Geoffrey Hinton may be the "godfather" of deep learning, a suddenly hot field of artificial intelligence, or AI – but that doesn't mean he's resting on his algorithms.
Hinton, a University Professor Emeritus at the University of Toronto, recently released two new papers that promise to improve the way machines understand the world through images or video – a technology with applications ranging from self-driving cars to making medical diagnoses.
“This is a much more robust way to detect objects than what we have at present,” Hinton, who is also a fellow at Google's AI research arm, said today at a tech conference in Toronto.
“If you’ve been in the field for a long time like I have, you know that the neural nets that we use now – there’s nothing special about them. We just sort of made them up.”
Hinton’s latest approach, detailed in a recent story in Wired magazine, relies on something he calls “capsule networks.” Here’s how it works: At present, deep learning algorithms must be trained on millions of images before they can reliably distinguish a picture of, say, a cat from something else. In part, that’s because the software isn’t very good at applying what it’s already learned to brand new situations – for example, recognizing a cat that’s being viewed from a slightly different angle. Capsule networks, by contrast, can help track the relationship between various parts of an object – in the case of a cat, one example might be the relative distance between its nose and mouth.
Hinton talked about his research, co-authored with Sara Sabour and department of computer science alumnus, Nicholas Frosst, at Google’s Go North conference, held at Toronto’s Evergreen Brick Works.
Read the Wired magazine story on Hinton's latest work
Read the full story at U of T News