Jennifer Tour Chayes

Now, exactly what form that was going to take, you know, I can say, uh, in twenty twenty-- in the fall of twenty twenty-four when ten forty-seven was being vetoed, there was not already a, a bill that had been drafted that was in someone's coat pocket ready to pull out and put forward.

The, the f- the process that Dr. Lee ran and Tino Cuellar, uh, who was at Carnegie at the time, now at Anthropic, and Jennifer Chayes, uh, the dean at, uh, College of Computing at Berkeley, the process that they ran to write the report they wrote, um, over the end of twenty twenty-four and into the beginning of twenty twenty-five was instructive in the design of the bill that eventually became SB fifty-three.

So there was not a preconceived notion of what the components of the subsequent bill would be, but there was definitely a clear acknowledgment that California is not gonna sit idly by if the federal government doesn't do its job.

So let's turn to, to SB fifty-three, um, quickly.

These concepts can be hard to wrap your head around, but fundamentally they describe how massive networks behave.

One of the people who helped create that field of math is Jennifer Chayes.

Her game-changing ideas in the mid-2000s helped lay the theoretical foundations of machine learning and modern AI.

She spent twenty years at Microsoft building interdisciplinary research labs.

I love that.

Thank you.

Jennifer Chayes is the Dean of the College of Computing Data Science & Society at UC Berkeley.

This has been such a privilege.