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Greetings from spring in Berkeley. In the last couple of weeks, some of us have taken off our masks in Calvin Lab for the first time, and are enjoying a sense of renewal overall.

In this talk from our Theoretically Speaking public lecture series, Dina Katabi presented sensing technologies that track people’s gait and movements based purely on the radio signals that bounce off their bodies.

Chris Watkins introduced the Q-function in the 1980s as part of a methodology for reinforcement learning. Given its importance for over three decades, it is not surprising that the question of the true meaning of Q was a hot topic for discussion during the Simons Institute's Fall 2020 program on Theory of Reinforcement Learning.

Greetings from Berkeley, where spring is already upon us. A shout out to those who went to school here at some time or another — the magnolias are in full bloom, and that has not changed one bit. 

“Every traditional statistics text includes a warning not to mistake correlation for causality. But until very recently, there has been almost nothing else to say about causality,” observes Daniel Goroff, director of the Sloan Foundation’s economics program. “Research on the theory and practice of causal inference, as facilitated by the Simons Institute, will be critical to progress on everything from machine learning to evidence-based policymaking.”

It has been only a few months since the last time this column appeared. Yet there is so much to write about that it feels like a year has passed. For one area in particular, a decade’s worth of developments seems to have emerged in a few months’ time. I am talking of course about the phenomenal developments on error-correcting codes that we witnessed in the past few months.