Artificial Intelligence Still Waiting for the “Ah-ha” Moment

This is part of a week(-ish) blog series where I deep-dive on a cool technology. I am an investor at Dell Technologies Capital in Silicon Valley, and occasionally reminisce about my previous life in academia. Follow me on Twitter and LinkedIn.

To add variety to my blog, I’m trialing some new content. I am interviewing researchers to get their perspectives on emerging industry trends.

This week, I sat down with Stephen Tu, a last-year PhD student at UC Berkeley studying machine learning. He has an interesting background. He worked at Google, Facebook, and Qadium, and is a systems researcher turned AI/ML researcher.

You can find more details on his research on his website.

What has been the biggest AI breakthrough in the last three years?

Stephen: There hasn’t been what one could consider an “ah-ha” breakthrough in the last three years. Of course, there have been some very amazing progress.

One of my favorites is Deepmind’s very impressive AlphaGo program (the first computer program to defeat a professional Go player). But if you look at how AlphaGo was constructed, it was mostly a clever combination of existing techniques in reinforcement learning in conjunction with a lot of computational resources.

This is not to diminish Deepmind’s accomplishments, but rather to say that a lot of the successes we are seeing nowadays are not the result of a “breakthrough” in the traditional sense, but rather modifications of existing techniques with clever engineering and an intensive amount of computing resources.

What problem do you focus on?

Stephen: My research focuses on provable guarantees for autonomous systems controlled by machine learning techniques. We are starting to see more robots deployed in the real-world, interacting with humans in non-trivial ways. The most prominent example of this is the self-driving car.

I’m interested in being able to provide guarantees of safety and performance for these robots. For instance, if we train a robot using machine learning in the lab, how do we know that when we deploy the robot in the wild, it will perform as we expect it to?

Researchers have proposed a lot of algorithms that have very impressive performance in simulation, but are wary of deploying these algorithms in the real world because of a lack of safety guarantees. This is a very active area of research right now.

If you solve this problem, what big applications will be enabled?

Stephen: The sky is really the limit here in terms of applications. Anything that you imagine could be automated would immediately be up for consideration.

What are some interesting companies in this space and why?

Stephen: Google Brain, Deepmind, OpenAI, and to a lesser extend FAIR (Facebook) are what I would consider the big players in this space from the machine learning side, having teams of researchers dedicated to robotics. Microsoft Research in NYC has a very reputable team of academics who study the theoretical questions behind reinforcement learning. I am personally a big fan of the company Boston Dynamics, which constantly puts out very impressive videos of their robots.

If you have questions, comments, future topic suggestions, or just want to say hi, please send me a note at frank.y.wang@dell.com.