A Pioneering Scientist Explains Deep Learning

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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.

The Verge spoke to Sejnkowski about how “deep learning” suddenly became everywhere, what it can and cannot do, and the problem of hype.

First, I’d like to ask about definitions. People throw around words like “artificial intelligence” and “neural networks” and “deep learning” and “machine learning” almost interchangeably. But these are different things — can you explain?

AI goes back to 1956 in the United States, where engineers decided they would write a computer program that would try to imitate intelligence. Within AI, a new field grew up called machine learning. Instead of writing a step-by-step program to do something — which is a traditional approach in AI — you collect lots of data about something that you’re trying to understand.

Read more at The Verge