We want to take AI and CIFAR to wonderful new places, where no person, no student, no program has gone before. — Geoffrey Hinton Copy Share Image
Early AI was mainly based on logic. You're trying to make computers that reason like people. The second route is from biology:… — Geoffrey Hinton Copy Share Image
Now that neural nets work, industry and government have started calling neural nets AI. And the people in AI who spent all… — Geoffrey Hinton Copy Share Image
Most people in AI, particularly the younger ones, now believe that if you want a system that has a lot of knowledge… — Geoffrey Hinton Copy Share Image
Irony is going to be hard to get. You have to be master of the literal first. But then, Americans don't get irony either.… — Geoffrey Hinton Copy Share Image
My main interest is in trying to find radically different kinds of neural nets. — Geoffrey Hinton Copy Share Image
We want to take AI and CIFAR to wonderful new places, where no person, no student, no program has gone before. — Geoffrey Hinton Copy Share Image
In deep learning, the algorithms we use now are versions of the algorithms we were developing in the 1980s, the 1990s. People were very… — Geoffrey Hinton Copy Share Image
The role of radiologists will evolve from doing perceptual things that could probably be done by a highly trained pigeon to doing far more… — Geoffrey Hinton Copy Share Image
I get very excited when we discover a way of making neural networks better - and when that's closely related to how the brain… — Geoffrey Hinton Copy Share Image
Deep learning is already working in Google search and in image search; it allows you to image-search a term like 'hug.' It's used to… — Geoffrey Hinton Copy Share Image
In A.I., the holy grail was how do you generate internal representations. — Geoffrey Hinton Copy Share Image
As soon as you have good mechanical technology, you can make things like backhoes that can dig holes in the road. But of course… — Geoffrey Hinton Copy Share Image
