A Quote by Andrew Ng

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.
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.
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.
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.
Every company has messy data, and even the best of AI companies are not fully satisfied with their data. If you have data, it is probably a good idea to get an AI team to have a look at it and give feedback. This can develop into a positive feedback loop for both the IT and AI teams in any company.
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.
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.
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.
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.
I believe in the future of AI changing the world. The question is, who is changing AI? It is really important to bring diverse groups of students and future leaders into the development of AI.
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.
I don't think there's a particular technology that will set the trajectory for us moving forward. We don't want to be one of the companies that say AI is the next big thing, let's go build an AI application for Robinhood. That might not work. It might be awkward.
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.
Beyond helping other people build AI systems with Deeplearning.ai, I also hope to build some AI systems myself!
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.
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.
My own work falls into a subset of AI that is about building artificial emotional intelligence, or Emotion AI for short.
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