UCF Professor Talks About Why Uber Acquired His Startup
For Ken Stanley, an associate professor of computer science at the University of Central Florida, the past two years have been a whirlwind that’s landed him in the international spotlight.
First, in 2015, he co-founded Geometric Intelligence, a startup designed to be a unique, research-oriented, private-sector lab that would focus on the cutting edge of artificial intelligence and machine learning.
Then, last December, Geometric Intelligence was acquired by Uber, the ride-sharing giant whose disruptive technology has made it the highest-valued private tech company in the world, with a valuation around $70 billion.
Uber was drawn to Geometric Intelligence because of its approach of focusing on multiple avenues of artificial intelligence, the chance of adding a ready-made lab to its stable, and the work of Stanley and his co-founders: Gary Marcus, a renowned scientist and researcher from New York University; Zoubin Ghahramani, a leader in the field of machine learning and a professor at the University of Cambridge; and Doug Bemis, a veteran of startups with a Ph.D. in neurolinguistics from NYU.
Now, Stanley’s days are spent at Uber headquarters in San Francisco integrating what is now known as Uber AI Labs, while also juggling advising his Ph.D. students and overseeing his lab at UCF.
We talked with him about how it happened, the future of AI, and what it means for UCF.
First off, will your work with Uber impact UCF?
I think this is overall a really good outcome for UCF. There are several reasons for that. First, when large technology companies like a Google or a Facebook or a Microsoft move into a new space, usually there are headlines about it and you’ll see the usual suspect universities in those headlines – something like Stanford or MIT. What this has done is put UCF in those headlines and shown that UCF plays in that league. Another impact is that UCF now has new job opportunities in Silicon Valley for Ph.D. students in particular, but also for all students. Here at Uber AI Labs we already employ three UCF alumni [Joel Lehman, Sebastian Risi and Paul Szerlip]. So we have scientists from UCF who are now working in one of the top research labs in the world – and the opportunities will continue because people will see that UCF has produced this kind of expertise. UCF also now has an opportunity for partnerships with Uber. Finally, on a personal note I’d add that my own experience in industry from this acquisition at a company revolutionizing transportation around the world has given me valuable perspective that improves my ability to connect my work as a professor to the opportunities and challenges that students will face in the future.
What makes your lab different?
We brought together people who are from areas that usually don’t intersect with one another. I come from an area called neuroevolution, which is a combination of artificial neural networks and evolutionary computation. Gary Marcus is a prominent psychologist. Zoubin Ghahramani is known for work with Bayesian learning. And Doug Bemis is a neurolinguistics Ph.D. Gary had a vision that by combining areas that don’t normally practice together, something new would come out. Right now, in the field of machine learning the overwhelmingly dominant idea is deep learning, but many of the ideas in the area are beginning to converge to a similar set of research directions. We had a vision that we would try to push current trends in completely different directions than they’re going right now – in effect not as much following the most obvious path that the community seems to be going.
What does Uber gain from acquiring the startup?
Uber saw there was an opportunity to compete with the top tech companies in the world in the space of machine learning and the value of technology we were developing. In the technology industry, artificial intelligence and machine learning are becoming so important to every business that even businesses that are not direct competitors are still a threat if they have superior technology in this area. Uber has very good machine learning practitioners on their staff. But they understood and had the foresight to see that they needed something that will push the cutting edge to go somewhere different and be able to anticipate the future. A company like ours fit well in filling that gap to push in new directions.
Was Uber’s interest solely about self-driving cars or is it more than that?
That is obviously something they’re looking at. But if you think of Uber, they have innumerable AI-type applications. Their entire system – the way that they dispatch vehicles so you can get a ride, the way that they anticipate where things will be when you need them and try to optimize all of that information – it’s a massive machine learning problem. There are opportunities for AI all throughout their business.
What are your days like now?
I’m spending a lot of time helping to create this lab from the ground up. This doesn’t happen that often, especially in industry. And Uber is a huge organization, and the lab itself has resources and funding to expand, which is again very unusual. We’re trying to decide how this whole thing will work – what are our customs, our incentives to inspire people, what are the things we want to accomplish. For example, we want to engage with the academic community, we don’t want to be isolated. We have to come up with all these new ways of doing business that weren’t formerly part of Uber. Then I spend a lot of time advising my UCF students. So my time is really tight.
Humans behind the wheel are able to pick up on cues like a wave from another driver to go first at a stop sign. How far are we away from when self-driving cars will be able to figure that out?
That is a tough question, and there are a lot of really tough problems in self-driving cars. There are a lot of differing opinions from experts and no one knows for sure. But what I would say is that I think you will see it trickle into practice rather than happening all at once. You might see cars doing one type of driving, like daytime highway driving, and then it will grow into other areas. I think you’ll see a gradual expansion that at first you may not even notice. Some of the most complex types of applications you probably won’t see for many years.