Quotes from Geoffrey Hinton

In A.I., the holy grail was how do you generate internal representations.

I had a stormy graduate career, where every week we would have a shouting match. I kept doing deals where I would say, 'Okay, let me do neural nets for another six months, and I will prove to you they work.' At the end of the six months, I would say, 'Yeah, but I am almost there. Give me another six months.'

All you need is lots and lots of data and lots of information about what the right answer is, and you'll be able to train a big neural net to do what you want.

The role of radiologists will evolve from doing perceptual things that could probably be done by a highly trained pigeon to doing far more cognitive things.

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 getting you Smart Replies to your Gmail. It's in speech and vision. It will soon be used in machine translation, I believe.

I feel slightly embarrassed by being called 'the godfather.'

The NSA is already bugging everything that everybody does. Each time there's a new revelation from Snowden, you realise the extent of it.

Machines can do things cheaper and better. We're very used to that in banking, for example. ATM machines are better than tellers if you want a simple transaction. They're faster, they're less trouble, they're more reliable, so they put tellers out of work.

Take any old classification problem where you have a lot of data, and it's going to be solved by deep learning. There's going to be thousands of applications of deep learning.

Everybody right now, they look at the current technology, and they think, 'OK, that's what artificial neural nets are.' And they don't realize how arbitrary it is. We just made it up! And there's no reason why we shouldn't make up something else.

Humans are still much better than computers at recognizing speech.

Now that neural nets work, industry and government have started calling neural nets AI. And the people in AI who spent all their life mocking neural nets and saying they'd never do anything are now happy to call them AI and try and get some of the money.

The question is, can we make neural networks that are 1,000 times bigger? And how can we do that with existing computation?

In the brain, you have connections between the neurons called synapses, and they can change. All your knowledge is stored in those synapses.

We want to take AI and CIFAR to wonderful new places, where no person, no student, no program has gone before.