Sebastian Thrun made robots smarter, and built “Stanley,” the driverless car that won the 2005 DARPA Challenge. Now he’s making humans smarter with Udacity, the online education company he co-founded in 2011.
Udacity is one of several companies leading the charge in offering affordable courses online, and serves hundreds of thousands of students globally. Courses include modern programming, computer science, product design, and web development classes, as well as curriculum tracks in Data Science & Big Data.
Thrun will be speaking at the upcoming DataBeat/Data Science Summit, Dec. 4-5 in Redwood Shores, Calif., as part of a stellar lineup of data scientists, data analysts, and engineers building the next generation of Big Data tools.
So what does a robotics expert have to do with online education — and big data? A lot, as it turns out.
Online education might be an unusual outcome for a man whose early career was spent as a professor of computer science at Carnegie Mellon University and Stanford University in the 1990s. He blended his two subjects of expertise, computer science and statistics, to help make robots smarter and more able to learn and interact with their environment. From 2003 on, he guided the team that built Stanley, helping give vehicle the necessary sensors to “see” the road, and software to make the right decisions and complete the race.
At the same time, he contributed to the machine learning algorithms that enabled the software to learn faster by incorporating human corrections, becoming better over time. Thrun went on the become a Google Fellow and publish a book on probabilistic programming techniques in robotics.
So what does that all have to do with Udacity? Why would a statistics and artificial intelligence guru start such a venture, if not to apply his machine learning chops to a grand challenge such as education?
Hundreds of thousands of students working on the classes give Udacity millions of interactions, generating gigabytes of progress data to mine in order to make the system smarter and more efficient.
In other words, the kind of probabilistic data analysis that helped make Stanley so smart could soon help make our educators smarter, teaching students more effectively and adapting to their needs more quickly, based on what they are actually doing in their classes.
And that, in turn, could make all of us smarter.