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The Ezra Klein Show

A.I. Could Solve Some of Humanity’s Hardest Problems. It Already Has.

The Ezra Klein Show

New York Times Opinion

Society & Culture, Government, News

4.611K Ratings

🗓️ 11 July 2023

⏱️ 88 minutes

🧾️ Download transcript

Summary

Since the release of ChatGPT, huge amounts of attention and funding have been directed toward chatbots. These A.I. systems are trained on copious amounts of human-generated data and designed to predict the next word in a given sentence. They are hilarious and eerie and at times dangerous. But what if, instead of building A.I. systems that mimic humans, we built those systems to solve some of the most vexing problems facing humanity? In 2020, Google DeepMind unveiled AlphaFold, an A.I. system that uses deep learning to solve one of the most important challenges in all of biology: the so-called protein-folding problem. The ability to predict the shape of proteins is essential for addressing numerous scientific challenges, from vaccine and drug development to curing genetic diseases. But in the 50-plus years since the protein-folding problem had been discovered, scientists had made frustratingly little progress. Enter AlphaFold. By 2022, the system had identified 200 million protein shapes, nearly all the proteins known to humans. DeepMind is also building similar systems to accelerate efforts at nuclear fusion and has spun off Isomorphic Labs, a company developing A.I. tools for drug discovery. Demis Hassabis is the chief executive of Google DeepMind and the leading architect behind AlphaFold. So I asked him on the show to talk me through how AlphaFold actually works, the kinds of problems similar systems could solve and what an alternative pathway for A.I. development could look like. Mentioned: “The Curse of Recursion” by Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross Anderson “DeepMind’s CEO Helped Take AI Mainstream. Now He’s Urging Caution” by Billy Perrigo Book Recommendations: The Fabric of Reality by David Deutsch Permutation City by Greg Egan Consider Phlebas by Iain M. Banks Listen to this podcast in New York Times Audio, our new iOS app for news subscribers. Download now at nytimes.com/audioapp Thoughts? Guest suggestions? Email us at [email protected]. You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs. This episode of “The Ezra Klein Show” was produced by Rogé Karma. Fact checking by Michelle Harris. Fact checking by Michelle Harris with Rollin Hu. Our senior engineer is Jeff Geld. The show’s production team also includes Emefa Agawu, Annie Galvin and Kristin Lin. Original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. Special thanks to Sonia Herrero.

Transcript

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0:00.0

From New York Times' opinion, this is the Ezra Klein Show.

0:23.0

So I think you can date this era in artificial intelligence back to the launch of ChatGPT.

0:29.0

And what is weird if you talk to artificial intelligence people about that is they'll tell you ChatGPT, it was just a wrapper, an interface system.

0:38.0

The underlying system, GPD3 had been around for a while. I mean, I'd had access to GPD3 for quite a while before ChatGPT came around.

0:46.0

What ChatGPT did was it allowed you to talk to GPD3 like you were a human and it was a human.

0:53.0

So it made AI more human. It made it more able to communicate back and forth with us by doing a better job mimicking us and understanding us.

1:03.0

Which is amazing. I don't mean to take anything away from it, but it created this huge land rush for AI's that functionally mimic human beings.

1:11.0

AI's that relate as if they are human beings and try to fool us into thinking that they're human.

1:16.0

But I've always been more interested in more inhuman AI systems. When you ask somebody who's working on artificial intelligence, including people who believe it could do terrible harm to the world, why are you doing it? What's a point of this?

1:29.0

They don't say, oh, we should risk these terrible consequences because it's fun to chat with ChatGPT.

1:36.0

They say, oh, AI, it's going to solve all these terrible scientific problems we have, clean energy and drug discovery.

1:42.0

And it's going to create an era of innovation like nothing humanity's ever experienced.

1:48.0

There aren't that many examples though of AI doing that yet.

1:52.0

But there is one which you may have heard me mention before. And that's alpha fold. The system built by DeepMind that solve the protein folding problem.

2:01.0

And the protein folding problem is that there are hundreds of millions of proteins. The way they function has to do with their 3D structure.

2:08.0

But even though it's fairly straightforward to figure out their amino acid sequence, it's very hard to predict how they will be structured based on that.

2:16.0

We were never able to do it. We were doing it one by one, studying each one for years to try to figure out and basically map it.

2:22.0

And then they build the system alpha fold, which solves a problem, is able to predict the structure of hundreds of millions of proteins, a huge scientific advance.

2:32.0

So how did they build that? And what could other systems like that look like? What is his other path for AI, this more scientific path where you're tuning these systems to solve scientific problems, not to communicate with us, but to do what we truly cannot do.

2:49.0

Demis Asabis is a founder of DeepMind. DeepMind is owned by Google and recently Asabis was put in charge of all Google AI. So now it's called Google DeepMind and he runs all of it.

3:00.0

That makes him one of the most important people in the world, charting the future of artificial intelligence.

3:05.0

So I asked him to come on the show to talk me through the development of alpha fold, how it was built, what came before it, what could come after it.

...

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