What Happens When AI Gets a Lab?

What happens when AI moves beyond the chatbot and into the laboratory? From Claude-designed proteins that bind scorpion toxins to robots that assemble optics experiments, we examine what AI can do for science and what still has to be proven in the real world. Lester Nare and Krishna Choudhary explore the gap between generating a promising design and demonstrating that it works. We also unpack BrainChat, a research platform that lets epilepsy patients interact with their brain-recording data and contribute feedback to seizure detection, and a 15-year study of orangutan mothers facilitating playdates for their young. Along the way: why binding a toxin is not the same as neutralizing it, how patient feedback can help personalize models, and what cloud laboratories and automated experiments could mean for scientific discovery. Which part of science do you think AI will change most? CHAPTERS 00:00 Playdates, brain data and robot labs 00:29 Hello Internet and Signal Award celebration 02:40 Orangutan mothers and infant playdates 15:18 What 15 years of orangutan observations reveal 25:16 Testing the playdate hypothesis 39:30 BrainChat and patient-guided learning 44:57 Putting patients in the feedback loop 1:02:00 Personalizing seizure detection 1:05:33 What the BrainChat study does not yet establish 1:07:59 Anthropic scorpion-toxin binders 1:15:49 Why a toxin binder is not an antivenom 1:18:31 Fast AI designs, slower laboratory testing 1:19:40 Connecting AI to the physical lab 1:23:22 Robotic optics and automated laboratories 1:28:04 Cloud laboratories and remote experiments 1:33:00 Closing the loop with Lila Sciences 1:37:48 Wrap-up RESEARCH & FURTHER READING Orangutan maternal ranging and infant social play (PNAS): https://pubmed.ncbi.nlm.nih.gov/42837467/ BrainChat and human-AI collaboration in epilepsy (Nature Medicine): https://www.nature.com/articles/s41591-026-04680-4 Claude-designed scorpion-toxin binders (Anthropic): https://www.anthropic.com/research/claude-scorpion-toxin-binder-design Robotic optics experiments (MIT News): EDITORIAL NOTES The orangutan findings are observational evidence of mothers facilitating social play, not direct proof of conscious intent. BrainChat was a small feasibility study, not proof of improved clinical outcomes. The scorpion-toxin work demonstrated binding, not a validated antivenom. On-screen clarifications: 15:20 The first author is Odd T. Jacobson. Maria A. van Noordwijk is a coauthor. 16:06 About 32,600 observation hours were collected over 15 years. That is roughly 3.7 years of continuous observation. 40:39 In epilepsy, deep brain stimulation reduces seizure frequency. Tremor is the Parkinson's disease example here. 47:07 The study included 13 participants: 11 with scalp EEG and 2 with intracranial EEG. 51:01 and 51:35 GPT-4o-mini ran in Penn's HIPAA-compliant Azure OpenAI cloud environment, not locally. 1:18:03 This was an Anthropic announcement, in collaboration with Citrini Research. WATCH & LISTEN Episode notes and research: https://ffppod.com/episodes/ep65 Watch and listen: https://ffppod.com Apple Podcasts: https://podcasts.apple.com/us/podcast/id1830375095 Subscribe: https://www.youtube.com/@FromFirstPrinciplesPod?sub_confirmation=1 Support the show: https://ffppod.com/donate Contact: https://ffppod.com/contact Follow @FFPPod on Instagram, TikTok, X and Facebook.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Krishna Choudhary?

  Orangutan mothers arrange play dates for their infants in order to invest in their social development. You're turning the patient into a collaborator for training the neural network, for training the machine learning model.

• 0:16Lester Nare?

  This is so good. They have a robotic arm that in 50 steps in 30 minutes can set up the, these lenses for this optical experiment, which we've talked about previously. Hello, internet. This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Choudhary. We are back for a fun rundown episode this week with some special news about our recent award-winning show as the recipients of a bronze Signal Award, which we just got the news of this morning. And so a big shout out to all of

• 1:00Lester Nare?

  you in FFP Nation for watching the show, for giving us the inspiration. A quick question for our resident PhD, how does it feel to be recognized for the fifth annual Signal Award in best podcast for science?

• 1:14Krishna Choudhary?

  Um, it, it feels great. Um, in the sixth annual Signal Award, I would like to get gold.

• 1:21Lester Nare?

  Yes.

• 1:21Krishna Choudhary?

  So.

• 1:22Lester Nare?

  Yes.

• 1:22Krishna Choudhary?

  That's what I'm feeling, feeling, feeling.

• 1:24Lester Nare?

  Gold is on the mind and we are going to get into three science stories today for rundown. For those of you who joined us during the Nobel Prizes, welcome. Rundowns are where we do a little bit more of a high level overview of some science stories that we have found interesting, starting with an animal behavior story, the parenting techniques and sacrifices of orangutan mothers, followed by personalized medicine with AI, using a chatbot to make epilepsy treatment more effective. And we're going to end with automated scientific discovery. The AIs are telling us they're going to solve all human disease in 10 years. But what

• 2:05Lester Nare?

  does that really mean? And there's an interesting story that actually came out earlier today. It's going to help us look at the larger ecosystem of what is going to be required to have AI really impact clinical outcomes for humanity. As always, we're going to talk about the science from the ground up today because this is from first principles.

• 2:40Krishna Choudhary?

  The headline here is irresistible. Orangutan mothers arrange play dates for their infants in order to invest in their social development. I mean, as soon as I saw this, I was like, we gotta cover it.

• 2:55Lester Nare?

  We gotta cover it.

• 2:56Krishna Choudhary?

  Okay. Because I mean, it intrigues me in so many ways. As a new father, you know, I'm starting to think about the pros and cons of like putting my kid in daycare and like the social development that comes with it, but also the exposure to like, you know, just getting sick and like all sorts of nonsense that comes with that. So, you know, um, there's a lot hiding in that headline. And when I was diving into the paper, there's a lot of rich science behind the methods and things like that, that I thought would be perfect for this podcast. Um, there's a fact that this seems to be a deliberate decision by

• 3:36Krishna Choudhary?

  the orangutan mothers. Like they're not just like casually meeting up with other mothers that have babies. This is a deliberate decision. Um, there's a fact that they have to give up energy and time to do this. So there's a story where, you know, mothers are making sacrifices. It's so similar to how humans do things.

• 3:57Lester Nare?

  Hundred percent.

• 3:57Krishna Choudhary?

  Um, and, and the science underneath that headline is really interesting. Like the way they collected years of data, analyzed that data, and then they can convincingly state that, you know, orangutan mothers are consciously, or not consciously, but deliberately trying to do this. I think it's really, really cool. So.

• 4:18Lester Nare?

  Careful with the consciousness word with all the AI stuff.

• 4:21Krishna Choudhary?

  I know, right?

• 4:21Lester Nare?

  So it's up for debate right now.

• 4:24Krishna Choudhary?

  Exactly. I mean, if you ask me personally, my personal opinion is that of course orangutans are conscious. Like just look at them and look at the way that they respond to their environment. They respond to each other. They respond to stress. I mean, it's, it's obvious to me, but there's going to be people that like, you know, saying, yeah, you don't really know what consciousness is. Okay. Anyways, let's, let's get into some history and background. Orangutans, they're the great apes. They're part of the great apes. That includes gorillas, chimpanzees, bonobos, and us. The animals that are in this study are particularly the Bornean orangutan, specifically Pongo pygmeus wurum by, another

• 5:06Krishna Choudhary?

  taxonomical name that I'm struggling with. But these are the guys that live in Tuhanan in central Kalimantan in the Indonesian Borneo. So Borneo is a giant island and there's Indonesia has like a majority of that. And.

• 5:20Lester Nare?

  I'll just note for all of my Nickelodeon fans, the Wild Thorn Berries is the first time I ever heard of the location of Borneo because this family that traveled around as like wildlife videographers, uh, like that was kind of the, it was like, I think it's literally in the theme song, uh, that, and so it was really cool because they traveled the world, but that was the first time I understood where the location was.

• 5:42Krishna Choudhary?

  They must've been looking for orangutans in Borneo because Borneo is very famous for being like sort of a native habitat.

• 5:49Lester Nare?

  The family had a, a, an ape, Darwin, that could speak to Eliza because Eliza could speak to animals.

• 5:56Krishna Choudhary?

  Oh, I see.

• 5:57Lester Nare?

  That's the schtick of the show.

• 5:58Krishna Choudhary?

  That's the schtick.

• 5:58Lester Nare?

  And they had a, you know, Darwin was around as.

• 6:02Krishna Choudhary?

  It's a good name for an ape.

• 6:02Lester Nare?

  Yeah. And clearly was done with intent.

• 6:06Krishna Choudhary?

  Yeah, yeah, yeah. So orangutans are a bit different from other apes. Okay. Because they are not that social. Um, they are solitary in the sense that adult females, they occupy home regions that maybe overlap with neighbors, but in a real sense, like they're very solitary. Solitary here means that, um, the usual organization of travel and feeding, like they don't, they don't go in groups. They don't assemble in packs, you know?

• 6:37Lester Nare?

  So they're like Americans, very individualistic.

• 6:39Krishna Choudhary?

  Very individualistic in modern, modern Americans, very much so, right? Um, modern society, very much so in some sense. And, um, maybe that tells us something that we can gather from the study, but we'll touch on that later on, right? Um, the idea is that rather than traveling every single day in a stable troop, they actually move as separate units. So the mothers will be separate. And if a mother has an offspring, they're going to move as separate units. Okay. They're going to have some kind of home range of like some section of the forest that they like explore, but those are going to be mostly disjointed. Now, there are good ecological reasons for that. It actually has to do with,

• 7:19Krishna Choudhary?

  um, the diet of these orangutans and like where they, where they live. The diet is mostly like fruit, large fruit eaters. Now, fruit is something that is kind of rare in the forest and it's sporadic. It's not like, you know, at a regular cadence, you can be like, that tree is definitely going to have fruit and then this tree is definitely going to have fruit. So group living has its advantages, right? Because you can like share information, you can share protection, but the disadvantages are that there's a limited amount of food and you're going to be competing for that limited amount of food, right? In a space where the density of food is very low. So that's why orangutans, I mean, from a biophysical

• 8:03Krishna Choudhary?

  perspective, there's a lot of, um, theories out there for why they are solitary. It's actually has to do with the, the physics of the environment itself.

• 8:14Lester Nare?

  There's a resource constraint that they are conscious, not consciously, but actively.

• 8:19Krishna Choudhary?

  Yeah.

• 8:21Lester Nare?

  Operating around.

• 8:22Krishna Choudhary?

  Exactly. Um, this is very different from other great apes. Chimpanzees, very famously, they are in troops, right? There's a lot of resource abundance in the place that chimpanzees live, right? So they can, they can actually administer in that. So now in this space of solitary confinement of these orangutan individuals, the adults who are solitary in the forest, now let's consider the infant. Okay. Orangutans usually have one baby at a time and, um, usually that baby happens once every seven to eight years.

• 8:59Lester Nare?

  Oh, wow.

• 9:00Krishna Choudhary?

  So they don't have siblings.

• 9:02Lester Nare?

  Yeah.

• 9:03Krishna Choudhary?

  In the traditional sense, right? It's not like a, a litter of kittens or puppies where like they, they have a bunch of people their same age that they can play around with. These are single babies to a single, to, to a single mother effectively because the dad.

• 9:20Lester Nare?

  Went to go get the milk.

• 9:21Krishna Choudhary?

  Yeah, yeah, yeah. You know, the dad is not involved. Let's say, let's say that. So, so these, this is a single mom with a single baby, no siblings, and you've got to introduce some bit of social.

• 9:35Lester Nare?

  Yeah.

• 9:35Krishna Choudhary?

  You know?

• 9:36Lester Nare?

  Interaction.

• 9:37Krishna Choudhary?

  Interaction. Yes. You have to introduce some bit of that. You don't have group living. So what do you do? That's the challenge, right?

• 9:47Krishna Choudhary?

  That's the challenge. Um, and that creates a developmental problem inside of the ecological problem.

• 9:54Lester Nare?

  Uh, that's interesting. Yeah, there's there's layers.

• 9:56Krishna Choudhary?

  Right. The ecological problem as well, because of the forest that I'm living in and because of the constraints that I have, depending on the fruit and all of the choices that I have for food, I need to stay independent. But if I have a kid, I need the kid to socialize in order to like actually grow up and be sort of functionally accurate. Right? Like, play is a very, very important part of upbringing. So play is actually very important. It's been studied for a very long time. Play is difficult science-wise because it's easy to recognize, but it's hard to like quantify.

• 10:32Lester Nare?

  Yeah.

• 10:33Krishna Choudhary?

  Like, this is a photo of two bear cubs playing. Now, here, they're probably siblings, which is why they're close to one another, right? And they're playing amongst one another. I don't think bear cubs really mess with other bear cubs because mama bears are very, very cautious of other bears when they are nursing and when they're bringing up their kids. Right? So, so bears have this advantage of having siblings. Orangutans do not. Now, in orangutan research, social play means that you have non-aggressive interactions, such as tagging, wrestling, chasing. It's the stuff that like humans are pretty, are pretty used to. And there's a long history behind why play evolved.

• 11:17Krishna Choudhary?

  I mean, one idea is that it rehearses adult skills. So even if the adult is not meant to be in a social setting, play can still be important. If the adult is meant to be in a social setting, then of course play is important, right? Because you're getting used to socializing. But you could have the argument that like, well, orangutans, they're, they're asocial. Why do you need to play? Well, play gives you a kind of emotional regulation and it gives you the ability to respond to something that is unexpected.

• 11:50Lester Nare?

  Right.

• 11:50Krishna Choudhary?

  Right? If you're doing self-play, then it's kind of a self-feedback.

• 11:55Lester Nare?

  You know what's coming.

• 11:55Krishna Choudhary?

  You know what's coming. When you're, when you're playing with someone else, you don't know what the other person is going to do. And that ability to respond to an environment or to a stimulus that is unpredictable, that can have a lasting effect.

• 12:10Lester Nare?

  It's beneficial to your own survival, ultimately, because you want to be able to react to unknown stimuli in a way that is beneficial to your own survival, but that is not also an overreaction.

• 12:20Krishna Choudhary?

  Yes.

• 12:20Lester Nare?

  Unnecessarily.

• 12:22Krishna Choudhary?

  Exactly. Yeah. So, so, so there is, there is a benefit to play even if the population itself is asocial. That was the first thing that, when I was reading the paper, I was like, that's actually interesting to think about, right? Because it's trivial that play is important for social animals, because you need to be social. But even for asocial animals, there is a, there is a component of play that is important for your development.

• 12:48Krishna Choudhary?

  Any case, do orangutan youngsters play? Well, in 2024, Julia Kuntz, who is a co-author on the current study that we're going to be talking about in depth, she published this article that showed 11,000 observation hours from 99 young orangutans at two sites, one in Borneo and one in Sumatra. And she showed that social play was more frequent in the Sumatran site versus the Borneo site. But actually, if you controlled for the amount of times that

• 13:22Krishna Choudhary?

  play was presented, it was the same.

• 13:25Lester Nare?

  Yeah.

• 13:25Krishna Choudhary?

  So if they're given the opportunity to play, youngster orangutans will play. So now let's try to hone in on the other part, which is how many times are they given the opportunity to play? Because that's what this paper is about.

• 13:38Lester Nare?

  Because there's a difference.

• 13:39Krishna Choudhary?

  Mm-hmm. Right. It's like, it's like if I'm not given the opportunity, then I can't do it, right? And the new paper builds on that groundwork. This is the paper that came out in the Proceedings of the National Academy of Sciences in October 2026.

• 13:52Lester Nare?

  I see you've avoided the acronym.

• 13:54Krishna Choudhary?

  PNAS. That's what it is. That's what everyone says, okay?

• 13:59Lester Nare?

  Everyone says, stop saying the acronym and that's.

• 14:01Krishna Choudhary?

  No, no, no, we, we, everyone says it. This is from Max Planck Institute of Animal Behavior, and there's a few authors from the University of Zurich. So the earlier work established that orangutan youngsters play and that the opportunities for that play varies, right? And here, what we're saying is that the maternal association could provide for that opportunity and the moms actually make a concerted effort in providing that opportunity. Okay? That's what's so cool about this. The methods are incredible, I would, I would say. So since 2003, Van Noordwijk

• 14:46Krishna Choudhary?

  and her team have been following mother-infant pairs at Tuhanan Research Station on the island of Borneo. Since 2003, over a decade of research, they've observed encounters between mothers that are rare, but when they do happen, the mothers are very cautious, but the kids, as soon as they see each other, they just like run at each other and start playing. Yeah, they're having a blast. The mothers are very cautious. They've, you know, developed the whole adult thing of being solitary. The kids, they're having a great time. And one of the things that the first author Van Noordwijk noticed is that these infant encounters, they seem to come at a cost to the mothers.

• 15:28Lester Nare?

  Oh, okay.

• 15:29Krishna Choudhary?

  Okay? The cost being it's a really long and hard day for them when they have to arrange these play dates. Are they even arranging these play dates? And if they arrange these play dates, are they doing it at an expense to their own benefit, right? Because they could be doing other things.

• 15:46Lester Nare?

  Resource gathering.

• 15:47Krishna Choudhary?

  Yeah, they could be eating, they could be resting. Instead, they're doing this, right? So is it a concerted effort? After 15 years, they've amassed 32,000 hours of observation from 31 mother-infant pairs. That's actually incredible, first of all, right?

• 16:03Lester Nare?

  Yeah.

• 16:04Krishna Choudhary?

  Like 15 years of work, 31,000 hours. That's like on the order of like year, a year of just like continuous observing orangutans. It's incredible. The basic technique is something called a focal follow. This is a very famous photo of Jane Goodall doing a focal follow on chimpanzees. The idea is that you have researchers that choose an identified individual and then you basically follow its behavior from sundown to sunset or from sunrise to sunset. You monitor every two minutes where the person is. So you have like a GPS monitor and you're like, okay, I'm going to monitor where the orangutan is right now.

• 16:48Krishna Choudhary?

  I'm going to monitor what the animal is doing at that moment. So is the mother feeding? Is the youngster playing with whom? And roughly every 30 minutes, you log a GPS location, right? So you, you get a kind of GPS of, of where this person, where, where this animal is going. And you get, uh, every two minutes, some kind of log of the activity over the entire day.

• 17:13Lester Nare?

  That's a quite, that's a rich dataset.

• 17:16Krishna Choudhary?

  Yeah. Incredible, right?

• 17:18Lester Nare?

  Yeah.

• 17:18Krishna Choudhary?

  And, and it requires a human being just straight up following this orangutan or this mother-infant pair for the entire day. Pretty incredible.

• 17:27Lester Nare?

  For days and days.

• 17:29Krishna Choudhary?

  Yeah. And it's the same individual that you have to follow for days and days because you want to have some type of longitudinal data on the same individual.

• 17:36Lester Nare?

  Yeah.

• 17:37Krishna Choudhary?

  Now, the dataset here includes 3,000 plus follow days, 2,000 of which were full nest-to-nest days, meaning like, like there's like mothers and more. Only 6.3%, so only 6% did they record play.

• 17:57Lester Nare?

  That's, that's incredible. What we're saying is over the course of all this observation, a less than 10% proportion of it has this peer play concept, has the, the thing we're looking for. And that's why you need such a large dataset because it's so infrequent.

• 18:11Krishna Choudhary?

  Yeah. And you need a large dataset because if you have infrequent events, in order to do good statistics, you need a large dataset, right?

• 18:21Lester Nare?

  Reminds me of the Ice Cube Neutrino Detector.

• 18:23Krishna Choudhary?

  100%. It's the same thing. It's the same exact paradigm, right? Because you need a lot of events in order to hone down on a very rare, like per chance. So now let's get into the figures. They've collected all this data. What do they actually see? Let's start actually with figure C here. This is figure one. In C, on the x-axis, you have the age difference between two offspring that are meeting up for a play date. And meeting up, meaning, you know, they're playing with one another. This is obvious from a human. That's like looking up and seeing them. On the y-axis is their play rate, which is, you know, some normalized sense of how often do these two individuals get together. We're looking at C.

• 19:06Lester Nare?

  Okay, yes.

• 19:07Krishna Choudhary?

  Right? We're looking at this, this part right here.

• 19:09Lester Nare?

  Yes.

• 19:09Krishna Choudhary?

  And there, you can see there's a negative correlation, meaning that as the age difference gets bigger, the play rate diminishes.

• 19:17Lester Nare?

  Yeah, that makes sense.

• 19:18Krishna Choudhary?

  Kind of makes sense, right?

• 19:19Lester Nare?

  My little brother and I have a 13-year age gap. Yeah. So our play rate was much lower than siblings who were much closer together.

• 19:26Krishna Choudhary?

  Exactly. The other cool thing is on the y-axis, it's actually exponential. That's a log scale.

• 19:31Lester Nare?

  Okay.

• 19:31Krishna Choudhary?

  Because it goes like 10 to the minus one, 10 to the minus two, 10 to the minus three. So this is exponential decay. It's a very strong effect that you're seeing, which is like, it seems that like the, the closer you are to age, the longer you're going to play, right? Okay. On A and B, what we're actually seeing is the effect size based on different markers that the, the human beings that are on the ground are actually like marking. For example, you can track the relatedness of two individuals and you can track the fruit availability in the, in the forest. Because you could imagine, right? Fruit availability is a confound. If there's a lot of fruit, then of course the mothers are going to be foraging a lot.

• 20:15Lester Nare?

  Yeah.

• 20:15Krishna Choudhary?

  And so they're going to meet up a lot, right? You want to disentangle those and see what's the actual effect. Well, actually, fruit availability is not that, is not that high. In effect, the correlation is not that big. On the other hand, Related li- relatedness, like whether you're related to the other kid, that's high for occurrence, but if you control for occurrence, the amount of play that you do per occurrence is not that high.

• 20:45Lester Nare?

  That's if you if if your family, I'm not about that life.

• 20:48Krishna Choudhary?

  Yeah, like like we'll meet up a lot.

• 20:50Lester Nare?

  Yeah, but.

• 20:51Krishna Choudhary?

  But I would I would play with you just as much as I would play with a friend.

• 20:55Lester Nare?

  Hmm.

• 20:56Krishna Choudhary?

  You see what I'm saying?

• 20:57Lester Nare?

  Yeah, yup, yup.

• 20:57Krishna Choudhary?

  Like, there's a difference here, like maybe the occurrence is higher with family because like the moms know each other.

• 21:03Lester Nare?

  But just because your family doesn't mean I play with you more.

• 21:05Krishna Choudhary?

  Yeah, yeah, I play with you the same amount that I would with like a stranger's kid, right? Um, the two big anti correlations are again, age difference. That's the very top. It's like, if the age difference is very high, then the amount of play rate is very low and the occurrence is very low. So it seems to be that the mothers have honed in on who has the offspring that is about the same age as my kid.

• 21:30Lester Nare?

  Fascinating.

• 21:31Krishna Choudhary?

  Isn't that crazy?

• 21:32Lester Nare?

  So good.

• 21:33Krishna Choudhary?

  Like, I mean, they're they're keeping track.

• 21:37Lester Nare?

  Yeah, yeah, yeah.

• 21:37Krishna Choudhary?

  Of like of like who has the the right age group.

• 21:40Lester Nare?

  Mm hmm, mm hmm.

• 21:41Krishna Choudhary?

  Of kid. Now, the other the other cool thing, the ranges overlap for moms. Because I told you, right, the the the moms have a um, they're solitary, which means that the range of forest that they forage might not overlap all that much. But overlap is higher for moms that have similar age group kids.

• 22:04Lester Nare?

  Oh, that's really good.

• 22:05Krishna Choudhary?

  Right?

• 22:05Lester Nare?

  That's really good.

• 22:06Krishna Choudhary?

  Because you have this data.

• 22:08Lester Nare?

  Yeah.

• 22:08Krishna Choudhary?

  Over fifteen years, you've been collecting GPS data.

• 22:10Lester Nare?

  Yeah.

• 22:11Krishna Choudhary?

  You can actually tell where the moms forage and the overlap is higher. It's stronger for two moms that have similar aged kids.

• 22:20Lester Nare?

  Which is pointing to this idea of some intentionality of going to that place of overlap. Because I know the person, the other mom that comes around here.

• 22:27Krishna Choudhary?

  Mm hmm.

• 22:27Lester Nare?

  They're a little bit, they're a little Bobby.

• 22:29Krishna Choudhary?

  Yeah.

• 22:29Lester Nare?

  And my little Timmy.

• 22:30Krishna Choudhary?

  Yeah.

• 22:30Lester Nare?

  Are about the same age.

• 22:31Krishna Choudhary?

  About the same age, right? So they can they can like play around.

• 22:34Lester Nare?

  Yeah.

• 22:34Krishna Choudhary?

  This uh, this entire paper is going into that intentionality.

• 22:39Lester Nare?

  Yeah.

• 22:39Krishna Choudhary?

  Right? Can we prove intentionality in orangutan mothers? That's the point.

• 22:45Lester Nare?

  And the way they've structured these questions

• 22:48Krishna Choudhary?

  Mm hmm.

• 22:49Lester Nare?

  and the data points they're tracking in order to reach this this, the conclusions about intentionality are very clever in the areas that they're trying to cover to remove other confounding variables.

• 23:01Krishna Choudhary?

  Exactly. Yeah, and that's it. I've always find this fascinating because like, especially with animal behavior, it's very hard to think of all the confounds, but these guys are doing a pretty good job, I think.

• 23:12Lester Nare?

  Quite nice.

• 23:13Krishna Choudhary?

  Um, the next figure is figure two. Here we talk about um, what it costs for the mothers, right? Um, here the researchers are comparing three different categories. There's days when the mother was alone with her offspring. There's days when another orangutan is nearby, but there's no play. And then there's days when there is infant play. Okay, those are the three sort of categories. Um, the middle category is really important because here we're simply disentangling between like, okay, if another orangutan is nearby, does that have an effect versus another orangutan is nearby and my infant is playing, right? There's there's two different things that are happening.

• 23:56Krishna Choudhary?

  And here we're seeing an actual difference, right? On the left hand side, this is the daily amount of path that the mother takes. When the kid is playing, the path length that the mother traversed in the forest is a lot longer.

• 24:11Lester Nare?

  Yeah.

• 24:11Krishna Choudhary?

  Almost twenty percent longer.

• 24:13Lester Nare?

  Yeah.

• 24:13Krishna Choudhary?

  Okay, which means that she's she's exerting more effort to actually go from one place to another. On the right hand side, figure two C, that is the proportion of time feeding. Hmm. This is what's most important to me and most salient to me. The amount of time that the mother spent feeding herself was lower on days when her infant played with another friend.

• 24:39Lester Nare?

  There there's there's more caloric deficit from the amount of movement.

• 24:44Krishna Choudhary?

  Mm hmm.

• 24:45Lester Nare?

  While having a lower amount of caloric intake at that stage where you're having this higher caloric deficit.

• 24:51Krishna Choudhary?

  Yeah, yeah, yeah, it's it's a two found, it's a twofold.

• 24:54Lester Nare?

  Yeah.

• 24:55Krishna Choudhary?

  Yeah. Effect.

• 24:56Lester Nare?

  Yeah. And and you know, as human beings, part of this is gonna feel like be like, oh, obviously, like you talk about childcare and all these things.

• 25:06Krishna Choudhary?

  Yeah.

• 25:06Lester Nare?

  And you are experiencing this personally right now and and going through that. Yes, okay, with human beings for sure.

• 25:12Krishna Choudhary?

  Yeah, yeah, yeah.

• 25:13Lester Nare?

  But we're trying to empirically show this in a different species that does not have all of the things that we have, and so we have to ask what may seem like basic questions.

• 25:24Krishna Choudhary?

  Yeah.

• 25:25Lester Nare?

  Because we don't, we don't know unless we have the data.

• 25:27Krishna Choudhary?

  Right, and and that's that I think is what's key about this paper, right? It's like with human beings, I can just ask mothers and they'll tell me, yeah, it's hard, right? But here it's it's, I think it's really cool that using data from behavior over fifteen years, we can empirically show that orangutan mothers are kind of feeling the same stresses that human mothers feel, right? The last, the last figures that I'm going to talk about, I think are really, really cool. The next step is really asking whether mothers' movements change before and after play.

• 26:01Lester Nare?

  Mm hmm.

• 26:01Krishna Choudhary?

  Okay, like, is it intentional? You know, when we talk about like intentionality.

• 26:05Lester Nare?

  Yeah.

• 26:06Krishna Choudhary?

  Well, here, what we do is first we define each mother's reference core using the average of her GPS location from the preceding year, three hundred and sixty five days. So we've got a range, right? Because we need to be like, where was this mother before the before the foraging? Like, what is her control? And we're like, okay, this particular mother, um, Mindy goes in the green region of the forest and another mother, Johnak, goes in another region of the forest. And there's a little bit of overlap because they have similar aged infants. Okay, we've already covered that part, right? Now,

• 26:48Krishna Choudhary?

  let's talk about the day when the the infant play happened. Okay, let's look at the day before and the day after. The day before, you can see both mothers actually venturing into each other's zones, which means there's planning involved, right?

• 27:05Lester Nare?

  They're scoping the scene out.

• 27:06Krishna Choudhary?

  Yeah, they're like, they're like, let's, you know, let's try.

• 27:09Lester Nare?

  Yeah.

• 27:09Krishna Choudhary?

  Let's try to find the other

• 27:11Lester Nare?

  Right.

• 27:11Krishna Choudhary?

  woman who has the kid.

• 27:13Lester Nare?

  Yeah.

• 27:13Krishna Choudhary?

  That's the similar age as mine.

• 27:15Lester Nare?

  Yes.

• 27:15Krishna Choudhary?

  And then let's let's try to meet up. That's the yellow, right? You see, you see, you see Mindy like going into Johnax's zone.

• 27:22Lester Nare?

  Yup.

• 27:23Krishna Choudhary?

  And then the day of the play, they actually play around. And you see where the play starts and where the play ends.

• 27:29Lester Nare?

  Yes.

• 27:30Krishna Choudhary?

  And and they're they're moving around in their territory. And the day after.

• 27:34Lester Nare?

  Incredible.

• 27:35Krishna Choudhary?

  The day after Mindy's like, you know what? We're good. That was enough.

• 27:38Lester Nare?

  Yeah.

• 27:38Krishna Choudhary?

  We're good. We're good, we're gonna, we're gonna hang out entirely in my own zone.

• 27:43Lester Nare?

  This is incredibly like, it's so visceral and visual, because you can see it visually.

• 27:47Krishna Choudhary?

  Yeah.

• 27:48Lester Nare?

  You know, a a and again, the pre, during and post, this is why anytime you do data capture, like you want.

• 27:54Krishna Choudhary?

  Yeah.

• 27:54Lester Nare?

  The pre.

• 27:55Krishna Choudhary?

  You want controls.

• 27:55Lester Nare?

  Of the event you're tracking for. And it's the distinction between the day before and the day after is so

• 28:04Krishna Choudhary?

  in your face.

• 28:04Lester Nare?

  Yeah.

• 28:05Krishna Choudhary?

  Um, and they have statistics to prove this because this is obviously an anecdotal thing, right?

• 28:12Lester Nare?

  Sure.

• 28:12Krishna Choudhary?

  One of the statistics is this directional bias. I mean, these are the statistics on figure four E, they're actually showing the difference between like the correlation day before, day after, there is a huge difference in the behavior.

• 28:25Lester Nare?

  Yes.

• 28:26Krishna Choudhary?

  The directional difference is what's really cool.

• 28:28Lester Nare?

  Hmm.

• 28:29Krishna Choudhary?

  Um, in the day before, they're moving towards

• 28:33Lester Nare?

  Mm hmm.

• 28:34Krishna Choudhary?

  the other territory.

• 28:36Lester Nare?

  Mm hmm.

• 28:36Krishna Choudhary?

  That's the yellow compared to the control on the left.

• 28:39Lester Nare?

  Yes.

• 28:40Krishna Choudhary?

  And the day after, they're moving away.

• 28:42Lester Nare?

  Yeah, yeah.

• 28:43Krishna Choudhary?

  And it's

• 28:46Krishna Choudhary?

  you know?

• 28:46Lester Nare?

  I I, one of the things that as we've gone through so many episodes of our last year, almost in a half now, is really getting a strong understanding of the questions that are asked, the types of data that are gathered and the type of analysis that's done on that data. And understanding how those three component parts connect to

• 29:10Krishna Choudhary?

  Yeah.

• 29:11Lester Nare?

  conclusions you want to draw. And with animal behavior especially, we've done a lot of these stories. It's a very complex, all biology is complicated. Now we're adding behavior on top of just the biological substrate. Um, and when you see something like that, it's it's very.

• 29:32Lester Nare?

  I don't want to say the word convincing, but it is present.

• 29:36Krishna Choudhary?

  Yeah, I mean, to me, it's like, it's, it's, it's confirming a notion that I already had, which is that like, of course, these animals are like,

• 29:46Lester Nare?

  of course.

• 29:47Krishna Choudhary?

  Are like, doing all these things, right? But it's just, it's pretty awesome that like, with 15 years of data and all of this effort, we can like quantify these things. I think it's, I think it's absolutely amazing.

• 29:59Lester Nare?

  This makes me think of the whales have culture.

• 30:01Krishna Choudhary?

  Yeah.

• 30:01Lester Nare?

  Uh, story we did, and we were having this very emotive reaction about it. Because we were showing something that might feel obvious.

• 30:10Krishna Choudhary?

  Yeah.

• 30:11Lester Nare?

  If you just like, of course whales have.

• 30:12Krishna Choudhary?

  Yeah.

• 30:13Lester Nare?

  Culture and things like that. But like, how do we know?

• 30:15Krishna Choudhary?

  Yeah, I, I think that's kind of cool.

• 30:17Lester Nare?

  And I think it's cool that we can like, say, this is how we know.

• 30:21Krishna Choudhary?

  Yeah.

• 30:22Lester Nare?

  Like, if you were to say, oh, the orangutan mothers are solitary, but they, the headline, like, just looked at, oh, how do you know that orangutan mothers are setting up playdates for their kids? Well, actually, here's the.

• 30:34Krishna Choudhary?

  Yeah.

• 30:34Lester Nare?

  One, two, three, four. Here's how we removed other elements from it. And it's the well constructed, the methods are well constructed.

• 30:44Krishna Choudhary?

  Yeah, yeah. I really quite enjoyed this. Yeah. The evidence here supports active maternal facilitation.

• 30:51Lester Nare?

  Yeah.

• 30:52Krishna Choudhary?

  And is consistent with planning, as we were, as we were talking about just now. Now, the broader scientific contribution here concerns paternal investment. Because in evolutionary biology, we think about like, investment meaning effort or resources devoted to an offspring that have a cost or trade off for the parent, right? It doesn't require some kind of conscious calculation. We usually think about it in terms of nursing, caring and protection. Those are the obvious examples. In this case, the paper is making a case for including access to a social environment. Right? As something that we need to talk about when we talk about paternal parental investment. Right?

• 31:38Krishna Choudhary?

  The, the fact is that like, a mother can shape development by changing which other animals her youngster has a chance to meet.

• 31:48Krishna Choudhary?

  It's part of a growing movement. There's been a growing movement in the science of behavioral analysis these days, where

• 31:58Krishna Choudhary?

  the biologists are now starting to study solitary mammals. And they're saying, actually, like, play and socialization is important, even if these solitary mammals are kind of doing their own thing. Like, you can't count out social boundaries and social interactions, just because, you know, by tradition, these, these animals are solitary. Now, there is a second implication.

• 32:24Lester Nare?

  Okay.

• 32:24Krishna Choudhary?

  Which I found quite intriguing. It was in the discussion in their, in their paper. As an Indian, I found it quite intriguing. Here's why. So, group living means that, you know, young animals, they meet potential partners almost automatically. If you live in a group,

• 32:50Krishna Choudhary?

  you get introduced to potential partners pretty regularly. If you're like an orangutan and you're solitary and your mom's solitary, so, which means that you are solitary because you're tied to your mom, you're not going to meet a lot of the opposite sex on an organic basis, right? Just from Brownian motion of like, the orangutans. So perhaps, in a dispersed society,

• 33:17Krishna Choudhary?

  someone has to bear the cost of making these encounters possible.

• 33:23Lester Nare?

  Right.

• 33:23Krishna Choudhary?

  Kind of like an arranged marriage system.

• 33:25Lester Nare?

  Yeah, yeah, yeah.

• 33:26Krishna Choudhary?

  Right? And, and, and this is in the discussion of their paper. They're saying, look, like, you know, in a solitary society,

• 33:36Krishna Choudhary?

  this is an evolutionary advantage.

• 33:38Lester Nare?

  Yeah, the, the facts.

• 33:39Krishna Choudhary?

  Because, because the moms, like, okay, if we, if we get real nitty gritty down to it, right? The moms care about their genes being passed on. This is about evolutionary fitness, Darwinian fitness, so on and so forth, right? That's going to require their kid in a solitary society to like, kind of ingratiate themselves with individuals of the similar age, right? What if, what if that's the motive?

• 34:02Lester Nare?

  Yeah, yeah, yeah, right, right. Look, look.

• 34:04Krishna Choudhary?

  That's the question. Now we can ask that question. Now, and, and the more data we have, we can ask that question. And that's actually what's next, right? Is whether the investment actually ultimately pays off.

• 34:17Lester Nare?

  In an increase.

• 34:18Krishna Choudhary?

  In a, in an increase, like, fertility of the offspring. Right? We don't have the answer to that question yet. But a, a, as an Indian son that like, kind of reminded me a little bit about, about some of the struggles that I've had.

• 34:33Lester Nare?

  This is so, it's, I love the connection.

• 34:35Krishna Choudhary?

  You know, it's funny.

• 34:36Lester Nare?

  Yes, yes, I, I will make, and this is a big spoiler alert for anyone who's not watched the new Netflix TV show Below. So spoiler alert. Fast forward like, a minute or two. What's funny about what you just said is, in this show, Below, which is about this sort of local town in Newfoundland that gets moved around because they, some rich entities want access to resources where they live. The main character got moved when he was a kid and now is an adult in an island, next to the island he grew up on. In this Bay Area.

• 35:18Lester Nare?

  And one of the plotlines in the show is that they thought where they were moved from was abandoned. But there was actually a family, a family and a few other individuals that had stayed there that were solitary. And there was a daughter that the dad was struggling to find a partner for. And there was this sort of scheme where the main character's son got whisked away to this solitary island to be the potential partner. And this idea of the, the parent having this intent around this evolutionary advantage, like, wanting their, it was, it was, and it was a core tension point in the show that had, anyway, but like,

• 36:04Lester Nare?

  I'm just bringing a popular culture reference to something that you're talking about here, because it's, it's literally narrative storylines. And like, the number two show on Netflix right now.

• 36:13Krishna Choudhary?

  Yeah, yeah. And that's what the authors are proposing here, right? In their discussion, they're saying like, look, like, as a solitary society, the moms are also thinking, look, I kind of want grandkids.

• 36:25Lester Nare?

  I would love that.

• 36:25Krishna Choudhary?

  Even these orangutan moms are thinking the same thing. They're like, look, I want orangutan grandkids, which means that you're going to need to meet people of similar age. It kind of explains the negative correlation between, you know, age difference and like meetup.

• 36:41Lester Nare?

  Very good point.

• 36:42Krishna Choudhary?

  You know, like, it kind of explains like, the intentionality behind all of this. I, I think, so, so if, if I like the theory, you know? And of course, like, there's going to be a lot more data that's going to be involved, because what you're going to have to do as scientists, you're going to have to now follow the offspring.

• 37:00Lester Nare?

  Yeah, right.

• 37:01Krishna Choudhary?

  And see what their reproductive success is over several decades, right?

• 37:05Lester Nare?

  This is a longitudinal study.

• 37:07Krishna Choudhary?

  Yeah.

• 37:07Lester Nare?

  Long, long, longitudinal study.

• 37:09Krishna Choudhary?

  Exactly. But, uh, but I'm willing to bet that I think that's what's happening.

• 37:15Lester Nare?

  The data suggests.

• 37:16Krishna Choudhary?

  Yeah.

• 37:17Lester Nare?

  Very good, very good. I just want to know, because usually, in the context of Below, the mother was dead, which is why the dad was doing the job. And so that's part of the gender difference there, but like, obviously.

• 37:28Krishna Choudhary?

  Here, it's like, all moms.

• 37:29Lester Nare?

  It's the moms.

• 37:30Krishna Choudhary?

  Yeah, it's, here it's.

• 37:31Lester Nare?

  Generally speaking, generally speaking.

• 37:33Krishna Choudhary?

  General speaking, look, we, we know how it is, okay? Even in humans, it's, it's usually the moms.

• 37:39Lester Nare?

  That's how it goes.

• 37:40Krishna Choudhary?

  Yeah.

• 37:40Lester Nare?

  Fantastic first story. The animal behavior stories are always so interesting, because it's kind of not obvious for a layman to like, understand, like, how would you even go about asking the question?

• 37:51Krishna Choudhary?

  Yeah.

• 37:52Lester Nare?

  And they're so clever at it. We are going to transition into our next story, which is going to be about AI chatbots and epilepsy. But before we do so, I'm going to do a quick commercial break here for some housekeeping. Welcome, for those of you who are joining us. This is from First Principles, the best science show on the planet. I have one very amazing announcement about how you can watch the show. If you're an audio listener or have been an audio listener, we do a lot of overlays and visuals in our show to kind of talk through the fundamental science, the methods. It really helps you get an understanding of why these stories are so important. And I'm very proud to announce that finally, after weeks of promo,

• 38:37Lester Nare?

  that the podcast is now available in video on Apple Podcasts. So for those of you who have iPhones or listen previously on Apple, you can now watch the show. It's also available in video on Spotify and YouTube. On social, we have so many clips that are a way that so many of you listen to us. If you've come from an Instagram clip and are now watching the full podcast, leave a comment below letting us know you've come from TikTok, Facebook, etc. We'd love to know that you've made the transition to the full show. It's super helpful. You can always get all of the research papers that we talk about at our website, ffppod.com.

• 39:19Lester Nare?

  And we are now going to move in to our second story of the rundown. AI, chatbots, epilepsy. How do they all connect?

• 39:30Krishna Choudhary?

  Well, we've covered a lot of AI doom and gloom.

• 39:33Lester Nare?

  Yes.

• 39:35Krishna Choudhary?

  Because, well, there's a lot of AI doom and gloom.

• 39:38Lester Nare?

  Yes.

• 39:39Krishna Choudhary?

  But this one, I think, is genuinely uplifting because it's the promise of AI that got me excited about it in the first place. All those years ago, when we got the first chat GPT moment, and everyone was like, oh, this is gonna change the world.

• 39:52Lester Nare?

  The future.

• 39:53Krishna Choudhary?

  You know, it's gonna be the future. Um, this isn't the apocalypse. This is actually great. Now it seems like, oh, I don't know. Um, in any case, this particular story, it's about personalized medicine. This time it's for epilepsy patients. The story comes out of the University of Pennsylvania. The headline reads, AI platform allows real time conversation between patients and their medical devices. I think it's really, really cool.

• 40:20Lester Nare?

  This is interesting.

• 40:20Krishna Choudhary?

  Now, in this case, when I say personalized medicine, I'm talking about really a personalized machine for medicine. Okay, we've covered things like this in the past. We have pacemakers that regulate your heart. We have cochlear implants that digitize sound, and we've got deep brain stimulators that suppress the tremors of Parkinson's disease and epilepsy. Now, these are marvels of modern engineering, but they're all entirely silent to the patient that is housing these machines, right? Like, if you get a medical implant today, the device records your physiological data, and then it stores it in some black box effectively, right? Some kind of USB drive or something.

• 41:05Krishna Choudhary?

  And then months go by, weeks go by, depending on what the treatment is. You go to the clinic, the physician downloads whatever the data is, and then only the physician really understands what's going on. And then he or she or they are gonna be going through and understanding. And you're essentially locked out of that loop.

• 41:25Lester Nare?

  That's actually really good. I never thought about that. Yeah.

• 41:27Krishna Choudhary?

  Yeah, like the patient who is creating the medical data is out of the loop, right? The physician kind of just like uses the patient for the data and then tries to understand the patient from the data, but there's no real feedback that's happening, right? Nowhere is this disconnect even more critical than with the treatment of epilepsy. Globally, around 70 million people live with epilepsy. Epilepsy, a third of them get seizures that simply do not respond to medication. A seizure is basically a temporary episode of abnormal excessive activity in the brain. It's like synchronized neural networks. It can sometimes make a person experience convolutions,

• 42:11Krishna Choudhary?

  brief loss of awareness, unusual sensations, disturbance in memory. There's a myriad of different things that a seizure can do. And a single seizure does not automatically mean epilepsy, but if you're prone to this recurrence over time, then it's reason to diagnose with epilepsy. Now, sometimes drugs work, but then there are drug resistant patients. As with almost every brain disease, there are drug resistant patients. In this case, um, one of the ways that we try to combat it is with implantable neuromodulation devices.

• 42:51Krishna Choudhary?

  This is where we stick an electrode into the brain that is, that has the ability to read the electrical signals and then pump electricity in. So what this thing is going to do is read brainwaves, and in principle, it can detect seizures using the brainwave profile, and then it can also provide current into the brain to mess with the neural circuits, and then perhaps stop the seizure, right? And the history of this, of these types of devices has a rich, rich, um, history. You've got the earliest FDA approved devices in 1997, something like the vagus nerve stimulation.

• 43:36Krishna Choudhary?

  This is purely open loop. This is something where you actually stimulate the vagus nerve that's like down near your neck, not actually in the brain. And what you're actually doing, you can think of it as like a lawnmower, not a lawnmower, a lawn sprinkler.

• 43:50Lester Nare?

  Okay.

• 43:50Krishna Choudhary?

  Okay, that just like turns on, and then it like sprinkles. It's doing that, but with electricity. It's, this is what we would call open loop.

• 44:00Lester Nare?

  Okay.

• 44:01Krishna Choudhary?

  Because there's no feedback coming from the brain, and there, there's no, it's just, it's on a timer, and it's just like going. It's just stimulating the vagus nerve at regular intervals.

• 44:15Lester Nare?

  It doesn't know if the grass is growing. It doesn't know what patches of the grass are growing more than others, any of that kind of stuff.

• 44:20Krishna Choudhary?

  Any of that kind of stuff is just blindly implementing.

• 44:23Lester Nare?

  I know it needs water.

• 44:24Krishna Choudhary?

  Yeah, and, and, and it, it had its effects.

• 44:29Lester Nare?

  Sure.

• 44:29Krishna Choudhary?

  And it was great. 2013, there was a massive leap into closed loop systems. So this is the RNS system, which is the responsive neurostimulation. Meaning, I've got some kind of read mechanism that is going to maybe identify that a seizure is happening, and then only stimulate when the seizure is happening, right? So this is where the responsive thing comes in.

• 44:57Krishna Choudhary?

  Now, instead of open loop, we've got a closed loop kind of thing, right? The device knows when the seizure is coming, but at the same time, it's not closed in the sense that the patient is aware of what is going on, right? You can't, you can't tell the patient, maybe you need to sit down, maybe you need to pull the car over.

• 45:16Lester Nare?

  Alert, alert.

• 45:17Krishna Choudhary?

  Like, yeah, alert, you're about to have a seizure.

• 45:21Lester Nare?

  Right, which would be obviously great.

• 45:24Krishna Choudhary?

  That would be great. And that's where the breakthrough currently comes in hand.

• 45:28Lester Nare?

  This, this, the patient was never involved in any of the, the communication cycle of this. Obviously, we weren't there yet.

• 45:34Krishna Choudhary?

  Yeah.

• 45:34Lester Nare?

  But.

• 45:35Krishna Choudhary?

  Because it required like a human being with a trained experience to understand all of these things. Now, with the advent of machine learning, you can put the patient in the loop of his or her own medical destiny.

• 45:51Lester Nare?

  Yeah.

• 45:52Krishna Choudhary?

  It's pretty incredible.

• 45:53Lester Nare?

  It's, this is a really big deal, um, because I think it also, you know, there's this whole, been this whole era of like quantified self, and you're getting all this medical information for all these devices we have around us. It's gonna help you improve your well-being, but part of, and this, that's a different thing than medical and clinical treatments and solutions. I'm not saying that the same thing, but there's been this big promise.

• 46:15Krishna Choudhary?

  Yeah.

• 46:16Lester Nare?

  With the rise of technology is bringing us closer to understanding our own bodies.

• 46:20Krishna Choudhary?

  Yeah.

• 46:20Lester Nare?

  In real time. Um, and this is a perfect example of how that can have a hugely important impact for epilepsy patients.

• 46:33Krishna Choudhary?

  Yeah, and I think, I think it's not only limited to epilepsy. This particular case is taking epilepsy as kind of like this microcosm.

• 46:42Lester Nare?

  Yeah.

• 46:42Krishna Choudhary?

  Because it's, it's such a rich dataset in terms of like, you know, you get intracranial stimulation, there's a lot of data that's going on. It requires a lot of expertise that perhaps a machine learning model can elucidate. But, um, it's, it's absolutely an incredible paper. It's a team out of the University of Pennsylvania. And what they've done is fundamentally reimagine the patient-device relationship by bridging this EEG that comes with the, the patients of epilepsy with agentic large language models, right? That's the key.

• 47:19Lester Nare?

  Yeah.

• 47:19Krishna Choudhary?

  You're tying in the brainwaves from inside the brain of these epileptic patients with now agentic models that use large language.

• 47:30Lester Nare?

  This, this, this is the, the promise that has been, again, large language models are not just a tool in the context of a chatbot.

• 47:37Krishna Choudhary?

  Yes.

• 47:38Lester Nare?

  Is kind of the point here.

• 47:40Krishna Choudhary?

  Yeah, they've built a kind of interface that allows the patient to maybe interface with the chatbot, but the chatbot now has access to all of the data from within the patient's brain itself.

• 47:52Lester Nare?

  Which is.

• 47:53Krishna Choudhary?

  It's incredible, dude.

• 47:54Lester Nare?

  Yeah.

• 47:54Krishna Choudhary?

  Now, now, I actually, like, you know, from a personal standpoint,

• 48:01Krishna Choudhary?

  my PhD had to do with electrophysiology, right? Where, you know, you're taking electrodes, you're sticking it inside, um, the rodent brain while the rodent is freely behaving inside mazes, inside, you know, behavioral tasks. One of the, in, one of the, the bane of my existence during my PhD was deciphering between real neural signal and all of the artifacts that come with behavior. Because if the rodent is chewing, that's gonna cause electrical signal, right? If the rodent is, um, going into like a micro nap, that's gonna cause electrical signal. This problem is real even for epilepsy patients, right?

• 48:46Krishna Choudhary?

  Because if you've got electrodes that are going into the brain, there's a giant signal to noise problem. Because brainwaves are incredibly chaotic and highly variable. Here we're seeing brainwaves where chewing artifacts come in. All of those, all of those jagged things, that's when the person is chewing.

• 49:08Lester Nare?

  That's crazy.

• 49:09Krishna Choudhary?

  If you have a really terrible machine learning classifier that just thinks you're having a seizure every time you chew.

• 49:19Lester Nare?

  Yeah, not great.

• 49:20Krishna Choudhary?

  That's not gonna be great for two reasons. There's this thing called alarm fatigue, which is basically the boy who cried wolf. Right? If every time, yeah, if every time the thing just says, hey, you're gonna have a seizure and you're not having a seizure, there's so many false positives. Well, then you're gonna just disregard the thing, right? And two, if you're kind of someone who has seizures and has anxiety, that's gonna add to your anxiety and it's gonna like be detrimental to your well being at the end of the day. Like, the whole point is to get better and like having these alerts is making it worse, right?

• 49:56Lester Nare?

  You're constantly put in a state of heightened.

• 49:59Krishna Choudhary?

  Exactly.

• 49:59Lester Nare?

  You know, uh, defense mode, anyway.

• 50:02Krishna Choudhary?

  Right. And, and the, the reason why this is so hard is because historically, tuning these algorithms requires an expert neurologist to manually go through all of the data from your brain and be like, yep, that was you chewing, that was you not chewing, that was an actual seizure right there. Right? You gotta have some M D be like, that was an actual seizure. We gotta tag that, not tag the other thing. Now.

• 50:28Lester Nare?

  I can't believe.

• 50:28Krishna Choudhary?

  You flipped the script entirely, right? You're, you're turning the patient into a collaborator for training the neural network, for training the machine learning model.

• 50:38Lester Nare?

  This is so good.

• 50:39Krishna Choudhary?

  It's, I, dude, I think, I think it's so cool. I mean, there are things that are involved here that they were very careful about. You've got to be HIPAA compliant. And, um, these guys at UPenn, they made a system that features a HIPAA compliant instance of the GPT four O mini. So this is one of those mini models that OpenAI has, right? Which means that you can like, like, put it on a local thing.

• 51:04Lester Nare?

  Right, it doesn't need to go to the cloud.

• 51:05Krishna Choudhary?

  It doesn't, yeah, yeah. So now patient data is not going to the cloud and like, you know, uh, uh, the, the, the Levant, El Pogay and Buck Master.

• 51:15Lester Nare?

  Uh, a problem.

• 51:16Krishna Choudhary?

  Nightmare where, right? Where it's like they, if, if you guys don't know the Navier-Stokes episode, right? Buck Master was talking to OpenAI Codex about his solution for Navier-Stokes. And now.

• 51:28Lester Nare?

  Lo and behold.

• 51:29Krishna Choudhary?

  Lo and behold.

• 51:30Lester Nare?

  A week later.

• 51:30Krishna Choudhary?

  Yeah, OpenAI came out with Navier-Stokes, right? Like, I don't know, I don't know how that happened. Well, in this case, they've got a GPT four O mini that is local, right? So there's no way that HIPAA, that the data from the patient is going into the cloud.

• 51:45Lester Nare?

  It's, it's, and it's not trivial to be able to execute something like this on, on one of these smaller mini models. Like, that's been part of the challenge.

• 51:55Krishna Choudhary?

  Exactly.

• 51:55Lester Nare?

  Generally is, is you want the capability.

• 51:57Krishna Choudhary?

  Yep.

• 51:58Lester Nare?

  As you go to smaller parameter based, the smaller parameter models, you lose some of the capability you're looking for in an L L M. So to be able to, uh, work within that constraint and still get the solution is, I think, an interesting part of the, the, the success here.

• 52:13Krishna Choudhary?

  Exactly. And one thing that I want to note here is the L L M is not actually doing the medical diagnosing. Okay? They've got a specific model, a neural network that they've trained for earlier purposes that is doing the medical diagnosis. Like, there, there's a separate model that is like taking in the brainwaves and being like, hey, is this epilepsy, is this none, blah, blah, blah? The L L M is being used to interface with the patient because that's the whole point of the language, right? Is like, as a patient, I want to use natural language to interact with whatever data modality.

• 52:48Lester Nare?

  Crazy.

• 52:49Krishna Choudhary?

  Right? This is, so it's really agent based. Like, there's the agent that's the L L M. Then there's the agent that's like, has nothing to do with English.

• 52:58Lester Nare?

  Right.

• 52:58Krishna Choudhary?

  But just trying to classify brainwaves.

• 53:02Lester Nare?

  Mate.

• 53:03Krishna Choudhary?

  Yeah. I, I, I think, I think, I think it's really cool. So, so you can imagine, right? Like, the deep learning model detects anomalous brain synchrony. The A I then texts the patient, hey, did you just experience a seizure?

• 53:17Lester Nare?

  So crazy.

• 53:18Krishna Choudhary?

  And then the patient is like, uh, no, that was just me like jumping up and down. Or actually that felt weird, that might have been a seizure.

• 53:26Lester Nare?

  And now it's, again, you're taking the, the cycle of training goes from patient collects data over X period of time. It goes to the physician, the physician, physician spends X period of time with some assistant or someone else that's tagging manually.

• 53:42Krishna Choudhary?

  And then the physician doesn't even know the personal experience of the patient. Who's like experiencing the seizure.

• 53:48Lester Nare?

  So they're just looking at it and based on our past things, this is what normally. It's, it actually, it has both the combination of bringing the patient into their own.

• 53:57Krishna Choudhary?

  Mm hmm.

• 53:57Lester Nare?

  Uh, giving them a sense of control and their own experience. But it also has a very functional benefit to improving the detection. Because the, the part of the challenge is always patients translating their experience to the physician and then like that back and forth, which there's, it's a very noisy. The signal to noise there is also not very high.

• 54:20Krishna Choudhary?

  Right, right, right. I mean, it's absolutely incredible. I think, I think we're standing at a new dawn.

• 54:25Lester Nare?

  This is crazy.

• 54:26Krishna Choudhary?

  Medical technology, right? We're moving away from passive hardware that treats patients as biological substrates and towards semi-autonomous conversational partners that help us navigate our own biology. Like, imagine you have, you, you have like agency over your own kind of outcome in some sense.

• 54:48Lester Nare?

  And, and I want to make a small note here. There's, I know there's a lot of, uh, people have a lot of feelings about specific providers of foundational models.

• 54:58Krishna Choudhary?

  Yeah.

• 54:59Lester Nare?

  Uh, I think part of the point here is, is this could be done on an open source mini model.

• 55:03Krishna Choudhary?

  Yeah.

• 55:03Lester Nare?

  It's not, it's not OpenAI mini specific. It happens to be what they used in this case.

• 55:09Krishna Choudhary?

  The large language model is doing a very small amount.

• 55:12Lester Nare?

  Right, honestly. Right, right. And, but I just wanted to make that distinction that, you know, the, now that we understand it can be done, this can be extrapolated to whatever provider you prefer.

• 55:24Krishna Choudhary?

  Yes, exactly. Um, yeah. Um, it's incredible. I want, I want to take a brief note about how they actually did it. Because, you know, even though this is a rundown episode, I want to get into like some of the.

• 55:34Lester Nare?

  We gotta hit two hours, you know how it is.

• 55:36Krishna Choudhary?

  Yeah, yeah, yeah. I'm sorry, guys, but you know, these Penn researchers, they recruited 13 adults in the epilepsy monitoring unit. The setting here matters because it's an epilepsy monitoring unit, which is one of these things where you go in, high quality recording, there's clinical supervision, there's detailed event review. And also sometimes they even take reduced medication to induce more and more epilepsy and more and more seizures to actually get more and more data. So that matters a little bit, right?

• 56:07Lester Nare?

  Sure.

• 56:07Krishna Choudhary?

  Um, the platform, actually, there's three parts. There's a stream of E E G and clinical information. And then there's software that extracts whatever the useful measurements are. And then that goes into a phone interface that then connects to the patients. And the patients can be like, just like in the epilepsy monitoring unit with their phone, talking to this agent that is also talking to like the models that are interpreting data from his brain.

• 56:33Lester Nare?

  This is so good.

• 56:35Krishna Choudhary?

  Dude, it's, it's absolutely incredible.

• 56:37Lester Nare?

  It's clever too.

• 56:38Krishna Choudhary?

  Yeah.

• 56:38Lester Nare?

  It's so obvious, like, it's so obvious in retrospect.

• 56:41Krishna Choudhary?

  Yeah, and, and there's so many multiple modalities here. Like, there's a system that estimates sleep stages so that that is not confounding.

• 56:49Lester Nare?

  Yep.

• 56:49Krishna Choudhary?

  The algorithm. There's a system that met, that estimates medication because there's medication information in the patient's notes, right? That'll tell you, okay, the patient took the medication at this time. And then there's a model that tells you how the medication is deteriorating with time.

• 57:07Lester Nare?

  Yep.

• 57:07Krishna Choudhary?

  And that's gonna inform this ensemble of machine learning.

• 57:14Krishna Choudhary?

  Models.

• 57:14Lester Nare?

  We have all these inputs into the model that gets to decide, do I set the alarm on or off? And you also now have the feedback of the patient, which is arguably one of the most important inputs.

• 57:24Krishna Choudhary?

  Yeah.

• 57:24Lester Nare?

  Into that, uh, let's call it detection system. Um, and to be able to have them all connected and streaming.

• 57:32Krishna Choudhary?

  Mm hmm.

• 57:32Lester Nare?

  Real time.

• 57:33Krishna Choudhary?

  Yeah, it's absolutely very, very cool.

• 57:35Lester Nare?

  Quite nice.

• 57:35Krishna Choudhary?

  And the result is a structured collection of measurements, right? You can, you can have these measurements be queried, plotted and discussed. And the chatbot and the seizure detector are different models that are doing different jobs. But they're all coming together for the seamless sort of loop. The patient has a brain interface. It goes through all of the stuff, but the patient owner only interacts with it using natural language. Which means that, you know, it's kind of a democratized system. The patient doesn't have to understand what the machine learning nonsense is happening, right?

• 58:10Lester Nare?

  Right.

• 58:10Krishna Choudhary?

  The chatbot is going to go ahead and ask it questions and get the feedback.

• 58:16Lester Nare?

  This is something we talked about at the end of our Navier-Stokes episode, which is when we talk about these concepts of AI, we have to think about them as systems, not as individual component parts. Because this is a perfect example of why that framework of how to think about these things matters. Because they can be individual modules with discrete functions that can work in collaboration towards an end goal. And they're also not just L L Ms. There's a whole, right? The word AI has a whole ecosystem of meaning and capability.

• 58:46Krishna Choudhary?

  Yeah.

• 58:46Lester Nare?

  Uh, and this is an example of combining two things, the machine learning and the L L M.

• 58:50Krishna Choudhary?

  Exactly.

• 58:51Lester Nare?

  Uh, to, for an outcome that has clinical relevance.

• 58:53Krishna Choudhary?

  Yeah, and, and actually to piggyback back off of that, um, you, you can ask like, what exactly is this machine? This, uh, there's a bunch of agents, right? What exactly is happening? Well, let, let's suppose a patient asks how much sleep they got last night and how good it was. Well, they've published kind of a flow chart of how things happen.

• 59:15Lester Nare?

  Yeah.

• 59:15Krishna Choudhary?

  You've, you received the patient message. Now, if the message is unsafe, then you don't do anything. You, you maybe, you know, alert the physician who's in charge and things like that. But if it's safe, then you have a message router.

• 59:29Lester Nare?

  Yes.

• 59:30Krishna Choudhary?

  And the router asks, okay, is this a general chat? Or do I need to actually do a data query?

• 59:36Lester Nare?

  Yep.

• 59:36Krishna Choudhary?

  Right? If it's a data query, then it actually instantiates a SQL query.

• 59:43Lester Nare?

  Yep.

• 59:44Krishna Choudhary?

  Like there, there's, there's an agent that goes, okay, I'm going to go into the SQL database of all of the data on this patient. And like instantiate, get that bit of EEG or whatever, brainwave. And then now instantiate another thing that asks maybe a clarifying question that asks, what are the tools that I need to understand this data? And then you go back and send the AI message through the chatbot to the phone. Right? It's, it's able to actually look at the raw data, analyze the raw data and kind of understand what to do next.

• 1:00:19Lester Nare?

  And, and from this flow chart, I'm seeing there's actually also two safety, uh, gates. One at the front end to moderate the patient, patient message if it's unsafe. And then once all of the processing is done, like you just mentioned, it also moderates the AI response.

• 1:00:34Krishna Choudhary?

  Yes.

• 1:00:34Lester Nare?

  If it's unsafe prior to getting to the patient. So again, they're, they're, you know, they've thought about both the front and back end from a, a safety perspective. Um, and, and you know, it, the medical researchers, clinical researchers are some of the most, uh, anal retentive about details like this.

• 1:00:54Krishna Choudhary?

  Yeah.

• 1:00:54Lester Nare?

  Uh, in ways that maybe, uh, people wish the frontier labs would be more conscientious.

• 1:00:59Krishna Choudhary?

  Hundred percent, dude. Hundred percent. Um, and I, I, one last thing that I want to talk about is the fact that the, the system can ask questions back. So it's not just that the patient asks questions. Um, for example, suppose it has detected a possible seizure. It sends the patient an alert asking, hey, did you consciously experience a seizure? Sometimes you have these kinds of mini seizures where you don't really experience anything and sometimes you do. Um, in any case, the, the patient answers yes, no, or uncertain. And in machine learning, that means that you've established a label to the EEG.

• 1:01:39Lester Nare?

  Yeah.

• 1:01:39Krishna Choudhary?

  That the, the model had triggered as something that possibly could use a label. Now, usually you only want high quality labels, but actually even in the case of these low quality, weak labels, you can do a bit of learning. There's a technique in machine learning called distillation. Here's the idea. You've got a pre-trained model, right? Let's say, let's say you have a bunch of data on epilepsy and you've got EEG recordings and you know when the epileptic seizures happened. So you've got this model that is pre-trained on thousands of hours of epileptic data. And now you want to sort of fine tune it to this specific patient, right?

• 1:02:21Krishna Choudhary?

  Because this specific patient's brain is different and the way that they experience seizures is different and so on and so forth. You can use this technique called distillation. That's going to do two things. One, it's going to try to get it right for that specific patient. But at the same time, it's going to penalize changing up what you already know.

• 1:02:41Lester Nare?

  Hmm.

• 1:02:42Krishna Choudhary?

  Right? There's, there's a two-fold thing that you want to do. You want to, you want to gear towards what the patient specifically is experiencing. But you don't want to forget all of the.

• 1:02:54Lester Nare?

  Yeah, yeah, yeah.

• 1:02:55Krishna Choudhary?

  Stuff that you've learned from general epilepsy data, right? And that's this idea of model distillation. It's a penalty for changing too much on the basis of limited evidence. Because you have all this hours of evidence and now you're getting, what, maybe a few hours of evidence on this patient? You can't, you can't totally upend your world model.

• 1:03:15Lester Nare?

  Hundred percent.

• 1:03:16Krishna Choudhary?

  Based on everything that you've sort of realized beforehand, right? And now the, the, the question is, are, do the models actually do better? In fact, they do. The models do better from this closed loop arrangement between the patient and the training, right? On the left-hand side, what we're seeing is, um, from baseline, the, that's called the, the area under the P R C curve. The area under the precision recall curve. It's a, it's effectively like how well is the machine learning model doing in terms of precision and recall? You can see that the baseline model is highlighted in green and the fine tuning steadily increases.

• 1:03:59Lester Nare?

  Hmm.

• 1:04:00Krishna Choudhary?

  The F1 score, again, another metric for how well, um, precision and recall is working is increasing. The false alarm per hour is decreasing.

• 1:04:12Lester Nare?

  Yep.

• 1:04:12Krishna Choudhary?

  And that's crucially very important.

• 1:04:15Lester Nare?

  We are on the foothills of continual learning in clinical application. And I, I, continual learning is a very specific thing. I know that this is not literally continual learning.

• 1:04:28Krishna Choudhary?

  No.

• 1:04:28Lester Nare?

  But, but it, I said, that's why I said foothills.

• 1:04:30Krishna Choudhary?

  Mm hmm.

• 1:04:31Lester Nare?

  Because part of the challenge with all of these model architectures is they're all pre-trained.

• 1:04:36Krishna Choudhary?

  Yeah.

• 1:04:36Lester Nare?

  And so they can't learn from new information.

• 1:04:39Krishna Choudhary?

  Exactly.

• 1:04:39Lester Nare?

  The argument that's said a lot.

• 1:04:41Krishna Choudhary?

  And, and even in this case, right? They, they're scaffolding off of pre-trained models. The, the L L M is the chat G P T four O mini. That's a pre-trained model.

• 1:04:50Lester Nare?

  Yep.

• 1:04:50Krishna Choudhary?

  The, the seizure detector is a pre-trained model.

• 1:04:55Lester Nare?

  But especially because of the complexity of biological variation and individualized medicine, you are going to kind of necessarily need this foothills of continual learning capability, which is, can we allow this distillation to happen on a per patient basis? In a way that takes the benefit of all of the massive amounts of data, but can correctly weight how much to take the influence of the individual patient in future decision-making? It's a very narrow thread.

• 1:05:29Krishna Choudhary?

  Yeah.

• 1:05:29Lester Nare?

  To, to, to pull, but the effect, efficacy that we've seen in this study in particular is very encouraging and again, being able to now kind of take something out of the box and make it evolutionary in tandem with a patient.

• 1:05:50Krishna Choudhary?

  Yeah, I think, I think it's.

• 1:05:51Lester Nare?

  That's really cool.

• 1:05:51Krishna Choudhary?

  A really, really cool.

• 1:05:53Lester Nare?

  It's really cool.

• 1:05:54Krishna Choudhary?

  Um, study. It's, it's a small study. It's short and it was conducted at only one center.

• 1:05:59Lester Nare?

  Sure.

• 1:05:59Krishna Choudhary?

  Right? Um, the participants, they need sufficient English cognitive capacity and smartphone ability, I guess. Um, so you could think of that as a kind of limitation, but I think a convincing next study would be to measure the outcomes that matter to patients. For example, like the seizure burden, the quality of life, anxiety, the amount of emergency visits.

• 1:06:22Lester Nare?

  Yep.

• 1:06:22Krishna Choudhary?

  The clinical workload.

• 1:06:25Lester Nare?

  Yep.

• 1:06:26Krishna Choudhary?

  Um, I, I think, I think. The fact that we're seeing this, though, is very encouraging to me because, you know,

• 1:06:33Lester Nare?

  Very much so.

• 1:06:34Krishna Choudhary?

  We, we, we hear a lot of AI doom and gloom.

• 1:06:37Lester Nare?

  Yeah.

• 1:06:37Krishna Choudhary?

  You know, this is some good news. This is, this is what was promised to me.

• 1:06:42Lester Nare?

  Yep.

• 1:06:42Krishna Choudhary?

  Okay?

• 1:06:42Lester Nare?

  Yep.

• 1:06:43Krishna Choudhary?

  When, when, when AI came in, they were like, we're gonna make medicine better. We're gonna make your health better.

• 1:06:48Lester Nare?

  That's what they said.

• 1:06:49Krishna Choudhary?

  This is what was promised.

• 1:06:50Lester Nare?

  This is what was promised.

• 1:06:51Krishna Choudhary?

  Right?

• 1:06:51Lester Nare?

  And we're, we're starting to see it slowly come out. And it's not just the frontier lab companies that are using these technologies. And a fascinating second story, and not to burden you with another AI-related story, but unfortunately, uh, when we were in our twenties, the phrase was software is eating the world. And now in our thirties, AI is eating the world. And so there are just a multitude of areas, especially in science research, where it comes up and we are going to now move into our last story, which is gonna be a little

• 1:07:31Lester Nare?

  bit of a zoom in, but also zoom out of what we mean by this whole idea of automated science research or AI going end to end from hypothesis to clinical outcome. And I think this will be an interesting, there's a story that came out today that I think will give us a nice on-ramp to discuss what is the current state of play in what is a pipe dream at this point around AI scientific discovery. So earlier today, we'd planned this episode and then this story dropped and I was like, oh, this will be interesting.

• 1:08:06Krishna Choudhary?

  Mm-hmm.

• 1:08:06Lester Nare?

  And so this is a last minute addition about the other frontier lab that we did not talk about in our last story, which is OpenAI. This is an Anthropic story about scorpion binders and the path from AI to real world science. And so what are we talking about? We've seen all these stories, like we mentioned, every week about AI is gonna change science and make all these new discoveries. And today Anthropic published this research article, How Claude Designed Protein Binders Against Scorpion Neurotoxins.

• 1:08:44Lester Nare?

  And it was a collaboration with, uh, Citrini Research and the laboratories, Adapti Bio and Twist Bioscience.

• 1:08:53Krishna Choudhary?

  Mm-hmm.

• 1:08:54Lester Nare?

  The ideas are trying to design proteins for these dangerous venoms that are in scorpion venom, potentially leading to a treatment. So interestingly, there are, there are some antivenoms that exist, uh, but they're very specific to different species, they're species specific.

• 1:09:12Krishna Choudhary?

  Yeah, okay.

• 1:09:13Lester Nare?

  And even with any individual species, there's also some regional things. And so it's very complex, as all the biology we talk about, it's, it's very complicated. And so one of the holy grails is this idea of discovering a universal antivenom.

• 1:09:28Krishna Choudhary?

  Uh, obviously.

• 1:09:29Lester Nare?

  Yeah, yeah, yeah. Cause then you currently see the antivenoms that have been created are not in underserved areas or areas with less of this clinical and medical infrastructure.

• 1:09:41Krishna Choudhary?

  Yep.

• 1:09:41Lester Nare?

  Um, and so, you know, the challenge becomes when you don't have these antivenoms for, for scorpion, uh, stings, the, the current clinical solution is this idea of aggressive, supportive stabilization, right? You're basically waiting for the body to naturally metabolize, but you have to keep the person alive while that's

• 1:10:04Krishna Choudhary?

  Okay.

• 1:10:05Lester Nare?

  like happening, right?

• 1:10:06Krishna Choudhary?

  That's the stabilization part, you just like keep him alive.

• 1:10:09Lester Nare?

  Right. And so that can be done by supporting, for example, uh, artificial, uh, air, like if they can breathe and things like that. And they also have this idea of utilizing secondary drugs to counteract the specific damage that the venom is causing, which in some cases can work and it impacts the nervous system directly. So the, this story was immediately all over the place.

• 1:10:30Krishna Choudhary?

  Okay.

• 1:10:30Lester Nare?

  Because again, marketing.

• 1:10:31Krishna Choudhary?

  Yeah.

• 1:10:32Lester Nare?

  And so the headline read, they tested 211 designs in the lab and 17 met their binding criteria for four toxins. So like, what is, I'm reading this, I'm like, what is this?

• 1:10:45Krishna Choudhary?

  Mm-hmm.

• 1:10:45Lester Nare?

  What does this actually mean? Um, crucially, they've not shown that this can actually neutralize the toxin, protect an animal, or actually help a human patient. Um, and they explicitly made that clear, that they did not create an, an antivenom, but this is sort of this incremental stepping stone process that we've talked about previously. Another small detail point is none of the, uh, candidates also could bind to multiple toxins. So in the journey for a universal antivenom.

• 1:11:22Krishna Choudhary?

  That's not.

• 1:11:22Lester Nare?

  It, it's still not that, but it was still an interesting design challenge where they were able to find the ability for the protein to bind, um, which is still interesting and impressive for the system to have hypothesized and designed on its own. So the first thing I want to look at here is like, what did this experiment actually establish? Um, and so they act, they have this video overview, we'll walk through really quick. So this was the scorpion from a, a Mexican, it's a Mexican scorpion, a scorpion from Mexico, that the CH2 is the, the toxin. And so we've talked about sodium ion channels, uh,

• 1:12:00Krishna Choudhary?

  Yep.

• 1:12:01Lester Nare?

  that allow things to transition. And so this toxin comes in and activates so that it causes the ion channels to become overactive.

• 1:12:10Krishna Choudhary?

  Okay.

• 1:12:11Lester Nare?

  Right? And so that's obviously a problem. So there are current antibodies, this LR antibody is actually a lab engineered antibody that we know can bind to it to prevent, uh, this from happening. So Claude designed this C N2BI protein that is, is able to bind to the venom, but it conflicts, what it looks like conflicts with where the antibody is able to connect, but not all antibodies, this RU1 antibody, uh, the Anthropic designed one does not have a conflict with them. What's interesting is what they were basically trying to identify is with LR, the initial one we saw, can we bind to the same location for a known antibody that we know, uh, is

• 1:12:55Lester Nare?

  able to combat the venom itself? So we're gonna repeat this one more time and I wanna come back to this really quick. So they use two antibodies.

• 1:13:02Krishna Choudhary?

  Yeah.

• 1:13:03Lester Nare?

  The LR and the RU1. They bind to different locations on the, on the, this venom itself. This binding test was trying to figure out, can we design a protein that can bind to the same location as a known working antibody? And so it was a competition, uh,

• 1:13:21Krishna Choudhary?

  Yeah.

• 1:13:22Lester Nare?

  uh, experiment here. So now, again, we see the Claude designed protein is able to connect. When the LR antibody is there, it's a, it's in conflict.

• 1:13:33Krishna Choudhary?

  Yeah, it's in conflict, it's not able to.

• 1:13:35Lester Nare?

  So it's, it's kind of in the same location.

• 1:13:36Krishna Choudhary?

  Yeah.

• 1:13:37Lester Nare?

  But when RU1 is there, it's like, it's, it's a little bit, but it's only at 16%.

• 1:13:41Krishna Choudhary?

  Yeah, yeah, yeah, yeah, yeah.

• 1:13:42Lester Nare?

  And so the idea, what they're trying to say here is that, look, we can kind of have a binding in a similar location to something we know that works, but that does not mean it neutralizes the toxin. We're just saying that it can bind.

• 1:13:55Krishna Choudhary?

  Mm-hmm.

• 1:13:55Lester Nare?

  Right? Am I doing okay so far?

• 1:13:57Krishna Choudhary?

  Yeah, yeah. I think that makes sense.

• 1:13:59Lester Nare?

  So LR came from an earlier antivenom research from, uh, UNAM in Mexico. They engineered a human derived antibody fragment that showed, uh, that it protected in mice.

• 1:14:09Krishna Choudhary?

  Mm-hmm.

• 1:14:09Lester Nare?

  Um, and it's a laboratory reference. A lot of times with these antivenoms, what they end up doing is they'll inject a horse with the venom, then take the antibodies from the horse and then do some voodoo magic.

• 1:14:20Krishna Choudhary?

  Oh, yeah, yeah, yeah.

• 1:14:21Lester Nare?

  And then give it to the human.

• 1:14:22Krishna Choudhary?

  Fair enough.

• 1:14:23Lester Nare?

  Um, and then the, the RU1 is a similar, that it just attaches and binds to a different location. So they wanted to see

• 1:14:31Krishna Choudhary?

  Yeah.

• 1:14:32Lester Nare?

  how it worked in, in both cases. The idea here is like, you know, neutralization is the idea of capping the ability of the venom to create that overactivation.

• 1:14:41Krishna Choudhary?

  Yeah.

• 1:14:42Lester Nare?

  And ultimately, like, that's a solution here. So the, the headlines and the analysis are always related. So what we're saying that they've been able to do is they designed a bunch of, uh, options for what could potentially be successful at binding to a similar location as the LR antibody, uh, 211 of them. Uh, they found as a result of that, that they were able to get 17 to meet the binding criteria.

• 1:15:11Krishna Choudhary?

  Okay.

• 1:15:11Lester Nare?

  Across four different toxin types.

• 1:15:13Krishna Choudhary?

  Mm-hmm.

• 1:15:14Lester Nare?

  Uh, so that's interesting.

• 1:15:16Krishna Choudhary?

  Yeah, it's, it's for, it's like generalized across four different toxins. That's pretty good.

• 1:15:22Lester Nare?

  It's interesting, okay. And so, but this is still not helping a patient.

• 1:15:30Krishna Choudhary?

  Mm-hmm.

• 1:15:31Lester Nare?

  Right? Cause we've not even gone to the point of being able to actually confirm that it neutralizes the toxin. So I wanted to take a step back and say, what are the steps between this binding mechanism, which was the result of this, and a medicine to a patient?

• 1:15:46Krishna Choudhary?

  Yeah.

• 1:15:47Lester Nare?

  Um, and, uh, Citrini actually put this roadmap out that we can look at. That's kind of how they sort of define it. So step one, right, is does it neutralize the toxin? We haven't even gotten to step one yet. We found a binding mechanism and so now step one is making sure that it can actually neutralize the toxin. Cause it could bind, but not necessarily neutralize it's like the actual.

• 1:16:09Krishna Choudhary?

  Oh, interesting.

• 1:16:10Lester Nare?

  You know what I'm saying?

• 1:16:10Krishna Choudhary?

  I see, yeah.

• 1:16:11Lester Nare?

  And so there's a difference between those two things. We know it can connect, but we don't know that it makes, uh, that it protects

• 1:16:17Krishna Choudhary?

  Yeah.

• 1:16:17Lester Nare?

  the actual nerve function itself. So then number two, does it cover the relevant venoms that we're trying to

• 1:16:23Krishna Choudhary?

  Mm-hmm.

• 1:16:23Lester Nare?

  address? Number three, does it work in a body? Uh, in this case, in animal testing. Uh, then we get to this number, stage number four, which is, is it ready from a safety perspective for human testing?

• 1:16:36Krishna Choudhary?

  Mm-hmm.

• 1:16:37Lester Nare?

  And then five, does it actually help patients once it gets through that developmental process? They estimate that whole pipeline for this specific use case to be about four to seven years plus. Um, and all we're talking about is not even yet having solved step one.

• 1:16:54Krishna Choudhary?

  Step one, yeah.

• 1:16:55Lester Nare?

  Uh, but this is the pipeline that all these AI is gonna cure X, Y, and Z. This is the pipeline they kind of have to go through. And they've identified a few things in their chart about where they think AI is gonna be the most impactful. So improving the neutralization of the venom itself, they're like, I think, we think it will have moderate impact in being able to then cover, once they know it can neutralize, they think it has the highest impact of being able to then generalize it across, uh, all these different classes of venom.

• 1:17:24Krishna Choudhary?

  Yeah.

• 1:17:24Lester Nare?

  This universal antivenom. They also think that in that first stage of animal testing, that it can have high impact as it relates to animal testing. But when you get to human, preparing for human trials and the trials and acceptance process, it goes to moderate to limited, which is obviously

• 1:17:40Krishna Choudhary?

  Yeah.

• 1:17:40Lester Nare?

  very understandable. I think the point here that is sort of being brought up is There is, there's a real world aspect to this stuff that is just naturally going to be slower, unless and until some of the processes, the regulatory environment and the way in which we do those downstream processes fundamentally changes. So that's kind of part one of what this OpenAI announcement was about. And it's interesting because it dovetails with the wet lab story we talked about a few weeks ago, where everyone's like, oh, Anthropic has a wet lab.

• 1:18:13Krishna Choudhary?

  Yeah.

• 1:18:14Lester Nare?

  You know, but it was where the AI was designing and the human scientists were in the lab, actually conducting the entire process.

• 1:18:24Krishna Choudhary?

  Yeah.

• 1:18:25Lester Nare?

  Itself. So I want to sort of briefly talk about the timing, because the promise here is that this is going to accelerate and it's going to happen very quickly.

• 1:18:35Krishna Choudhary?

  Yes.

• 1:18:36Lester Nare?

  So this next chart kind of looks at how long did it take them to create the design candidates? And how long did it take them to test those candidates in a laboratory with the two partners we mentioned earlier? They were able to get the first 180 designs in 64 hours, which is quite quick.

• 1:18:54Krishna Choudhary?

  That's quite quick.

• 1:18:55Lester Nare?

  Quite fast. Easy as part of the process, maybe, but it's still quite fast.

• 1:19:00Krishna Choudhary?

  Yeah, two and a half days, though, is quite fast for 180 designs.

• 1:19:04Lester Nare?

  It's quite quick.

• 1:19:05Krishna Choudhary?

  Yeah.

• 1:19:06Lester Nare?

  The first binding results arrived after 39 days, which is from the beginning of when they started the design process, not after they got the design process. So about five and a half, five, five, five and a half weeks to get to this point where we have the designs done. We've tested them in the lab and we have these candidates. In between there, there's synthesis, there's protein production, there's getting the reagent, scheduling the work, actually measuring what happens. There's quite a bit.

• 1:19:34Krishna Choudhary?

  Yeah, there's quite a bit that hasn't been optimized for an AI lab ecosystem. Let's say that, right?

• 1:19:43Lester Nare?

  That's exactly right.

• 1:19:43Krishna Choudhary?

  Like, like this, this is, there's so many parts of this that are, are still sort of, not even optimized, I would say, just like catering to human time scales, like PhD student time scales.

• 1:19:58Lester Nare?

  Right, right. And, and I think part of what we're trying to talk about in this story is looking at each of the stages of this process and where is AI, but also the combination of automation and robotics, because ultimately you need to interact with the real world.

• 1:20:17Krishna Choudhary?

  That's right.

• 1:20:18Lester Nare?

  And so this is where sort of the AI and robotics together fit in that process. So I wanted to, again, I was trying to help myself understand what are we looking at here? And so I created this map of how AI is going to like meet the physical lab and where the current state of play is in some of the sort of, this is not a complete map of every player in the space, but it's just to give a kind of representative example. So it kind of breaks down into these three stages. There's the design and prioritize. So we just talked about that in this Anthropic story with the scorpion antivenom.

• 1:20:55Krishna Choudhary?

  So the AI is going to design and prioritize.

• 1:20:59Lester Nare?

  Correct.

• 1:21:00Krishna Choudhary?

  In this model.

• 1:21:01Lester Nare?

  We, we may still define the question.

• 1:21:04Krishna Choudhary?

  Right.

• 1:21:04Lester Nare?

  Scorpion antivenom.

• 1:21:06Krishna Choudhary?

  Right. Be like, give me an antivenom to all of scorpions.

• 1:21:11Lester Nare?

  Right. And I think the original prompt for this was about 35 pages.

• 1:21:15Krishna Choudhary?

  Oh, wow.

• 1:21:16Lester Nare?

  That they fed. They had, I think, 300 GPUs. It cost a total of about $40,000 to $60,000 to run this design and prioritize process. Which is not crazy.

• 1:21:28Krishna Choudhary?

  No, but.

• 1:21:29Lester Nare?

  It's a little expensive, but it's, you know, not crazy.

• 1:21:32Krishna Choudhary?

  No, it's, it's not the usual stuff that I asked Claude and ChatGPT to do, right? My prompts are not 35 pages.

• 1:21:40Lester Nare?

  Right. And if one of those ultimately became something that was able to get to the end of process, that would be fantastic. Okay, so a variety of the frontier labs are doing that, that, that design and prioritize, but we're talking about this Anthropic story in combination with Citrini. So then sort of stage two is this idea of physical automation, not necessarily AI anything. We have a couple of players in here, MIT, Emerald, Cloud Lab, Stratos and Ginkgo. They're trying to now take the physical process that humans do in a lab and have it be run by a robotic automation. Obviously, it's a connective tissue because you can take part one and then send it to part two.

• 1:22:19Krishna Choudhary?

  Yep.

• 1:22:20Lester Nare?

  Now, there are some companies that are trying to do the full closed loop cycle, going back to our previous story of the learn and repeat.

• 1:22:30Krishna Choudhary?

  Right.

• 1:22:30Lester Nare?

  This is Lila Science Madeira, where it is the combination of both the AI designing prioritizing, the robotic lab executing, getting the measurements, feeding that back directly into the AI to then decide what to change and then retry, right? Human oversight, but.

• 1:22:49Krishna Choudhary?

  Yep.

• 1:22:51Lester Nare?

  Kind of a closed loop cycle. These processes are also using a variety of shared infrastructure. For example, Carnegie Mellon is providing some of this shared infrastructure as well as Slack, the National Lab.

• 1:23:05Krishna Choudhary?

  Stanford Linear Accelerator.

• 1:23:06Lester Nare?

  Lab, yep, is, is giving some of the infrastructure around these pieces. So that's, that's where, it's sort of where we're trying to go is where Lila Sciences is effectively saying we are right now. And so the first step that the frontier labs think they can take is this design and prioritize, because it does not require this connection to the downstream.

• 1:23:30Krishna Choudhary?

  Right.

• 1:23:31Lester Nare?

  Robotics and automation piece, which is not their bread and butter. So now let's transition. And this is something we've talked about a lot, as I said, with the methods is this equipment and measurement. We talk a lot about tools on the pod. We talk a lot about being precise in our measurement and our methods. And MIT actually gives us a great example of this automating step. This is not necessarily new, as in new in the last couple of years. It's been happening for some time. But understanding how this now connects to these AI workflows is a different way to start to think about these steps. So researchers at MIT working in collaboration with Nokia Bell Labs and Arizona State

• 1:24:13Lester Nare?

  built a robotic system that can assemble and align an optical experiment. And this was actually highlighted recently in September. This research, always talk about the funders, supported by National Science Foundation, Army Research Office and some other funders, private funders, including Shell. And this was one of their demonstration videos where they have a robotic arm that in 50 steps in 30 minutes can set up the, these lenses for this optical experiment, which we've talked about previously. And not only can it set up the lenses, if any, it's because it's constantly taking measurements.

• 1:24:55Lester Nare?

  It can also make a change to fix something when there becomes, when a misalignment arises as well. So it's both things. It's a human.

• 1:25:05Krishna Choudhary?

  This is crazy.

• 1:25:06Lester Nare?

  Human designed setup.

• 1:25:07Krishna Choudhary?

  Yeah.

• 1:25:08Lester Nare?

  But it can set up and iterate when there's a misalignment.

• 1:25:12Krishna Choudhary?

  I mean, I mean, so already this is giving me flashbacks to my PhD days. Like, for example, the table that they're on, you see all those holes, those little screws that you can put in and you can fasten like mirrors and lenses.

• 1:25:29Krishna Choudhary?

  Any type of tabletop AMO experiment, meaning atomic molecular optical, like if you want to do molecular traps, a lot of quantum computing experiments are literally just this, which is establishing lenses everywhere and then, you know, aligning them to the point where you can suspend atoms in a laser or you can, you know, isolate a single atom to, to do some type of spectroscopy measurement.

• 1:26:00Lester Nare?

  This reminds me of the MIT double slit experiment episode.

• 1:26:03Krishna Choudhary?

  Yeah, this, this is all that is. Like, a lot of these AMO experiments are literally just this.

• 1:26:10Lester Nare?

  And just, so I, you know, I want to ask you briefly, like, the, the sensitivity and complexity, because someone who's watching this might be like, oh, this looks pretty.

• 1:26:21Krishna Choudhary?

  No, this is incredible. Like, if, if they're able to align a laser and like make sure that the transition frequency is exactly aligned to whatever, you know, system that they're trying to control, like, that's, that's pretty incredible. Like, if they're able to do that just generally, like, that's, you know, that's like, that's like half the work of these AMO PhD students, like my roommates, you know, like Pratik Purie.

• 1:26:49Lester Nare?

  Yeah, yeah, yeah.

• 1:26:50Krishna Choudhary?

  I don't, I don't mean to call you out, but this is half your job. Like, you know, like, so Elliot Bora, another friend of the pod.

• 1:26:57Lester Nare?

  Yes, yes.

• 1:26:58Krishna Choudhary?

  Like, this is something that he does a lot of, I mean, obviously there's a lot more that's in his head.

• 1:27:03Lester Nare?

  Sure.

• 1:27:03Krishna Choudhary?

  Right? There's a lot more stuff that they could do, perhaps, if they didn't have to spend their time.

• 1:27:09Lester Nare?

  Doing that.

• 1:27:09Krishna Choudhary?

  Aligning lenses and lasers.

• 1:27:14Lester Nare?

  And this is, this gets to, this is, this was a demonstration, going back to our three stages, of mainly this idea of execution and measurement. Because AI can design all these things, but we still have to be able to execute and measure.

• 1:27:28Krishna Choudhary?

  Yeah.

• 1:27:28Lester Nare?

  And this is all for optical experiments, not biological experiments. It's a different category, but I'm just trying to give the listeners a sense of where these component parts are that people are ultimately trying to connect in order to get this closed loop end to end AI-based research cycle.

• 1:27:49Lester Nare?

  You know, but that step might close the equipment control loop.

• 1:27:55Krishna Choudhary?

  Yeah.

• 1:27:56Lester Nare?

  As one component part of that.

• 1:27:57Krishna Choudhary?

  Yeah.

• 1:27:57Lester Nare?

  Now, the next video that, this is sort of a promo video, so that there's some history about how we've done these things for quite some time.

• 1:28:07Krishna Choudhary?

  Right.

• 1:28:07Lester Nare?

  So this is from Emerald Cloud Lab and Stratos, and they kind of address a slightly different part of this execution, which is making physical laboratories accessible through software, through the cloud remotely. So I'm in one location, I need to do X, Y, or Z in another location, and the entirety of the process that I need to be done is remotely executed by automation and robotics, but I have a software interface that I can interact with in real time to execute. So like, a lot of folks might go, go to the doctor and then they need to get, you know, a blood test and then they send it to Quest Diagnostics, right?

• 1:28:50Lester Nare?

  This idea that you have one person doing the question, one person doing the execution, and then it comes back to your physician to give you the result. The difference here, again, with still some elements of human involvement for some of the component parts, many of the components are now directly cloud connectable and fully automated.

• 1:29:13Lester Nare?

  This is, I think, a big area.

• 1:29:15Krishna Choudhary?

  This is crazy.

• 1:29:16Lester Nare?

  I think this is very big.

• 1:29:17Krishna Choudhary?

  No, this is crazy. This is absolutely.

• 1:29:21Lester Nare?

  And you get how long it takes to run each. You get the real time data back in the exact format you want.

• 1:29:26Krishna Choudhary?

  It's a, it's a wet lab that you can access through the cloud.

• 1:29:31Lester Nare?

  Isn't that crazy?

• 1:29:33Krishna Choudhary?

  I mean, no, they're doing mass spec, I mean, or something, you know? This seems like, this seems like stuff that would require specialized equipment, but there you go, like, right?

• 1:29:44Krishna Choudhary?

  No, I mean, that's actually incredible, right? Because for the longest time, we've seen machine learning researchers do this. You just access GPUs from the cloud, AWS, and then, and then, you know, you're doing machine learning experiments. Well, now you're actually doing like real biological experiments. You've got pipettes that are automated. You've got, I think I saw a centrifuge that was automated, right? And they did it correctly.

• 1:30:11Lester Nare?

  I'm like, the Ryan Gosling movie.

• 1:30:16Krishna Choudhary?

  Look, it was, it was one of the only flaws that I brought up.

• 1:30:20Lester Nare?

  I was wondering if you were gonna say it or miss it, but.

• 1:30:23Krishna Choudhary?

  But like, they did it right.

• 1:30:24Lester Nare?

  They did it right.

• 1:30:25Krishna Choudhary?

  The AI knows to balance the centrifuge, unlike Ryan Gosling. So, but no, this is incredible.

• 1:30:34Lester Nare?

  And what, again, this is not a promo for any particular entity. There are a variety of companies that are trying to do so.

• 1:30:40Krishna Choudhary?

  No, I'm just saying this is a new era in, in scientific.

• 1:30:45Lester Nare?

  Correct.

• 1:30:46Krishna Choudhary?

  Yeah, it's like, so, I mean, one, one thing I, I wanna understand is like, is Emerald Cloud Lab gonna be a co-author? On, I, I wonder, like, as a, as a former researcher in like a PhD or a postdoc setting, it's like, I'm always like, who's gonna be co-author on the paper if I were to use this data, right? Do they have some kind of thing in their contract being like, we gotta be, acknowledgements are fine, co-author, you know?

• 1:31:15Lester Nare?

  Like, okay, maybe, maybe a footnote.

• 1:31:17Krishna Choudhary?

  Yeah.

• 1:31:17Lester Nare?

  May, maybe a footnote. And one of the things that this brings up that I was kind of thinking about, that's not necessarily as obvious, because like, the idea is like, oh, you know, a lot of the thoughts around these AI implementations is it's gonna allow the rich to get richer, which is true in some.

• 1:31:36Krishna Choudhary?

  Probably.

• 1:31:40Lester Nare?

  In a lot of contexts, that's true. But what it does also do, like, what type of research teams that might not have access to these types of resources, who are in underserved environments, what kind of questions can they ask? Now that, and it's the idea of like AWS create and all this cloud computing allowed people to build websites and do video, and we can do this show because of cloud computing and distributed networking and, you know, this wouldn't exist without that. So it does bring up the question, like, what kind of research questions? And what access that teams that otherwise would not have been able to access these kind of high fidelity resources, particularly in my family, who's worked in, you know, global health and neglected diseases.

• 1:32:25Lester Nare?

  It becomes a very interesting democratization of the access to the types of tooling when you have this cloud accessibility. And that, that was a non-obvious conclusion, after kind of digging into this, is like, it, it is a different paradigm that allows us to ask some different questions. One last sort of note I wanna bring up, going to the third stage. We talked about design and how to prioritize what questions to ask. We then looked at automation at two different levels, right? One where the humans are still involved in the lab, one that's cloud accessible. Lila Sciences is trying to connect the full loop, right? They had this catalyst study.

• 1:33:08Lester Nare?

  The AI helped select compositions, automated the equipment, made the test materials, and the measurements informed subsequent choices. So it was a very narrow question.

• 1:33:16Krishna Choudhary?

  Yeah, but it's closed loop.

• 1:33:17Lester Nare?

  But it's closed loop in some sense. They evaluated almost 3,000 compositions related to hydrogen production. And this diagram here kind of shows how they look at that cycle.

• 1:33:31Lester Nare?

  You know, and what they're ultimately trying to do in terms of this closed-loop catalyst discovery. And so, the, the, the goal here was trying to give a baseline lay of the land. There are more than just the frontier companies doing this. People are tacking different component parts of the process. Some people are trying to do the whole thing. Some people are just trying to do a module of the thing. And there is sort of this shared infrastructure. So Carnegie Mellon has AI Science Foundry, which is trying to connect these cloud labs and scientific infrastructure to like a shared, this sort of shared distributed research infrastructure to make sure it's evenly distributed and accessible for many people.

• 1:34:16Lester Nare?

  Slack has this new Spire project, which is trying to connect their robotic sample handling, beam control, and real-time analysis. And we actually have a reference video here. This is from, from Slack's Spire project. Now, it's, it's just part of the execution and measurement infrastructure. It's not the completed future system. But again, I think that there are multiple national labs, universities, private companies that are all moving in this direction. And when we go back to one of the reasons we started the show, the thesis was, we're gonna be in a Cambrian explosion of a whole variety of breakthroughs.

• 1:34:56Krishna Choudhary?

  Yeah.

• 1:34:57Lester Nare?

  And we wanna be here having a front seat and bringing you along with us for what will happen. Again, there are very complex social, economic, political, and other issues as it relates to, quote, AI.

• 1:35:12Lester Nare?

  But on this show, we talk about the researchers who are using this in very specific ways that are ultimately looking to create opportunities to benefit us collectively. And it's important not to just say AI is bad in its entirety when there are really interesting and valuable use cases that are coming up. Now, I will just end on this note of all the doom scenarios. Expect there to be automated wet labs.

• 1:35:43Krishna Choudhary?

  Yeah.

• 1:35:44Lester Nare?

  And then an accidental release.

• 1:35:46Krishna Choudhary?

  Yes.

• 1:35:47Lester Nare?

  Obviously, none of the things that we, we discussed are related to pathogens that are extremely deadly, but there are certainly a lot of consequences as it relates to the progress we've already made with cloud wet labs.

• 1:36:04Krishna Choudhary?

  Yeah.

• 1:36:05Lester Nare?

  And.

• 1:36:06Krishna Choudhary?

  Dude, I mean, it, it's, it's, uh, it's the best of times, it is the worst of times. As Charles Dickens said, right?

• 1:36:15Krishna Choudhary?

  And it really feels that way. There's, if we, if we do nothing, it's gonna be both.

• 1:36:21Lester Nare?

  Right.

• 1:36:22Krishna Choudhary?

  And the scales only need to tip one way or the other. It's kind of like the chemistry Nobel Prize, right? You only need a slight asymmetry, and then all of a sudden, it's very much not a great thing, right? Or all of a sudden, it's a very good thing, right? So.

• 1:36:38Lester Nare?

  We, we, uh, we want our homo chirality to be positive.

• 1:36:42Krishna Choudhary?

  Positive, in our direction.

• 1:36:43Lester Nare?

  In our direction, right? Not in the AI's direction. It's, and so I, I, I thought you'd find this, because we kind of touch on aspects of this, but as we continue in future episodes, this is gonna continue to come up. And I successfully talked through a live molecular diagram.

• 1:37:04Krishna Choudhary?

  Yeah, that was pretty good.

• 1:37:05Lester Nare?

  It's not bad.

• 1:37:06Krishna Choudhary?

  Yeah.

• 1:37:06Lester Nare?

  I'll get better. I'm learning, guys. I'm trying to get better.

• 1:37:11Krishna Choudhary?

  But I, I thought that was, that was pretty cool. I mean, the, the laser stuff.

• 1:37:20Krishna Choudhary?

  It was crazy to me, because I've been in those labs, right? In the physics department at UCLA, like, we, those, those tables are the bread and butter of every tabletop experiment. So that was crazy, that like, there was just a robot that could just hang out and do that, which was like half the job of the PhDs.

• 1:37:42Lester Nare?

  30 minutes, 50 moves.

• 1:37:44Krishna Choudhary?

  Right, that's crazy. Yeah. It's not bad.

• 1:37:46Lester Nare?

  Yeah.

• 1:37:47Krishna Choudhary?

  And no breaks, no, it could just work overnight kind of thing, you know?

• 1:37:51Lester Nare?

  Yeah, it could adjust when there's an issue.

• 1:37:53Krishna Choudhary?

  Yeah. Yeah. It's not bad.

• 1:37:56Lester Nare?

  That's pretty good.

• 1:37:57Krishna Choudhary?

  It's not bad.

• 1:37:58Lester Nare?

  We are going to continue to bring you the frontier of science research globally. Not just here in the US, but from all around the world. My name is Lester Nare, joined as always by my co-host and our resident PhD. And I also just wanna remind everybody, Signal Award-winning podcast.

• 1:38:22Lester Nare?

  Bronze, we're going for gold.

• 1:38:24Krishna Choudhary?

  We're going for gold, but it'll, it'll go, it'll go like, my point.

• 1:38:29Lester Nare?

  Here, there it is. Krishna got the point. And for those of you watching on Apple Podcasts on video for the first time, welcome. We know it's been a long time coming. We will see you all next week.