミフルと異常行動 「因果関係は明確ではない」… "厚生労働省はインフルエンザ治療薬のタミフルについて、飛び降りなど子どもの異常行動との因果関係は明確ではないとする報告書をまとめました。"

— konso (@oiroppa) 2018年11月6日 - 11:33

1961年ニューヨーク・セントラルパーク、ベンチで本を読む女性たち。 #本を読む人

— 愛書家日誌 (@aishokyo) 2018年11月6日 - 11:50
          The Truth About AI And Businesses In India: It's A Win-Win-Win Situation - Analytics India Magazine      Cache   Translate Page      

Analytics India Magazine

The Truth About AI And Businesses In India: It's A Win-Win-Win Situation
Analytics India Magazine
The hype around artificial intelligence is very real in India with the country building deep tech pockets in robotics and deep learning areas. Research centres are rife in Bengaluru and Hyderabad where these solutions are being applied in the local ...

          What is the Difference between Machine Learning and Deep Learning?      Cache   Translate Page      

There is often a lot of confusion around the differences between machine learning and deep learning.  Both are classed as techniques to enable artificial intellgence or AI.  But what is AI? AI is the ability to create a program or computer system that can fool a human into thinking it is another human.  There is […]

The post What is the Difference between Machine Learning and Deep Learning? appeared first on ALC Training.

          Inteligência artificial consegue prever Alzheimer anos antes do diagnóstico      Cache   Translate Page      

De acordo com o que descreve um novo estudo publicado na revista Radiology, a tecnologia de inteligência artificial já consegue analisar imagens do cérebro com precisão a ponto de identificar sinais de Alzheimer anos anos de um diagnóstico.

Identificar a doença antecipadamente é essencial pois os tratamentos e intervenções para o controle de seu avanço são mais eficazes no início do curso da doença — após sua manifestação, a degeneração é rápida. Contudo, diagnosticar Alzheimer precocemente é um desafio e tanto para a comunidade médica. A pesquisa em questão liga o processo da doença a mudanças no metabolismo, como é o caso da captação de glicose em certas regiões do cérebro, sendo que tais mudanças são difíceis de reconhecer.

É aí que entra a tecnologia da IA com deep learning, em que as máquinas aprendem por meio de exemplos reais e conseguem encontrar mudanças sutis no metabolismo cerebral a fim de prever a doença de Alzheimer. Os pesquisadores treinaram o algoritmo a partir de tomografias cerebrais específicas, que mostram a captação de glicose.

A equipe, então, usou dados da Iniciativa de Neuroimagem da Doença de Alzheimer (ADNI), que é um grande estudo focado em ensaios clínicos para melhorar a prevenção e tratamento da doença. O estudo abriga mais de 2.100 imagens cerebrais de mais de mil pacientes, com 90% desse montante de informações sendo usado pelo algoritmo em seu processo de aprendizagem.

Então, os pesquisadores testaram seu algoritmo em exames de 40 pacientes que não fizeram parte do estudo do ADNI, com a IA alcançando 100% de sensibilidade na detecção da doença em média seis anos antes do diagnóstico final. E, ainda que sejam necessários mais testes para determinar uma taxa de acerto válida proporcionalmente falando, os autores do estudo acreditam que seu algoritmo seja uma ferramenta útil para complementar o trabalho de radiologistas e bioquímicos, fornecendo uma oportunidade para terapias precoces.

          アルツハイマー病がAIとイメージング技術を活用することで早期に見つけられるようになるかもしれない      Cache   Translate Page      
by jesse orrico世界で数千万人を悩ませているアルツハイマー病は、早期に見つけることがとても難しい病気です。カリフォルニア大学サンフランシスコ校(UCSF)放射線医学画像診断学科のジェ・ホン・ソン教授らは、脳のスキャン画像を用いてニューラルネットワークのトレーニングを行い、40件の事例で、アルツハイマー病の早期診断に成功しました。A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain | Radiologyhttps://pubs.rsna. 全文
GIGAZINE(ギガジン) 11月07日 12時00分

          Matrox Imaging: Flowchart software      Cache   Translate Page      
Matrox® Imaging announces Matrox Design Assistant X flowchart-based vision application software. This integrated development environment (IDE) allows developers to build intuitive flowcharts instead of writing traditional program code. It enables the development of a graphical web-based operator interface for modifying the vision application.

This update integrates a host of new features and functionality, including image classification using deep learning, a photometric stereo tool that highlight surface imperfections, and the ability to interface directly with third-party 3D sensors.

Deep learning for image classification

The classification tool leverages deep learning—specifically, convolutional neural network (CNN) technology—to categorize images of highly textured, naturally varying, and acceptably deformed goods. All inference is performed on a mainstream CPU, eliminating the dependence on third-party neural network libraries and the need for specialized GPU hardware. Matrox Imaging handles the intricate design and training of the neural network, utilizing the deep technical experience, knowledge, and skill of its machine learning and machine vision experts.

A Q&A video offers more insight into deep learning technology.

Photometric stereo for emphasizing surface irregularities

A new registration tool features photometric stereo technology, which creates a composite image from a series of images taken with light coming in from different directions. Creation of these images utilizes directional illumination light controllers, such as the Light Sequence Switch (LSS) from CCS, LED Light Manager (LLM) from Smart Vision Lights, or others similar. This composite image emphasizes surface irregularities, such as embossed or engraved features, scratches, or indentations.

A primer on photometric stereo techniques was outlined in a Q&A video.

Third-party 3D sensor interfacing

Matrox Design Assistant X makes it possible to capture and process depth-map data byinterfacing with third-party 3D sensors. Initially, the software will support LMI Gocator® line profilers and snapshot sensors and Photoneo® PhoXi® scanners, with other scanner options to be added in the future.

Other updates and additions include multiple run-times for running multiple independent projects simultaneously on the same platform; dedicated shape-finding tools for locating circles, ellipses, rectangles, and line segments; and addition of a code-grading step.

Field-proven Matrox Design Assistant X software is a perfect match for the Matrox 4Sight EV6 vision controller or the Matrox Iris GTR smart camera.

"This new version delivers on the three cornerstones of our development methodology," said Fabio Perelli, product manager, Matrox Imaging. "These are to extend Matrox Design Assistant’s capabilities while incorporating recent evolutions to the underlying vision library and also striving to simplify the overall user experience."

Matrox Design Assistant X will be officially released in Q2 2019.

          Data scientist      Cache   Translate Page      
Looking for someone to contribute to an ongoing project as data analyst. You are expected to be having experience on machine learning and deep learning modules using Python. Minimum 4 hours a days is needed to be spent on the project... (Budget: ₹100 - ₹400 INR, Jobs: Data Mining, Machine Learning, Python, Software Architecture, Statistics)
          Deep Learning Research Engineer Intern - TandemLaunch - Montréal, QC      Cache   Translate Page      
The project is in development phase, where we are implementing academic works and our patent portfolio into our technology....
From TandemLaunch - Thu, 11 Oct 2018 16:08:59 GMT - View all Montréal, QC jobs
          (USA-CA-San Jose) Lead Machine Learning Software Engineer - up to 220k + bonus      Cache   Translate Page      
Lead Machine Learning Software Engineer - up to 220k + bonus Lead Machine Learning Software Engineer - up to 220k + bonus - Skills Required - Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Programming with languages such as Python/C/Java, Full lifecycle experience Location: Work remote initially, once established office will be either Redwood City OR San Jose. Will need to be in the office 2-3 days per week minimum. Salary: Up to 220k base plus bonus Skills: Machine learning, full lifecycle experience, programming with a variety of languages Work for an industry leader which is one of the largest consumer products brands around the globe! It's an exciting time for our brand as we continue to move forward with our digital/IoT strategy. If you are a Lead Machine Learning Software Engineer please read on........ **Top Reasons to Work with Us** - Work for an industry leading consumer products brand - Excellent benefits including 401k contribution, bonuses and much more..... - Excellent work/life balance and positive company culture **What You Will Be Doing** As the Lead Machine Learning Engineer you will be very hands on defining and delivering solutions which will bring delightful user experiences globally. Key responsibilities: - Work with a cross functional team which is developing products for consumers across the globe - Utilize machine learning, computer vision, NLP and speech recognition techniques to create innovative products - Be the SME for Machine Learning in our product group - Stay abreast of the latest machine learning techniques and technologies and advise the company on how they can be applied to our products - Architect and implement smart IoT products - Mentor more junior engineers - Participate in code reviews **What You Need for this Position** Required: 5+ years in software engineering Strong Machine learning skills Programming with languages such as Python, C and Java Ideally you will have experience with at least some of these specific areas: computer vision, speech recognition, natural language processing **What's In It for You** Market rates salaries (150-220k) plus bonus and full benefits package! So, if you are a Lead Software Engineer that specializes in Machine Learning, please apply today! Applicants must be authorized to work in the U.S. **CyberCoders, Inc is proud to be an Equal Opportunity Employer** All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or any other characteristic protected by law. **Your Right to Work** – In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification document form upon hire. *Lead Machine Learning Software Engineer - up to 220k + bonus* *CA-San Jose* *SJ2-LeadML-SJ*
          (USA-CA-San Jose) Lead Software Engineer: Embedded      Cache   Translate Page      
Lead Software Engineer: Embedded (IoT) Lead Software Engineer: Embedded (IoT) - Skills Required - RTOS, Embedded Android, IOT, Android, Linux, BSP, Android AOSP, AOSP, Android NDK, NDK Location: Work remote initially, once established office will be either Redwood City OR San Jose. Will need to be in the office 2-3 days per week minimum. Salary: Up to 220k base plus bonus Skills: system level embedded programming in Linux or Android or other lower layer system Work for an industry leader which is one of the largest consumer products brands around the globe! It's an exciting time for our brand as we continue to move forward with our digital/IoT strategy. If you are a Lead Embedded Engineer please read on.......... **Top Reasons to Work with Us** - Work for an industry leading consumer products brand - Excellent benefits including 401k contribution, bonuses and much more..... - Excellent work/life balance and positive company culture **What You Will Be Doing** The Lead Software Engineer will play a key role in the development of embedded software platforms to drive innovation across the full range of products. The software platforms range from RTOS to Embedded Android. The platforms provide IoT features, middleware to control appliance, framework for running machine learning models, and user-interface framework to allow development of interfaces. -Architect, design and develop software platforms using Android, Linux or RTOS -Lead the development of various software components that include interfacing to appliance control, appliance middleware, application layer networking protocols, device drivers, computer vision, deep learning inference middleware, GUI middleware, etc. -Working with partners to develop Android, Linux, or RTOS BSP including board-bringup, hardware debugging, and optimizing low-level OS features -Be the expert in Linux and Android System Software and especially develop expertise in Android's wireless and networking architecture, security architecture, low-power, over-the-air upgrades, and development tools -Provide technical leadership **What You Need for this Position** Required Qualifications: 5+ years in embedded engineering Android, Linux or RTOS Preferred Qualifications: -Experience working with the Linux open-source community is highly desired -Experience working within the Android development environment including with Android AOSP and Android NDK -Excellent with debugging of complex software systems -Have a deep understanding of full product development life-cycle **What's In It for You** Market rates salaries (150-220k) plus bonus and full benefits package! So, if you are a Lead Embedded Software Engineer with at least 5 years of experience, please apply today! Applicants must be authorized to work in the U.S. **CyberCoders, Inc is proud to be an Equal Opportunity Employer** All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or any other characteristic protected by law. **Your Right to Work** – In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification document form upon hire. *Lead Software Engineer: Embedded* *CA-San Jose* *SJ2-Lead-IoT-SJ*
          (USA-PA-Pittsburgh) Computer Vision / Deep Learning Engineer      Cache   Translate Page      
Computer Vision / Deep Learning Engineer Computer Vision / Deep Learning Engineer - Skills Required - Computer Vision, C++, Caffe, Tensorflow, Lidar, Geometry-Based Vision, Deep Learning, Multi-view stereo I am currently working with several companies in the area who are actively hiring in the field of Computer Vision and Deep Learning. AI, and specifically Computer Vision and Deep Learning are my niche market specialty and I only work with companies in this space. I am actively recruiting for multiple levels of seniority and responsibility, from experienced Individual Contributor roles, to Team Lead positions, to Principal Level Scientists and Engineers. I offer my candidates the unique proposition of representing them to multiple companies, rather than having to work with multiple different recruiters at an agency, or applying directly to many different companies without someone to manage the process with each of those opportunities. In one example, I am working with a candidate who is currently interviewing with 10 different clients of mine for similar roles across the country with companies applying Computer Vision and Deep Learning to various different applications from Robotics, Autonomous Vehicles, AR/VR/MR, Medical Imaging, Manufacturing Automation, Gaming, AI surveillance, AI Security, Facial ID, 3D Sensors and 3D Reconstruction software, Autonomous Drones, etc. I would love to work with you and introduce you to any of my clients you see as a great fit for your career! Please send me a resume and tell me a bit about yourself and I will reach out and offer some times to connect on the phone! **Top Reasons to Work with Us** Some of the current openings are for the following brief company overviews: Company 1 - company is founded by 3x Unicorn (multi-billion dollar companies) founders and are breaking into a new market with advanced technology, customers, and exciting applications including AI surveillance, robotics, AR/VR. Company 2 - Autonomous Drones! Actually, multiple different companies working on Autonomous Drones for different applications - including Air-to-Air Drone Security, Industrial Inspection, Consumer Drones, Wind Turbine and Structure Inspection. Company 3 - 3D Sensors and 3D Reconstruction Software - make 3D maps of interior spaces using our current products on the market. We work with builders, designers, Consumers and Business-to-Business solutions. Profitable company with strong leadership team currently in growth mode! Company 4 - Industrial/Manufacturing/Logistics automation using our 3D and Depth Sensors built in house and 3D Reconstruction software to automate processes for Fortune 500 clients. Solid funding and revenue approaching profitability in 2018! Company 5 - Hand Gesture Recognition technology for controlling AR/VR environments. We have a product on the market as of 2017 and are continuing to develop products for consumers and business applications that are used in the real and virtual world. We have recently brought on a renowned leader in Deep Learning and it's intersection with neuroscience and are doing groundbreaking R&D in this field! Company 6 - Full facial tracking and reconstruction for interactive AR/VR environments. Company 7 - massively scalable retail automation using Computer Vision and Deep Learning, currently partnered with one of the largest retailers in the world. Company 8 - Products in the market including 3D Sensors, and currently bringing 3D reconstruction capabilities to mobile devices everywhere. Recently closed on a $50M round of funding and expanding US operations. Company 9 - Mobile AI company using Computer Vision for sports tracking and real time analytics for players at all levels from beginner to professional athletes to track, practice and improve at their craft. Company 10 - Digitizing human actions to create a massive new dataset in manufacturing - augmenting the human/robot working relationship and giving manufacturers the necessary info to improve that relationship. We believe that AI and robotics will always need to work side by side with humans, and we are the only company providing a solution to this previously untapped dataset! Company 11 - 3D facial identification and authentication for security purposes. No more key-fobs and swipe cards, our clients use our sensors and software to identify and permit employees. **What You Will Be Doing** If you are interested in discussing any of these opportunities, I would love to speak with you! I am interested in learning about the work you are currently doing and what you would be interested in for your next step. If the above opportunities are not quite what you're looking for but would still like to discuss future opportunities and potential to work together, I would love to meet you! I provide a free service to my candidates and work diligently to help manage the stressful process of finding the right next step in your career. The companies that I work with are always evolving so I can keep you up to date on new opportunities I come across. Please apply to this job, or shoot me an email at and let's arrange a time to talk on the phone. **What You Need for this Position** Generally, I am looking for Scientists/Engineers in the fields of Computer Vision, Deep Learning and Machine Learning. I find that a lot of my clients are looking for folks who have experience with 3D Reconstruction, SLAM / Visual Odometry, Object Detection/Recognition/Tracking, autonomy, Point Cloud Processing, Software and Algorithm development in C++ (and C++11 and C++14), GPU programming using CUDA or other GPGPU related stuff, Neural Network training, Sensor Fusion, Multi-view stereo, camera calibration or sensor calibration, Image Segmentation, Image Processing, Video Processing, and plenty more! - Computer Vision - C+- Python - Linux - UNIX **What's In It for You** A dedicated and experienced Computer Vision placement specialist! If you want to trust your job search in the hands of a professional who takes care and pride in their work, and will bring many relevant opportunities your way - I would love to work with you! So, if you are a Computer Vision Scientist or Engineer and are interested in having a conversation about the market and some of the companies I am working with, please apply or shoot me an email with resume today! Applicants must be authorized to work in the U.S. **CyberCoders, Inc is proud to be an Equal Opportunity Employer** All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or any other characteristic protected by law. **Your Right to Work** – In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification document form upon hire. *Computer Vision / Deep Learning Engineer* *PA-Pittsburgh* *RM2-1492758*
          COSCon Bridges East & West, Open Source Powers Now & Future      Cache   Translate Page      

The OSI was honored to participate in the 2018 China Open Source Conference (COSCon'18) hosted by OSI Affiliate Member KAIYUANSHE in Shenzhen, China. Over 1,600 people attended the exciting two-day event, with almost another 10,000 watching via live-stream online. The conference boasted sixty-two speakers from twelve countries, with 11 keynotes (including OSI Board alum Tony Wasserman), 67 breakout sessions, 5 lightning talks (led by university students), 3 hands-on camps, and 2 specialty forums on Open Source Education and Open Source Hardware.

COSCon'18 also served as an opportunity to make several announcements, including the publication of "The 2018 China Open Source Annual Report", the launch of "KCoin Open Source Contribution Incentivization Platform", and the unveiling of KAIYUANSHE's "Open Hackathon Cloud Platform".

Since its foundation in October of 2014, KAIYUANSHE has continuously helped open source projects and communities thrive in China, while also contributing back to the world by, "bringing in and reaching out". COSCon'18 is one more way KAIYUANSHE serves to: raise awareness of, and gain expereince with, global open source projects; build and incentivise domestic markets for open source adoption; study and improve open source governance across industry sectors; promote and serve the needs of local develoeprs, and; identify and incubate top-notch local open source projects.

In addition to all of the speakers and attendees, KAIYUANSHE would like to thank their generous sponsors for all of their support in making COSCon'18 a great success.

2018 China Open Source Annual Report - Created by KAIYUANSHE volunteers over the past six months, the 2018 Open Source Annual Report describes the current status, and unique dynamics, of Open Source Software in China. The report provides a global perspective with contributions from multiple communities, and is now available on GitHub: contributions welcome.

KCoin - Open Source Contribution Incentivization Platform - KCoin, an open source, blockchain-based, contribution incentivization mechanism was launched at COSCon'18. KCoin is curently used by three projects including, KFCoding--a next generation interactive developer learning community, ATN--an AI+Blockchain-based open source platform, and Dao Planet--a contribution-based community incentive infrastructure.

Open Hackathon Platform Donation Ceremony - Open Hackathon Platform is a one-stop cloud platform for hosting or participating online in hackathons. Originally developed by and run internally for Microsoft development, the platform was officially donated to KAIYUANSHE by Microsoft during the conference. Since May of 2015 the open source platform has hosted more than 10 hackathons and other collabrative development eforts including hands-on camps and workshops, and is the first project to be contributed by a leading international corporation to a Chinese open source community. Ulrich Homann, Distinguished Architect at Microsoft who presided over the dedication offered, “We are looking forward to contributions from the KAIYUANSHE community which will make the Open Hackathon Cloud Platform an even better platform for your needs. May the source be with you!”

Open Source 20-Year Anniversary Celebration Party - Speakers, sponsors, community and media partners, and KAIYUANSHE directors and officers came together to celebrate the 20-year anniversary of Open Source Software and the Open Source Initiative. The evening was hosted by OSI Board Director Tony Wasserman, and Ross Gardler of the Apache Software Foundation, who both shared a few thoughts about the long journey and success of Open Source Software. Other activities included, a "20 Years of Open Source Timeline", where attendees added their own memories and milestones; "Open-Source-Awakened Jedi" cosplay with Kaiyuanshe directors and officers serving OSI 20th Anniversary cake as Jedi warrior's (including cutting the cake with light sabers!).

The celebration also provided an opportunity to recognize the outstanding contributions to KAIYUANSHE and open source by two exceptional individuals. Cynthia Xin and Junbo Wang were both awarded the "Open Source Star" trophy. Cynthia was recogmized for her work as both the Event Team Lead and Community Partnership Team Lead, while Junbo Wang, was recognized for contributions as the Open Hackathon Cloud Platform Infrastructure Team Lead, and KCoin Project Lead.

"May the source be with you!" Fun for all at the 20th Anniversary of Open Source party during COSCon'18.


Other highlights included:

  • A "Fireside Chat" with Nat Friedman, GitHub CEO, and Ted Liu, Kaiyuanshe Chairman
  • Apache Project Incubation
  • Implementing Open Source Governance at Scale
  • Executive Roundtable: "Collision of Cultures"
  • 20 years of open source: Where can we do better?
  • How to grow the next generation of university talent with open source.
  • Open at GitLab: discussions and engagement.
  • Three communities--Open Source Software (OSS), Open Source Hardware (OSHW) and Creative Commons (CC)--on stage, sharing and brainstorming.
  • Made in China, "Xu Gu Hao": open source hardware and education for the fun of creating!
Former OSI Board Director Tony Wasserman presents at COSCon'18


COSCon'18 organizers would like to recognize and thank their international and domestic communities for their support, Apache Software Foundation (ASF), Open Source Initiative (OSI), GNOME, Mozilla, FreeBSD and another 20+ domestic communities. As of Oct. 23rd, there were more than 120,000 viewerships from the retweet of the articles published for the COSCon'18 by the domestic communities and more retweets to come from the international communities. We are grateful for these lovely community partners. The board of GNOME Foundation also sent a greeting video for the conference.

Many attendees also offered their thoughts on the event...

COSCon was a great opportunity to meet developers and learn how GitHub can better serve the open source community in China. It is exciting to see how much creativity and passion there is for open source in China.
---- Nat Friedman, CEO, GitHub

COSCon is the meetup place for open source communities. No matter where you are, on stage or in the audience crowd, the spirits of openness, freedom, autonomy and collaboration run through the entire conference. Technologies rises and falls, only the ecosystem sustains over the community.
---- Tao Jiang, Founder of CSDN

When I visited China in 2015, I said "let's build the bridge together", in 2018 China Open Source Conference, I say "let's cross the bridge together!"
---- Ross Gardler, Executive Vice President, Apache Software Foundation

The conference was an excellent opportunity to learn about "adoption and use of FOSS from industry leaders in China and around the world."
---- Tony Wasserman, OSI Board Member Alumni, Professor of Carnegie Mellon University

I'm very glad to see the increasing influence power of KAIYUANSHE and wish it gets better and better.
---- Jerry Tan, Baidu Open Source Lead & Deep Learning Evangelist

It is a great opportunity to share Microsoft’s Open source evolution with the OSS community in China through the 2018 ConsCon conference. I am honored to officially donate the Microsoft Open Hackathon platform to the Kayuanshe community. Contributing over boundaries of space and time is getting more important than ever – an open platform like the Microsoft Open Hackathon environment can bring us together wherever we are, provide a safe online environment enabling us to solve problems, add unique value and finally have lots of fun together.
---- Ulrich Homann,Distinguished Architect, Microsoft

I was impressed by the vibrant interest in the community for OSS and The Apache Software Foundation, particularly by young developers.
---- Dave Fisher, Apache Incubator PMC member & mentor

Having the China Open Source Conference is a gift for the 20-year anniversary of the birth of open source from the vast number of Chinese open source fans. In 2016, OSI officially announced that Kaiyuanshe becomes an OSI affiliate member in recognizing Kaiyuanshe's contribution in promoting open source in China. Over the years, the influence of Kaiyuanshe has been flourishing, and many developers have participated & contributed to its community activities. In the future, Huawei Cloud is willing to cooperate with Kaiyuanshe further to contribute to software industry growth together.
---- Feng Xu, founder & general manager of DevCloud, Huawei Cloud

          AMD Radeon Instinct MI50 and MI60 bring 7-nm GPUs to the data center      Cache   Translate Page      

Alongside a preview of its first 7-nm Epyc CPUs built with the Zen 2 microarchitecture, AMD debuted its first 7-nm data-center graphics-processing units today. The Radeon Instinct MI50 and Radeon Instinct MI60 take advantage of a new 7-nm GPU built with the Vega architecture to crunch through tomorrow's high-performance computing, deep learning, cloud computing, and virtualized desktop applications.



          ONNX and Intel nGraph API Deliver AI Framework Flexibility – Intel Chip Chat – Episode 611      Cache   Translate Page      
In this Intel Chip Chat audio podcast with Allyson Klein: Prasanth Pulavarthi, Principal Program Manager for AI Infrastructure at Microsoft, and Padma Apparao, Principal Engineer and Lead Technical Architect for AI at Intel, discuss a collaboration that enables developers to switch from one deep learning operating environment to another regardless of software stack or hardware [...]
          (USA-WA-Bellevue) Machine Learning Scientist - NLP, Recommender/Ranking Systems      Cache   Translate Page      
Machine Learning Scientist - NLP, Recommender/Ranking Systems Machine Learning Scientist - NLP, Recommender/Ranking Systems - Skills Required - Machine Learning, NLP, Recommender Systems, Python, Deep Learning Theory, Hadoop, SPARK, Building Data Pipelines If you are a Machine Learning Scientist with experience, please read on! One of the largest and most well-known travel agencies is looking for a Machine Learning Scientist. We are an online travel agency that enables users to access a wide range of services. We books airline tickets, hotel reservations, car rentals, cruises, vacation packages, and various attractions and services via the world wide web and telephone travel agents. Our team helps power many of the features on our website. We design and build models that help our customers find what they want and where they want to go. As a member of our group, your contributions will affect millions of customers and will have a direct impact on our business results. You will have opportunities to collaborate with other talented data scientists and move the business forward using novel approaches and rich sources of data. If you want to resolve real-world problems using state-of-the-art machine learning and deep learning approaches, in a stimulating and data-rich environment, lets talk. **What You Will Be Doing** You will provide technical leadership and oversight, and mentor junior machine learning scientists You will understand business opportunities, identify key challenges, and deliver working solutions You will collaborate with business partners, program management, and engineering team partners You will communicate effectively with technical peers and senior leadership **What You Need for this Position** At Least 3 Years of experience and knowledge of: - PhD (MS considered) in computer science or equivalent quantitative fields with 3+ years of industry or academic experience - Expertise in NLP or recommender systems (strongly preferred) - Deep understanding of classic machine learning and deep learning theory, and extensive hands-on experience putting it into practice - Excellent command of Python and related machine learning/deep learning tools and frameworks - Strong algorithmic design skills - Experience working in a distributed, cloud-based computing environment (e.g., Hadoop or Spark) - Experience building data pipelines and working with live data (cleaning, visualization, and modeling) **What's In It for You** - Vacation/PTO - Medical - Dental - Vision - Bonus - 401k So, if you are a Machine Learning Scientist with experience, please apply today! Applicants must be authorized to work in the U.S. **CyberCoders, Inc is proud to be an Equal Opportunity Employer** All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or any other characteristic protected by law. **Your Right to Work** – In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification document form upon hire. *Machine Learning Scientist - NLP, Recommender/Ranking Systems* *WA-Bellevue* *GK2-1493004*
          Data Architect/Data Science      Cache   Translate Page      
CA-SAN JOSE, Role : Data Architect/Data Science Location : San Jose California Duration : 6+ Months Expert programming skills in Python, R Experience in writing code for various Machine learning algorithms for classification, clustering, forecasting, regression, Neural networks and Deep Learning Hands-on experience with modern enterprise data architectures and data toolsets (ex: data warehouse, data marts, dat
          L'intelligence artificielle pour les nuls      Cache   Translate Page      

Apprendre à créer des systèmes d'intelligence artificielle avec un simple niveau de mathématiques scolaire est à la portée de qui veut The Economist Ces cinq dernières années, les chercheurs en intelligence artificielles sont devenus les rock stars de la technologie. Une branche de l’intelligence artificielle, le deep learning, ou apprentissage profond, utilise des réseaux neuronaux […]

L’article L'intelligence artificielle pour les nuls est apparu en premier sur Le nouvel Economiste.

          Precision farming startup Taranis gets $20M Series B for its crop-monitoring tech      Cache   Translate Page      
Taranis, an ag-tech startup that uses aerial scouting and deep learning to identify potential crop issues, announced today that it has raised a $20 million Series B led by Viola Ventures. Existing investors Nutrien (one of the world’s largest fertilizer producers), Wilbur-Ellis venture capital arm Cavallo Ventures and Sumitomo Corporation Europe also participated. Tel Aviv-based […]
          Scientifique en apprentissage profond / Deep Learning Scientist - Huawei Canada - Montréal, QC      Cache   Translate Page      
Located in Hong Kong, Shenzhen, Beijing, London, Paris, Montreal, Toronto and Edmonton, Noah’s Ark Lab is the flagship AI research lab of Huawei Technologies....
From Huawei Canada - Thu, 11 Oct 2018 23:46:40 GMT - View all Montréal, QC jobs
          Data Elixir - Issue 207      Cache   Translate Page      

In the News

Harvard Converts Millions of Legal Documents into Open Data

Inspired by the Google Books Project, the new Caselaw Access Project from the Library Innovation Lab at Harvard puts the entire corpus of published U.S. case law online for anyone to access for free. The project involved scanning and digitizing 100,000 pages per day over two years. This is a big deal that will enable new analytical insights, research, and applications.

⚽ How data analysis helps football clubs make better signings

They said it could never be done. The game was too fluid, too chaotic. The players’ movements too difficult to track reliably. But, decades after sports like baseball first embraced statistics, football - known as soccer in the U.S. - is starting to play the data game.

Sponsored Link

10 Guidelines for A/B Testing

Online experimentation, or A/B testing, is the gold standard for measuring the effectiveness of changes to a website. But while A/B testing can appear simple, there are many issues that can complicate an analysis. In this presentation, Emily Robinson, data scientist at DataCamp, will cover 10 best practices that will help you avoid common pitfalls.

Tools and Techniques

Importance of Skepticism in Data Science

Great discussion about one of the most important aspects of analyzing data - being skeptical of the results. Includes lots of useful examples.

Scaling Machine Learning at Uber with Michelangelo

In 2015, machine learning was not widely used at Uber. Just three years later, Uber has advanced capabilities and infrastructure, and hundreds of production machine learning use-cases. This post describes the wide variety of ways that Uber uses machine learning and how they've managed to scale their systems so quickly and effectively.

Why Jupyter is data scientists’ computational notebook of choice

Last week's article in Nature about Jupyter Notebooks sparked a great discussion in Hacker News. There's a lot here, including tips, debate, and links to further resources.

Grokking Deep Learning

Andrew Trask's new book, Grokking Deep Learning aims to be the easiest introduction possible to deep learning. Each section teaches how to build a neural component from scratch in NumPy. This repo contains the code examples for each lesson.

Bringing machine learning research to product commercialization

Rasmus Rothe, Founder at Merantix, explores the differences between academia and industry when applying deep learning to real-world problems. This article goes into detail about differences regarding workflow, expectations, performance, model design and data requirements.

Find A Data Science Job Through Vettery

Vettery specializes in tech roles and is completely free for job seekers. Interested? Submit your profile, and if accepted onto the platform, you can receive interview requests directly from top companies growing their data science teams.

// sponsored


Data Science With R Workflow Cheatsheet

Nice map of R cheatsheets that's organized around common workflows. It's like a cheatsheet for cheatsheets.

Python Learning Resources

Rachel Thomas from asked for Python learning recommendations on Twitter and the resulting thread was amazing. These two recommendations, in particular, stand out:


My Weaknesses as a Data Scientist

Identifing your weaknesses is one of most important things you can do to become effective in your career. In this post, William Koehrsen explores his particular weaknesses as a data scientist and the steps he's taking to overcome them. The approach he models here may be uncomfortable for some but it's a super effective strategy.

Jobs & Careers


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

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More data science jobs >>


Data Elixir is curated and maintained by @lonriesberg. For additional finds from around the web, follow Data Elixir on Twitter, Facebook, or Google Plus.

This RSS feed is published on You can also subscribe via email.

          Robot arm assists with PET/CT-guided needle biopsies      Cache   Translate Page      
By combining a robotic arm-assisted needle navigation system with PET/CT, researchers...Read more on AuntMinnie.comRelated Reading: FDG-PET/CT fails for peripheral T-cell lymphoma outcomes FDG-PET/CT helps stage men with breast cancer, too PET/CT key to NIH's tuberculosis research plan PET/CT makes case for directing cervical cancer treatment Can deep learning monitor lesions on F-18 NaF PET/CT? (Source: Headlines)
          DDN to showcase World’s Fastest Storage and more at SC18      Cache   Translate Page      

Today DDN announced it will showcase a number of new innovations optimized for HPC, AI and hybrid cloud at SC18 in Dallas. Designed, optimized and right-sized for commercial HPC, AI, deep learning (DL), and exascale computing, DDN’s new products and solutions are fully integrated for any data-at-scale need. “DDN continues to advance powerful new storage solutions, and we’re excited to see its latest offerings at SC18 next week.”

The post DDN to showcase World’s Fastest Storage and more at SC18 appeared first on insideHPC.

          Deep Learning AI diagnosed Alzheimer’s 6-years earlier than conventional methods      Cache   Translate Page      
One of the most important things for combating Alzheimer’s disease is early diagnosis so treatments for the condition can start before damage is severe. The earlier interventions start, the better the outcome for the person suffering from the condition. A new study was published to the medical journal Radiology has found that early prediction for Alzheimer’s disease later in life … Continue reading
          Deep Learning Solution Architect - NVIDIA - Santa Clara, CA      Cache   Translate Page      
NVIDIA is widely considered to be one of the technology world’s most desirable employers. 5+ years delivering Enterprise Accelerated Computing (HPC, Deep...
From NVIDIA - Tue, 06 Nov 2018 01:54:48 GMT - View all Santa Clara, CA jobs
          AMD Unveils World's First 7nm Datacenter GPUs with PCIe 4.02 Interconnect      Cache   Translate Page      
AMD unveiled the world's first lineup of 7nm GPUs for the datacenter that will utilize an all new version of the ROCM open software platform for accelerated computing. "The AMD Radeon Instinct MI60 and MI50 accelerators feature flexible mixed-precision capabilities, powered by high-performance compute units that expand the types of workloads these accelerators can address, including a range of HPC and deep learning applications." They are specifically designed to tackle datacenter workloads such as rapidly training complex neural networks, delivering higher levels of floating-point performance, while exhibiting greater efficiencies. The new "Vega 7nm" GPUs are also the world's first GPUs to support the PCIe 4.02 interconnect which is twice as fast as other x86 CPU-to-GPU interconnect technologies and features AMD Infinity Fabric Link GPU interconnect technology that enables GPU-to-GPU communication that is six times faster than PCIe Gen 3. The AMD Radeon Instinct MI60 Accelerator is also the world's fastest double precision PCIe accelerator with 7.4 TFLOPs of peak double precision (FP64) performance. "Google believes that open source is good for everyone," said Rajat Monga, engineering director, TensorFlow, Google. "We've seen how helpful it can be to open source machine learning technology, and we're glad to see AMD embracing it. With the ROCm open software platform, TensorFlow users will benefit from GPU acceleration and a more robust open source machine learning ecosystem." ROCm software version 2.0 provides updated math libraries for the new DLOPS; support for 64-bit Linux operating systems including CentOS, RHEL and Ubuntu; optimizations of existing components; and support for the latest versions of the most popular deep learning frameworks, including TensorFlow 1.11, PyTorch (Caffe2) and others. Discussion
          Balbix Earns Acclaim from Frost & Sullivan for its Risk-Based Vulnerability Management Platform, BreachControl™      Cache   Translate Page      

SANTA CLARA, Calif., Nov. 7, 2018 /PRNewswire/ -- Based on its recent analysis of the North American vulnerability management market, Frost & Sullivan recognizes Balbix, Inc. with the 2018 North American Technology Innovation Award for leveraging advancements in deep learning (DL),...

          Symantec Acquisitions Further AI Strategy      Cache   Translate Page      

Symantec this week moved this week to expand its portfolio of security offerings by acquiring Appthority and Javelin Networks as part of a larger artificial intelligence (AI) strategy being driven by a combination of internal organic research and development and inorganic acquisitions.

Sri Sundaralingam, head of product marketing for enterprise security products at Symantec, said the acquisitions of Appthority and Javelin Networks will advance an AI security strategy dubbed Integrated Cyber Defense Platform, which feeds data collected from various endpoints into a set of cloud-based security intelligence applications based on machine and deep learning algorithms.

Recent Articles By Author

Check Point Acquires Dome9 to Advance Cloud Security FireEye Focuses on Email Security Analysis with Free Offering McAfee Report Points to Cyber Collusion Between China, North Korea
Symantec Acquisitions Further AI Strategy

Appthority is a provider of mobile application security analysis software, while Javelin Networks provides software that prevents cybercriminals from exploiting Microsoft Active Directory software to better target their attacks. Symantec intends to rebrand these offerings and incorporate them into a larger cybersecurity AI framework, Sundaralingam said.

The rise of AI is driving a wave of vendor consolidation across the IT security industry, he noted, as smaller IT security vendors discover they are not able to collect enough data on their own to drive AI models. Those vendors either will be acquired by larger companies that can collect enough data to drive those AI models or they will need to find a way to add value by participating in a larger ecosystem by taking advantage of open application programming interfaces (APIs) to share and access data, he said.

Signature and behavioral-based approaches to cybersecurity will no longer suffice,Sundaralingam said, noting the tactics and scale of the attacks being launched require a more real-time response that’s only possible when employing machine and deep learning algorithms.

In fact, the need to rely on AI to provide that level of response has made endpoint security more relevant than ever, he added. Not only are cybercriminals launching targeted attacks such as ransomware against endpoints, but the data collected by endpoint security tools as they combat those attacks informs the rest of cybersecurity frameworks on how to identify and respond better to that specific type of attack.

Sundaralingam said the IT security industry is still in the early to mid-stage of AI adoption. No one expects AI to replace the need for cybersecurity experts anytime soon. But given the shortage of cybersecurity expertise, there is a need to employ machines to augment the capabilities of cybersecurity personnel, who are already overtaxed. That’s especially critical as cybersecurity criminals employ machine and deep learning algorithms to further their own nefarious aims.

There may never be such a thing as perfect security. But the current state of security leaves much to desired. As the number of applications deployed by an organization increases, the risks associated with a cybersecurity breach exponentially increase. The only way to reduce those odds going forward is to rely more on AI to level a playing field that today tips in favor of the attacker.

          Micron's bet: Quad-level cell NAND SSDs will finally replace HDDs      Cache   Translate Page      
The company's Micron 5210 ION enterprise SATA SSD is now generally available and aimed at artificial intelligence, machine learning, deep learning and other intensive workloads.

          Hyundai invests in to bring deep learning to our cars      Cache   Translate Page      
The startup is researching ways for vehicle vision to tackle real-world problems.

          Machine Learning Engineer / Algorithm 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
          Scientifique en apprentissage profond / Deep Learning Scientist - Huawei Canada - Montréal, QC      Cache   Translate Page      
Located in Hong Kong, Shenzhen, Beijing, London, Paris, Montreal, Toronto and Edmonton, Noah’s Ark Lab is the flagship AI research lab of Huawei Technologies....
From Huawei Canada - Thu, 11 Oct 2018 23:46:40 GMT - View all Montréal, QC jobs
          Machine Learning Engineer / Algorithm 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
          Mesh-TensorFlow: Deep Learning for Supercomputers. (arXiv:1811.02084v1 [cs.LG])      Cache   Translate Page      

Authors: Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman

Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters. We introduce Mesh-TensorFlow, a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the "batch" dimension, in Mesh-TensorFlow, the user can specify any tensor-dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A Mesh-TensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce. We use Mesh-TensorFlow to implement an efficient data-parallel, model-parallel version of the Transformer sequence-to-sequence model. Using TPU meshes of up to 512 cores, we train Transformer models with up to 5 billion parameters, surpassing state of the art results on WMT'14 English-to-French translation task and the one-billion-word language modeling benchmark. Mesh-Tensorflow is available at .

          Classification of 12-Lead ECG Signals with Bi-directional LSTM Network. (arXiv:1811.02090v1 [cs.CV])      Cache   Translate Page      

Authors: Ahmed Mostayed, Junye Luo, Xingliang Shu, William Wee

We propose a recurrent neural network classifier to detect pathologies in 12-lead ECG signals and train and validate the classifier with the Chinese physiological signal challenge dataset (this http URL). The recurrent neural network consists of two bi-directional LSTM layers and can train on arbitrary-length ECG signals. Our best trained model achieved an average F1 score of 74.15% on the validation set.

Keywords: ECG classification, Deep learning, RNN, Bi-directional LSTM, QRS detection.

          Simple, Distributed, and Accelerated Probabilistic Programming. (arXiv:1811.02091v1 [stat.ML])      Cache   Translate Page      

Authors: Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous

We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single abstraction---the random variable. Our lightweight implementation in TensorFlow enables numerous applications: a model-parallel variational auto-encoder (VAE) with 2nd-generation tensor processing units (TPUv2s); a data-parallel autoregressive model (Image Transformer) with TPUv2s; and multi-GPU No-U-Turn Sampler (NUTS). For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips. With NUTS, we see a 100x speedup on GPUs over Stan and 37x over PyMC3.

          DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. (arXiv:1811.02114v1 [q-bio.QM])      Cache   Translate Page      

Authors: Ingoo Lee, Jongsoo Keum, Hojung Nam

Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors are shown to be not informative enough to predict accurate DTIs. Thus, in this study, we employ a convolutional neural network (CNN) on raw protein sequences to capture local residue patterns participating in DTIs. With CNN on protein sequences, our model performs better than previous protein descriptor-based models. In addition, our model performs better than the previous deep learning model for massive prediction of DTIs. By examining the pooled convolution results, we found that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches.

          Modeling and Predicting Popularity Dynamics via Deep Learning Attention Mechanism. (arXiv:1811.02117v1 [cs.SI])      Cache   Translate Page      

Authors: Sha Yuan, Yu Zhang, Jie Tang, Huawei Shen, Xingxing Wei

An ability to predict the popularity dynamics of individual items within a complex evolving system has important implications in a wide range of domains. Here we propose a deep learning attention mechanism to model the process through which individual items gain their popularity. We analyze the interpretability of the model with the four key phenomena confirmed independently in the previous studies of long-term popularity dynamics quantification, including the intrinsic quality, the aging effect, the recency effect and the Matthew effect. We analyze the effectiveness of introducing attention model in popularity dynamics prediction. Extensive experiments on a real-large citation data set demonstrate that the designed deep learning attention mechanism possesses remarkable power at predicting the long-term popularity dynamics. It consistently outperforms the existing methods, and achieves a significant performance improvement.

          Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures. (arXiv:1811.02130v1 [cs.SD])      Cache   Translate Page      

Authors: Prem Seetharaman, Gordon Wichern, Jonathan Le Roux, Bryan Pardo

Separating an audio scene into isolated sources is a fundamental problem in computer audition, analogous to image segmentation in visual scene analysis. Source separation systems based on deep learning are currently the most successful approaches for solving the underdetermined separation problem, where there are more sources than channels. Traditionally, such systems are trained on sound mixtures where the ground truth decomposition is already known. Since most real-world recordings do not have such a decomposition available, this limits the range of mixtures one can train on, and the range of mixtures the learned models may successfully separate. In this work, we use a simple blind spatial source separation algorithm to generate estimated decompositions of stereo mixtures. These estimates, together with a weighting scheme in the time-frequency domain, based on confidence in the separation quality, are used to train a deep learning model that can be used for single-channel separation, where no source direction information is available. This demonstrates how a simple cue such as the direction of origin of source can be used to bootstrap a model for source separation that can be used in situations where that cue is not available.

          In-the-wild Facial Expression Recognition in Extreme Poses. (arXiv:1811.02194v1 [cs.CV])      Cache   Translate Page      

Authors: Fei Yang, Qian Zhang, Chi Zheng, Guoping Qiu

In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.

          CarePre: An Intelligent Clinical Decision Assistance System. (arXiv:1811.02218v1 [cs.HC])      Cache   Translate Page      

Authors: Zhuochen Jin, Jingshun Yang, Shuyuan Cui, David Gotz, Jimeng Sun, Nan Cao

Clinical decision support systems (CDSS) are widely used to assist with medical decision making. However, CDSS typically require manually curated rules and other data which are difficult to maintain and keep up-to-date. Recent systems leverage advanced deep learning techniques and electronic health records (EHR) to provide more timely and precise results. Many of these techniques have been developed with a common focus on predicting upcoming medical events. However, while the prediction results from these approaches are promising, their value is limited by their lack of interpretability. To address this challenge, we introduce CarePre, an intelligent clinical decision assistance system. The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his/her historical medical records. The system includes an interactive framework together with intuitive visualizations designed to support the diagnosis, treatment outcome analysis, and the interpretation of the analysis results. We demonstrate the effectiveness and usefulness of CarePre system by reporting results from a quantities evaluation of the prediction algorithm and a case study and three interviews with senior physicians.

          Deep Learning Solution Architect - NVIDIA - Santa Clara, CA      Cache   Translate Page      
NVIDIA is widely considered to be one of the technology world’s most desirable employers. 5+ years delivering Enterprise Accelerated Computing (HPC, Deep...
From NVIDIA - Tue, 06 Nov 2018 01:54:48 GMT - View all Santa Clara, CA jobs
          Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks. (arXiv:1811.02290v1 [q-bio.NC])      Cache   Translate Page      

Authors: Qi Yan, Yajing Zheng, Shanshan Jia, Yichen Zhang, Zhaofei Yu, Feng Chen, Yonghong Tian, Tiejun Huang, Jian K. Liu

Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the visual system in neuroscience. However, it is still not clear what are learned by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNN with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex. Here we address this issue by focusing on single retinal ganglion cells with biophysical models and recording data from animals. By training CNNs with white noise images to predict neuronal responses, we found that fine structures of the retinal receptive field can be revealed. Specifically, convolutional filters learned are resembling biological components of the retinal circuit. This suggests that a CNN learning from one single retinal cell reveals a minimal neural network carried out in this cell. Furthermore, when CNNs learned from different cells are transferred between cells, there is a diversity of transfer learning performance, which indicates that CNNs are cell-specific. Moreover, when CNNs are transferred between different types of input images, here white noise v.s. natural images, transfer learning shows a good performance, which implies that CNN indeed captures the full computational ability of a single retinal cell for different inputs. Taken together, these results suggest that CNN could be used to reveal structure components of neuronal circuits, and provide a powerful model for neural system identification.

          An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding. (arXiv:1811.02353v1 [eess.SP])      Cache   Translate Page      

Authors: Xian-Rui Zhang, Meng-Ying Lei, Yang Li

Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG) is one of most commonly used brain signals and EEG recognition is an important part of BCI system. Recently, convolutional neural networks (ConvNet) in deep learning are becoming the new cutting edge tools to tackle the problem of EEG recognition. However, training an effective deep learning model requires a big number of data, which limits the application of EEG datasets with a small number of samples. In order to solve the issue of data insufficiency in deep learning for EEG decoding, we propose a novel data augmentation method that add perturbations to amplitudes of EEG signals after transform them to frequency domain. In experiments, we explore the performance of signal recognition with the state-of-the-art models before and after data augmentation on BCI Competition IV dataset 2a and our local dataset. The results show that our data augmentation technique can improve the accuracy of EEG recognition effectively.

          Micro-Attention for Micro-Expression recognition. (arXiv:1811.02360v1 [cs.CV])      Cache   Translate Page      

Authors: Chongyang Wang, Min Peng, Tao Bi, Tong Chen

Micro-expression, for its high objectivity in emotion detection, has emerged to be a promising modality in affective computing. Recently, deep learning methods have been successfully introduced into micro-expression recognition areas. Whilst the higher recognition accuracy achieved with deep learning methods, substantial challenges in micro-expression recognition remain. Issues with the existence of micro expression in small-local areas on face and limited size of databases still constrain the recognition accuracy of such facial behavior. In this work, to tackle such challenges, we propose novel attention mechanism called micro-attention cooperating with residual network. Micro-attention enables the network to learn to focus on facial area of interest. Moreover, coping with small datasets, a simple yet efficient transfer learning approach is utilized to alleviate the overfitting risk. With an extensive experimental evaluation on two benchmarks (CASMEII, SAMM), we demonstrate the effectiveness of proposed micro-attention and push the boundary of automatic recognition of micro-expression.

          Multi-Level Sensor Fusion with Deep Learning. (arXiv:1811.02447v1 [cs.CV])      Cache   Translate Page      

Authors: Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux, Frédéric Jurie

In the context of deep learning, this article presents an original deep network, namely CentralNet, for the fusion of information coming from different sensors. This approach is designed to efficiently and automatically balance the trade-off between early and late fusion (i.e. between the fusion of low-level vs high-level information). More specifically, at each level of abstraction-the different levels of deep networks-uni-modal representations of the data are fed to a central neural network which combines them into a common embedding. In addition, a multi-objective regularization is also introduced, helping to both optimize the central network and the unimodal networks. Experiments on four multimodal datasets not only show state-of-the-art performance, but also demonstrate that CentralNet can actually choose the best possible fusion strategy for a given problem.

          Double Adaptive Stochastic Gradient Optimization. (arXiv:1811.02525v1 [stat.ML])      Cache   Translate Page      

Authors: Kin Gutierrez, Jin Li, Cristian Challu, Artur Dubrawski

Adaptive moment methods have been remarkably successful in deep learning optimization, particularly in the presence of noisy and/or sparse gradients. We further the advantages of adaptive moment techniques by proposing a family of double adaptive stochastic gradient methods~\textsc{DASGrad}. They leverage the complementary ideas of the adaptive moment algorithms widely used by deep learning community, and recent advances in adaptive probabilistic algorithms.We analyze the theoretical convergence improvements of our approach in a stochastic convex optimization setting, and provide empirical validation of our findings with convex and non convex objectives. We observe that the benefits of~\textsc{DASGrad} increase with the model complexity and variability of the gradients, and we explore the resulting utility in extensions of distribution-matching multitask learning.

          Interpretation of Neural Networks is Fragile. (arXiv:1710.10547v2 [stat.ML] UPDATED)      Cache   Translate Page      

Authors: Amirata Ghorbani, Abubakar Abid, James Zou

In order for machine learning to be deployed and trusted in many applications, it is crucial to be able to reliably explain why the machine learning algorithm makes certain predictions. For example, if an algorithm classifies a given pathology image to be a malignant tumor, then the doctor may need to know which parts of the image led the algorithm to this classification. How to interpret black-box predictors is thus an important and active area of research. A fundamental question is: how much can we trust the interpretation itself? In this paper, we show that interpretation of deep learning predictions is extremely fragile in the following sense: two perceptively indistinguishable inputs with the same predicted label can be assigned very different interpretations. We systematically characterize the fragility of several widely-used feature-importance interpretation methods (saliency maps, relevance propagation, and DeepLIFT) on ImageNet and CIFAR-10. Our experiments show that even small random perturbation can change the feature importance and new systematic perturbations can lead to dramatically different interpretations without changing the label. We extend these results to show that interpretations based on exemplars (e.g. influence functions) are similarly fragile. Our analysis of the geometry of the Hessian matrix gives insight on why fragility could be a fundamental challenge to the current interpretation approaches.

          Learning Restricted Boltzmann Machines via Influence Maximization. (arXiv:1805.10262v2 [cs.LG] UPDATED)      Cache   Translate Page      

Authors: Guy Bresler, Frederic Koehler, Ankur Moitra, Elchanan Mossel

Graphical models are a rich language for describing high-dimensional distributions in terms of their dependence structure. While there are algorithms with provable guarantees for learning undirected graphical models in a variety of settings, there has been much less progress in the important scenario when there are latent variables. Here we study Restricted Boltzmann Machines (or RBMs), which are a popular model with wide-ranging applications in dimensionality reduction, collaborative filtering, topic modeling, feature extraction and deep learning.

The main message of our paper is a strong dichotomy in the feasibility of learning RBMs, depending on the nature of the interactions between variables: ferromagnetic models can be learned efficiently, while general models cannot. In particular, we give a simple greedy algorithm based on influence maximization to learn ferromagnetic RBMs with bounded degree. In fact, we learn a description of the distribution on the observed variables as a Markov Random Field. Our analysis is based on tools from mathematical physics that were developed to show the concavity of magnetization. Our algorithm extends straighforwardly to general ferromagnetic Ising models with latent variables.

Conversely, we show that even for a contant number of latent variables with constant degree, without ferromagneticity the problem is as hard as sparse parity with noise. This hardness result is based on a sharp and surprising characterization of the representational power of bounded degree RBMs: the distribution on their observed variables can simulate any bounded order MRF. This result is of independent interest since RBMs are the building blocks of deep belief networks.

          A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities. (arXiv:1807.01257v2 [cs.CV] UPDATED)      Cache   Translate Page      

Authors: Bo Zhou, Yuemeng Li, Jiangcong Wang

We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medical imaging data with region-level annotations, our model was merely trained on imaging data with image-level labels to classify diseases, and is able to identify abnormal image regions simultaneously. Our model consists of a customized pooling structure and an adaptive DenseNet front-end, which can effectively recognize possible disease features for classification and localization tasks. Our method has been validated on the publicly available ChestX-ray14 dataset. Experimental results have demonstrated that our classification and localization prediction performance achieved significant improvement over the previous models on the ChestX-ray14 dataset. In summary, our network can produce accurate disease classification and localization, which can potentially support clinical decisions.

          Aplican “deep learning” para detectar HLB en cítricos      Cache   Translate Page      
Investigadores del INTA Misiones utilizan técnicas de inteligencia artificial para identificar síntomas de la enfermedad y carencias nutricionales en hojas de plantas.
          Deep Learning Research Engineer Intern - TandemLaunch - Montréal, QC      Cache   Translate Page      
The project is in development phase, where we are implementing academic works and our patent portfolio into our technology....
From TandemLaunch - Thu, 11 Oct 2018 16:08:59 GMT - View all Montréal, QC jobs
          Senior iOS Engineer - Stealth Mode, Well-Funded AI Company!      Cache   Translate Page      
CA-San Mateo, If you are a Senior iOS Engineer with 3+ years of INDUSTRY experience, please read on! Situated in the heart of Silicon Valley, where innovation is at the forefront, we are a well-funded stealth mode startup located in San Mateo, CA! We are a company leveraging Deep Learning and Computer Vision to transcend the shopping experience. So much of our shopping is done online and yet, it lags behind. We
          AMD Radeon Instinct Mi50 și Mi60: primele acceleratoare grafice pe 7 nm      Cache   Translate Page      

Ieri AMD a dezvăluit primele acceleratoare grafice pe 7 nm. AMD Radeon Instinct Mi60 și Mi50, căci așa se numesc, sunt cele mai rapide GPU-uri pentru data centere. Nu sunt proiectate pentru gaming, ci pentru activități intensive, cum ar fi deep learning, computing și rendering. Sunt bazate pe arhitectura AMD Vega 20 și sunt realizate […]

Articolul AMD Radeon Instinct Mi50 și Mi60: primele acceleratoare grafice pe 7 nm apare prima dată în Arena IT.

          The Koch Brothers Are Watching You -- And New Documents Reveal Just How Much They Know      Cache   Translate Page      
Billionaire brothers have built personality profiles of most Americans, and use them to push right-wing propaganda

New documents uncovered by the Center for Media and Democracy show that the billionaire Koch brothers have developed detailed personality profiles on 89 percent of the U.S. population; and are using those profiles to launch an unprecedented private propaganda offensive to advance Republican candidates in the 2018 midterms.

The documents also show that the Kochs have developed persuasion models — like their "Heroin Model" and "Heroin Treatment Model" — that target voters with tailored messaging on select issues, and partner with cable and satellite TV providers to play those tailored messages during “regular” television broadcasts.

Over the last decade, big data and microtargeting have revolutionized political communications. And the Kochs, who are collectively worth $120 billion, now stand at the forefront of that revolution — investing billions in data aggregation, machine learning, software engineering and Artificial Intelligence optimization.

In modern elections, incorporating AI into voter file maintenance has become a prerequisite to producing reliable data. The Kochs’ political data firm, i360 states that it has “been practicing AI for years. Our team of data scientists uses components of Machine learning, Deep Learning and Predictive Analytics, every day as they build and refine our predictive models.”

Thanks to that investment (and the Supreme Court’s campaign finance rulings that opened the floodgates for super PACs), the Koch network is better positioned than either the Democratic Party or the GOP to reach voters with their individually tailored communications.

That is a dangerous development, with potentially dramatic consequences for our democracy.

The Kochs and i360

The Kochs formally entered the data space nine years ago, developing the “Themis Trust” program for the 2010 midterms — an uncommonly impactful election cycle where Republican operatives executed their REDMAP program and algorithmically gerrymandered congressional maps across the country in their favor.

In 2011, the Kochs folded Themis into a data competitor it acquired, i360 LLC, which was founded by Michael Palmer, the former chief technology officer of Sen. John McCain’s 2008 presidential campaign. Palmer still leads the organization.

Back then, as journalists Kenneth Vogel and Mike Allen documented, the Kochs’ long-term funding commitments to i360 allowed the organization to think bigger than their political competitors.

“Right now, we’re talking about and building things that you won’t see in 2016, because it’s not going to be ready until 2018,” Michael Palmer said in the wake of the 2014 midterm cycle.

Those programs are now operational. And according to a successful GOP campaign manager, i360 is the “best in the business” at providing Republicans with voter data.

i360’s client list reflects that data superiority. The country’s most notorious and effective political spenders, like the National Rifle Association, use the platform to identify and influence voters, as do Republican party committees, and U.S. House and Senate campaigns.

(A full list of i360’s clients is available here. Some clients, like the Republican Party of Wisconsin, have multiple sub-campaigns they run. It is also important to note that many Koch political groups, like Americans for Prosperity and the Libre Initiative, signed data sharing agreements with i360 in 2016 that are most likely still in effect.)

i360 sweetens the deal to its clients by offering its services at below-market rates. And once clients are locked into the i360 platform, they have access to the company’s voter file — the beating heart of modern political campaigns.

Conservatives agree that the Kochs are subsidizing i360. The losses they sustain by undercharging clients, however, are a pittance compared to the down-stream public policy returns and political power the Kochs receive from operating what amounts to a shadow political party in the United States — one that vigilantly guards the fossil fuel subsidies, deregulatory schemes, and regressive tax structures that enable Koch Industries to bring in $115 billion annually in private revenue.

Inside the i360 Voter File

i360’s voter file identifies “more than 199 million active voters and 290 million U.S. consumers,” and provides its users with up to 1,800 unique data points on each identified individual.

As a result, i360 and the Kochs know your vitals, ethnicity, religion, occupation, hobbies, shopping habits, political leanings, financial assets, marital status and much more.

They know if you enjoy fishing — and if you do, whether you prefer salt or fresh water. They know if you have bladder control difficulty, get migraines or have osteoporosis. They know which advertising mediums (radio, TV, internet, email) are the most effective. For you.

i360 has the following attribute tags, among hundreds of others, ranked 1-10, or subdivided otherwise in their voter file.

Here’s an example of an i360 attribute tag and code name, using a 1-10 value scale:

But i360 attribute codes are not limited to that 1-10 scale. Their knowledge of your financial standing is granular, from how much equity you have in your home to your net wealth and expendable income.

They know where you live, what your mortgage status is and even how many bathrooms are in your house.

i360 has also created a set of 70 “clustercodes” to humanize its data for campaign operatives. These categories range from “Faded Blue Collars” to “Meandering Millennials,” and have flamboyant descriptions that correspond with their attribute headings.

Here are some examples:

Koch Persuasion Models

Additionally, i360 has developed a series of persuasion models for its voter file. These models are often regionally sensitive — since voters have regional concerns — and are being used in federal elections and down-ballot races to assist Republicans across the country.

In 2016, i360 created a set of regional models while working with Sen. Rob Portman’s 2016 re-election campaign in Ohio. Portman started out the race polling nine points behind his Democratic opponent, Gov. Ted Strickland, but ultimately won with 58 percent of the vote.

The company developed a model that could predict whether a voter supported Portman or Strickland with 89 percent accuracy, and others that predicted voter policy preferences. Well aware of the 2016 landscape, i360 also made a Trump/Clinton model, an Anti-Hillary model, and a Ticket Splitter model.

Much of i360’s success in the race, however, was linked to understanding (after conducting extensive polling) that a “key local issue facing Ohio was the opioid epidemic.” In response, the company created a “heroin model” and a “heroin treatment model” that were particularly effective at convincing voters to support Portman.

When describing how they employed their “heroin model,” i360 was clear that Portman’s “position” on the crisis depended on the voter, emphasizing health care solution communications for some, and criminal justice solution communications for others.

Here is i360 on the subject:

…the issue of opioid abuse was particularly complex in that it was relatively unknown whether it was considered a healthcare issue or a criminal justice issue. The answer to this would dictate the most effective messaging. In addition, this was a particularly personal issue affecting some voters and not others.

By leveraging two predictive models — the Heroin model identifying those constituents most likely to have been affected by the issue of opioid abuse and the Heroin Treatment model determining whether those individuals were more likely to view the issue as one of healthcare or of criminal justice — the campaign was able to effectively craft their messaging about Senator Portman’s extensive work in the Senate to be tailored to each individual according to their disposition on the topic.

This manipulation of the opioid crisis for political gain has a perverse irony given the Kochs’ long-running work to provide corporate interests, including health care and pharmaceutical interests, with undue political power and influence over public policy decisions. The Kochs have gifted over a million dollars to ALEC, for example, an organization that counts Purdue Pharma — the unconscionable manufacturer of OxyContin — as a member.

The company also stated it joined Portman’s campaign 21 months before the election, and that, “Together, i360 and the campaign strategized a plan to execute one of the most custom-targeted, integrated campaigns to date with a focus on getting the right message to the right voter wherever that might be.”

This is notable because during the 2016 election, i360 also ran $11.7 million worth of “independent” expenditures for the National Rifle Association Political Victory Fund, Freedom Partners Action Fund, and Americans for Prosperity in Portman’s race.

These outside spenders, two of which are Koch-funded groups, and Portman’s campaign all used i360 to coordinate their digital marketing, phone banks and television ad buys, in the same market, in the same election.

Additionally, i360 supplied Portman’s campaign with other issue-based models on gun control, gay marriage and abortion that the company continues to supply to its clients in 2018.

Here are some examples of i360’s issue-based models:

The list goes on, but the structure stays the same. The Kochs are tailoring their advertising to you, because they know nearly everything about you.

          AMD annonce les Radeon Instinct MI60 & MI50, des GPU Vega 20 gravés en 7 nm, embarquant jusqu'à 32 Go HBM2 et optimisés pour le Deep Learning      Cache   Translate Page      
AMD annonce les Radeon Instinct MI60 & MI50, des GPU Vega 20 gravés en 7 nm
Embarquant jusqu'à 32 Go HBM2 et optimisés pour le Deep Learning

À l'occasion de sa conférence Next Horizon qui s'est tenue hier à San Francisco, la société Advanced Micro Devices (AMD) a levé le voile sur sa nouvelle architecture pour processeurs x86 baptisée Zen 2 qui va succéder à Zen+. Les processeurs qui bénéficieront en premier de cette nouvelle architecture devraient faire leur apparition dès l'an prochain dans...
          Deep Learning AI diagnosed Alzheimer’s 6-years earlier than conventional methods      Cache   Translate Page      
One of the most important things for combating Alzheimer’s disease is early diagnosis so treatments for the condition can start before damage is severe. The earlier interventions start, the better the outcome for the person suffering from the condition. A new study was published to the medical journal Radiology has found that early prediction for Alzheimer’s disease later in life … Continue reading
          AI may not suffice to analyse data across multiple health systems      Cache   Translate Page      

[USA], Nov 7 (ANI): Researchers have observed that artificial intelligence (AI) tools trained to detect pneumonia using chest X-rays suffered significant decreases in performance when tested on data from outside health systems.

According to a study conducted at the Icahn School of Medicine and published in a special issue of PLOS Medicine, these findings suggest that AI in the medical space must be carefully tested for performance across a wide range of populations; otherwise, the deep learning models may not perform as accurately as expected.

As interest in the use of computer system frameworks called convolution neural networks (CNN) to analyse medical imaging and provide a computer-aided diagnosis grows, recent studies have suggested that AI image classification may not generalise to new data as well as commonly portrayed.

Researchers assessed how AI models identified pneumonia in 158,000 chest X-rays across three medical institutions: the National Institutes of Health; The Mount Sinai Hospital; and Indiana University Hospital. They chose to study the diagnosis of pneumonia on chest X-rays for its common occurrence, clinical significance, and prevalence in the research community.

In three out of five comparisons, CNN's performance in diagnosing diseases on X-rays from hospitals outside of its own network was significantly lower than on X-rays from the original health system. However, CNNs were able to detect the hospital system where an X-ray was acquired with a high degree of accuracy and cheated at their predictive task based on the prevalence of pneumonia at the training institution.

Researchers found that the difficulty of using deep learning models in medicine is that they use a massive number of parameters, making it challenging to identify specific variables driving predictions, such as the types of CT scanners used at a hospital and the resolution quality of imaging.

"Our findings should give pause to those considering rapid deployment of artificial intelligence platforms without rigorously assessing their performance in real-world clinical settings reflective of where they are being deployed," said senior author Eric Oermann, MD. "Deep learning models trained to perform medical diagnosis can generalise well, but this cannot be taken for granted since patient populations and imaging techniques differ significantly across institutions."(ANI)

          Azure Data Architecture Guide – Blog #3: Advanced analytics and deep learning      Cache   Translate Page      
We'll continue to explore the Azure Data Architecture Guide with our third blog entry in this series. The previous entries for this blog series are: Azure Data Architecture Guide – Blog #1: Introduction Azure Data Architecture Guide – Blog #2: On-demand big data analytics Like the previous post, we'll work from a technology implementation seen directly in...
          דרושים פרילנסרים לייעוץ בתחום ה- DEEP LEARNING      Cache   Translate Page      
דרוש/ה יועץ/ת לפרויקט בתחום ה- Deep Learning.תיאור התפקיד:חניכה וליווי בפרויקט העוסק ב-People tracking, זיהוי מסלול ודמויות, מיפוי אנשים בסרטון/תמונה.*לא מדובר בביצוע הפרויקט עצמו אלא בליווי וחניכה של אנשי המקצוע המבצעים את הפרויקט.דרישות:ניסיון ב- Deep Learning/Computer Vision.חובה ניסיון קודם ב- People tracking.שליטה ב- Python.
          Comment on Deep Learning AI diagnosed Alzheimer’s 6-years earlier than conventional methods by German Romero      Cache   Translate Page      
This is great progress, at least people will know when to start taking treatments for this disease. No doubt as AI continues developing it will soon find by itself the opportunities to eliminate this disease for good.
          Comment on Deep Learning AI diagnosed Alzheimer’s 6-years earlier than conventional methods by Johnny_Fever      Cache   Translate Page      
Ahh, this is real nice, you find out 6 years ahead of time that your brain will be mush in 5-7 years- what good does that do? It's like saying you'll have cancer in 10 years, but we don't have any treatment to stop it? We need a concentrated "1960's Space Program" style all-out scientific research program to not only ID brain destructive diseases, and cancer, but many more unique choices in how to get rid of them once they occur, and if detected to stop them from developing down the road. To me, this article is the same as a "Bridge Out Ahead" sign on a road, but you still know 6 miles down the road that your car is going to drive off into a river?
          G. Carucci - Deep Learning from zero to hero      Cache   Translate Page      
          R. Lancellotti, G. Di Brino - Deep Learning in Computer Vision      Cache   Translate Page      
          U. Cakmak - Recent advancements in NLP and Deep Learning: A Quant's Perspective      Cache   Translate Page      
          New mobile device identifies airborne allergens using deep learning      Cache   Translate Page      
UCLA researchers invented a portable device that uses holograms and machine learning to measure bioaerosols from living organisms like plants or mold.
           【预告】“智能图形计算前沿进展与应用”讲习班开始报名       Cache   Translate Page      


第8期CSIG图像图形学科前沿讲习班(Advanced Lectures on Image and Graphics,简称IGAL)于2018年11月17日-18日在杭州举办,本期讲习班的主题为“智能图形计算前沿进展与应用”,由浙江大学周昆教授任学术主任,邀请智能图形计算及相关领域的知名专家作报告,使学员在了解学科前沿,提高学术水平的同时,增强与国内外顶尖学者的学术交流。








周昆,教育部长江学者特聘教授,国家杰出青年科学基金获得者,国际电气电子工程师协会会士(IEEE Fellow),现任浙江大学计算机辅助设计与图形学国家重点实验室主任。2002年获浙江大学工学博士学位,2002至2008年就职于微软亚洲研究院,2008年回到浙江大学工作。研究领域包括计算机图形学、人机交互、虚拟现实和并行计算。近年来在ACM/IEEE汇刊上发表论文80余篇,获得发明专利40余项。现(曾)担任《IEEE TVCG》、《ACM TOG》、《The Visual Computer》、《Frontiers of ComputerScience》、《中国科学:信息科学》、《计算机研究与发展》等期刊编委,担任《IEEE Spectrum》编辑顾问委员会委员,担任中国图象图形学学会智能图形专委会主任和中国人工智能学会智能交互专委会副主任。曾获得2010年中国计算机图形学杰出奖、2011年中国青年科技奖、2011年麻省理工学院《技术评论》全球杰出青年创新奖(MIT TR35 Award)、2013年国家自然科学二等奖、2016年陈嘉庚青年科学奖、2017年浙江省自然科学一等奖。








Charlie C.L.Wang


报告题目:Geometric Computing for Multi-Axis Additive Manufacturing

摘要:In this talk,I will present our recent development of using multi-axis motion to conduct material accumulation along dynamically varied directions.Our development results in two approaches that mainly focus on how to avoid the additional supporting structures in a framework of volume-to-surface and then surface-to-curve decomposition.I will discuss a few future extensions of this framework so that models can be printed faster and in a more accurate way.


Tien-Tsin Wong


报告题目:Learning for Graphics When Training Data is Scarce

摘要:Deep learning has been demonstrated to be an effective tool for solving many problems that are ambiguous in nature.It outperforms many tailormade solutions and offers stable results,in a real-time speed.It seems to be an ultimate solution for many hard problems.However,its major drawback is its high dependency on training data,because it transforms the problem from“method”to“data.”In many cases,training data is scarce or hard/impractical to obtain.

In this talk,I will talk about a few learning-for-graphics projects we have done in the past few years at CUHK.In some of these projects,we face the problem of lacking groundtruth training data.We have worked on computational manga for many years.A key problem we want to solve since the beginning is to remove the screentones that manga artists typically used to enrich the manga.Removing the screentones can significantly ease the digital migration from paper to digital platform.However,the definition of screentones by itself is ambiguous and cannot be easily defined by mathematical equations.More importantly,we do not have the unscreened manga as groundtruth,as it is prohibitively expensive to manually trace the structural lines from legacy manga.We shall describe how we overcome the problem of data scarcity.I will discuss how to extend our strategy to other applications such as sketch colorization,and also unsupervised learning framework for invertible grayscale application,in this talk.
















讲座嘉宾将介绍其近两年基于深度神经网络学习风格迁移(style transfer)的一系列科研成果。首先介绍如何设计深度神经网络学习语义信息和艺术风格,然后将其艺术风格根据语义应用到新的照片,视频,VR/AR中;其次介绍一些相关的扩展应用包括照片颜色转换(比如白天变黑夜、夏季变冬季),人像化妆迁移,黑白照片上色等。














2018年11月16日(含)前注册并缴费:CSIG会员1600元/人,非会员报名同时加入CSIG 2000元/人(含1年会员费);现场缴费:会员、非会员均为3000元/人;CSIG团体会员参加,按CSIG会员标准缴费;同一单位组团(5人及以上)报名,均按CSIG会员标准缴费。



















          How to install Ubuntu 16.04 and Lambda Stack on a TensorBook (Gen 2)      Cache   Translate Page      

Many deep learning teams have software that depends on Ubuntu 16.04 (and not 18.04). However, the installation process for 16.04 has some quirks with the TensorBook. This tutorial walks you through the entire process of installing 16.04 from scratch with Lambda Stack.

Download a version of the Ubuntu 16.04 ISO that is 16.04.5 or greater. Burn the ISO onto a USB drive. For a tutorial on this see here: Insert the USB drive into the TensorBook and boot off of it by pressing [Del] during your boot sequence. Select Install Ubuntu in the GRUB menu and hit [e] . Add the following text to the end of the linux line acpi_os_name=Linuxacpi_osi= acpi_backlight=vendor modprobe.blacklist=nouveau . Hit [F10] to boot. Perform a normal installation of Ubuntu 16.04.5. Install Lambda Stack with this command: LAMBDA_REPO=$(mktemp) && \ wget -O${LAMBDA_REPO} && \ sudo dpkg -i ${LAMBDA_REPO} && rm -f ${LAMBDA_REPO} && \ sudo apt-get update && sudo apt-get install -y lambda-stack-cuda

That's it! You should now have a working TensorBook installed with Ubuntu 16.04.5 and Lambda Stack.

          Medical News Today: Alzheimer's: Artificial intelligence predicts onset      Cache   Translate Page      
A deep learning algorithm trained to analyze brain scans accurately predicted who would develop Alzheimer's more than 6 years before diagnosis.
          Comment on This week’s poll: AI in healthcare by Richard H      Cache   Translate Page      
A recent BBC programme cast doubt on the use of AI for diagnostics. Horizon 2018: 12. Diagnosis on Demand? The Computer Will See You Now Could a machine replace your doctor? Dr Hannah Fry explores the incredible ways AI is revolutionising healthcare - and what this means for all of us. This film chronicles the inside story of the AI health revolution, as one company, Babylon Health, prepare for a man vs machine showdown. Can Babylon succeed in their quest to prove their AI can outperform human doctors at safe triage and accurate diagnosis? Artificial intelligence is starting to transform healthcare beyond recognition - and tech companies large and small see almost limitless commercial opportunity. The ultimate vision is for accessible, affordable, better healthcare for almost everyone with a phone. In Britain this is already radically changing how some of us see our GPs. And in a world with a chronic shortage of doctors, but where even the very poor own mobile phones, it could be truly revolutionary. To witness this revolution from the inside, this film has privileged, behind-the-scenes access to ambitious British tech start-up Babylon Health, whose CEO Dr Ali Parsa declares with complete conviction 'we're going to do with healthcare what Google did with information.' Babylon launched its GP at Hand app in London in late 2017 and has already persuaded 30,000 Londoners to quit their old GPs to register instead for this NHS 'digital first' service, where patients discuss symptoms with an AI chatbot and see a doctor in minutes 24/7 via their phone. But GP at Hand's arrival has proved controversial - with many traditional GPs worried about the disruptive consequences for them and their patients, and others seeking to thwart its expansion nationwide. As this film reveals, there is a fundamental culture clash at play - between the 'move fast and break things' world of tech, and the cautious, diligent, often slow-moving world of medical science. So how will both camps respond when Babylon's AI attempts to pass the diagnostic sections of the Royal College of GPs exam? Amazingly, the NHS is today the largest purchaser of fax machines in the world - and the British government are eagerly embracing AI as the remedy for our public health system's antiquated inefficiencies. British health secretary Matt Hancock is an unabashed evangelist for tech - boasting Babylon's GP at Hand as his GP. Yet some scientists are increasingly alarmed, questioning the current hype and asking where is the proof that AI health apps, now in widespread use, are effective and safe. How should they be evaluated and regulated? And what needs to happen before we all trust our health to AI? As well as following a tumultuous year inside Babylon, both in the UK and Rwanda, the film also explores how another British AI Health start-up, Kheiron Medical, has successfully used deep learning to train its AI to detect breast cancer and now outperforms human radiologists at spotting the tell-tale signs of cancer in mammograms.
          Deep Learning Research Engineer Intern - TandemLaunch - Montréal, QC      Cache   Translate Page      
The project is in development phase, where we are implementing academic works and our patent portfolio into our technology....
From TandemLaunch - Thu, 11 Oct 2018 16:08:59 GMT - View all Montréal, QC jobs
          New SightLine Software Release 2.25 Introduces Deep Learning Classification for C-UAS Systems      Cache   Translate Page      

SightLine Applications recently launched its latest software release, 2.25, which improves existing functions and adds appealing new features to their onboard...

The post New SightLine Software Release 2.25 Introduces Deep Learning Classification for C-UAS Systems appeared first on Inside Unmanned Systems.

          Applied Math Seminar, Nov 15      Cache   Translate Page      
A deeper understanding of the principles of deep learning can consolidate and boost its already-spectacular empirical success. I will introduce some of the recent progress in the theory of deep learning, including some of my own work. We will discuss the core ML issues, such as optimization, generalization, and expressivity, and their rich interactions, in the contexts of supervised learning with (deep) non-linear models.
          Artificial intelligence predicts Alzheimer's      Cache   Translate Page      

Artificial intelligence (AI) technology improves the ability of brain imaging to predict Alzheimer's disease. Early diagnosis of Alzheimer's disease has proven to be challenging.

Research has linked the progression of Alzheimer's disease to changes in metabolism, as shown by glucose uptake in certain regions of the brain, but these changes can be difficult to recognize.

Implications for Patient Care
  • A deep learning algorithm can be used to improve the accuracy of predicting the diagnosis of Alzheimer disease from fluorine 18 fluorodeoxyglucose PET of the brain.
  • A deep learning algorithm can be used as an early prediction tool for Alzheimer disease, especially in conjunction with other biochemical and imaging tests, thereby providing an opportunity for early therapeutic intervention.

How to Test Your Memory for Alzheimer's and Dementia (5 Best Tests)

By Alzheimer's Reading Room

To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither.

"If we diagnose Alzheimer's disease when all the symptoms have manifested, the brain volume loss is so significant that it's too late to intervene,"

"If we can detect Alzheimer's earlier, this could lead to better ways to slow down or even halt the disease process."

What is the Difference Between Alzheimer’s and Dementia

Dr. Benjamin Franc, University of California in San Francisco (UCSF), was interested in applying deep learning, a type of Artificial Intelligence in which machines learn by example much like humans do, to find changes in brain metabolism predictive of Alzheimer's disease.

Subscribe to the Alzheimer's Reading Room

"Differences in the pattern of glucose uptake in the brain are very subtle and diffuse. People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process." said study co-author Jae Ho Sohn
  • Researchers trained the deep learning algorithm on a special imaging technology known as 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET).
  • In an FDG-PET scan, FDG, a radioactive glucose compound, is injected into the blood. PET scans can then measure the uptake of FDG in brain cells, an indicator of metabolic activity.

The researchers had access to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a major multi-site study focused on clinical trials to improve prevention and treatment of this disease.

The ADNI dataset included more than 2,100 FDG-PET brain images from 1,002 patients.

Researchers trained the deep learning algorithm on 90 percent of the dataset and then tested it on the remaining 10 percent of the dataset. Through deep learning, the algorithm was able to teach itself metabolic patterns that corresponded to Alzheimer's disease.

4 Memory Systems of the Brain and Dementia

Finally, the researchers tested the algorithm on an independent set of 40 imaging exams from 40 patients that it had never studied.
  • The algorithm achieved 100 percent sensitivity at detecting the disease an average of more than six years prior to the final diagnosis.

"We were very pleased with the algorithm's performance. It was able to predict every single case that advanced to Alzheimer's disease." ~ Dr.Jae Ho Sohn.

He did caution that their independent test set was small and needs further validation with a larger multi-institutional prospective study,

Dr. Sohn said that the algorithm could be a useful tool to complement the work of radiologists--especially in conjunction with other biochemical and imaging tests--in providing an opportunity for early therapeutic intervention of Alzheimer's.
  • Future research directions include training the deep learning algorithm to look for patterns associated with the accumulation of beta-amyloid and tau proteins, abnormal protein clumps and tangles in the brain that are markers specific to Alzheimer's disease, according to UCSF's Youngho Seo, Ph.D., who served as one of the faculty advisers of the study.
"If FDG-PET with AI can predict Alzheimer's disease this early, beta-amyloid plaque and tau protein PET imaging can possibly add another dimension of important predictive power," he said.

By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis.

Related Articles

Alzheimer's Clock Draw Test -- Detect the Signs of Alzheimer's Early

Can An Undetected Urinary Tract Infection Can Kill an Alzheimer's Patient

Alzheimer's Care Using the Brain to Create Happiness

Learn More from Our Award Winning Knowledge Base - Topics Pages

My mom has dementia and is mean

How do you talk and communicate effectively with a dementia patient

How to live with someone who has Alzheimer's

The Award Winning Alzheimer’s Reading Room Knowledge Base is considered to be the highest quality, deepest collection, of information on Alzheimer’s and dementia in the world. Ranked #1 by Healthline for 7 straight years (2012-2018).

Need Help? Search Our Award Winning Knowledge Base for Answers to Your Questions About Alzheimer's and Dementia

Originally published in the Alzheimer's Reading Room


"A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain". Radiology, 2018; 180958 DOI: 10.1148/radiol.2018180958"

Drs. Sohn, Franc, and Seo and Ms. Ding were Michael G. Kawczynski, M.S., Hari Trivedi, M.D., Roy Harnish, M.S., Nathaniel W. Jenkins, M.S., Dmytro Lituiev, Ph.D., Timothy P. Copeland, M.P.P., Mariam S. Aboian, M.D., Ph.D., Carina Mari Aparici, M.D., Spencer C. Behr, M.D., Robert R. Flavell, M.D., Ph.D., Shih-Ying Huang, Ph.D., Kelly A. Zalocusky, Ph.D., Lorenzo Nardo, Ph.D., Randall A. Hawkins, M.D., Ph.D., Miguel Hernandez Pampaloni, M.D., Ph.D., and Dexter Hadley, M.D., Ph.D.
          25: Sexy Data Science and its Analysis of IoT      Cache   Translate Page      

See the complete show analysis notes at:

First it was Big Data and now it’s the Internet of Things; the science of data is becoming increasingly sexy, maybe not Victoria’s Secret sexy but it certainly get the juices flowing for business leaders in the know. Hot or not? Definitely hot. In this episode of the IoT Business Show I speak with Ajit Jaokar about his passion, data science, and the application of machine learning, deep learning and predictive analytics in IoT. 


Read the rest of the show analysis notes at:


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