To get greater efficiency, Google DeepMind's researchers did what chip designers have long done, built a pipeline so that the learning rule for machine learning -- backpropagation -- is more efficient.
Scientists at Germany's Technische Universität Darmstadt have developed a procedure for a human domain expert to look at the inner workings of an AI model as it is trained to solve a simple problem, and to correct where the machine goes wrong. Can it work for more complex problems?
Google’s latest refinement to the popular “Transformer” language model promises to make state of the art available to those with a smaller computer budget. But the advances could just as well be used with those commanding vast compute resources to squeeze even more performance out of the biggest machines.
Luminaries of AI such as Yoshua Bengio gathered in Vancouver last month to champion ways machine learning can mitigate global warming. Despite energy, enthusiasm, and brilliant ideas, the work may still need lots of regulation to make sure greenhouse gas emissions are reduced.
Every year, a new crop of stuff at the Consumer Electronics Show is crowned the "best of." Some of it lasts, a lot of it is forgotten and discontinued within years. A review of recent years' highlights is a cautionary tale not to take what happens in Vegas too seriously.
After almost seven years at the helm of data integration specialists Talend S.A., Mike Tuchen is passing the baton to a fifteen-year veteran of financial software maker Concur, Cristal Bemont. It's part of the company's pivot to go from roughly a quarter billion in annual revenue to an eventual goal of one billion annually, said Tuchen in an interview with ZDNet.
Scholars at the high-profile Institute for Humanity propose a scheme in which companies agree to pay back to society a portion of excess profits produced by AI, to compensate for lost jobs. Can it be gamed by corporations is one of the questions that springs to mind.
Google's Google Health and its DeepMind unit, along with London's Imperial College, report in this week's Nature magazine about how a trio of deep-learning networks can in some cases best human radiologists in reading a mammogram. But the fine print shows we're not yet at the point of replacing radiologists.
Yoshua Bengio and Gary Marcus held a debate in Montreal on Monday about the future of artificial intelligence. Then they held another debate on Medium and Facebook about what the term "deep learning" means.
An historic debate between two of the artificial intelligence illuminati was mostly simpatico on the big questions -- creating hybrid systems of AI, finding the right "priors" for knowledge -- but it was also punctuated by sharp differences on some of the details.