I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anandkumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.

The Deep Learning Glossary from NVIDIA The postulation of a principle of causality, "to every effect there is a cause," has been a continuing central problem for philosophy (Popper, 1972). Its role as a source of contention in modern science (Jauch, 1973) is epitomized by Einstein's remark that, "I can't believe that God plays dice." Many of the arguments about the application of the principle are very relevant to systems science and to problems of system identification and machine learning, on the one hand,and to epistemology and behavioural psychology, on the other. In current system science the theory of causal deterministic systems is most well developed and generally applied, while the theory of modeling with alternative structures, e.g., stochastic automata, indeterminate automata, products of asynchronous automata, etc., has not been developed to the same degree. Brian R. Gaines Hoy traemos a este espacio esta slideshare de NVidia, que nos presentan así: Learn the most important terminology from "A" to "Z" utilized in deep learning linked with resources for more in-depth exploration in our glossary.

Deep learning may need a new programming language that's more flexible and easier to work with than Python, Facebook AI Research director Yann LeCun said today. It's not yet clear if such a language is necessary, but the possibility runs against very entrenched desires from researchers and engineers, he said. LeCun has worked with neural networks since the 1980s. "There are several projects at Google, Facebook, and other places to kind of design such a compiled language that can be efficient for deep learning, but it's not clear at all that the community will follow, because people just want to use Python," LeCun said in a phone call with VentureBeat. "The question now is, is that a valid approach?"

AI algorithms can quickly detect and localize wrist fractures in X-ray images, which can augment the work of harried emergency physicians and radiologists. Missing a fracture on an emergency department radiograph is one of the most common causes of diagnostic errors and subsequent litigation. Such errors are due to clinical inexperience, distraction, fatigue, poor viewing conditions and time pressures. The study authors, from the National University of Singapore, hypothesized that automated analysis using artificial intelligence (AI) would be "invaluable" in reducing these misreadings and that an object detection convolutional neural network (CNN) would work better than other CNNs. Object detection CNNs are extensions of image classification models that not only recognize and classify objects on images, but also localize the position of each object.

Facebook Inc.'s chief AI researcher has suggested the company is working on a new class of semiconductor that would work very differently than most existing designs. Yann LeCun said that future chips used for training deep learning algorithms, which underpin most of the recent progress in artificial intelligence, would need to be able to manipulate data without having to break it up into multiple batches. Most existing computer chips, in order to handle the amount of data these machine learning systems need to learn, divide it into chunks and processes each batch in sequence. "We don't want to leave any stone unturned, particularly if no one else is turning them over," he said in an interview ahead of the release Monday of a research paper he authored on the history and future of computer hardware designed to handle artificial intelligence. Intel Corp. and Facebook have previously said they are working together on a new class of chip designed specifically for artificial intelligence applications.

Facebook's chief artificial intelligence (AI) researcher has suggested the company is working on a new class of semiconductor that would work very differently than most existing designs. Yann LeCun said that future chips used for training deep learning algorithms, which underpin most of the recent progress in artificial intelligence, would need to be able to manipulate data without having to break it up into multiple batches. Most existing computer chips, in order to handle the amount of data these machine learning systems need to learn, divide it into chunks and processes each batch in sequence. "We don't want to leave any stone unturned, particularly if no one else is turning them over," he said in an interview ahead of the release of a research paper he authored on the history and future of computer hardware designed to handle artificial intelligence. Intel and Facebook have previously said they are working together on a new class of chip designed specifically for artificial intelligence applications.

In the inaugural edition of TWiML Live, Sam Charrington is joined by Amanda Askell, Anima Anandkumar, Miles Bundage, Robert Munro, and Stephen Merity to discuss the recently released OpenAI Language Model, GPT-2.

We are in the midst of the Artificial Intelligence Revolution (AIR), the next major epoch in the history of technological innovation. Artificial intelligence (AI) is globally gaining momentum not only in scientific research, but also in business, finance, consumer, art, healthcare, esports, pop culture, and geopolitics. As AI becomes increasingly pervasive, it is important to examine at a macro level whether AI gains from disorder. Antifragile is a term and concept put forth by Nassim Nicholas Taleb, a former quantitative trader and self-proclaimed "flâneur" turned author of New York Times bestseller of "The Black Swan: The Impact of the Highly Improbable." Taleb describes antifragile as the "exact opposite of fragile" which is "beyond resilience or robustness" in "Antifragile: Things That Gain From Disorder."

A storm is brewing over a new language model, built by non-profit artificial intelligence research company OpenAI, which it says is so good at generating convincing, well-written text that it's worried about potential abuse. That's angered some in the community, who have accused the company of reneging on a promise not to close off its research. OpenAI said its new natural language model, GPT-2, was trained to predict the next word in a sample of 40 gigabytes of internet text. The end result was the system generating text that "adapts to the style and content of the conditioning text," allowing the user to "generate realistic and coherent continuations about a topic of their choosing." The model is a vast improvement on the first version by producing longer text with greater coherence.

In this paper, we demonstrate a computationally efficient new approach based on deep learning (DL) techniques for analysis, design, and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus, the computational complexity, without imposing considerable errors. By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique, we redefine the conventional many-to-one design problem in EM nanostructures into a one-to-one problem plus a much simpler many-to-one problem, which can be simply solved using an analytic formulation. This approach reduces the computational complexity in solving both the forward problem (i.e., analysis) and the inverse problem (i.e., design) by orders of magnitude compared to conventional approaches. In addition, it provides analytic formulations that, despite their complexity, can be used to obtain intuitive understanding of the physics and dynamics of EM wave interaction with nanostructures with minimal computation requirements. As a proof-of-concept, we applied such an efficacious method to design a new class of on-demand reconfigurable optical metasurfaces based on phase-change materials (PCM). We envision that the integration of such a DL-based technique with full-wave commercial software packages offers a powerful toolkit to facilitate the analysis, design, and optimization of the EM nanostructures as well as explaining, understanding, and predicting the observed responses in such structures.