Aria Finger

And I'm Aria Finger.

I think so many people outside of sort of the AI realm would sort of be surprised that sort of it all starts with gaming, but that's sort of gospel for what we're doing. It's like, that's how we created these systems.

And so switching gears from board games to video games, can you give us just like the elevator pitch explanation for what exactly makes an AI that can play StarCraft II like AlphaStar so much more advanced and fascinating than the one that can play chess or go?

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Can you double click on that for a moment? Like you said, it is in vogue to talk about, are we running out of data? Do we need synthetic data? Like, where do you stand on that issue?

In 2020, a team of researchers at DeepMind successfully created a model called AlphaFold that could predict how proteins will fold. This model helped answer one of the holy grail questions of biology. How does a long line of amino acids configure itself into a 3D structure that becomes the building block of life itself?

In October 2024, three scientists involved with AlphaFold won a Nobel Prize for these efforts. This is just one of the striking achievements spearheaded by our guest today.

I mean, that's wild. And thinking about benchmarks and what we're going to need these digital assistants to do, when we look under the hood of these big AI models, well, some people would say it's attention. So the trade-offs is thinking time versus output quality. We need them to be fast, but of course, we need them to be accurate.

And so talk about what is that trade-off and how is that going in the world right now?

Reid and I sat down for an interview with Demis in which we talked about everything from game theory to medicine to multimodality and the nature of innovation and creativity.

So I want to go back to the world of multimodal that we were talking about before with sort of robots in the real world. And so right now, most AI doesn't need to be multimodal in real time because the internet is not multimodal. And for our listeners, that means absorbing many types of input, voice, text, vision at once.

And so can you go deeper in what you think the benefits of truly real-time multimodal AI will be? And like, what are the challenges to get to that point?

So Demis, one of the areas of AI that when anyone asks me like, hey, Aria, I know you're interested in AI, but like, well, you can write my emails. Like, why is it so special? I just say, no, think about what it can do in medicine. I always talk about AlphaFold. I tell them about what Reed is doing. Like, I'm just so excited for those breakthroughs.

Can you give us just a little bit, you had the seminal breakthrough in AlphaFold and what is it going to do for the future of medicine?

I mean, it's just amazing. I mean, Demis, there's a reason they give you the Nobel Prize. Thank you so much for all of your work in this area. It's truly amazing.

What is a question that you wish people asked you more often?

Final question. Can you leave us with a final thought on what is possible over the next 15 years if everything breaks humanity's way? And what's the first step to get there?

Special thanks to Surya Yalamanchili, Syeda Sepiyeva, Thanasi Dilos, Ian Alice, Greg Beato, Parth Patil, and Ben Relis. And a big thanks to Leila Hajjaj, Alice Talbert, and Denise Owusu-Afrie.

I mean, it's so funny. I was saying to read before this, my seven year old school just won the New York State Chess Championship. So they have a long way to go before they get to you. But he takes it on faith like, oh, yeah, mom, I'm just going to go play chess kid on the computer. Like I'll go play against the computer a few games, which, of course, was sort of a revelation sort of decades ago.

And I remember, you know, when I was in middle school, it was obviously the deep blue versus Garry Kasparov. And this was like a man versus machine moment. And one thing that you've gestured at about this moment is that it illustrated, like in this case, based on Grandmaster Data, it was like brute force versus like a self-learning system. Can you say more about that dichotomy?