ATRIUMsearch → argument graph
ClaimVideo · 42:04 — 43:34

Building AI systems that can detect diseases like cancer or malaria via scent requires collecting massive, broad data (e.g., sniffing 10,000 people regardless of health status) rather than small targeted studies, because the disease signal is a subtle pattern across many molecules rather than one obvious marker.

Wiltschko argues that scent-based disease detection can't be cracked with small targeted studies; it requires massive indiscriminate data collection, akin to what animals like dogs can already do intuitively. ✦ AI generated

Alex Wiltschko · The TWIML AI Podcast · 2026-07-08 · original ↗

starts at this moment · 42:04

Elicited by

Meaning because there's not enough signal and the correlation between scent and disease or some other factor. Are you speaking?

We just need to go get a ton of data, right? Like we need to like band together and build a huge effort where we just like let's go sniff 10,000 people. I don't care if they're healthy or sick. I don't care how old they are. I don't care.

verbatim transcript · starts at 42:04

Transcript · around this moment

42:04know dogs can do it. Like animals can actually detect these patterns. So there's something, >> right? That's what I was thinking of when you raised. >> It's for sure there. It's for sure there. Um, but we can't get computers to do it. >> We just need to go get a ton of data, right? Like we need to like band together and build a huge effort where we just like let's go sniff 10,000

42:21people. I don't care if they're healthy or sick. I don't care how old they are. I don't care. I mean, let's record all that information, of course, but like let's just go get a ton of data. Let's go to the grocery store and get cucumbers and bananas, flowers, and like steaks and whatever. Um, and let's just go smell everything and then build a huge oldactory data set of what the

42:41world smells like. And if we can do that, then then >> it sounds a lot harder than scraping Reddit one more time. [laughter] >> Whole lot harder than scraping Reddit. But it's worth it because literally nobody's going to do it unless we pull pull ourselves together. So like but this it's like worth doing, right? Like you know this is the thing that maybe as an aside but like I have a lot of

43:02friends in the world of AI and ML and you know I came in through this as a biologist and as a biologist like you go do experiments and it's like hard and like you got to go to the bench and get your own data and you know it's gritty. Um and in the world of AI like you kind of just assume the data is out there and

43:18if the data is not out there you kind of assume you can give somebody a credit card to create the data. Um, and like I think that's amazing. But like the amount of infrastructure that you have access to is just astoundingly huge. And like you just have to understand that if you want to like continue to expand the realm of like what AI can do and compute

43:36can do, like it might get hard. Like you might reach a point where there's not a vendor that can handle everything for you and, you know, shine your shoes or whatever. Like you might have to go do the work yourself. And so we're very much in that regime. And I love like it's it's so hard. It's just it's beyond brutally hard. Um but uh I love it

43:55because nobody else is doing it, right? And like that's very much my preference for how I want to spend my life is I want to do weird things that nobody else is doing that matter, right? And so that's that's kind of my selection criteria. Like there's plenty of people that can go build, you know, AI voice agents for customer service. I think we're going to need that to like make commerce better,

Related moments