Geoffrey Hinton Quotes

I'm fascinated by artificial intelligence.

Note that I am not proposing that AI research be ignored or less funded.

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.

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.

Today, there are industries employing hundreds of thousands of engineers creating artificial intelligence.

Artifical Intellegence is the most important of the coming technologies. Whoever can crack that will have a company worth 10 Microsofts.

The deep learning techniques, while relatively easy to learn, are quite foreign to traditional engineering modalities. It takes a different mindset and a relaxation of the presumption of control. The practitioners are like magi, sequestered from the rest of a typical engineering process.

Even companies like Baidu and Google, which have amazing AI teams, cannot do all the work needed to get us to an AI-powered society. I thought the best way to get us there would be creating courses to welcome more people to deep learning.

Having been trained as a computer scientist in the '90s, everybody knew that AI didn't work. People tried it. They tried neural nets, and none of it worked.

Google Brain, which I led, was arguably the single biggest force for turning Google into a great A.I. company. I'm pretty sure I led the team that transformed Baidu as well. So one thing that really excites me is the potential for other companies to become great A.I. companies.

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.

The tools and technologies we've developed are really the first few drops of water in the vast ocean of what AI can do.

I often tell my students not to be misled by the name 'artificial intelligence' - there is nothing artificial about it. AI is made by humans, intended to behave by humans, and, ultimately, to impact humans' lives and human society.

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.

I'd like to make a fundamental impact on one of the most exciting, intelligent questions of all time. Can we use software and hardware to build intelligence into a machine? Can that machine help us solve cancer? Can that machine help us solve climate change?

Once you acknowledge that human brains are basically made of atoms and acknowledge that atoms are governed by simple laws of physics, then there is no reasoning principle why computers couldn't do anything that people are doing, and we don't really see any evidence that this is not the case.

I want to build the next CNN with 'VICE' - it's within my grasp.

It's going to be interesting to see how society deals with artificial intelligence, but it will definitely be cool.

AI does not keep me up at night. Almost no one is working on conscious machines. Deep learning algorithms, or Google search, or Facebook personalization, or Siri or self driving cars or Watson, those have the same relationship to conscious machines as a toaster does to a chess-playing computer.

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.

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.

When you apply computer science and machine learning to areas that haven't had any innovation in 50 years, you can make rapid advances that seem really incredible.