Ai Research Quotes

One thing ImageNet changed in the field of AI is suddenly people realized the thankless work of making a dataset was at the core of AI research.

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

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

AI-assisted driving is a perfect platform for advancing fundamental human-centric artificial intelligence research while also producing practical applications.

Baidu Research has three labs - two in Beijing that are already largely built up, and the Silicon Valley one is being built from scratch. We're hiring pretty rapidly, about one person a week, but we are about a month in, so honestly, we haven't done that much work yet.

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

Artificial Intelligence (AI) is the science of how to get machines to do the things they do in the movies.

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.

Besides publishing its own work, the Google AI China Center will also support the AI research community by funding and sponsoring AI conferences and workshops and working closely with the vibrant Chinese AI research community.

Note that I am not proposing that AI research be ignored or less funded.

There's a great phrase, written in the '70s: 'The definition of today's AI is a machine that can make a perfect chess move while the room is on fire.' It really speaks to the limitations of AI. In the next wave of AI research, if we want to make more helpful and useful machines, we've got to bring back the contextual understanding.

Deep learning is a subfield of machine learning, which is a vibrant research area in artificial intelligence, or AI.

I'm fascinated by artificial intelligence.

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.

With the Google Brain project, we made the decision to build deep learning processes on top of Google's existing infrastructure.

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.

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.

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.

One of the things that Baidu did well early on was to create an internal platform for deep learning. What that did was enable engineers all across the company, including people who were not AI researchers, to leverage deep learning in all sorts of creative ways - applications that an AI researcher like me never would have thought of.

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.

Silicon Valley and Beijing are the leading hubs of AI, followed by the U.K. and Canada. I am seeing a lot of excitement in India, going by the number of people who are taking Coursera courses on AI.

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

I think Lenat is headed in the right direction, but someone needs to include a knowledge base about learning.

The main reason I backed DeepMind was strategic: I see my role as bridging the AI research and AI safety communities.