Geoffrey Hinton Quotes

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.

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.

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.

There's a long history of artificial intelligence programs that try to mimic what the brain is doing, but they've all fallen short.

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.

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.

Understanding vision and building visual systems is really understanding intelligence.

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.

The real goal of AI is to understand and build devices that can perceive, reason, act, and learn at least as well as we can.

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

I'm working on artificial intelligence. Actually, natural language understanding, which is to get computers to understand the meaning of documents.

There are three basic approaches to AI: Case-based, rule-based, and connectionist reasoning.

None of us today know how to get computers to learn with the speed and flexibility of a child.

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.

Most of the value of deep learning today is in narrow domains where you can get a lot of data. Here's one example of something it cannot do: have a meaningful conversation.

You could claim that moving from pixelated perception, where the robot looks at sensor data, to understanding and predicting the environment is a Holy Grail of artificial intelligence.

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

The human brain has an amazing ability for pattern recognition, sometimes even better than a computer.

Artificial intelligence is the science of making machines do things that would require intelligence if done by men.

You may not realize it, but artificial intelligence is all around us.

I'm fascinated by artificial intelligence.

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.

When people speak of creating superhumanly intelligent beings, they are usually imagining an AI project.

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