Vilayanur S. Ramachandran Quotes

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'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.

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.

The only path to build intelligent machines is to enable it with powerful visual intelligence, just like what animals did in evolution.

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.

We may hope that machines will eventually compete with men in all purely intellectual fields. But which are the best ones to start with? Many people think that a very abstract activity, like the playing of chess, would be best. It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English.

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.

Having been trained as a computer scientist in the '90s, everybody knew that AI didn't work. People tried it. They tried neural nets, and none of it worked.

Google Brain, which I led, was arguably the single biggest force for turning Google into a great A.I. company. I'm pretty sure I led the team that transformed Baidu as well. So one thing that really excites me is the potential for other companies to become great A.I. companies.

The big AI dreams of making machines that could someday evolve to do intelligent things like humans could - I was turned off by that. I didn't really think that was feasible when I first joined Stanford.

I thought the best place to advance the AI mission is at Baidu.

Baidu's AI is incredibly strong, and the team is stacked up and down with talent; I am confident AI at Baidu will continue to flourish. After Baidu, I am excited to continue working toward the AI transformation of our society and the use of AI to make life better for everyone.

We're making progress, but getting machines to replicate our ability to perceive and manipulate the world remains incredibly hard.

Artificial intelligence... I've been following that since I was in high school.

We think of computers as smart and powerful machines. But your goldfish is smarter.

Google seems to be more intelligent than some people.

We really believe that long-term, the way AI will drive is similar to the way humans drive - we don't break the problem down into objects and vision and localization and planning. But how long it will take us to get there is questionable.

Things like chatbots, machine learning tools, natural language processing, or sentiment analysis are applications of artificial intelligence that may one day profoundly change how we think about and transact in travel and local experiences.

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

If someone has a fantastic biology background, he or she can contribute in AI and health care. AI has many aspects.

Understanding vision and building visual systems is really understanding intelligence.

There's a lot that machine learning can't do that humans can do very, very well.

Computers will understand sarcasm before Americans do.

The Turing Test was a brilliant idea, but it's evolved into a competition of chatbots.