Hi and welcome to the A16Z podcast. I'm Hannah. Deep learning has come to the life sciences.

Lately it seems every week a published study comes out with code on top. In this episode, A16Z general

partner on the biofund Vijay and Bart Ramsundar talk about how AI and ML is unlocking the field

in a new way in a conversation around their recently published book, Deep Learning for

the Life Sciences, written along with co-authors Peter Eastman and Patrick Walters.

The book aims to give developers and scientists a toolkit on how to use deep learning for

genomics, chemistry, biophysics, microscopy, medical analysis, and other areas.

So why now?

What is it about ML's development that is allowing it to

finally make an impact in this field? And what is the practical toolkit, the right problems to

attack, the right questions to ask? Above and beyond that, as this deep learning toolkit becomes

more and more accessible, biology is becoming democratized through ML. So how is the hacker ethos coming to the world of biology and what might open source biology

truly look like?

So Bart, we spent a lot of time thinking about deep learning in life sciences.

It's a great time, I think, for people to become practitioners in this space, especially

for people maybe that's never done machine learning before from the life sciences side,

or maybe people from the machine learning side to get into life sciences.

But maybe the place to kick it off is, you know, what's special about now?

Like, why should people be thinking about this?

The challenge of programming biology has been that we don't know biology, and we make up theoretical models, and the computers are wrong, and, you know, biologists and chemists understandably get grumpy and say, why are you wasting my time?

But with machine learning, the advantages that we can actually learn from the raw data, and all of a sudden we have this powerful new tool there. It can find things that we didn't know before.

And this is why now is the time to get into it, really to enable that next wave of, you know, breakthroughs in the core sites.

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