Most people in AI, particularly the younger ones, now believe that if you want a system that has a lot of knowledge in, like an amount of knowledge that would take millions of bits to quantify, the only way to… — Geoffrey Hinton Copy Share Image
We now think of internal representation as great big vectors, and we do not think of logic as the paradigm for how to get things to work. We just think you can have these great big neural nets that learn,… — 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
