Welcome to Quantum Magazine's podcast.

Each episode we bring you stories about developments in science and mathematics.

I am Susan Vallett.

For more than 250 years, mathematicians have been trying to blow up some of the most important

equations in physics, those that describe how fluids flow.

If they succeed, then they'll have discovered a scenario in which those equations break down,

a vortex that spins infinitely fast perhaps, or a current that abruptly stops and starts,

or a particle that whips past its neighbors infinitely quickly. Beyond that point of blow-up,

known as the singularity, the equations will no longer have solutions. They will fail to

describe even an idealized version of the world we live in.

Then, mathematicians will have reason to wonder just how universally dependable they are as models of

fluid behavior. Now, a new approach to machine learning might be getting them closer to that blow-up.

That's next.

Imagine you're in a lab where you've synthesized ancient DNA sequences and spliced them

into modern bacteria just to see how they'd react.

They needed each other, but they didn't want each other.

So, you know, it was like a very complicated relationship unfolding in front of me.

This isn't Jurassic Park or some sci-fi movie.

I'm Steve Strogetz, and this is the Joy of Why.

A new podcast from Quantum Magazine that takes you into some of the biggest unanswered mysteries in science and math today.

Join me on The Joy of Why as we explore these questions.

We may not have all the answers yet, but I'm pretty sure the curiosity to figure them out is in our DNA.

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