Computer Vision 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.

There's a very long tail of all sorts of creative products - beyond our core web search, image search and advertising businesses - that are powered by deep learning.

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.

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.

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

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.

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.

The first autonomous cars date back to the late 20th century. But recent increases in sophistication and reductions in cost - reflected, for example, in cheap LIDAR systems, which can 'see' a street in 3D in a way similar to that of the human eye - are now bringing autonomous cars closer to the market.

There is great potential to use computer vision technology in a constructive and benevolent way.

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

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

I think Tesla will most likely develop its own autopilot system for the car, as I think it should be camera-based, not Lidar-based. However, it is also possible that we do something jointly with Google.

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.

Some Google employees have their self-driving vehicles take them to work. These car robots don't look like something from 'The Jetsons'; the driverless features on these cars are a bunch of sensors, wires, and software. This technology 'works.'

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

Pretend I asked, now answer the question.

The paradigm shift of the ImageNet thinking is that while a lot of people are paying attention to models, let's pay attention to data. Data will redefine how we think about models.

Most of the robots being developed for home use are functional in design - Gecko's homecare robot looks rather like the Star Wars robot R2-D2. Honda and Sony are designing robots that look more like the same movie's 'android' C-3PO.

Everybody thought that screen time was bad for vision. Well, it happens that spending a lot of time reading on a computer is bad for your vision, but spending a lot of time playing first-person-shooter games is good for your vision.

It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries.

I'm not a big fan of self-driving cars where there's no steering wheel or brake pedal. Knowing what I know about computer vision and AI, I'd be pretty uncomfortable with that. But I am a fan of a combined system - one that can brake for you if you fall asleep at the wheel, for example.

The company I invested in is probably a leader in that area. They're a company called Second Spectrum, which happens to be based in LA but was started by two USC computer-science professors. It's filled with guys who love sports, who played sports, but really look like programmers.

In the past, Google has used teams of humans to 'read' its street address images - in essence, to render images into actionable data. But using neural network technology, the company has trained computers to extract that data automatically - and with a level of accuracy that meets or beats human operators.

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.

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