Steve Jurvetson Quotes

If you step back a little and say we want to do A.I., then you will realize that A.I. needs knowledge, reasoning, and explanation.

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.

I'm fascinated by artificial intelligence.

If you want to solve very complex problems, you will have to end up letting machines work out a lot of the details for themselves, and in ways that we don't understand what they are doing.

Graphics has lately made a great shift towards machine learning, which itself is about understanding data.

The key to artificial intelligence has always been the representation.

Artificial intelligence is always 20 years away.

This is a magical time for computer programming. We are moving towards the artifical intelligence holy grail of programming the computer to simulate the human brain.

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.

Whatever you are studying right now, if you are not getting up to speed on deep learning, neural networks, etc., you lose. We are going through the process where software will automate software, automation will automate automation.

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

Many researchers are exploring other forms of AI, some of which have proved useful in limited contexts; there may well be a breakthrough that makes higher levels of intelligence possible, but there is still no clear path yet to this goal.

The revolution in deep nets has been very profound. It definitely surprised me, even though I was sitting right there.

Machine learning allows us to build software solutions that exceed human understanding and shows us how AI can innervate every industry.

With the Google Brain project, we made the decision to build deep learning processes on top of Google's existing infrastructure.

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.

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.

Deep learning is a subfield of machine learning, which is a vibrant research area in artificial intelligence, or AI.

Artificial Intelligence (AI) is the science of how to get machines to do the things they do in the movies.

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.

Interactive computers and software will, I think, provide a less costly method of doing some kinds of inquiry, in knowledge acquisition and even reasoning and interaction.

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.

My background really comes from geekdom and the idea of building a smart machine. If we're going to build a smart machine, let's have it read good books.

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.

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?