The following is a conversation with Judea Pearl, professor at UCLA and a winner of the Turing Award that's generally recognized as the Nobel Prize of Computing.

He's 1 of the seminal figures in the field of artificial intelligence, computer science, and statistics.

He has developed and championed probabilistic approaches to AI, including Bayesian networks, and profound ideas in causality in general.

These ideas are important, not just to AI, but to our understanding and practice of science.

But in the field of AI, the idea of causality, cause and effect, to many, lie at the core of what is currently missing and what must be developed in order to build truly intelligent systems.

For this reason and many others, his work is worth returning to often.

I recommend his most recent book called Book of Why that presents key ideas from a lifetime of work in a way that is accessible to the general public.

This is the Artificial Intelligence Podcast.

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And now, here's my conversation with Judea Pearl.

You mentioned in an interview that science is not a collection of facts, but a constant human struggle with the mysteries of nature.

What was the first mystery that you can recall that hooked you, that kept you curious?

And when I learned about Descartes, analytic geometry, And I found out that you can do all the construction in geometry using algebra.

So what kind of world does analytic geometry unlock?

Okay so Descartes had the idea that geometrical construction and geometrical theorems and assumptions can be articulated in the language of algebra.

Which means that all the proof that we did in high school trying to prove that the 3 bisectors meet at 1 point and that, okay, all this can be proven by just shuffling around notation.

The connection, the traumatic experience.

So it's the connection between the different mathematical disciplines that they all...

So which mathematic discipline is most beautiful?

But there's a visual element to geometry being a-

But once you get over to algebra, then a linear equation is a straight line.

And to pass a tangent to a circle, you know, you have the basic theorems and you can do it with algebra.

So but the transition from 1 to another was really, I thought that Descartes was the greatest mathematician of all times.

So you have been, if you think of engineering and mathematics as a spectrum.

You have walked casually along this spectrum throughout your life.

You know, a little bit of engineering, and then, you know, done a little bit of mathematics here and there.

I mean, We got a very solid background in mathematics because our teachers were geniuses.

Our teachers came from Germany in the 1930s, running away from Hitler.

They left their careers in Heidelberg and Berlin and came to teach high school in Israel.

And we were the beneficiary of that experiment.

Their cousins and their nieces and their faces.

And how they jumped from the bathtub when they scream, Eureka!

So you're almost educated as a historian of math.

No, we just got a glimpse of that history together with a theorem, so every exercise in math was connected with a person.

Mathematically speaking, yes, not the politics.

So, and then in university, you have gone on to do engineering.

Yeah, I get a BS in engineering and a Technion.

And then I moved here for graduate work and I did engineering in addition to physics in Rutgers.

And it combined very nicely with my thesis which I did in LCA Laboratories in superconductivity.

And then somehow thought to switch to almost computer science software, even not switch but long to become, to get into software engineering a little bit, programming, if you can call it that in the 70s.

If you were to pick a favorite, In terms of engineering and mathematics, which path do you think has more beauty?

You have permanent current swirling around 1 way or the other.

You can have a store 1 or 0 for computer, that's what we worked on in the 1960s in RCA.

And I discovered a few nice phenomena with the vortices.

Pearl Vortex, right, you can Google it, right?

I didn't know about it, but the physicist picked up on my thesis, on my PhD thesis, and it becomes popular.

I mean, thin film superconductors became important for high temperature superconductors.

So they called it pearl vortex without my knowledge.

You have footprints in all of the sciences.

Is the universe at the lowest level deterministic or stochastic in your amateur philosophy view?

We think because we have the Heisenberg Uncertainty Principle and we have some experiments to confirm that.

All we have is experiments to confirm it.

It's a puzzle that you have the dice flipping machine, a God, and the result of the flipping propagates with a speed faster than the speed of light.

But it only governs microscopic phenomena.

So you don't think of quantum mechanics as useful

So in your thinking, the world might as well be deterministic.

The world is deterministic, and as far as the new 1 firing is concerned, it's deterministic to first approximation.

Free will is an illusion that we AI people are gonna solve.

So What do you think, once we solve it, that solution will look like?

The solution will look like, first of all, it will look like a machine.

A machine that act as though it has free will.

It communicates with other machines as though they have free will, and you wouldn't be able to tell the difference between a machine that does and a machine that doesn't have free will.

So the illusion, it propagates the illusion of free will amongst the other machines.

Faking intelligence is intelligence because it's not easy to fake.

So Let's begin at the beginning with probability, both philosophically and mathematically.

What does it mean to say the probability of something happening is

It's a degree of uncertainty that an agent has about the world.

You're still expressing some knowledge in that statement.

If the probability is 90%, it's absolutely a different kind of knowledge than if it is

But it's still not solid knowledge, it's...

It is solid knowledge, but hey, if you tell me that

assurance smoking will give you lung cancer in 5 years versus

So the statistical view of the universe, why is it useful?

So we're swimming in complete uncertainty, Most of everything around us.

It allows you to predict things with a certain probability and computing those probabilities are very useful.

And you need prediction to be able to survive.

If you can't predict the future, then you're just crossing the street will be extremely fearful.

And so you've done a lot of work in causation, and so Let's think about correlation.

And so we'll dance back and forth between these levels of uncertainty.

What is it, so probability of something happening is something, but then there's a bunch of things happening.

And sometimes they happen together, sometimes not, they're independent or not, so how do you think about correlation of things?

Correlation occurs when 2 things vary together over a very long time, is 1 way of measuring it.

Or when you have a bunch of variables that they all vary cohesively, Then we call it, we have a correlation here.

And usually when we think about correlation, we really think causally.

Things cannot be correlated unless there is a reason for them to vary together.

If they don't see each other, why should they vary together?

Yes, hidden in our intuition, there is a notion of causation because we cannot grasp Any other logic except causation.

And how does conditional probability differ from causation?

Conditional probability, how things vary when 1 of them stays the same.

Now, staying the same means that I have chosen to look only at those incidents where the guy has the same value as the previous 1.

So things that are not correlated before could become correlated.

Like for instance, if I have 2 coins which are uncorrelated, okay, and I choose only those flippings experiments in which a bell rings, and a bell rings when at least 1 of them is a tail, then suddenly I see correlation between the 2 coins.

Because I only look at the cases where the bell rang.

You see, it's my design, with my ignorance essentially, with my audacity to ignore certain incidents, I suddenly create a correlation where it doesn't exist physically.

Right, so that's, you just outlined 1 of the flaws of observing the world and trying to infer something from the math about the world from looking at the correlation.

I don't look at it as a flaw, the world works like that.

Which, but the flaws comes if we try to impose causal logic on correlation, it doesn't work too well.

That's what, that has been the majority of science.

Statisticians know that if you condition on a third variable, then you can destroy or create correlations among 2 other variables.

It's nothing surprising, that's why they all dismiss the Simpson Paradox, ah, we know it.

Well, there's disciplines like psychology where all the variables are hard to account for.

And so oftentimes there's a leap between correlation to causation.

Who is trying to get causation from correlation?

You're not proving causation, but you're sort of discussing it, implying, sort of hypothesizing with our ability to prove.

I'll tell you if they are obsolete, or if they are outdated, or they're about to get outdated.

No, no, I was thinking of applied psychology studying.

For example, we work with human behavior in semi-autonomous vehicles, how people behave.

And you have to conduct these studies of people driving cars.

The research question, do people fall asleep when the car is driving itself?

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Judea Pearl: Causal Reasoning, Counterfactuals, and the Path to AGI | Lex Fridman Podcast #56