Lester pick — Episode 15 (OncoGAN & AI-generated cancer genomes)

Transcript
This chapter, from the episode video's captions · 382 words
54:40>> Uh the AI genome uh project in episode 15. >> Yeah. And I actually just talked to my my mom about this who works in clinical trials and is focused on the future of precision medicine and it was just the the experimental like what they all of the different pieces they put together. >> Yeah. >> And and the redundancy and really how wellought out it was was incredible >> to make sure that the genomes that they were generating with this AI was not linked to anything personal. Yes. you know, it truly was no hippo violations, but we're creating this data set that is going to be used to train AI models to detect cancer.
55:20>> And the fact that when they those with using those newly generated AI genomes using existing tools like deep tumor, they were actually able to increase the efficacy of detection in edge cases. Yeah. >> Uh basically proving that that the the genomes were not just junk data. >> Yeah. Right. It had >> like an existing tool was better off >> but was better off having it as a part of its data set. That was a great I love >> that was a great one. Yeah. Yeah. And that was that was originally because um it was from the University of Toronto and Toronto lost the World Series which >> Yeah. Yeah. Look, >> the LA Dodgers won. >> We're from LA. What do you
56:01>> May I just repeat what is already known? [laughter] The LA Dodgers are backto back world champion. >> We did give Toronto's love though. you know, we made sure we gave you guys, >> but that was an incredible scientific achievement >> from the University of Toronto >> and and the implications are just going to be >> Yeah. >> Um, and again, this is another we covered a lot of AI stories and for me what this was was like AI gets talked about as like oh image generation and AI slop but >> and AI girlfriend, >> right? But this is not this is going to save lives. >> No, straight up >> literally. And it is and it and it's important to understand that in a lot in multiple research environments
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