Duncan Jones Quotes

One of the best things to come out of the home computer revolution could be the general and widespread understanding of how severely limited logic really is.

Human players have their strengths and weaknesses and Watson is the same way. He just has different strengths and weaknesses than most people.

IA is something that is proceeding very naturally, in most cases not even recognized by its developers for what it is.

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.

We've seen computers play chess and beat grand masters. We've seen computers drive a car across a desert. But interestingly, playing chess is easy, but having a conversation about nothing is really difficult for a computer.

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

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 degree of intelligence that a man or a machine can show depends on many qualities of the ways that knowledge, goals, and problem-solving techniques are represented and put together, and not so much on the fine details.

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

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

To make real progress in A.I., we have to overcome the big challenges in the area of common sense.

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

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.

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.

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.

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.

The problem with Google is you have 360 degrees of omnidirectional information on a linear basis, but the algorithms for irony and ambiguity are not there. And those are the algorithms of wisdom.

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.

In my view, the fact that computers caught up to humans and completely dominate humans in chess and some other domains already, that says there's evidence that, yes, in principle, they can be better programmers than humans.

The popular dystopian vision of AI is wrong for one simple reason: it equates intelligence with autonomy. That is, it assumes a smart computer will create its own goals and have its own will and will use its faster processing abilities and deep databases to beat humans at their own game.

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.

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.

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

From one perspective, we're in the early stage in artificial intelligence, but exponentials start out slowly, and then they take off.