Marvin Minsky Quotes

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

If you want to solve very complex problems, you will have to end up letting machines work out a lot of the details for themselves, and in ways that we don't understand what they are doing.

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

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.

Many researchers are exploring other forms of AI, some of which have proved useful in limited contexts; there may well be a breakthrough that makes higher levels of intelligence possible, but there is still no clear path yet to this goal.

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.

A deep-learning system doesn't have any explanatory power.

If you step back a little and say we want to do A.I., then you will realize that A.I. needs knowledge, reasoning, and explanation.

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.

I'm fascinated by artificial intelligence.

Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition.

Twenty-five years ago, the notion was you could create a general problem-solver software that could solve problems in many different domains. That just turned out to be totally wrong.

The real goal of AI is to understand and build devices that can perceive, reason, act, and learn at least as well as we can.

Graphics has lately made a great shift towards machine learning, which itself is about understanding data.

When people speak of creating superhumanly intelligent beings, they are usually imagining an AI project.

I am suggesting that we recognize that in network and interface research there is something as profound (and potential wild) as Artificial Intelligence.

The human brain must continue to frame the problems for the electronic machine to solve.

It was a case of the technology catching up with the problem.

This is a magical time for computer programming. We are moving towards the artifical intelligence holy grail of programming the computer to simulate the human brain.

Artifical Intellegence is the most important of the coming technologies. Whoever can crack that will have a company worth 10 Microsofts.

I joined Baidu in 2014 to work on AI. Since then, Baidu's AI group has grown to roughly 1,300 people, which includes the 300-person Baidu Research. Our AI software is used every day by hundreds of millions of people.

I just thought making machines intelligent was the coolest thing you could do. I had a summer internship in AI in high school, writing neural networks at National University of Singapore - early versions of deep learning algorithms. I thought it was amazing you could write software that would learn by itself and make predictions.

Machine learning is the science of getting computers to learn without being explicitly programmed.

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

The deep learning techniques, while relatively easy to learn, are quite foreign to traditional engineering modalities. It takes a different mindset and a relaxation of the presumption of control. The practitioners are like magi, sequestered from the rest of a typical engineering process.