Geoffrey Hinton

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

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.

Computers will understand sarcasm before Americans do.

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.

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.

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.

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.

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

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.

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.

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.

In deep learning, the algorithms we use now are versions of the algorithms we were developing in the 1980s, the 1990s. People were very optimistic about them, but it turns out they didn't work too well.

Backhoes can save us a lot of digging. But of course, you can misuse it.

I think we should think of AI as the intellectual equivalent of a backhoe. It will be much better than us at a lot of things.

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.

As soon as you have good mechanical technology, you can make things like backhoes that can dig holes in the road. But of course a backhoe can knock your head off. But you don't want to not develop a backhoe because it can knock your head off, that would be regarded as silly.

I think people need to understand that deep learning is making a lot of things, behind-the-scenes, much better. Deep learning is already working in Google search, and in image search; it allows you to image search a term like \'hug.\'

You look at these past predictions like there's only a market in the world for five computers [as allegedly said by IBM founder Thomas Watson] and you realize it's not a good idea to predict too far into the future.

I refuse to say anything beyond five years because I don't think we can see much beyond five years.

In the brain, you have connections between the neurons called synapses, and they can change. All your knowledge is stored in those synapses. You have about 1,000-trillion synapses - 10 to the 15, it's a very big number.

To deal with a 14-dimensional space, visualize a 3-D space and say 'fourteen' to yourself very loudly. Everyone does it.