Words matter. These are the best Geoffrey Hinton Quotes, and they’re great for sharing with your friends.
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.
Humans are still much better than computers at recognizing speech.
My main interest is in trying to find radically different kinds of neural nets.
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.
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 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 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.’
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.
We want to take AI and CIFAR to wonderful new places, where no person, no student, no program has gone before.
The NSA is already bugging everything that everybody does. Each time there’s a new revelation from Snowden, you realise the extent of it.
In a sensibly organised society, if you improve productivity, there is room for everybody to benefit.
I feel slightly embarrassed by being called ‘the godfather.’
In the brain, you have connections between the neurons called synapses, and they can change. All your knowledge is stored in those synapses.
The pooling operation used in convolutional neural networks is a big mistake, and the fact that it works so well is a disaster.
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.
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.
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.
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.
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 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.
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 long run, curiosity-driven research just works better… Real breakthroughs come from people focusing on what they’re excited about.
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.
Making everything more efficient should make everybody happier.
Computers will understand sarcasm before Americans do.
My view is we should be doing everything we can to come up with ways of exploiting the current technology effectively.
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 A.I., the holy grail was how do you generate internal representations.
I have a Reagan-like ability to believe in my own data.
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.
