Judea Pearl

Today, we're back in New York City to meet Elias Bareinboim.

When he first met Judea Pearl, he felt that there was truth to what Pearl was saying, and that convinced him to study causality.

Ladies and gentlemen, please welcome Dr. Elias Bareinboim.

Let me pass it to your host, Alex Mola.

People are discussing causation for hundreds of years.

And philosophers can do that, and it's very useful in a way, but it is still philosophy.

Took the genius of someone like Judea, Judea Pearl, right? That, uh, you interviewed.

They, they say, "Oh, this is kind of after, would be a thousand years discuss.

So I think maybe around '85, you know, there was a big push away from logic and towards probability.

So, uh, Judea Pearl was, was probably the leader of that, and I think everybody else saw, yes, this is the way to go, that we were too constrained by Boolean logic, and AI should really be the study of uncertainty, and probability was the right way to do that.

So that was one change.

And then, uh, machine learning rather than hand coding was another change.

We'll hear next from Dan Gold, the Gilbert Foundation Chair and Executive Director of UCLA Hillel.

Following Dan, Judea Pearl, Daniel's father, one of our esteemed colleagues here at UCLA, will come up and give a few remarks of personal remembrance.

Following that, I will come back and I'll introduce Evan properly, then he and I will sit in those two chairs, have a conversation, talk about his new book, which I urge you to not only read, but also to buy, which is outside, signed copies by Evan.

So we'll talk a bit about that and about some broader issues and then, time permitting, we'll open it up to questions from all of you.

This year, Evan Gershkovich carries that conversation forward.

Like Daniel, he has approached journalism with a determination to understand people and places firsthand and to bear witness to what he encounters, even when doing so comes at great personal cost.

We are especially grateful to Judea Pearl, Daniel's father and a beloved member of our UCLA faculty, and we honor the memory of his late wife, Ruth.

Their resilience in the face of unimaginable loss has helped turn Daniel's legacy into an enduring commitment to dialogue and understanding.

It's, uh, something I have to map in my model.

I'm sure there is some, like, confounded factor example from Judea Pearl [laughs] that, that actually, like, will give you predictive power, but not causal power.

Uh, and you, you don't see it in the samples because you, you have this confounding variable that is operational in, like, a consistent way.

It's just useful in your prediction, but it's not, um, uh, but it's not passing the bar for causality because actually by...

This AI agent would, yes, would use reinforcement learning, but, but with one caveat.

Use, uh, Professor or rather Dr. Judea Pearl's idea of, um, causal inference, the relationship between cause and effect, to add to that algorithmic power.

Use vector databases.

Again, things from physical review letters rather than from Reddit or crap you found on the internet saying, for example, the Pais effect is wrong.

Because if we are outsourcing our consciousness to AI, we desperately need to understand the limits of what AI actually is.

To understand AI's limits mechanically, we have to look at computer scientist Judea Pearl and his ladder of causation.

This is such a great framework.

Pearl breaks down cognition into three rungs.