A Quote by Andrew Ng

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.
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.
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.
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.
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 thought the best place to advance the AI mission is at Baidu.
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.
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 are two companies that the AI Fund has invested in - Woebot and Landing AI - and the AI Fund has a number of internal teams working on new projects. We usually bring in people as employees, work with them to turn ideas into startups, then have the entrepreneurs go into the startup as founders.
One of the things Baidu did well early on was to create an internal platform that made it possible for any engineer to apply deep learning to whatever application they wanted to, including applications that AI researchers like me would never have thought of.
Google or other search engines are examples of AI, and relatively simple AI, but they're still AI. That plus an awful lot of hardware to make it work fast enough.
I am looking into quite a few ideas in parallel and exploring new AI businesses that I can build. One thing that excites me is finding ways to support the global AI community so that people everywhere can access the knowledge and tools that they need to make AI transformations.
I will continue my work to shepherd in this important societal change... In addition to working on AI myself, I will also explore new ways to support all of you in the global AI community so that we can all work together to bring this AI-powered society to fruition.
Beyond helping other people build AI systems with Deeplearning.ai, I also hope to build some AI systems myself!
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.
Now that neural nets work, industry and government have started calling neural nets AI. And the people in AI who spent all their life mocking neural nets and saying they'd never do anything are now happy to call them AI and try and get some of the money.
The main reason I backed DeepMind was strategic: I see my role as bridging the AI research and AI safety communities.
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