The Future of Machine Intelligence

Chapter 5. Benjamin Recht: Machine Learning in the Wild

Benjamin Recht is an associate professor in the electrical engineering and computer sciences department and the statistics department at the University of California at Berkeley. His research focuses on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning—exploring the intersections of convex optimization, mathematical statistics, and randomized algorithms.

Key Takeaways

• Machine learning can be effectively related to control theory, a field with roots in the 1950s.

• In general, machine learning looks to make predictions by training on vast amounts of data to predict the average case. On the other hand, control theory looks to build a physical model of reality and warns of the worst case (i.e., this is how the plane responds to turbulence).

• Combining control principles with reinforcement learning will enable machine learning applications in areas where the worst case can be a question of life or death (e.g., self driving cars).

You’re known for thinking about computational issues in machine learning, but you’ve recently begun to relate it to control theory. Can you talk about some of that work?

I’ve written a paper with Andy Packard and Laurent Lessard, two control theorists. Control theory is most commonly associated with aviation or manufacturing. So you might think, what exactly does autopilot have to do with machine learning? We’re making great progress in machine ...