Andrew Ng

Managing General Partner

A globally recognized leader in AI, Dr. Andrew Ng leads AI Fund as the Managing General Partner. Additionally, he is the Founder and CEO of Landing AI, founder of DeepLearning.AI, chairman and co-founder of Coursera, and an adjunct professor at Stanford University.

As a pioneer both in machine learning and online education, Andrew has changed countless lives through his work in AI, authoring or co-authoring more than 200 research papers in machine learning, robotics and related fields. He also led teams that helped with two leading technology companies’ AI adoption, as the founding lead of the Google Brain team, and Chief Scientist in Baidu.

“AI is the new electricity.”

“Just as electricity led to many new innovations, AI will fuel tens of thousands of new applications,” continues Andrew. “There are many opportunities for valuable AI projects and one of the most efficient ways to get valuable AI projects running is in building new teams and companies. AI Fund allows us to create from scratch or invest in existing businesses to bring such AI ideas to fruition.”

“I have a lot of respect for entrepreneurs that have passion and faith in what they believe in and work really hard to make it happen,” notes Andrew.

Andrew holds a Ph.D. from UC Berkeley, a master’s degree from MIT, and a bachelor’s degree from Carnegie Mellon University.

INSIGHTS

PUBLICATIONS AND PRESS

Don’t buy the ‘big data’ hype, says cofounder of Google Brain

The thing about big data is that it’s supposed to be “big.” But massive data sets aren't essential for A.I. innovation, says Andrew Ng, founder and CEO of startup Landing AI and cofounder of Google Brain, the tech giant's A.I. and deep-learning team.

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AI Doesn’t Have to Be Too Complicated or Expensive for Your Business

For most companies that are interested in using AI, there isn’t a clear model to follow. The approach to building AI used by massive internet companies like Amazon and Google just doesn’t translate — most companies don’t have overflowing troves of data they can...

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Does MLOps Live Upto The Hype?

Machine Learning (ML) model metrics are designed to monitor performance. But when a model goes into production, many factors influence its performance. The traditional checkpoints may no longer help as organisations look to scale these models (think: scaling from a million to billion credit card users).

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