Sebastian Thrun Quotes

One of the interesting applications of symbolic systems is artificial intelligence, and I spent some time thinking about how to create a brain that operates the way ours does.

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.

I became interested in AI in high school because I read 'Goedel, Escher, Bach,' a book by Douglas Hofstader. He showed how all their work in some ways fit together, and he talked about artificial intelligence. I thought 'Wow, this is what I want to be doing.'

If you wanted to design a robot that could learn as well as it possibly could, you might end up with something that looked a lot like a 3-year-old.

Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition.

The computer is not, in our opinion, a good model of the mind, but it is as the trumpet is to the orchestra - you really need it. And so, we have very massive simulations in computers because the problem is, of course, very complex.

I don't think there's anything unique about human intelligence.

The thing that's going to make artificial intelligence so powerful is its ability to learn, and the way AI learns is to look at human culture.

In order to be truly intelligent, computers must understand - that is probably the critical word.

Machine learning is the science of getting computers to learn without being explicitly programmed.

Watson augments human decision-making because it isn't governed by human boundaries. It draws together all this information and forms hypotheses, millions of them, and then tests them with all the data it can find. It learns over time what data is reliable, and that's part of its learning process.

Artificial intelligence is one of 50 things that Watson does. There is also machine learning, text-to-speech, speech-to-text, and different analytical engines - they're like little Lego bricks. You can put intelligence in any product or any process you have.

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 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.

We often attribute 'understanding' and other cognitive predicates by metaphor and analogy to cars, adding machines, and other artifacts, but nothing is proved by such attributions.

The computing world is very good at things that we are not. It is very good at memory.

I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.

I think what's really amazing is that given the scale of the web and getting the compute power we have today, we're starting to see things that appear intelligent but actually aren't semantically intelligent.

In fact, there was general agreement that minds can exist on nonbiological substrates and that algorithms are of central importance to the existence of minds.

We wanted to solve robot problems and needed some vision, action, reasoning, planning, and so forth. We even used some structural learning, such as was being explored by Patrick Winston.

My mission at Google is to develop natural language understanding with a team and in collaboration with other researchers at Google.

By 2020, most home computers will have the computing power of a human brain. That doesn't mean that they are brains, but it means that in terms of raw processing, they can process bits as fast as a brain can. So the question is, how far behind that is the development of a machine that's as smart as we are?

I am suggesting that we recognize that in network and interface research there is something as profound (and potential wild) as Artificial Intelligence.

People are good at intuition, living our lives. What are computers good at? Memory.

We could construct a machine that is more intelligent than we can understand. It's possible Google is that kind of thing already. It scales so fast.