Famed Yale professor David Gelernter and investment banker Rob Rosenthal field questions about Revolution Populi, their startup effort to build a blockchain-based alternative to Facebook. Will the desire to control our personal information prompt us to abandon the giants that control today's Internet?
Chip executives gathered in San Francisco to discuss what to do about the explosion in demand for deep learning forms of A.I. that is pushing at the limits of today’s chips. Possibilities include analog computing, optical circuits, and computing in memory, but it’s not clear if any of that will head off a potential warehouse-scale-computing bottleneck.
How to deal with a breakdown in theoretical support in machine learning? Researchers from Carnegie Mellon and Facebook describe winning many hands against the world’s top poker players by inventing smart search strategies to counter a lack of theoretical math used in most game-playing AI.
Retail can be a game, a game that Indian online apparel retailer Myntra is learning to play better by predicting people's propensity to return what's in their shopping cart before they purchase, and using rewards and punishments to block returns.
Researchers at Heidelberg's Ruprecht-Karls-Universität, the Max-Planck Institute for Intelligent Systems, and Montreal's prestigious MILA develop a neural network model that computes whether states of affairs are reversible or irreversible. They hope it adds up to giving a computer a sense of the "arrow of time."
Neuromorphic computing has had little practical success in building machines that can tackle standard tests such as logistic regression or image recognition. But work by prominent researchers is combining the best of machine learning with simulated networks of spiking neurons, bringing new hope for neuromorphic breakthroughs.
Machine learning, especially deep learning, is forcing a re-evaluation of how chips and systems are designed that will change the direction of the industry for decades to come.
To do machine learning right, some take the approach of getting in the middle of a big problem and hoping to amass the data to train the network. That's the bet of startup Owlcam, whose $349 camera and service offering claims to have received millions of videos from its customers, building its database of understanding of what happens on the road.
After 10 years of ImageNet, AI researchers are digging into the details of test sets and some are asking just how much knowledge has really been created with machine learning.
Carnegie Mellon and Google's Brain outfit have tried to undo some of the techniques of Google's BERT machine learning model for natural language processing. They propose a new approach called "XLNet." Built on top of the popular "Transformer" A.I. for language, it may be a more straightforward way to examine how language works.