Geoffrey Hinton Quotes

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

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

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.

Understanding of natural language is what sometimes is called 'AI complete,' meaning if you can really do that, you can probably solve artificial intelligence.

Language is too complex for a computer to understand. It's not going to be able to make sense of what people are saying en masse. We need a new type of discipline that puts together computer scientists and social scientists, who can add context to the situation.

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.

Our aim is to develop affectionate robots that can make people smile.

Emotion AI will be ingrained in the technologies we use every day, running in the background, making our tech interactions more personalized, relevant, authentic, and interactive.

I remember, once I was stressed, with an upcoming paper deadline. That little Microsoft Word clippy guy would show up in my face, jumping around and asking if I needed help. It had no understanding of my emotions and had zero empathy. That got me interested in this idea of tech being responsive to our emotions.

You won't remember what it was like when your technology didn't recognize when you are sad or angry.

I think, in the future, we'll assume that every device just knows how to read your emotions.

My own work falls into a subset of AI that is about building artificial emotional intelligence, or Emotion AI for short.

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.

Google created the intent graph. Facebook created the social graph. We are creating the emotional graph.

It turns out you can train a neural network on a big body of text. It can be Wikipedia; it can be all the works of Charles Dickens; it could be all of the Internet. They can use grammar and put words together in interesting and convincing ways - and, I think, unexpected and beautiful ways.

With Emotion AI, we can inject humanity back into our connections, enabling not only our devices to better understand us, but fostering a stronger connection between us as individuals.

Computers ought to help people find their own best path through lots of textual information.

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 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.

The most important thing for building a robot that you can interact with socially is its visual attention system. Because what it pays attention to is what it's seeing and interacting with, and what you're understanding what it's doing.

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.

I know it may seem surprising to people, but learning dialog that has a conversational flow to it is not that difficult.

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

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?