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Terence Tao

๐Ÿ‘ค Speaker
2047 total appearances

Appearances Over Time

Podcast Appearances

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

all the atoms in a box of air.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

That's, like, Avogadro's number is humongous, right?

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

There's a huge number of particles.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

If you actually have to track each one, it'll be ridiculous.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

But certain laws emerge at the microscopic scale that almost don't depend on what's going on at the macroscale, or only depend on a very small number of parameters.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

So if you want to model a gas of, you know,

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

quintillion particles in a box.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

You just need to know its temperature and pressure and volume and a few parameters, like 5 or 6.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

And it models almost everything you need to know about these 10 to 23 or whatever particles.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

So we have...

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

We don't understand universality anywhere near as we would like mathematically, but there are much simpler toy models where we do have a good understanding of why universality occurs.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

The most basic one is the central limit theorem that explains why the bell curve shows up everywhere in nature, that so many things are distributed by what's called a Gaussian distribution, a famous bell curve,

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

There's not even a meme with this curve.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

Even the meme applies broadly, the universality to the meme.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

Yes, you can go meta if you like.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

But there are many processes.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

For example, you can take lots of independent random variables and average them together in various ways.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

You can take a simple average or more complicated average, and we can prove in various cases that these bell curves, these calciums emerge, and it is a satisfying explanation.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

Sometimes they don't.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

So if you have many different inputs and they're all correlated in some systemic way, then you can get something very far from a bow curve show up.