Behind the Boom in Machine Learning


Terry Sejnowski, computational neuroscientist at the Salk Institute for Biological Studies, president of the Neural Information Processing System (NIPS) Foundation and project co-leader for the MMBioS TR&D2 project, was interviewed at the NIPS Conference in December about growth in machine learning. 

Machine learning is a core technology driving advances in artificial intelligence. This week, some of its earliest practitioners and many of the world's top AI researchers are in Long Beach, CA, for the field's big annual gathering—the Neural Information Processing Systems (NIPS) conference. In all, some 7,700 people are to attend AI's version of high tech's glitzy South by Southwest conference, and the electronic device industry's even bigger annual CES conference.

It's NIPS' 31st year in what originally drew just a few hundred participants — computer scientists, physicists, mathematicians and neuroscientists all interested in AI. Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies and president of the NIPS Foundation, spoke with Axios about growth in the field and what's next.

How machine learning has grown since NIPS' start in the '80s: "Over that period what happened was a convergence of a number of different factors, one of them being the fact that computers got a million times faster. Back then we could only study little toy networks with a few hundred units. But now we can study networks with millions of units. The other thing was the training sets — you need to have examples of what it is you're trying to learn. The internet made it possible for us to get millions of training examples relatively easily, because there's so many images, abundant speech examples, and so forth, that you can download from the internet. Finally, there were breakthroughs along the way in the algorithms that we used to make them more efficient. We understood them a lot better in terms of something called regularization, which is how to keep the network from memorizing — you want it to generalize."

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