Fei-Fei Li Quotes

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

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

I want to build the next CNN with 'VICE' - it's within my grasp.

As commentators like the American psychologist Gary Marcus have noted, it's extremely difficult to teach a computer to recognise cats. And that's not for want of trying.

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

One thing that humans still do better than computers is recognize images.

Today, there are industries employing hundreds of thousands of engineers creating artificial intelligence.

As the founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and now overall lead of Baidu's AI team of some 1,200 people, I've been privileged to nurture many of the world's leading AI groups and have built many AI products that are used by hundreds of millions of people.

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.

Computer vision and machine learning have really started to take off, but for most people, the whole idea of what is a computer seeing when it's looking at an image is relatively obscure.

By developing deep learning solutions that are faster, easier, and less expensive to use, Nervana is democratizing deep learning and fueling advances in medical diagnostics, image and speech recognition, genomics, agriculture, finance, and eventually across all industries.

You could claim that moving from pixelated perception, where the robot looks at sensor data, to understanding and predicting the environment is a Holy Grail of artificial intelligence.

Even companies like Baidu and Google, which have amazing AI teams, cannot do all the work needed to get us to an AI-powered society. I thought the best way to get us there would be creating courses to welcome more people to deep learning.

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.

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.

Understanding vision and building visual systems is really understanding intelligence.

I think the way we're doing computer vision is just wrong.

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.

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

And nowadays, the idea of AI is not really science fiction anymore - it's just science fact.

Deep learning is already working in Google search and in image search; it allows you to image-search a term like 'hug.' It's used to getting you Smart Replies to your Gmail. It's in speech and vision. It will soon be used in machine translation, I believe.

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

I think that the artificial-intelligence people are making a lot of noise recently, claiming that artificial intelligence is making huge progress and we're going to be outstripped by the machines.

The field of artificial intelligence is pushing new boundaries.

When I was a graduate student in computer science in the early 2000s, computers were barely able to detect sharp edges in photographs, let alone recognize something as loosely defined as a human face.