Quotes from Geoffrey Hinton

Early AI was mainly based on logic. You're trying to make computers that reason like people. The second route is from biology: You're trying to make computers that can perceive and act and adapt like animals.

Computers will understand sarcasm before Americans do.

The paradigm for intelligence was logical reasoning, and the idea of what an internal representation would look like was it would be some kind of symbolic structure. That has completely changed with these big neural nets.

I have a Reagan-like ability to believe in my own data.

Once your computer is pretending to be a neural net, you get it to be able to do a particular task by just showing it a whole lot of examples.

I am betting on Google's team to be the epicenter of future breakthroughs.

In a sensibly organised society, if you improve productivity, there is room for everybody to benefit.

My view is we should be doing everything we can to come up with ways of exploiting the current technology effectively.

The brain sure as hell doesn't work by somebody programming in rules.

I got fed up with academia and decided I would rather be a carpenter.

My main interest is in trying to find radically different kinds of neural nets.

The brain has about ten thousand parameters for every second of experience. We do not really have much experience about how systems like that work or how to make them be so good at finding structure in data.

Any new technology, if it's used by evil people, bad things can happen. But that's more a question of the politics of the technology.

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. Computers are going to reach the level of Americans before Brits.

Most people at CMU thought it was perfectly reasonable for the U.S. to invade Nicaragua. They somehow thought they owned it.

Making everything more efficient should make everybody happier.

The pooling operation used in convolutional neural networks is a big mistake, and the fact that it works so well is a disaster.

I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain. That is the goal I have been pursuing. We are making progress, though we still have lots to learn about how the brain actually works.

In the long run, curiosity-driven research just works better... Real breakthroughs come from people focusing on what they're excited about.

I get very excited when we discover a way of making neural networks better - and when that's closely related to how the brain works.

My father was an entomologist who believed in continental drift. In the early '50s, that was regarded as nonsense. It was in the mid-'50s that it came back. Someone had thought of it 30 or 40 years earlier named Alfred Wegener, and he never got to see it come back.

I am scared that if you make the technology work better, you help the NSA misuse it more. I'd be more worried about that than about autonomous killer robots.

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, and so, instead of programming, you are just going to get them to learn everything.

In science, you can say things that seem crazy, but in the long run, they can turn out to be right. We can get really good evidence, and in the end, the community will come around.