Ray Kurzweil Quotes

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

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

Computers will understand sarcasm before Americans do.

The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.

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

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.

I think a blog is a catalyst for a number of possible kinds of writing besides being its own medium.

Effective translation of natural languages comes awfully close to requiring a sentient translator program.

Humans are language machines, computers are language machines.

People have been trying to do kind of natural language processing with computers for decades and there has only been sort of slow progress in that in general. It turned out the problem we had to solve is sort of the reverse of the problem people usually have to solve. People usually have to solve the problem of you're given you know thousands, millions of pages of text, go have the computer understand this.

One of our big goals in search is to make search that really understands exactly what you want, understands everything in the world. As computer scientists, we call that artificial intelligence.

...what I write is smarter than I am. Because I can rewrite it.

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.

From a linguistic point of view, you can't really take much objection to the notion that a show is a show is a show.

Imagine if we can just talk to our computers and have it understand, 'Please schedule a meeting with Bob for next week.' Or if each child could have a personalized tutor. Or if self-driving cars could save all of us hours of driving.

Without your data, Google couldn't pursue the dream of trying to figure out what you're really thinking when you're asking a question, of trying to discern, from the imprecision of your language, the exact answer you're looking for.

Things that are so hard for people, like playing championship-level Go and poker, have turned out to be relatively easy for the machines. Yet at the same time, the things that are easiest for a person - like making sense of what they see in front of them, speaking in their mother tongue - the machines really struggle with.

Humans are still much better than computers at recognizing speech.

I work out most days, normally first thing, and then I just see where the day takes me. I recipe test most days, do lots of social media and emails, but nothing else is constant. Some days, I film YouTube videos; other days, I have lots of meetings, work on blog posts, brainstorm ideas, and work on upcoming projects.

I never really thought I would make a blog myself.

I want to start a blog.

Irony is going to be hard to get. You have to be master of the literal first. But then, Americans don't get irony either. Computers are going to reach the level of Americans before Brits.

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.

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.

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.