Donald Knuth Quotes

Although I'm not prepared to move up my prediction of a computer passing the Turing test by 2029, the progress that has been achieved in systems like Watson should give anyone substantial confidence that the advent of Turing-level AI is close at hand. If one were to create a version of Watson that was optimized for the Turing test, it would probably come pretty close.

As a science fiction fan, I had always assumed that when computers supplemented our intelligence, it would be because we outsourced some of our memory to them. We would ask questions, and our machines would give oracular - or supremely practical - replies.

Even though chess isn't the toughest thing that computers will tackle for centuries, it stood as a handy symbol for human intelligence. No matter what human-like feat computers perform in the future, the Deep Blue match demands an indelible dot on all timelines of AI progress.

The effort of using machines to mimic the human mind has always struck me as rather silly. I would rather use them to mimic something better.

I've come to a view that humans will continue to do what we do well, and that computers will continue to do what they do very well, and the two will coexist, but in different spaces.

I will argue that in the literal sense the programmed computer understands what the car and the adding machine understand, namely, exactly nothing.

By 2029, computers will have emotional intelligence and be convincing as people.

There is a real danger that computers will develop intelligence and take over. We urgently need to develop direct connections to the brain so that computers can add to human intelligence rather than be in opposition.

A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.

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.

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

Everyone talks about how much data's in the world. Except, actually, 80% of it is pretty blind to computers. I mean, it can store it. But if it's a movie, a poem, a song, it doesn't know what it's actually saying or doing.

Now with our Software Developer Kit (SDK), any developer can embed Emotion AI into the apps, games, devices, and digital experiences they are building, so that these can sense human emotion and adapt. This approach is rapidly driving more ubiquitous use of Emotion AI across a number of different industries.

We will not only use the machines for their intelligence, we will also collaborate with them in ways that we cannot even imagine.

Emotion AI uses massive amounts of data. In fact, Affectiva has built the world's largest emotion data repository.

I spent many, many hours with my computer, and it really bugged me that it was very oblivious to my emotional state. And that kind of inspired and motivated me to build an emotionally intelligent computer.

It's paradoxical that things that are hard for people are easy for the computer, and things that are hard for the computer, any child can understand.

I'm fascinated by artificial intelligence.

The sad thing about artificial intelligence is that it lacks artifice and therefore 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.

I've always been interested in technology, but specifically how we can use machines to engage the imagination. I started using computers when I was young and was fascinated by creating rules and instructions that allow a computer to engage in a dialogue with humans. The stories found in the data all around us can do just that.

If computers remain far worse than us at image recognition, a certain over-confident combination of man and machine can elsewhere take inaccuracy to a whole new level.

Perhaps one day we will have machines that can cope with approximate task descriptions, but in the meantime, we have to be very prissy about how we tell computers to do things.

What's casual for a robot isn't necessarily what's casual for a human.

A smart model is a good model.