The Verge: A pioneering scientist explains ‘deep learning’

The Verge: A pioneering scientist explains ‘deep learning’. “Buzzwords like ‘deep learning’ and ‘neural networks’ are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies. Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution (out next week from MIT Press). He argues that the hype about killer AI or robots making us obsolete ignores exciting possibilities happening in the fields of computer science and neuroscience, and what can happen when artificial intelligence meets human intelligence.”

TechCrunch: DARPA wants to teach and test ‘common sense’ for AI

TechCrunch: DARPA wants to teach and test ‘common sense’ for AI . “It’s a funny thing, AI. It can identify objects in a fraction of a second, imitate the human voice and recommend new music, but most machine ‘intelligence’ lacks the most basic understanding of everyday objects and actions — in other words, common sense. DARPA is teaming up with the Seattle-based Allen Institute for Artificial Intelligence to see about changing that.”

Forbes: A Two-Minute Guide To Artificial Intelligence

Forbes: A Two-Minute Guide To Artificial Intelligence. “If you keep hearing about artificial intelligence but aren’t quite sure what it means or how it works, you’re not alone. There’s been much confusion among the general public about the term, not helped by dramatic news stories about how ‘AI’ will destroy jobs, or companies that overstate their abilities to ‘use AI.’ A lot of that confusion comes from the misuse of terms like AI and machine learning. “

The Register: Microsoft open-sources Infer.NET AI code just in time for the weekend

The Register: Microsoft open-sources Infer.NET AI code just in time for the weekend . “The sharing of Microsoft’s toys continued today with the open-sourcing of its model-based machine-learning framework, Infer.NET. A team at Microsoft’s research centre in Cambridge, UK, kicked off development of the framework in 2004, and it was released for academic use in 2008. In Microsoft’s brave new world of AI, the technology has found itself evolving into a machine-learning engine and creeping into Office and Azure as well as gaming applications on Xbox.”

EurekAlert: Educating the next generation of medical professionals with machine learning is essential

EurekAlert: Educating the next generation of medical professionals with machine learning is essential. “Artificial intelligence (AI) driven by machine learning (ML) algorithms is a branch in the field of computer science that is rapidly gaining popularity within the healthcare sector. However, graduate medical education and other teaching programs within academic teaching hospitals across the U.S. and around the world have not yet come to grips with educating students and trainees on this emerging technology.”

Georgia Tech: New Visualization Tool Helps Non-Experts Understand Neural Networks

Georgia Tech: New Visualization Tool Helps Non-Experts Understand Neural Networks. “‘Visual analytics helps people make sense of complex systems that use large data and discover insights by effectively visualizing them and let people interact with them,’ said Minsuk (Brian) Kahng, a Ph.D. student in the School of Computational Science and Engineering (CSE). Kahng has recently been working alongside Associate Professor Polo Chau to build visualization tools for deep learning models, with an emphasis on public accessibility. Two examples of his work are ActiVis, a visualization system for industry-scale deep neural network models that is deployed at Facebook, and the newly released GAN Lab, which is now available to the public online.”

Engadget: AI can identify objects based on verbal descriptions

Engadget: AI can identify objects based on verbal descriptions. “Modern speech recognition is clunky and often requires massive amounts of annotations and transcriptions to help understand what you’re referencing. There might, however, be a more natural way: teaching the algorithms to recognize things much like you would a child. Scientists have devised a machine learning system that can identify objects in a scene based on their description. Point out a blue shirt in an image, for example, and it can highlight the clothing without any transcriptions involved.”