AI-Generated Genomes, Retinal Implants, and Palomar’s Mystery Lights Explained (EP. 15)

AI, Eyes, and the Sky — From Synthetic Genomes to Restored Vision and Cosmic Mysteries Hosted by Lester Nare and Krishna Choudhary, this episode of From First Principles explores three cutting-edge breakthroughs connecting medicine, technology, and astronomy. Summary • AI for Oncology, Minus the Privacy Risk: University of Toronto researchers develop OncoGAN—a generative model that creates realistic synthetic cancer genomes to accelerate precision oncology while protecting patient data. • Restoring Sight: The PRIMA (PRIMAvera) trial in NEJM demonstrates how a wireless sub-retinal photovoltaic implant can restore central vision in people with advanced macular degeneration. • Revisiting Cosmic Transients: New analyses of Palomar’s POSS-I plates re-examine the “multi-point transients” with fresh alignment statistics and an innovative Earth’s-shadow control test. Show Notes • University of Toronto — OncoGAN / Synthetic Cancer Genomes (Cell Genomics) • NEJM — PRIMA (PRIMAvera) Wireless Sub-Retinal Implant Trial for Geographic Atrophy • Palomar POSS-I Plates — Multi-Point Transient Analysis (IOP PASP Paper) • Palomar Alignment vs Earth’s Shadow Control (Nature Scientific Reports 2025)

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

• 0:00Lester Nare?

  Hello, Internet. This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Choudhary. We have three great stories lined up for you this week, starting with a story out of the University of Toronto, which is around generative AI for cancer detection, which is published in Cell Genomics. This will be a good one. Our second story is a super story with a couple of institutions here involved. Science Corporation, Stanford University, UCSF, University of Pittsburgh, and University of Bonn. They've created a wireless retinal implant that is helping blind people see. This is out of the New England Journal of Medicine. And our final third story actually is two papers

• 0:44Lester Nare?

  around the UFO subject, which is now known as unidentified anomalous phenomenon. Interestingly, published in both Nature Scientific Reports and publications on the Astronomical Society of the Pacific. This is going to be really interesting, covering a lot of ground. As always, we're going to have a great time. This is from First Principles.

• 1:24Lester Nare?

  My friend.

• 1:26Krishna Choudhary?

  How's it going?

• 1:27Lester Nare?

  Quite well, quite well. Back from our travels.

• 1:30Krishna Choudhary?

  Well, yeah, you know what happened.

• 1:33Lester Nare?

  I mean, I do. I am.

• 1:36Krishna Choudhary?

  You know what happened? The Dodgers won the World Series.

• 1:41Lester Nare?

  Los Angeles brought home another world championship.

• 1:44Krishna Choudhary?

  Back to back, baby.

• 1:45Lester Nare?

  Back to back, as predicted.

• 1:47Krishna Choudhary?

  As predicted. We predicted the chemistry Nobel Prize.

• 1:50Lester Nare?

  Yes.

• 1:51Krishna Choudhary?

  And now we predicted the World Series.

• 1:52Lester Nare?

  Yes.

• 1:53Krishna Choudhary?

  I mean, what can we not do?

• 1:54Lester Nare?

  Well, what can we not do?

• 1:56Krishna Choudhary?

  Yeah.

• 1:57Lester Nare?

  I, LA is obviously just the greatest place to be right now.

• 2:02Krishna Choudhary?

  Yeah, yeah.

• 2:02Lester Nare?

  Whenever we.

• 2:03Krishna Choudhary?

  I'm still like rewatching. They're putting out like.

• 2:09Krishna Choudhary?

  World Series movies.

• 2:11Lester Nare?

  Yeah, yeah, yeah.

• 2:11Krishna Choudhary?

  And I'm watching that.

• 2:13Lester Nare?

  A great, a great game seven.

• 2:15Krishna Choudhary?

  Yeah, it was an amazing game.

• 2:16Lester Nare?

  Amazing series, amazing game seven, you know, shout out to the Jays, put out a good fight.

• 2:22Krishna Choudhary?

  Yeah.

• 2:22Lester Nare?

  Um, and we're gonna, we're gonna start off with a little bit of, you know, deference.

• 2:28Krishna Choudhary?

  Yeah.

• 2:29Lester Nare?

  To, to Toronto, an incredible city.

• 2:31Krishna Choudhary?

  An incredible city and incredible university.

• 2:34Lester Nare?

  Yes.

• 2:34Krishna Choudhary?

  University of Toronto is, you know, one of the great universities in the world of science and technology.

• 2:40Lester Nare?

  Yes.

• 2:41Krishna Choudhary?

  They're kind of like the

• 2:45Krishna Choudhary?

  Niels Bohr Institute when it comes to AI.

• 2:48Lester Nare?

  Yes.

• 2:48Krishna Choudhary?

  Because of Geoffrey Hinton.

• 2:49Lester Nare?

  Shout out to Geoffrey Hinton.

• 2:50Krishna Choudhary?

  Yeah, I mean, they've done a bunch of other things. They discovered stem cells. They discovered insulin. So it's an incredible storied university. And even though we defeated them.

• 3:01Lester Nare?

  Even though we won.

• 3:02Krishna Choudhary?

  Even though we won, let's, let's just say we won. Let's, let's just say we won. And all these people being like, you know, I'm, we got a comment on TikTok.

• 3:10Lester Nare?

  Yeah.

• 3:11Krishna Choudhary?

  Saying that, oh, um, have fun, have fun with the ring that you bought. You know, and I finally feel like the Yankees did in the two thousands. And now I get it because I don't care either. I don't, I don't care. Yeah, whatever. But, you know, the Toronto is a great city and the University of Toronto is a great university.

• 3:32Lester Nare?

  Phenomenal.

• 3:33Krishna Choudhary?

  Phenomenal research institution.

• 3:35Lester Nare?

  Phenomenal institution. And we're gonna start off with, with a story out of the University of Toronto, which is this new study about how AI-generated genomes promise to advance sort of precision cancer detection.

• 3:52Krishna Choudhary?

  Yes.

• 3:53Lester Nare?

  Uh, and so that's sort of the story.

• 3:55Krishna Choudhary?

  Mm hmm.

• 3:55Lester Nare?

  And we've, we cannot avoid AI.

• 4:00Krishna Choudhary?

  We cannot avoid AI.

• 4:01Lester Nare?

  It's, it's just, it's becoming much more prevalent. And it's interesting because a lot of times people think of generative AI as, oh, let me create an image about, you know.

• 4:10Krishna Choudhary?

  Or chat GPT.

• 4:11Lester Nare?

  Or chat GPT.

• 4:11Krishna Choudhary?

  The G in GPT is generative.

• 4:14Lester Nare?

  Uh, and so a lot of people think of the consumer use cases, but in sort of this early, early stage. Research arenas, there are some interesting things happening, this being one of them.

• 4:24Krishna Choudhary?

  Yeah.

• 4:24Lester Nare?

  So, so how exactly are the researchers at the University of Toronto using generative AI for this detection process?

• 4:32Krishna Choudhary?

  Right. Well, the problem is precision oncology. Okay? It needs a lot of large genomic data sets.

• 4:39Lester Nare?

  Yep.

• 4:40Krishna Choudhary?

  But we also want to preserve the privacy of the individuals that are donating that data set, right? And so this creates a kind of bottleneck between the research and tool development. For if we want to look at a genome, let's say of a patient and try to figure out what is the specific cancer that this person has? And how can we better mitigate that? What are the kinds of drugs that we need to actually do this? In order to actually train a model to do that job, we need a lot of data. But the data has to be, you know, without all of these privacy bottlenecks.

• 5:23Lester Nare?

  The, the sort of personal identifiable information.

• 5:27Krishna Choudhary?

  Exactly. Yeah. And that's what's tough. Okay? And this OncoGAN, which is a generative AI pipeline, it was published in,

• 5:36Krishna Choudhary?

  I believe, Cell Genomics.

• 5:38Lester Nare?

  Cell Genomics.

• 5:38Krishna Choudhary?

  Cell Genomics, OncoGAN.

• 5:40Lester Nare?

  Yes.

• 5:40Krishna Choudhary?

  It creates a highly realistic synthetic cancer genome, just completely within the computer. Okay? And it bypasses these privacy issues.

• 5:49Lester Nare?

  That's actually really interesting. Okay.

• 5:50Krishna Choudhary?

  Right? And the key feature is it mimics all of these like computational, complex, mutational landscapes that are characteristic of cancers. But it doesn't tie that data with the patient.

• 6:05Lester Nare?

  With an individual.

• 6:07Krishna Choudhary?

  Yeah. And the goal is basically to democratize access to this high fidelity genomic data, accelerate cancer diagnosis, treatment, prevention tools, all of the stuff that we want, without actually compromising patient confidentiality.

• 6:22Lester Nare?

  The idea being if you can provide this sort of substrate of the data set, variety of research teams and institutions can do of work towards solving the problem without running.

• 6:35Krishna Choudhary?

  You know. Into all of the regulations. Regulation issues. Which are there for a good reason. Which are there for a good reason. Um, so let's go through what precision oncology means. Okay. Why it's actually hard to actually simulate these genomes. Yeah. And then what this current paper is doing. Okay. Okay, what this current paper is announcing. So the vision of precision oncology is we want to shift from things like chemotherapy and radiation, which is kind of just a general sort of shotgun approach to this tailored treatment based on a patient's tumor, the molecular and genomic alterations in that tumor. In that tumor. Okay, and we want to make it precision. That's, that's where precision comes from, right?

• 7:17Krishna Choudhary?

  And so what we want to do is identify mutations and then administer targeted drugs. Uh huh. There's a data bottleneck where we have this paradox between sharing and privacy, right? Because the success of precision oncology and in AI to precisely identify what type of oncology is going on, depends on a vast diverse data set, right? Yeah, yeah, yeah. But every single genomic identifier is something that is unique to every individual, right? You've got three billion base pairs, and if I know your three billion base pairs, I can figure out who you are. Right? So re-identification is really possible, like even if you de-identify data. Right.

• 7:58Krishna Choudhary?

  Like you took, you took some patient's genomic data and you took out some parts. It would actually be pretty easy to look up who you are based on genome, genealogical websites, public records, the whole like 23andMe thing, um, ancestry.com, like that, that whole thing, right? So there's privacy risks and that can reveal dispositions to disease, potential discrimination. And you've got stuff like HIPAA that actually controls. And creates barriers for that kind of data sharing. It's a very good thing that we're doing that, right? Because then, without it, you can, you can have potential discrimination on life insurance, long-term care.

• 8:38Lester Nare?

  Right, I was gonna say that the big, the big issue is you don't want insurance providers to be able to basically say, oh, because we know that you have, this is the whole Obamacare pre-existing conditions issue.

• 8:49Krishna Choudhary?

  Yeah.

• 8:49Lester Nare?

  If we know who you are and what your pre-existing conditions are, we're gonna charge you more because you're a higher risk and liability for us as the insurance provider.

• 8:57Krishna Choudhary?

  Exactly, yeah, you don't want that. That's just blatant nonsense.

• 9:01Lester Nare?

  Yeah, yeah, yeah.

• 9:01Krishna Choudhary?

  The other, the other thing actually that is tackling is, um, if you have an AI-generated tool, whenever we make AI, you wanna have a ground-truth data set.

• 9:11Lester Nare?

  Yes.

• 9:11Krishna Choudhary?

  That tells you what is right or wrong, right? So that you can compare one AI model from another. Well, with, with cancer data sets, it's really hard to do that kind of benchmarking because the, the real patient samples have sequencing errors, you don't actually know what the tumor complexity is, there's no actual ground truth. So if we were to generate data sets that have a ground truth, then we can easily benchmark all of these other AI models. So when someone says, hey, I've got an AI model that I think does better, we've actually got a data set where we can test that.

• 9:49Lester Nare?

  Yeah.

• 9:49Krishna Choudhary?

  Right?

• 9:49Lester Nare?

  Yeah.

• 9:50Krishna Choudhary?

  So, so that's, that's a separate issue from the privacy thing, but this thing is actually tackling both of them.

• 9:55Lester Nare?

  Both of them.

• 9:56Krishna Choudhary?

  At the same time.

• 9:57Lester Nare?

  Interesting.

• 9:57Krishna Choudhary?

  And obviously, there's a huge economic and human imperative, right? Like, there's a global camp, cancer burden, 20 million new cases every year, um, 9.7 million deaths every year, there's a huge economic toll, um, drug development is long and expensive. Takes hundreds of millions of dollars. And if we have this kind of data set, we can train models that do it way better, right? We can, we can limit all of that and democratize this approach.

• 10:26Lester Nare?

  Uh, this is actually totally an organic anecdote, but literally, I was in a work meeting earlier today, and unfortunately, one of my coworkers, you know, has a partner that, um, was just diagnosed with stage four.

• 10:41Krishna Choudhary?

  Ah.

• 10:41Lester Nare?

  And like, you know, I mean, it, it, it happens every day.

• 10:44Krishna Choudhary?

  Yeah.

• 10:45Lester Nare?

  It, it is present. Personally, for millions of people, one, two degrees of separation for so many people.

• 10:51Krishna Choudhary?

  Yeah, everyone knows someone.

• 10:52Lester Nare?

  And literally today, I mean, I, I, so this is clearly, obviously.

• 10:57Krishna Choudhary?

  Yeah.

• 10:58Lester Nare?

  A highly important and prevalent issue.

• 11:02Krishna Choudhary?

  Yes, exactly. And so let's talk about like cancer itself.

• 11:06Lester Nare?

  Okay.

• 11:07Krishna Choudhary?

  And why it's even like hard to do this.

• 11:10Lester Nare?

  Okay.

• 11:10Krishna Choudhary?

  It's hard to like make these genomic data sets that are synthetic, the way that we want. Okay. Cancer is a disease of the genome. It's caused by DNA errors. At the end of the day, that's what's happening. Okay. There's three billion DNA letters, ATGC, in our genome. And we're going to use this analogy of the genome being a library of cookbooks. Every single chromosome is a single cookbook that, let's say, one cookbook is for Indian food. One cookbook is for desserts and so on and so forth. Right? So the genome is a library of these cookbooks. Each cookbook is a chromosome and then each recipe is a gene inside that cookbook.

• 11:51Lester Nare?

  Okay.

• 11:52Krishna Choudhary?

  Okay. If we have errors in critical recipes, that's going to lead to cancer. And there's really two types of big mutations that happen, these typos that happen in our recipe. Okay. There's germline mutations, which is basically when a mutation is right, when a letter gets substituted for another letter or some mistake happens in the genomic reading of the thing. Or I delete a bunch of letters, so on and so forth. Right? There's, there's two types of mutations that happen. There's the germline mutation. That's something that's inherited from your parents and that's present in every single one of your cells. Right? Because that's the, you became, you came from a zygote, which is a single cell.

• 12:36Krishna Choudhary?

  And so whatever mutation was in that zygote.

• 12:39Lester Nare?

  Permeates.

• 12:40Krishna Choudhary?

  That's all of you. Right? Then there's somatic mutations. These are acquired during your lifetime. It happens within a single cell. And then the progeny of that single cell inherits that somatic mutation. Right? Cancer is actually primarily driven by accumulated somatic mutations.

• 12:57Lester Nare?

  Not germline.

• 12:59Krishna Choudhary?

  It's, it's, there are obviously cases where germline mutations create the cancer off the bat.

• 13:04Lester Nare?

  Yes.

• 13:05Krishna Choudhary?

  But usually it happens with age and there's these accumulated somatic mutations that cause your cells to do weird things, become tumorous, become carcinogenic and then.

• 13:17Krishna Choudhary?

  Create the cancer, right? And one key aspect is oncogen, which is this generative AI. It's trained on and it generates only somatic mutations. It doesn't get trained on the germ line info. So it's, it's already getting rid of that sort of privacy.

• 13:35Lester Nare?

  Issue, which is important to understand that there's two entry points. One of which has less of a privacy concern.

• 13:42Krishna Choudhary?

  Yes.

• 13:42Lester Nare?

  By default. Um, and that's where this oncogen is focused on.

• 13:46Krishna Choudhary?

  Exactly, exactly. Yeah. Now, when it comes to the architects of cancers, there's two types of cancers. There's a driver and a passenger. So the driver mutation, these are critical alterations that give a growth advantage to some kind of cell. Okay. It can either be something like you turn on an oncogene, which is something that turns on the cancer part of the genome, or it turns off a tumor repressor, right? A tumor suppressor. So then if, if the cell wants to go become tumorous, this gene isn't being active, telling it, no, you don't want to do that.

• 14:20Lester Nare?

  Not today.

• 14:22Krishna Choudhary?

  Right? And then, so these are the driver mutations. Then there's passenger mutations, which are kind of like hitchhikers. They accumulate by chance. There's no growth advantage. It's kind of like just there, but it doesn't really affect the, the cellular machinery in the way that it like turns it on and becomes cancerous, right? Tumors have very few driver mutations amidst thousands of passenger mutations. Okay. So there's this distribution where there's a few that are targeted for these oncogenes or these tumor suppressor genes. And then there's a bunch of noise.

• 14:59Lester Nare?

  Yes.

• 15:00Krishna Choudhary?

  Right? And any kind of generative model like oncogen needs to model that discrepancy, where there's a few of the stuff that matters and there's a lot of noise, right? And oncogen does exactly that, right? It replicates this tumor architecture by making a bunch of random passengers and not that many driver mutations. Okay. And the final thing I want to touch on is these mutations have fingerprints when it comes to cancer. Okay. There's characteristic patterns of mutations that.

• 15:34Krishna Choudhary?

  Give certain processes a leg up that becomes cancerous. For example, like UV light, right? UV light causes a C to T mutation. Basically, you get this dimerization where like one leg of your DNA, you know, your DNA is a twisted ladder, but one part of your ladder is gonna like staple itself. Like one leg of your ladder, it's got two legs. One leg of your ladder is gonna staple itself onto itself and become a dimer. And then that's gonna cause melanoma. For example, right? Tobacco smoke is linked to another single base substitution that'll cause lung cancer. So Uncle Gan, which is this thing that people have made,

• 16:18Krishna Choudhary?

  it accurately reproduces that tissue specific signature, right? Where it's like the lung cancer is gonna do this and the skin cancer is gonna do that, so on and so forth. It's actually learned how to do all of that stuff.

• 16:32Lester Nare?

  That's fascinating.

• 16:32Krishna Choudhary?

  Right? And then the other thing, we're not done with all of the stuff that can cause cancer. There's also something called large scale vandalism, right? There's copy number alterations, CNAs. This means you've taken a whole chunk of your chromosome and you've duplicated it.

• 16:50Lester Nare?

  Okay.

• 16:51Krishna Choudhary?

  Okay? So now there's a duplication or even a deletion sometimes of large chromosomal segments. So you've got large parts, like large chapters of your cookbook that are now been copied or completely deleted. You've also got structural variance where you take a big chunk of your DNA and you flip it and now it's reversed. Now, it's just all sorts of chaos, right? Like, there's so many different things that can happen with cancer genomes, right?

• 17:19Lester Nare?

  That are the driver and the cause of it.

• 17:21Krishna Choudhary?

  Right? And the real, and the real sort of challenge is how do we make a single architecture, a single framework that captures all of these different myriad effects, right? Into a single pipeline that then can be used to train other AI, to train other tools, right? And that's what this paper is doing. So they use something called

• 17:46Krishna Choudhary?

  Generative AI. We've seen this a lot, right? Like a cat on a thing. This one, this particular architecture is called a GAN. It's a generative adversarial network. Okay. There's two competing neural networks. This is a very interesting way to actually train AI and to architecture it up. I think it's very cool. It's been used actually for image generation as well.

• 18:09Lester Nare?

  Yep.

• 18:10Krishna Choudhary?

  But this time what they're doing is they're using it to generate genomic data.

• 18:15Lester Nare?

  It's interesting because I've, I've been watching this space for a while and I've, I've seen GANs being used in the, the image generation context. Now mapping it to this problem set is an interesting use of

• 18:28Krishna Choudhary?

  Yeah.

• 18:29Lester Nare?

  the architecture of general, uh, generative adversarial networks. That's

• 18:32Krishna Choudhary?

  Yeah, yeah. And, and the basic idea behind general adversary, generative adversarial networks is you've got two networks. Okay. You've got one that's the forger and one that's the discriminator. Okay. The forger or the generator, this is the guy who's going to create forgeries from random noise and try to attempt realism. And then there's the discriminator, which is like the judge who's trying to tell whether the thing that was generated is a real thing or a fake thing. So both of these guys have the training set, which is all the stuff that is actually real. And the, the generator is creating fake versions of that.

• 19:14Lester Nare?

  Yep.

• 19:15Krishna Choudhary?

  And over many, many iterations, both of these guys are going to get really good at their jobs. The generator is going to get really good at creating fake data and the discriminator is going to get really good at telling whether it's fake or not. And it's this competing adversarial, that's where we get adversarial from, this competing effect that actually causes the generator to get really, really good at its job.

• 19:41Lester Nare?

  So there's this substrate, there's a substrate of truth that these two processes have access to. One's job is to create synthetic data based on that substrate.

• 19:53Krishna Choudhary?

  Yeah.

• 19:53Lester Nare?

  The other is to say whether

• 19:54Krishna Choudhary?

  Mm hmm.

• 19:55Lester Nare?

  you're fake or not. And and because there's this sort of iterative back and forth between these two, you get to a place

• 20:05Krishna Choudhary?

  Yeah.

• 20:05Lester Nare?

  where, you know, the generator has now generated something the discriminator views as being

• 20:13Krishna Choudhary?

  Real.

• 20:14Lester Nare?

  Real.

• 20:14Krishna Choudhary?

  Yeah.

• 20:14Lester Nare?

  Because it's gone through that back and forth.

• 20:16Krishna Choudhary?

  Exactly. And now it's like really good at making real looking.

• 20:19Lester Nare?

  Real looking.

• 20:20Krishna Choudhary?

  Data.

• 20:21Lester Nare?

  Data.

• 20:21Krishna Choudhary?

  Right?

• 20:21Lester Nare?

  Which goes back to our privacy issue and some of the other things we just.

• 20:24Krishna Choudhary?

  Exactly. Yeah. Yeah. And so the cycle repeats until the generator produces stuff that's indistinguishable from the real data. And this can be used for tabular data, which is a lot of this genomic data, right? It's not like images and things like that. It's like tables of like, how much of this is there? How much of this is there? OncoGAN uses something called C tab GAN plus, which is just a specific architecture that's used for tabular data.

• 20:50Lester Nare?

  Got it.

• 20:51Krishna Choudhary?

  And it handles mixed data types, imbalanced distributions. So like the distribution is not like completely uniform. You have some stuff happening all the time. Some mutations happen all the time. Other mutations are rare, so on and so forth. So this takes care of that.

• 21:04Lester Nare?

  It handles the complexity of of this kind of data set.

• 21:07Krishna Choudhary?

  Exactly. Exactly. Now, the other thing that they're using is something called variational auto encoders. These are simpler than GANs. And what they do is they basically take whatever input you have, they try to squish it down into a tiny little latent space, right? And that latent space has fewer dimensions than the original data.

• 21:34Lester Nare?

  Yeah.

• 21:34Krishna Choudhary?

  And then from that latent space, we have an decoder that tries to recreate

• 21:42Lester Nare?

  Hmm.

• 21:42Krishna Choudhary?

  what was originally

• 21:44Lester Nare?

  Complexity.

• 21:44Krishna Choudhary?

  compressed.

• 21:45Lester Nare?

  Yeah.

• 21:45Krishna Choudhary?

  Okay?

• 21:46Lester Nare?

  Yeah.

• 21:46Krishna Choudhary?

  So this is used a lot. This is used actually a lot in like image generation. When we have, let's say, a 256 by 256 image, that has 256 times 256 dimensions, right? Because each pixel has a value. And you can imagine if if there's only three pixels, you can imagine putting that on an XYZ plane, right? Where every single pixel's value.

• 22:13Krishna Choudhary?

  Has to do like the first pixel is how far it is in the X direction. The second pixel is how far it is in the Y direction and so on and so forth. Well, now you have 256 times 256 dimensions. Every single image is highly dimensional. But if I were to take a bunch of photos of just a bunch of faces, right? The essence of the face is actually not that highly dimensional. There's always a nose in the middle. There's always a mouth. There's always eyes, right? Maybe, maybe one of the directions could be how dark is the face, right? For us, it would be like maxed out.

• 22:52Lester Nare?

  Very dark.

• 22:53Krishna Choudhary?

  Yeah, very dark. Like there'd be one dimension that would tell the variational autoencoder. This is a very dark face, right? Then there'd be one dimension that would tell how far up in the nose is the nose? And how, how wide is the mouth and things like that? But the fact that there is a mouth is not something that needs to be encoded in the latent space because it's always there, right? The stuff that's varying is the stuff that's going to be encoded in that compressed middle part. Right? And then you have a decoder that takes that middle part that says, okay, how much is the, how much is, where is this and where is that? And puts it, puts it into the actual image that it reconstructs. And this is something that we're seeing in MNIST. For example, the MNIST dataset is a bunch of handwritten digits.

• 23:37Lester Nare?

  Yeah, yeah.

• 23:38Krishna Choudhary?

  The key part about variational autoencoders is that it's a continuous latent space. Meaning that all of my real data points are somewhere in my latent space. But I can pick any point in between and that'll result in something real. Okay, it's continuous. It's not discrete, like this and this, and there's nothing in between, right? There is actually meaning in between. And so you can see in that latent space of the MNIST. It's going from the thing that looks like a nine, which is over here,

• 24:12Lester Nare?

  Yeah.

• 24:13Krishna Choudhary?

  To a three, to a six, to a zero, right? And it's continuously deforming.

• 24:18Lester Nare?

  Yeah.

• 24:19Krishna Choudhary?

  And all they're doing is reconstructing what happens if I move on that latent space, like as a walk.

• 24:25Lester Nare?

  Yeah.

• 24:25Krishna Choudhary?

  And I see, what is the decoder reconstruct?

• 24:28Lester Nare?

  Reconstruct.

• 24:28Krishna Choudhary?

  Does that sort of make sense?

• 24:29Lester Nare?

  No, 100%.

• 24:31Krishna Choudhary?

  It's like, it's like, it's taking all of the information of the stuff that it's trained on and saying, what is the essence?

• 24:38Lester Nare?

  Yes.

• 24:38Krishna Choudhary?

  Of the thing?

• 24:39Lester Nare?

  Yes.

• 24:39Krishna Choudhary?

  I'm going to put that into a landscape.

• 24:41Lester Nare?

  Yes.

• 24:41Krishna Choudhary?

  That I can traverse.

• 24:42Lester Nare?

  You can traverse, yeah, exactly.

• 24:43Krishna Choudhary?

  Right? And then, and then as I move through that landscape, it's going to create meaning with my decoder.

• 24:49Lester Nare?

  Because there's, there's different points on that landscape, for example, in the numbers that we find meaning in, which is like the nine, the three, the zero. But the, there is this, there is this, this transformation.

• 25:01Krishna Choudhary?

  A continuous.

• 25:02Lester Nare?

  Continuous in between there where you can say, oh, this is like a nine, three-ish.

• 25:08Krishna Choudhary?

  Mm hmm.

• 25:09Lester Nare?

  Thingy.

• 25:10Krishna Choudhary?

  Yeah.

• 25:10Lester Nare?

  On my way from nine to three.

• 25:12Krishna Choudhary?

  Exactly. Yeah, exactly. Exactly.

• 25:15Lester Nare?

  Yeah.

• 25:15Krishna Choudhary?

  So, so, so they're using both. And, and the key to using both is that you've got a GAN that's used for complex, interdependent features like mutation counts, driver co-occurrence, that, that's there due to high fidelity matching. And then you have this variational autoencoder that's there for continuous variables like genomic position. The position is a continuous thing, right? But the mutation count, that's a discrete thing.

• 25:43Lester Nare?

  That's a discrete thing.

• 25:43Krishna Choudhary?

  Right? So you're, you're combining both into this hybrid approach to create this like realism in the data set.

• 25:51Lester Nare?

  That's fat, that's actually really, really interesting.

• 25:54Krishna Choudhary?

  It's, they're not, they're not limiting themselves to like one type of architecture.

• 25:59Lester Nare?

  Right, right.

• 25:59Krishna Choudhary?

  They're really. Harnessing the true power.

• 26:03Lester Nare?

  It's like, there's this blended architecture, which is using the, the value of both of these like modalities. And taking the best of both.

• 26:10Krishna Choudhary?

  Yeah.

• 26:10Lester Nare?

  And, and sort of using them in the context that they are most.

• 26:15Krishna Choudhary?

  They're good at.

• 26:15Lester Nare?

  That they're good at.

• 26:16Krishna Choudhary?

  Yeah.

• 26:16Lester Nare?

  In, in, in parallel or together.

• 26:19Krishna Choudhary?

  Yeah, exactly, exactly. Because the genome is, is both continuous and discrete.

• 26:24Lester Nare?

  And discrete.

• 26:24Krishna Choudhary?

  Right? And so now we're, we're, we're getting like two soldiers here.

• 26:29Lester Nare?

  Yeah, yeah.

• 26:30Krishna Choudhary?

  That are, that are doing the work, right? In order to train on CoGAN, they use the training data from Pan Cancer Analysis of Whole Genomes, PCAWG. It's a data set that has 2,658 whole cancer genomes, like entire genomes, okay? All three billion base pairs.

• 26:49Lester Nare?

  Massive.

• 26:50Krishna Choudhary?

  Massive.

• 26:50Lester Nare?

  Massive.

• 26:51Krishna Choudhary?

  Yeah, and this, this data set is treated like sensitive information, because it should be, right? Because this has actual markers that can identify patients and things like that. But using that, they've now created something that will generate data that doesn't have those limitations.

• 27:10Lester Nare?

  They've created a derivative product from source data that is personal identifiable information. That now has that degree of separation to, to not run afoul of the.

• 27:21Krishna Choudhary?

  Exactly, yeah, yeah, exactly. And from what I counted in the paper, there's five GAN models and one TVA model. So there's five GAN generative adversarial network models, and then one variational autoencoder model. All of these guys are working together to create this giant genome, right? Multiple giant genomes. And obviously, the question is to ask is like, you know, okay, how real are you making this, right? The photo that you're making, could it fool me?

• 27:51Lester Nare?

  This becomes the, with photos, it's easy, because we sort of have the eye test, right? This is the whole uncanny valley concept, which is like, okay, I can look at something and be like, oh, the fingers.

• 28:02Krishna Choudhary?

  Yeah.

• 28:02Lester Nare?

  Aren't right.

• 28:03Krishna Choudhary?

  I mean, now, dude, I'll be honest, dude, there's like videos on Instagram that I get fooled. I do a double take and I'm like, oh, this is, this is definitely.

• 28:13Lester Nare?

  There's a, there's a Neil deGrasse, Neil deGrasse Tyson just did a video of himself talking about how Earth is flat. And he, he held up an iPad to the camera. You didn't know, like, the video starts, it's just Neil deGrasse Tyson.

• 28:23Krishna Choudhary?

  Yeah.

• 28:24Lester Nare?

  Talking about how the Earth is flat. And then like, he just moves the iPad and he's like, that wasn't me. And it's, it's like.

• 28:31Krishna Choudhary?

  Oh, my God.

• 28:32Lester Nare?

  And it's the same, it's like him in the same space.

• 28:34Krishna Choudhary?

  Oh, my God.

• 28:35Lester Nare?

  In the same outfit and the same voice. And it's effectively indistinguishable.

• 28:40Krishna Choudhary?

  Yeah, dude, it's, it's getting, it's getting really good, right?

• 28:43Lester Nare?

  It's quite good.

• 28:43Krishna Choudhary?

  So at least we're harnessing it for like something useful.

• 28:46Lester Nare?

  Something useful, yeah.

• 28:47Krishna Choudhary?

  Yeah. Um, so one of the things they looked at to see, okay, is the stuff that's being output by this neural network? Is it something that can be real, right? They looked at mutational density and type. They found a high similarity between the synthetic and the real data. They looked at mutational signatures. They found that it's like, pretty nicely correlated. The real thing that got me was the genomic distribution. So you can look at the entire genome of the human from, you know, chromosome one all the way to chromosome 23 with the sex chromosomes. And you can follow where mutations are happening. Okay? And in cancer, mutations do not happen uniformly. Okay?

• 29:32Krishna Choudhary?

  There's going to be parts of the genome where it happens all the time. And there's parts of the genomes where there's very few mutations. For example, if you're closer to the center of the chromosome, you're not going to get that many mutations. But if you're closer to the edge, you're going to get a lot more mutations just because of the physics of like, you know, you got a little bit more freedom towards the edge than towards the center.

• 29:52Lester Nare?

  Yep.

• 29:52Krishna Choudhary?

  Right? And so they, they actually showed that mutations in their data set would follow non-uniform distribution. So on the, on the top, they have the distribution of. Mutations in a real sample?

• 30:08Lester Nare?

  Yes.

• 30:09Krishna Choudhary?

  On the bottom, you've got your generated, it looks pretty much the same.

• 30:13Lester Nare?

  The eye test, you know?

• 30:14Krishna Choudhary?

  The eye test, yeah, exactly. And what they've done is like the axis on the X axis is all the genes.

• 30:21Lester Nare?

  Yeah.

• 30:22Krishna Choudhary?

  Like all 23 chromosomes worth of genomes.

• 30:24Lester Nare?

  Yeah.

• 30:25Krishna Choudhary?

  And they've binned it in like, I think, one kilo base bins. And they've counted how many mutations are happening, right?

• 30:32Lester Nare?

  That's incredible.

• 30:34Krishna Choudhary?

  This is actually, it's very, it's very similar to what we would actually see.

• 30:38Lester Nare?

  What we, what we've been basically able to do is now create a, a system that on the fly, it's not fixed.

• 30:47Krishna Choudhary?

  Yeah.

• 30:48Lester Nare?

  It's, it's a, it's a, it's a generative model.

• 30:50Krishna Choudhary?

  Yeah.

• 30:51Lester Nare?

  So on the fly, any team can now if, you know, take this and say, we want this kind of dynamics. And they can generate now these cancer data sets that mimic real cancer genomes to a degree of accuracy or, or comparable.

• 31:14Krishna Choudhary?

  Yeah.

• 31:14Lester Nare?

  Or, uh, high fidelity that is usable.

• 31:18Krishna Choudhary?

  Yeah.

• 31:18Lester Nare?

  In, in practical work in this space.

• 31:20Krishna Choudhary?

  Exactly. And that's that key that you just touched on, usable, right? What does it mean to be usable? Well, they actually tried to test it with something called DeepTumor. DeepTumor is a, again, another AI that's been developed to identify cancers. Okay? They could fool DeepTumor really well.

• 31:39Lester Nare?

  Oh, wow.

• 31:40Krishna Choudhary?

  But here's the key, here's the key. DeepTumor is really bad at rare cancer subtypes.

• 31:46Lester Nare?

  Okay.

• 31:47Krishna Choudhary?

  Okay? Like lymph MCLL, there's only 35 samples in the training set. Okay? And if you have low mutations, you're going to struggle with like creating an AI that can identify that. Well, they substituted.

• 32:01Krishna Choudhary?

  Generated samples into the training set and deep tumor did better. Once deep tumor trained on those, the F1 scores improved.

• 32:10Lester Nare?

  That's fascinating. So the synthetic data from this GAN, this ONCO GAN, when fed into an existing detection.

• 32:19Krishna Choudhary?

  Yeah.

• 32:19Lester Nare?

  Models.

• 32:20Krishna Choudhary?

  That's like kind of established. People use it all the time.

• 32:22Lester Nare?

  It's incredible. All these things.

• 32:24Krishna Choudhary?

  Yeah.

• 32:25Lester Nare?

  It has now improved its ability for these edge.

• 32:27Krishna Choudhary?

  These like edge niche cases because now I can, I can generate genomes. Right. For that specific thing that's not really prevalent. In a lot of the real world data sets.

• 32:39Lester Nare?

  Because the idea is you can take the deep tumor and then use it on like real world.

• 32:43Krishna Choudhary?

  Yeah.

• 32:43Lester Nare?

  Patient and like, and then know that they do or do not have it.

• 32:47Krishna Choudhary?

  Exactly.

• 32:47Lester Nare?

  And then if it comes out with a result, that is really powerful.

• 32:51Krishna Choudhary?

  It's really, it's really augmenting the data sets that we already have.

• 32:55Lester Nare?

  Right.

• 32:55Krishna Choudhary?

  With these synthetic data sets, which by the way, this is something that people do all the time. In AI research, right? You do data augmentation.

• 33:03Lester Nare?

  Right.

• 33:03Krishna Choudhary?

  Where you take whatever data you have, you like apply transforms to it. When it comes to image data, you like squish it. You'll like turn it from black and white to red, red, white and blue, or vice versa. You'll try to make the model more robust.

• 33:16Lester Nare?

  Yeah.

• 33:17Krishna Choudhary?

  To the data quality.

• 33:21Lester Nare?

  Mm hmm.

• 33:21Krishna Choudhary?

  But here they're actually able to create new data that can target these really rare cancer subtypes. And establish a new paradigm for medical AI, right? We can generate more data instead of we just need to collect more data.

• 33:36Lester Nare?

  Right, right, right. Which, which, you know, there's a bottleneck on the collecting data piece.

• 33:40Krishna Choudhary?

  Exactly.

• 33:41Lester Nare?

  And so you can much more quickly scale. Uh, if you can, uh, create usable synthetic data sets that actually map to the real world. Uh, use cases in a way that is demonstrably true.

• 33:59Krishna Choudhary?

  Yes.

• 33:59Lester Nare?

  Which is kind of the point, part of the point of this.

• 34:02Krishna Choudhary?

  Yeah, exactly, exactly. And there's no one to one correspondence with the real patients.

• 34:06Lester Nare?

  Right.

• 34:06Krishna Choudhary?

  You don't have to worry about HIPAA.

• 34:08Lester Nare?

  You, you solve the privacy issue while accelerating the ability to. Both create the synthetic data sets and then have those applied to existing toolsets or create new toolsets around all this process.

• 34:20Krishna Choudhary?

  And the team already, they've got 800 synthetic genomes that are openly available. No ethics approval needed, right? So anyone can go and try to train a model based on that. If they've got a new architecture in their mind that really is specific to genomic data, they can test it on this. It's a new gold standard for benchmarking, right? Because now, when I generate some kind of data set, I have a ground truth on what type of cancer it is. What are the mutations that should be flagged, so on and so forth? So it's a catalyst for medical oncology, I think. I think it's very cool.

• 34:57Lester Nare?

  It's a huge new tool in the toolbox for oncology studies.

• 35:01Krishna Choudhary?

  Yeah, I mean, there's still limitations, right? Like, the fact that we're doing this factorization where we first create the mutations and then we have another model that goes in and distributes it across the genome. It's not fully capturing this, like, complex interplay between features. Like, for example, if you have a copy number alteration where you have these giant chunks of chromosome that get duplicated or deleted, that's going to influence local mutation range. This is not something that captures that, right? It can't simulate complex events like chromothropesis and like tumor subclones, but it's still a huge, huge step forward, right?

• 35:44Lester Nare?

  We have to, it's one step at a time. This has obviously been an issue that is not only affecting so many people, but it's been happening for so long. And any, any new step towards our ability to solve this more robustly is hugely important.

• 36:02Krishna Choudhary?

  Yeah, yeah, yeah. Because cancer is just like, it's just such an insanely difficult problem, right? It's not a single cause.

• 36:12Krishna Choudhary?

  There's no single, like, panacea.

• 36:14Lester Nare?

  Right, solution.

• 36:15Krishna Choudhary?

  Yeah, it's just, yeah. So, so any incremental, and this, I think, is actually a huge step, because I think this opens up. Other people to do this kind of stuff.

• 36:25Lester Nare?

  Hundred percent.

• 36:25Krishna Choudhary?

  To create large data sets to then have really AI going in full fledged.

• 36:32Lester Nare?

  Right, right, right, right.

• 36:33Krishna Choudhary?

  To try and help out.

• 36:34Lester Nare?

  Right, right, right. In a, in a way that again, keeps in mind some of the aspects of data privacy.

• 36:39Krishna Choudhary?

  Yes.

• 36:40Lester Nare?

  That are, you know, are very important when you talk about medical studies. For all the reasons we talked about at the top of the story. This is actually, this is really, really, again, we said, we're gonna make sure we give Toronto.

• 36:54Krishna Choudhary?

  Yeah, University of Toronto. I mean, dude, their AI is.

• 36:57Lester Nare?

  This is a big deal.

• 36:58Krishna Choudhary?

  Their, their AI is always like on point, you know, their AI research is, is ridiculous. So.

• 37:04Lester Nare?

  This is a really big deal.

• 37:05Krishna Choudhary?

  This is a really big deal. I mean, you, yeah, you guys lost the World Series.

• 37:09Lester Nare?

  But you.

• 37:09Krishna Choudhary?

  We'll see you next year, hopefully, you know, that would be great, you know, round three.

• 37:13Lester Nare?

  Uh, round three, you've given us an incredible new tool in the box.

• 37:17Krishna Choudhary?

  Well done on that.

• 37:18Lester Nare?

  To, to do cancer detection and expand the amount of people that can start really working at this issue.

• 37:24Krishna Choudhary?

  Yeah.

• 37:25Lester Nare?

  In earnest. Uh, that's our story. Number one.

• 37:27Krishna Choudhary?

  Yep.

• 37:28Lester Nare?

  Number two, the super story, the Avengers came together.

• 37:33Krishna Choudhary?

  Oh, yeah.

• 37:34Lester Nare?

  For, for this wireless retinal chip that's helping solve for blindness.

• 37:41Krishna Choudhary?

  Yeah.

• 37:41Lester Nare?

  This is in the New England Journal of Medicine. Again, we have Science Corporation, Stanford, UCSF, University of Pittsburgh and University of Bun that all collaborated on this story. You were really excited to talk to me about this.

• 37:54Krishna Choudhary?

  I was really excited to talk to you about this because um, my undergrad, uh, my undergrad PhD thesis, not PhD, my undergrad bachelor's thesis in Princeton had to do with the retina. And, you know, we were doing fundamental science, but this is a really cool story because it's, it's out in NBC News. Fox News even covered it. Um, with a tiny eye implant and of set of glasses, we can now cure age-related macular degeneration.

• 38:28Lester Nare?

  Which is crazy.

• 38:28Krishna Choudhary?

  Which is crazy, right?

• 38:29Lester Nare?

  Yeah, that, that.

• 38:30Krishna Choudhary?

  The solution is pretty ridiculous. Now this, to, to be mindful, this age-related macular degeneration, which is basically age-related blindness in the center of your. Of your visual field. This had no prior restorative therapies. Right? This is the first of its kind that is trying to tackle that. Okay. So basically if you had AMD, you were just. Yeah, you were just, you were just blind in the middle of your, in the middle of your field. You could see in the peripheral, but in the middle, you couldn't see anything. And now we have, actually have something that is restorative. Right? Not just slowing it down. Restorative. Brings it. Brings it back. Okay. Okay.

• 39:14Krishna Choudhary?

  The solution is something called the Prima system. It's a photovoltaic retina implant microarray. It's a novel neuroprosthetic, so it's wireless. It's subretinal. And we're going to get into what, what all that means. Okay. This was published in the New England Journal of Medicine. The key trial is called Primavera. I don't know if the authors are fans of the music festival in Barcelona. But that's what they called it, the Primavera. It's, it's an incredible thing. Okay. 81% of participants, 81% of participants said they got profound improvement. Meaningful improvement.

• 39:54Krishna Choudhary?

  Okay. And remember those, um, those like eye exams that you had to do with like a bunch of big, big like letters and then these guys, the guys who had this over five additional lines on the eye chart. Really? Right? That's. That's, that's a lot, dude. Bad astigmatism and can barely see. Yeah. So I. And, and, and some patients regained the ability to read letters, numbers and words. That's crazy. Okay. So let's talk about, let's talk about how the eye works because that's going to tell you about how the mechanism of this thing works. Okay. The eye is basically a camera. Okay. You've got a lens. You've got.

• 40:38Krishna Choudhary?

  A detector in the back, just like a camera. A camera has a lens and then a CCD in the back that the image falls on and then the CCD captures how many photons are coming here versus there. What are the colors of those things? The eye is very, very similar, okay? The retina is an image sensor in the back. And this retina has photoreceptors, which are your rods and codes. You can think of that as your CCD. That's the thing that actually interacts with the light. And then if a little photon interacts with it, it's gonna set off a little electrical signal. Then it goes through a bunch of different cells. They're called bipolar cells. Some of them are called amicron cells. And at the end of the day, you've got ganglion cells,

• 41:23Krishna Choudhary?

  which are the cells at the very end that take in all of that light information, convert it into electrical pulses, action potentials, and that goes through the optic nerve through your eye to your brain, to the occipital lobe. And that's what's transmitting visual information to the brain's visual cortex, okay? But it's really, it's like mechanistically, the eye is remarkably like a camera. You've got a lens and that lens you can squish. There's muscles on the, on the end of your lens that you can squish and so on and so forth to let in more light, less light. That's your aperture, right? In, in a normal DSLR, you've got the magnifying, right?

• 42:07Krishna Choudhary?

  And the magnification is like how much, um, like the, the distance between your lens and the focal plane?

• 42:17Lester Nare?

  Yes, yes.

• 42:18Krishna Choudhary?

  But for us, we can, I can focus on the microphone right in front of me. And I can also focus on a mountain that's way, way, way far away, right? And the way that our eye can do that, we don't have the luxury of actually extending our lens. So what we do is we actually change the curvature of the lens.

• 42:38Lester Nare?

  In order to do-

• 42:38Krishna Choudhary?

  In order to actually get different focal, focal lengths, right? So it's remarkably like a camera, okay? And in the back of your camera, in the back of the eye, you have the retina, which is this mini-computer that takes in all of that information and then relays that back to the brain.

• 42:55Lester Nare?

  It's sort of like the processing center. Once this comes, once the raw data comes in, it kind of packages it up to send it upstairs.

• 43:02Krishna Choudhary?

  To send it upstairs, exactly. And- In the center of your retina, you have something called the macula and the fovea. Okay, notice like I want you to just do this experiment where you're like looking at stuff. Notice that you have a lot of detail in the center of your vision.

• 43:18Lester Nare?

  Yeah.

• 43:19Krishna Choudhary?

  Compared to the periphery.

• 43:20Lester Nare?

  Yeah.

• 43:21Krishna Choudhary?

  Right?

• 43:21Lester Nare?

  Yeah.

• 43:21Krishna Choudhary?

  Like if you really wanted to read something like that stuff that's on over, over there, you would focus on it so that it's at the center of your vision.

• 43:31Lester Nare?

  Right.

• 43:31Krishna Choudhary?

  If you focus somewhere else, you wouldn't be able to read it.

• 43:34Lester Nare?

  Yeah.

• 43:34Krishna Choudhary?

  And the reason for that is you've got this structure called the macula and the fovea that has a high density of rods and cones, specifically cones, actually, that, that are the color receptors. So you've, you've got, you've got like higher resolution there because the pixel density is higher.

• 43:55Lester Nare?

  We're not, everything is not totally in focus all of the time.

• 43:59Krishna Choudhary?

  Yes, exactly. And macular degeneration is actually a progressive disease that damages that macula. So the, the macula is in the, in the middle, right? And you can have two types of AMD, macular, age-related macular degeneration. There's the wet AMD where basically the abnormal blood vessel growth, there's a bunch of blood vessels in your eyes, right? That are keeping these cells alive, get, giving it oxygen, things like that. Those blood vessels can leak, rapid vision loss. And then there's dry atrophic AMD, okay? And that's the gradual thinning of the macula. You get these extracellular deposits where the, the cells are sort of just dumping their trash and then that kills everything.

• 44:43Krishna Choudhary?

  And all of that accumulation happens and it kills all of those cones and that are, that are there.

• 44:49Lester Nare?

  In that center.

• 44:50Krishna Choudhary?

  In that center. And so that's center of your, the center of your visual field.

• 44:56Lester Nare?

  Yes.

• 44:57Krishna Choudhary?

  Is the thing that goes away.

• 44:59Lester Nare?

  Which is like, obviously.

• 45:00Krishna Choudhary?

  And that's the whole point.

• 45:01Lester Nare?

  Yeah, right, right, right.

• 45:02Krishna Choudhary?

  That's, that's where most of my information.

• 45:05Lester Nare?

  Right.

• 45:05Krishna Choudhary?

  Is coming from.

• 45:06Lester Nare?

  Right.

• 45:07Krishna Choudhary?

  And that's the one that's more, more common, right?

• 45:09Lester Nare?

  Okay.

• 45:09Krishna Choudhary?

  And then there's geographic atrophy, um, which it's, it's at the end stage of AMD. You get these well demarcated patches of your retinal tissue where they die off. You get the photoreceptors that are dying off. And it results in like absolute scotoma. So you get a blind spot right in the middle. And when you get the blind spot, you literally can't see in the middle of your visual field.

• 45:35Lester Nare?

  Which is so crazy.

• 45:36Krishna Choudhary?

  Right? It's a complete loss of phototransduction. Okay? That is what this particular study is trying to handle. Okay? And i- i- it's, it's pretty common, actually. Irreversible blindness among older place, older patients globally. You've got something like 200 million people. That, that have, that have this. In the US itself, it's a million people. It's, it's, it's an incredibly like big problem that we need to solve, right? Obviously, quality of life goes away. There's a loss of independence. It's got a devastating impact on.

• 46:16Lester Nare?

  I mean.

• 46:16Krishna Choudhary?

  All aspects of your life.

• 46:17Lester Nare?

  Especially like when, you know, not that this is, it's true. It's been true forever, but particularly when everything is so device driven and, and you know.

• 46:28Krishna Choudhary?

  Oh, yeah, I didn't even think of that.

• 46:29Lester Nare?

  You know what I mean? So it's like, you have to be able to like, you know, read and interact with screens. And so it's impossible to do so.

• 46:37Krishna Choudhary?

  Exactly.

• 46:38Lester Nare?

  Um, especially in a device driven kind of era.

• 46:41Krishna Choudhary?

  Yeah, yeah. And, you know, previously it was just like, oh, you're just, you're, you're totally not having a good time. Okay? There are a few drugs, like there's a, there's, the first drug was approved in 2023. Iserve.

• 46:57Krishna Choudhary?

  There's another drug called, uh, Cyphovray, I guess. The mechanism is you basically inhibit this cascade of stuff that's going wrong in the back of your retina. It slows the lesion growth by 20%. But at the end of the day, it's not restorative.

• 47:15Lester Nare?

  It's just stopping.

• 47:16Krishna Choudhary?

  It's just slowing it down.

• 47:17Lester Nare?

  Degeneration.

• 47:18Krishna Choudhary?

  Right? And that's, that's great.

• 47:20Lester Nare?

  Yes.

• 47:21Krishna Choudhary?

  But.

• 47:21Lester Nare?

  Yes.

• 47:22Krishna Choudhary?

  For someone who's already had it. It's not really doing much.

• 47:25Lester Nare?

  It's not, yeah.

• 47:26Krishna Choudhary?

  Right? And so now we've got this Primo system. It's a new paradigm in vision restoration. It is restoring things. Okay, it's got two primary parts. There's an internal component. There's a tiny, tiny photovoltaic implant. Okay, it's a little cluster of neuro implants, right? It's, it's, it's a tiny little electronic device at the end of the day that goes in the back of your head, okay? And it surgically gets placed in the back of your retina, in the back of your eye. And then there's an external component, which is specialized glasses with a video camera. And what that video camera is gonna do is project light through your eye

• 48:09Krishna Choudhary?

  to that implant in the back of your retina and stimulate that implant to create electrical signals to trick your eye into thinking your photoreceptors are still alive.

• 48:23Lester Nare?

  That's, that's, okay, so the idea is you have this two-part system.

• 48:27Krishna Choudhary?

  Yeah.

• 48:28Lester Nare?

  Which is effectively mimicking the real effect of photons hitting the back of your retina. But because all the, the stuff in the macula is, is now dead.

• 48:39Krishna Choudhary?

  Yeah.

• 48:39Lester Nare?

  We're not like regenerating the cells itself, but we are creating a synthetic version of the exact same input such that when it goes into your brain, it's receiving an identical signal to what you would in a natural sense.

• 48:55Krishna Choudhary?

  Exactly.

• 48:55Lester Nare?

  But in an artificial, very interesting.

• 48:58Krishna Choudhary?

  Exactly. It's, it's like simple, but at the same time extremely hard to do.

• 49:02Lester Nare?

  Obviously, because it hasn't been done yet.

• 49:03Krishna Choudhary?

  Yeah, yeah. And it's only being done because of today's technology. So now, let's get into, let's get, first, let's tackle the implant, okay? What is the thing that is going in the back of your eye?

• 49:12Lester Nare?

  Okay.

• 49:12Krishna Choudhary?

  Okay, it's a two nanometer by two nanometer little chip, okay? Thirty microns thick, a silicon chip. It's a pixel array of 378 different independent hexagonal chip pixels. Two nanometers by two nanometers. It's way, way smaller than a penny. It's like, it's like, you know, uh, smaller than even Lincoln's. It's like the size of Lincoln's eye.

• 49:36Lester Nare?

  On the pen.

• 49:36Krishna Choudhary?

  On the penny.

• 49:37Lester Nare?

  In the, in the Lincoln Memorial.

• 49:38Krishna Choudhary?

  In the, yeah.

• 49:42Krishna Choudhary?

  That's how small this thing is, right?

• 49:44Lester Nare?

  Crazy small.

• 49:45Krishna Choudhary?

  Each of the pixels, there's 378 pixels. Each of those pixels has a little mini solar panel and an electrode. Now you're thinking, why solar panels?

• 49:55Lester Nare?

  Yeah.

• 49:55Krishna Choudhary?

  Well, solar panels create electricity out of light. Now, I've created electricity from the light that is going in into my eye. I don't need an external battery or anything like that.

• 50:10Lester Nare?

  I'm so mad.

• 50:11Krishna Choudhary?

  That is, I thought that was so cool, dude.

• 50:14Lester Nare?

  So genius.

• 50:15Krishna Choudhary?

  I thought that was so cool.

• 50:17Lester Nare?

  That, which matters because a lot of times you can even use phones as an example. A lot of the weight that goes into the phone is the battery.

• 50:26Krishna Choudhary?

  This is the first one that is wireless.

• 50:28Lester Nare?

  That's.

• 50:28Krishna Choudhary?

  The reason why it's wireless is because they use this photovoltaic cell.

• 50:32Lester Nare?

  That makes sense.

• 50:33Krishna Choudhary?

  Right?

• 50:33Lester Nare?

  That's genius.

• 50:34Krishna Choudhary?

  And I think that's so genius.

• 50:35Lester Nare?

  Yeah, yeah.

• 50:36Krishna Choudhary?

  Right? Like, there's an electrode and that electrode is what's actually creating the electric field to then tag all the other neurons in the retina to make it seem like, right? There is a photoreceptor there. There's a rod and cone there. But the elec, the energy for that electricity is coming from the photon itself.

• 50:55Lester Nare?

  And it makes sense because your eyes are, anyway.

• 50:57Krishna Choudhary?

  Right?

• 50:58Lester Nare?

  Yeah, that's smart.

• 50:59Krishna Choudhary?

  That's so smart.

• 51:00Lester Nare?

  Very smart.

• 51:01Krishna Choudhary?

  That's so smart. I love that. And, and as soon as I saw it, I was like, oh, that's dope. That's dope.

• 51:09Lester Nare?

  That's good.

• 51:09Krishna Choudhary?

  Yeah. And so what they do is they take this little chip, two nanometers by two nanometers, and they put it under the retina. Okay, this is deliberate where the electrodes are in proximity to the bipolar cells. So it's, it's where the, it's where the photoreceptors would be. And there you can see on the left, you've got a giant patch of where the photoreceptors have died. You're putting that chip right underneath where those photoreceptors should be. Okay? And that chip is now going to stimulate those bipolar cells, which will then in turn stimulate the ganglion cells, which will then go all the way to the back of your brain. Right? It's natural. It's efficiently processed signal and it's more sophisticated than these older retinal implants.

• 51:56Krishna Choudhary?

  That were stimulating the ganglion cells that were directly going into the brain. These guys are mimicking the photoreceptors. Itself, yeah. You see? Yeah, yeah, yeah. It's so, it's so nice, dude. I. I love that. And what I, what I love about this as well is that the, the, the pixel has a, has an input. So that's where the source of the electric field, like the, the source of the current, but it's also got a sink. Hmm. Because if you don't have a sink, then, then you put a lot of current out, that current is going to spread out and it's gonna, it's, it's going to trigger all the rest of the cells everywhere else in the retina. But if you have a sink, then it becomes localized, the current goes out, it triggers the stuff in its neighborhood and it goes back in.

• 52:39Krishna Choudhary?

  It becomes for like more, it becomes discreet. Yes, exactly. Yeah. Exactly. So now your pixel size, right? For like what I'm trying to see, that becomes smaller. They've thought of it. They've thought of everything, mate. This is really good. It's really nice, right? Okay, so that's the implant. Okay. Now let's get into the outside, right? Yes. There, there, there needs to be something that actually goes and puts light to that chip that's in the back of your retina. Yes. Right? And that's where these glasses come in. So there's a user interface, there's a smart component. You put on these glasses, you also have a user interface, like it's kind of like a remote. And that's your user interface that, um, you can like zoom in, zoom out. Oh.

• 53:22Krishna Choudhary?

  Yeah, yeah, yeah, yeah. You can adjust the contrast and things like that. And then, and then your, your, your glasses, they've got a little video camera, kind of like the meta glasses, you know, the meta glasses have a video camera. Yeah. And those, that, those glasses then send in near infrared signals to the back of your retina, which is where the chip is. Why near infrared? Well, all the other natural photoreceptors in your eye are not sensitive to near infrared. Ah. If you were sending invisible stuff, then you'd be blinded by like a giant. Like flashlight that's like right next to your eye.

• 54:02Krishna Choudhary?

  But because it's near infrared, the only thing that is getting stimulated is the chip.

• 54:08Lester Nare?

  The chip itself. That's, that's actually, that's really good. No, that's, that's, that's really good.

• 54:14Krishna Choudhary?

  It's so nice.

• 54:16Lester Nare?

  That's, I'm, I'm.

• 54:17Krishna Choudhary?

  Right?

• 54:18Lester Nare?

  Yeah, yeah.

• 54:19Krishna Choudhary?

  You're, you're seeing why I was like deciding about this. This is so cool.

• 54:22Lester Nare?

  I, I just, I, I'm like, I don't have anything to say because it's just, it's, it's so good.

• 54:25Krishna Choudhary?

  It's so good. Like they thought of it, right?

• 54:27Lester Nare?

  It's so good.

• 54:28Krishna Choudhary?

  The fact that it doesn't have any cables, like usually these things used to come with cables, but the fact that it doesn't have any cables simplifies the surgery. So you can go into the corner of your eye. I mean, it's still a crazy surgery. I'll be honest. Like you're going in with a needle of the corner of your eye, but you're depositing it in the back of your eye, but it's a relatively simple surgery. Because all you do is you stick it in, you leave the little bit of chip where you want it to be. And then you take the, you, you take the, the needle out, right? And it's, it's, it's, it's actually incredible. The, the, this chip efficiently absorbs 30 microns of silicon photo diodes, right? It's, it's, it's 30 microns of silicon photo diodes. You get this pulse light that's coming in at 30 hertz.

• 55:13Krishna Choudhary?

  And 30 hertz is, you must know 30 hertz is about where film.

• 55:18Lester Nare?

  Yeah, yeah, yeah, exactly.

• 55:20Krishna Choudhary?

  Uh, frame rate is.

• 55:21Lester Nare?

  Yeah.

• 55:21Krishna Choudhary?

  And that's because the ganglion cells that are going to the back of your head, right? That are relaying this information.

• 55:27Lester Nare?

  Yes.

• 55:27Krishna Choudhary?

  Anything faster than 30 hertz and it's really not discernible. Anything slower and it would look like stop motion. So at 30 hertz, you're getting this flicker free image.

• 55:36Lester Nare?

  Yes.

• 55:37Krishna Choudhary?

  Of pulse light that's going in.

• 55:40Lester Nare?

  This is really clever.

• 55:42Krishna Choudhary?

  Bro, it's so cool.

• 55:43Lester Nare?

  This is really, really, because they sort of, because you have to both solve for the like technical. So there's a variety of stages of this process, right? There's the actual like. Where in the eye do you make the intervention in order to get the restorative benefit? They made that sort of innovation by putting it in that like where the cones and rods are.

• 56:04Krishna Choudhary?

  Yeah.

• 56:04Lester Nare?

  Then you have to think about how do you do, how do you get power?

• 56:08Krishna Choudhary?

  Yeah.

• 56:08Lester Nare?

  Then that's the solar.

• 56:09Krishna Choudhary?

  That's the, yeah, you made a solar cell.

• 56:11Lester Nare?

  Which is so brilliant. Then you have to think about like, how are you actually going to turn it on? Meaning like, how are you going to direct the light to make it actually work the way you want it to?

• 56:20Krishna Choudhary?

  But not affect.

• 56:21Lester Nare?

  But not affect everything else. And so the near infrared as the methodology to do so is so genius.

• 56:26Krishna Choudhary?

  Yeah, dude.

• 56:27Lester Nare?

  Oh, man, that's so good.

• 56:29Krishna Choudhary?

  It's so good.

• 56:30Lester Nare?

  That's quite, that's quite nice.

• 56:31Krishna Choudhary?

  It's so nice.

• 56:32Lester Nare?

  That's quite.

• 56:32Krishna Choudhary?

  Yeah, it's so nice. And let's talk about the study itself that came out in the New England Journal of Medicine, right? It's a multi-center, so 17 sites across five European countries. You mentioned the University of Bonn. I mentioned them specifically because they kind of did a lot of the groundwork. But a bunch of other European institutions were part of the clinical trial, right?

• 56:55Lester Nare?

  Sure, sure.

• 56:55Krishna Choudhary?

  But you're increasing generalizability because of this 17 different places are trying it.

• 57:01Lester Nare?

  Because people culturally, genetically, they want to have it spread across.

• 57:04Krishna Choudhary?

  Exactly, yeah. So 38 participants, all of them over the age of 60, and all of them confirmed atrophy in both eyes.

• 57:13Lester Nare?

  Okay.

• 57:13Krishna Choudhary?

  Right? And what they looked at was something called the Log Mar, which is the logarithm of minimal angle of resolution. It's effectively saying like, in the center of your field of vision, like, how, how big can you see things? It's this thing. It's the, it's the letters and the driving test, you know? When you go to the DMV and you're like, okay, can you see or are you blind? Those letters tell you how small of a letter you can discern.

• 57:40Lester Nare?

  What line can you see on this one?

• 57:43Krishna Choudhary?

  I mean, the screen is pretty small, but I think I can see.

• 57:49Krishna Choudhary?

  But in any case, these guys, after 12 month follow up, after, after getting the, after getting the implant, 81%, so 26 out of the 32 participants met the endpoint, highly significant. And it's equivalent to five lines worth of stuff, 20, 59 more letters than they could have originally in that eye test, you know?

• 58:15Lester Nare?

  I mean, this is, this is such a huge, I mean, we talk about this all the time, which is like, why, you know, um, these, these studies are so important, like, the unlock that you get from something like this, um, for a huge population of people who otherwise were just going to lose the ability to see.

• 58:35Krishna Choudhary?

  Yeah.

• 58:37Lester Nare?

  Permanently.

• 58:37Krishna Choudhary?

  Yeah.

• 58:38Lester Nare?

  Is now no longer like necessarily true.

• 58:42Krishna Choudhary?

  Yeah, dude, there was one dude who went from legal blindness to like, I can drive now.

• 58:47Lester Nare?

  That's crazy.

• 58:48Krishna Choudhary?

  I mean, that's one dude, but still like.

• 58:50Lester Nare?

  And the, and this is like state, this is V1.

• 58:53Krishna Choudhary?

  Yeah.

• 58:53Lester Nare?

  Right?

• 58:53Krishna Choudhary?

  Yeah, this is V1.

• 58:54Lester Nare?

  It's, it's the first iteration of this. Um, in which you now have, I think that the, the innovation is in like all the kind of steps we just talked about, because now you can iterate on each of those component parts for any number of different.

• 59:11Krishna Choudhary?

  Exactly.

• 59:11Lester Nare?

  Permutations or use cases, et cetera.

• 59:14Krishna Choudhary?

  Exactly.

• 59:15Lester Nare?

  That's a really, really interesting.

• 59:16Krishna Choudhary?

  It's really cool. I mean, there were detractors, like if you looked at user satisfaction, 69%, so 22 out of the 32 reported medium to high overall satisfaction, which it's still like, if you weren't able to see, and now you're able to see like, that's 30% being like, I'm not satisfied. And I looked into more of that, I think part of that is because of these things called serious adverse events that happen at the end of any surgery. Like you're, look, you're, you're sticking a needle into someone's eye, you're putting in a chip, like, there's gonna be serious adverse events, right?

• 59:50Lester Nare?

  Sure, sure.

• 59:51Krishna Choudhary?

  And most of the stuff had to do with the trauma and healing from the surgery.

• 59:55Lester Nare?

  Not from the actual.

• 59:56Krishna Choudhary?

  Not from the actual device, right? So if we can make that, that process better.

• 1:00:02Krishna Choudhary?

  Perhaps this can be a lot better.

• 1:00:05Lester Nare?

  Satisfaction.

• 1:00:05Krishna Choudhary?

  Yeah, the satisfaction can be higher, right?

• 1:00:07Lester Nare?

  Makes sense. That does make sense.

• 1:00:09Krishna Choudhary?

  But at the end of the day, I think like this Prima is a quantum leap in terms of retinal implants. Like before, we used to, the first FDA approved retinal implant was something called the Argus II, which had 60 electrodes.

• 1:00:23Lester Nare?

  That was this image I just pulled up.

• 1:00:24Krishna Choudhary?

  Yeah, and that was, that was wired, though.

• 1:00:27Lester Nare?

  You're right. You can see it coming out of the.

• 1:00:29Krishna Choudhary?

  Yeah, yeah. And, and you could see that like in the retina itself, there's a little like wire that like, right? This thing is just complete, it's just a chip naked on its own.

• 1:00:38Lester Nare?

  Wireless.

• 1:00:39Krishna Choudhary?

  Wireless, dude. Wireless, no battery.

• 1:00:42Lester Nare?

  No battery.

• 1:00:43Krishna Choudhary?

  It's just a photovoltaic cell.

• 1:00:45Lester Nare?

  It's, it's, it's so clever.

• 1:00:48Krishna Choudhary?

  It's so clever, dude. And what, what they're doing next, you talked about how this is just the beginning, right? These, these pixels are about 100 microns in size. They're trying to get, get it down to 220 micron. 20 micron pixels from 100 microns. So now we'll be able to fit 10,000 pixels instead of the previous 378. So now I'll, I'll be able to get as much fidelity as I do now.

• 1:01:14Lester Nare?

  Which is, and again, now it's become an engineering problem, which is like much easier to deal with.

• 1:01:19Krishna Choudhary?

  Yes, yes.

• 1:01:20Lester Nare?

  Like, like I'll take an engineering problem all day.

• 1:01:22Krishna Choudhary?

  Yes, yes. 20 microns, right? That's, that's about the size of the rods and cones themselves, right? You're now starting to, to get to the limit the biology was getting to. Obviously, there's going to be challenges, right? You can't, smaller pixel size means that the electric field penetration to the upper layers of the retina is going to be not as good. So maybe you need these 3D electrode structures, right? Where you have like pillars, right? Where the, the pixel is like this and you've got a pillar that injects electricity into the upper layers of the retina. But again, engineering problems.

• 1:01:58Lester Nare?

  Problem, which, which is infinitely more solvable.

• 1:02:01Krishna Choudhary?

  Yeah, dude.

• 1:02:02Lester Nare?

  Than going from zero to one.

• 1:02:04Krishna Choudhary?

  I mean, this, this thing is like decades in the making, you know? But it's working and just, it's so clever. I loved it.

• 1:02:12Lester Nare?

  Yeah, no, that, that's really.

• 1:02:13Krishna Choudhary?

  It took a while to get access to the, the paper.

• 1:02:16Lester Nare?

  Oh, yeah.

• 1:02:17Krishna Choudhary?

  New England Journal of. Medicine, I don't know why you guys, yeah, I don't, I don't want to pay like, I think it was like a hundred, I'll be honest, I think it was like a hundred dollars. So I, I asked a friend of mine who, who is a doctor, who does research. Yeah, I need you to give me a PDF.

• 1:02:34Lester Nare?

  We're trying, we're trying to give you guys some traffic here. Okay, we're trying to give some promo.

• 1:02:38Krishna Choudhary?

  Yeah.

• 1:02:39Lester Nare?

  So, you know, free advertising.

• 1:02:42Krishna Choudhary?

  Yeah.

• 1:02:43Lester Nare?

  On the first principle pod, we'll, we'll do deep dives.

• 1:02:46Krishna Choudhary?

  Yeah, they're like, who are you?

• 1:02:47Lester Nare?

  And like honestly, like give like good, good understanding because a lot of times again, like for the average person, it's hard to really like parse and decipher what's actually happening in these stories. And like what the implications are and what, what's like the real world, you know, meaning. And the real world meaning for this is like a big deal.

• 1:03:05Krishna Choudhary?

  Yeah, this is a big deal, right? Like now imagine in the future, right now I'm thinking you can, you can bring AI into it.

• 1:03:12Lester Nare?

  Of course.

• 1:03:12Krishna Choudhary?

  Right? You could have like the, the, the Iron Man thing where like the thing automatically zooms.

• 1:03:17Lester Nare?

  Yeah.

• 1:03:18Krishna Choudhary?

  So you don't have to press a button to zoom. It'll just know what you're looking at and zoom in. Yeah. Like.

• 1:03:23Lester Nare?

  Yep. No, I mean, this kind of like ties to the first story we had around the, how AI is now being used for cancer detection. I mean, there's going to be clever ways in which to implement it in this context.

• 1:03:35Krishna Choudhary?

  Yeah.

• 1:03:35Lester Nare?

  I mean, you're, you know, there's all this stuff. I mean, these implants and these things, you know, we've talked about Neuralink before on the pod. Now we're having these retinal implants. Um, you know, there, there is a bottleneck around the surgery process itself. Like we're still, you know, meat.

• 1:03:51Krishna Choudhary?

  Yeah.

• 1:03:51Lester Nare?

  We're still meat sacks.

• 1:03:53Krishna Choudhary?

  Yeah.

• 1:03:53Lester Nare?

  So that's still a bottleneck.

• 1:03:55Krishna Choudhary?

  Yep. It's still a bottleneck, yeah. And it's going to be for a while.

• 1:03:59Lester Nare?

  For a while, but we're, we're getting, we're getting into real interesting places. I mean, as someone who already has bad vision, you know, my, you know, my wife has bad vision too. She actually has, I didn't do LASIK or anything. I just wear contacts and glasses.

• 1:04:14Krishna Choudhary?

  Mm hmm.

• 1:04:14Lester Nare?

  But her vision was so bad that at the time, like LASIK wasn't good enough for her eyes. So she got the implanted contact lens.

• 1:04:22Krishna Choudhary?

  Oh.

• 1:04:22Lester Nare?

  Like, like in her, in her eyes.

• 1:04:24Krishna Choudhary?

  Wow.

• 1:04:25Lester Nare?

  Which is like, and I was like, they could.

• 1:04:27Krishna Choudhary?

  That's crazy that you can do that.

• 1:04:28Lester Nare?

  I know. This was like 15 years, you know, whatever ago. This was a while ago. Um, and at the time it was state of the art and this and that and yada, yada. But I mean, this is like. Orders of magnitude.

• 1:04:40Krishna Choudhary?

  Crazy.

• 1:04:41Lester Nare?

  More difficult than just, just, just embedding a contact lens, you know, in the eye. Fascinating, fascinating, fascinating.

• 1:04:48Krishna Choudhary?

  Yeah, I thought that was such a cool story.

• 1:04:50Lester Nare?

  That's a great story. That is a great, great story. Um, it, it gives me hope that if I start getting blind later in life, there's still a chance.

• 1:04:58Krishna Choudhary?

  Yeah, there's still a chance.

• 1:04:59Lester Nare?

  There's still a chance.

• 1:05:00Krishna Choudhary?

  You're saying there's a chance.

• 1:05:01Lester Nare?

  You're saying there's a chance. We're going to end with our story number three, which is our story about, or related to at least unidentified anomalous phenomenon, which is more commonly known as UFOs. And before we kind of get into the two papers themselves, I kind of wanted to set the table a little bit because this is a subject and, you know, an issue that is extremely popular, not only in the US but globally. Um, and has in recent years gotten a boost because of a variety of events that have been happening, particularly within the apparatus of the US government, intelligence and military industrial complex structures.

• 1:05:44Krishna Choudhary?

  Yeah.

• 1:05:45Lester Nare?

  And, you know, this is a subject I've talked to you about a lot.

• 1:05:48Krishna Choudhary?

  Yeah, you're very passionate about it.

• 1:05:50Lester Nare?

  Uh, it's something that I, I have, I have interest in. I, I work at a nonprofit that's working this split space, the Disclosure Foundation. And one of the things that people always get caught up on is origin. And what I would ask is that we, we sort of keep origin to the side for a second in this conversation. Um, and just talk about what is without trying to ascribe an origin to it because these are all things that are happening, regardless of what you want to ascribe the origin to.

• 1:06:18Krishna Choudhary?

  Mm hmm.

• 1:06:18Lester Nare?

  Um, and it's interesting regardless of what the origin is. And so I'm going to do a quick retrospective on kind of the recent history of the UAP subject.

• 1:06:28Krishna Choudhary?

  Mm hmm.

• 1:06:29Lester Nare?

  Um, that is, is the context in which these two papers are coming out.

• 1:06:35Lester Nare?

  And so for many people, they're probably familiar with the 2017 New York Times article that came out that basically talked about the fact that there's these US government programs that are studying UFOs that have existed for some time. And that was sort of the catalyst for the modern era. The subject has a long history prior to that, that is outside of the scope of this episode. Yeah. And so we're not going to touch on sort of the, you know, the, what I call the legacy era of the subject. But after that New York Times article came out in 2017, it contained three videos. This was the first time we were getting videos with chain of custody from the US government that were showing these anomalous objects, right?

• 1:07:20Lester Nare?

  Okay. But it was still only put out by the New York Times, which people will have differing opinions about the quality of that institution as a newspaper. That's changed since that time period. But the Pentagon actually confirmed that the, what are colloquially known as the Gimbel, Fleer and Go Fast videos are legitimate videos that have chain of custody to the US government. That happened between 2017 and 2019. What sort of proceeded from there was an escalation of the issue from a national security perspective.

• 1:07:59Lester Nare?

  So in 2020, the then Senate intel minority leader, Marco Rubio, who is now the National Security Advisor. Oh, yeah. In the Trump administration, was one of several senators who called for there to be an unclassified report to be created by the Department of Defense around what are, what are these UAP? What is this unidentified anomalous phenomena? In 2021, the Office of the Director of National Intelligence delivered that report in which they stated that there were 143 unexplained cases that, you know, again, this was largely

• 1:08:40Lester Nare?

  driven by Navy and other pilots basically saying, look, this is a safety of flight issue and we need to like be able to report and address this just for our day to day operations. Right. Again, independent of origin or any of these other issues. The DOD then created, you know, this UAP task force, which was this sort of temporary task force that went out to both the intelligence agencies and the military sort of apparatus to try to understand, are you all seeing this? Where are you seeing this stuff? What is going on here? That eventually now rolled into what we now know as this permanent Pentagon office

• 1:09:25Lester Nare?

  that's called the All Domain Anomaly Resolution Office. That was established in 2022. And that office was congressionally mandated to be created after that initial report came out. Because Congress is basically alleging that the executive branch, which runs the intelligence agencies and the military, are not providing them the information about what's happening as it relates to this subject specifically. And it's important to note that the term UAP is a specific term of art that does not include any other form of unmanned aerial systems. So this is drones. This is, you know, undersea unmanned.

• 1:10:08Lester Nare?

  Like, those are all discreet and defined. And there's processes we have to deal with that. The reason why this became an issue within the government is that these platforms were exhibiting flight characteristics or behaviors that were beyond the scope of what was deemed as current or even next generation technologies. This has then been followed up by multiple hearings within the House and the Senate on this issue. And it's culminated with legislation that, part of which was passed, that was proposed initially in 2023, but then passed in 2024,

• 1:10:48Lester Nare?

  that mandated that executive agencies release information around the history of this subject. I mean, there's literally a... A colloquy between Senator Schumer, who was then Senate Majority Leader for the Democrats, and Senator Mike Rounds, who's a Republican, where they were sort of going back and forth, identifying that, you know, that this is something that has been an ongoing issue that Congress has been going after and the executive branch has been unwilling to provide information on. So this is sort of a live issue. NASA had a UAP Independent Study Group. They put out their own report around this issue. They are obviously struggling for funding,

• 1:11:33Lester Nare?

  so that it's not something that they're spending money on because they're trying to just survive as it is.

• 1:11:37Krishna Choudhary?

  Yeah.

• 1:11:38Lester Nare?

  There's been multiple whistleblower protections that have been passed. There's been defunding of UAP programs for the intelligence community. I mean, there's this long list of indirect evidence around this subject. But there's been a dearth of scientific research.

• 1:11:55Krishna Choudhary?

  Yes.

• 1:11:56Lester Nare?

  Really, the main primary thing that has existed in this space, particularly in the last couple of years, have been research papers that have been trying to define how to study the subject because of the reproducibility problem and the stigma, both of which are big issues. And the, the reference point that has been addressed is like multi-messenger astronomy studies has been the reference point for how to try to deal with this problem. But there are two papers that have just come out that are a first of its kind in terms of having actual data that, again, can be potentially either reproduced or replicated in other arenas from the Palomar-Sky Survey.

• 1:12:41Lester Nare?

  And we're going to kind of go in from a first principles perspective to understand what these papers actually were talking about.

• 1:12:48Krishna Choudhary?

  Yeah, yeah. So there's the lead author, Dr. Beatrice Villarreal.

• 1:12:55Lester Nare?

  Villarreal, yes.

• 1:12:56Krishna Choudhary?

  Villarreal.

• 1:12:56Lester Nare?

  Yes.

• 1:12:57Krishna Choudhary?

  I hope I'm saying that right.

• 1:12:58Lester Nare?

  Yes.

• 1:12:58Krishna Choudhary?

  So the lead author, Dr. Beatrice Villarreal, she actually covered this issue before in her 2021 paper.

• 1:13:06Lester Nare?

  Yes.

• 1:13:07Krishna Choudhary?

  That was published in Nature Scientific Reports. It was on nine simultaneous transients occurred in April 1950.

• 1:13:15Krishna Choudhary?

  And it's important to note that like, like, research on this has been done. Has been done. And this is, this is kind of a follow-up paper, but it's, I believe, a lot more thorough. And I just thought, I just thought that the way that the research was conducted, you know, the, the, the science sort of speaks for itself. And then the interpretations come afterwards. Sure. But, you know, on this podcast, we always focus on from first principles, what is the science that you did? And does it warrant the conclusions that you put out? Yes. Right? So we're going to start with these papers. The papers focus on the Palomar Observatory Sky Survey. Okay?

• 1:14:00Krishna Choudhary?

  This is P O S S one. It was a sky survey that was taken up by the Samuel Austen Telescope, which is a camera that is mounted on the Samuel Austen Telescope at Palomar Observatory. There you can see the, the telescope itself. It's got a Schmitt camera. And this thing is specialized for wide field photography. These cameras are actually pretty insane because, so that camera, the camera that is, that is attached to it, has a field of view of 6.6 degrees by 6.6 degrees. Okay. So 6.6 degrees by 6.6 degrees. The, the full moon for comparison is half a degree. It's about like, if you were to put your thumb right in front of your face, the, the size of your thumb is about half a degree.

• 1:14:45Krishna Choudhary?

  This is 13 full moons by 13 full moons. So it's 170 full moons. Big is the field of view of this camera, right? And because it's so big, there's a specialized camera called a Schmitt camera. And what it does is you've got, you've got a primary mirror, right? The mirror is about 1.2 meters. That mirror then goes into the camera. You can imagine the, the light is getting warped, right? As the light comes in, it gets warped. And so the edges of your field of view are going to get distorted. Yep, yep. This specific camera is specialized for that kind of wide field photography. So it corrects for that distortion.

• 1:15:30Krishna Choudhary?

  And even then, the focal plane is actually not flat. You know how in like a camera, your CCD is basically a flat piece of equipment? Because all of your light is coming onto a flat piece of equipment. Here, because it's such a large field of view and it's so, so big, the focal, the focal plane is actually curved. So in order to get a photograph, your photograph, the, the, the, the plate in this case, the photographic plate, and these are all photographic plates that are used, also has to be curved. And so you gotta like warp it before you slide it in there and get this photograph. I thought that was really cool. That's interesting. Like the, the amount of stuff that needed to happen in the 1950s. Yeah.

• 1:16:14Krishna Choudhary?

  In order to get these kinds of large field of view photographs, right? Given the technology at the time. This is purely photographic, which means that the, the other cool thing is that, that, that, that, um, the, the telescope that we just saw, that telescope is an equatorial mount. Okay. And what that means is that one of the axes is parallel to the earth's axis of rotation. And so all you have to do is rotate along that axis and it'll follow the stars as the earth rotates underneath, right? The, the, the stars are all gonna move in a circle around the north star. And so in order to track a single spot in the sky, the telescope has to rotate with the earth so that it points in one direction in the celestial sphere.

• 1:16:57Krishna Choudhary?

  And in order to actually do that, back then, they had a manual astronomer that would look through.

• 1:17:05Krishna Choudhary?

  A 10 inch smaller telescope to a guide star and then and then control the telescope manually to keep that guide star in the exact same position. So there was a lot of like manual stuff going on back in the day. And I think this is really important to see to to understand how how the data was collected, right? It's not with this modern technology where now everything is automated. And nothing is actually inequatorial because it's it's easy to it's easier to make bulkier telescopes in the azimuthal mount where I can control both axes with a computer. And I'll I just have to like do the calculation and control both axes and I'm fine.

• 1:17:44Lester Nare?

  Right.

• 1:17:45Krishna Choudhary?

  So this guy survey produced a thousand photographic plates. About a thousand, but one in red, one in blue. So then you'd have about 2,000. Um, this is an example of one of the photographic plates. Um, it's a one degree by one degree patch of the plate that is targeting Pleiades, the seven sisters. Um, and the the haziness that you see, that's actually not a defect. Those are actual clouds around those stars, those dust clouds around those stars that are that are creating that. It's a beautiful, beautiful example of one of the photographic plates that was actually taken during that time. The typical exposure was about 45 to 50 minutes. Um, what we're focusing on here are the red sensitive plates.

• 1:18:30Krishna Choudhary?

  They used to take red sensitive and blue sensitive. Here we're focusing on the red sensitive because that's what was used in the study here.

• 1:18:36Lester Nare?

  Okay.

• 1:18:37Krishna Choudhary?

  Um, and the limiting magnitude um is 22 in terms of apparent brightness. The higher the magnitude, the the dimmer the thingy is. And this is about 1 million times fainter than what the human eye can see.

• 1:18:50Lester Nare?

  Okay.

• 1:18:50Krishna Choudhary?

  Okay, so that's what's limiting the faintest object that this telescope can see.

• 1:18:55Lester Nare?

  Can see.

• 1:18:55Krishna Choudhary?

  Okay.

• 1:18:56Lester Nare?

  That's like the the lower bound.

• 1:18:57Krishna Choudhary?

  Yes, that is the lower bound. So that's the telescope that we're using. And that's how we're getting these images. Now, the photographic plates, we got to talk about the photographic.

• 1:19:08Lester Nare?

  We got to talk about the plates.

• 1:19:09Krishna Choudhary?

  What does that mean?

• 1:19:10Lester Nare?

  Yes.

• 1:19:10Krishna Choudhary?

  So photographic plates are 14 by 14 inches. And these 14 by 14 inches are placed at the back of the camera where all of the light is coming in. It's 1 millimeter thick. These are incredibly thin for how big they are. They're like the size of a record, you know, a vinyl record.

• 1:19:30Krishna Choudhary?

  They've got a light, they've got a photographic emulsion on them, okay? A light sensitive silver halide crystal layer. Usually it's silver bromide. And that's, that's suspended in gelatin. Okay, bromium is the halogen that they're using here. Halogen is like that column on the periodic table that's second to last on the right. These plates are bent, as I was saying, so that they can match the focal plane, which I thought was just really cool to think about. And then the process is a photon comes in from your telescope, and it liberates an electron in this silver bromide. And that electron, that liberation of the electron forms a latent image spec. You've got this free silver atom, okay?

• 1:20:15Krishna Choudhary?

  That creates a silver atom cluster. Then during development, that latent image spec catalyzes, and the silver bromide that's around there goes and turns into black metallic silver. Okay? And then finally, you have a washer, a kind of fixer, that removes any unexposed silver bromide. This is like the old school, you know, where you like wash the thingy? And then that removes any silver bromide. And what you're left with is a permanent negative image. And that's why in the, in the previous image that you saw, if you actually go back to photo two, you'll see in the previous image, the dark spots are actually where the light is. Hmm, right, right. Cause that's where the silver is getting deposited. Yes.

• 1:21:00Krishna Choudhary?

  Okay, so you get this negative image. Right. Of, of the dark is actually where the light is coming in. Got it. Okay, and the white is where there was no photo reaction that happened. Okay, and so that's how we actually get these photographic plates. That's what a photographic. That's what a photographic plate is. And that is what the chemical reaction of what a photographic plate means. Okay? Now, what this. What this Sky Survey is doing and the data that this particular group used is the digitized version of those photographic plates.

• 1:21:40Krishna Choudhary?

  Okay, so the digitized version in the 1980s and 1990s, we did a digitization process where those glass plates used micro densitometers that basically went through and digitized these photographic plates. You've got a light beam that measures the opacity of the photographic plate and that converts that opacity into a digital pixel value. So the more opaque it is, that means that there was more dark, right? There was more darkness. And then if there's more darkness, that means that there's more light over there.

• 1:22:16Krishna Choudhary?

  And so now you've converted from photographic plates to a digital pixel values, right? And there were two digitization processes that happened. Okay, there was the digital Sky Survey that was done with a pixel size of 25 microns. And then there was a super cosmos Sky Survey. And that was done at the Royal Observatory of Edinburgh with a higher resolution of 0.7 arc seconds per pixel.

• 1:22:52Lester Nare?

  Got it.

• 1:22:52Krishna Choudhary?

  Okay, so the first one was 1.7 arc seconds per pixel and then this one's 0.7 arc seconds. So it's just like more like granier detail, you know, sort of a thing about.

• 1:23:05Lester Nare?

  It's a slightly higher fidelity.

• 1:23:06Krishna Choudhary?

  Yes, exactly. And now you've got a vetting step, right? Where now we can create computer algorithms that look at these digital photographic versions and then try to find transients, little spots in the sky that aren't there in both, aren't there in current.

• 1:23:28Krishna Choudhary?

  Photographs of that patch of the sky.

• 1:23:30Lester Nare?

  But that were there.

• 1:23:31Krishna Choudhary?

  That were there back then.

• 1:23:32Lester Nare?

  Okay.

• 1:23:32Krishna Choudhary?

  Right? And what we can also do is we can rule out any scanning artifacts, because we've got two different digital versions of the photographic plate. So if something happened in the scanning where I got a little speck, that shouldn't be there in both at the same spot.

• 1:23:47Lester Nare?

  Yeah, yeah.

• 1:23:48Krishna Choudhary?

  Right? The probability of that is insane.

• 1:23:49Lester Nare?

  It's insanely high. The, the, the point is the, the, the digital scans, there's two, there's two versions. And so if someone was asleep at the wheel.

• 1:24:00Krishna Choudhary?

  Yeah.

• 1:24:00Lester Nare?

  When they scanned version one,

• 1:24:03Lester Nare?

  it should not be there in version two at the exact same spot in, in such a precise.

• 1:24:09Krishna Choudhary?

  Exactly.

• 1:24:10Lester Nare?

  Way. And so what, what you're sort of, there's a layer of, of, of validation.

• 1:24:16Krishna Choudhary?

  Yeah, there's like a redundancy here.

• 1:24:17Lester Nare?

  A redundancy here because we have two digital.

• 1:24:20Krishna Choudhary?

  Yeah, we have two digital versions of this.

• 1:24:22Lester Nare?

  Okay.

• 1:24:22Krishna Choudhary?

  And so now we get into the Vasco project, right? Which is the vanishing and appearing sources during a century of observations. This is led by Beatrice Villarreal. And the, the point of this project is to have a systematic comparison between the digitized versions and modern sky surveys, right? You look at what the Palomar Observatory saw back then.

• 1:24:48Lester Nare?

  Yes.

• 1:24:48Krishna Choudhary?

  And you look at what we're looking at right now.

• 1:24:50Lester Nare?

  Yes.

• 1:24:50Krishna Choudhary?

  And you've got an automated software that flags these vanishing, the present, present then, missing now, right? And there's also appearing. So not present then, but appearing now. Right? You've got an automated software that flags these things. And initially you've got a catalog of a hundred thousand transient candidates. And here we're seeing two different images, one from pos one, which is the first sky survey in pos two. And the circles represent two little spots that you can see are exactly not there. Right? It's like pretty obvious that those two dots were there before and now they're not there now. Right? And so that's a transient.

• 1:25:32Krishna Choudhary?

  You've got a point like source that's there in some plates, but in the later plates, it's not there.

• 1:25:37Lester Nare?

  And the idea is, you know, we have two points in time and we should be seeing identical things in both those points in time.

• 1:25:44Krishna Choudhary?

  Yes. If it's a star, then yeah.

• 1:25:46Lester Nare?

  Because of, and it's not identical.

• 1:25:49Krishna Choudhary?

  It's not identical. Right? So.

• 1:25:53Krishna Choudhary?

  People are gonna be skeptical about this, right? For example, it could just be plate defects. Sure. Right? Both this, this point of source of light or whatever, this thing that we're seeing in the photographic plate, it's there in both the digital versions. Well, that means it could just be in the plate, right? Itself. What is a hallmark of something that is in the plate? And this is something that comes from Nigel Hambly at the University of Edinburgh. He's one of the critics. He looks at something called the full width at half maximum of these points of light. Okay, so the full width at half max is basically, imagine you've got a Gaussian. Yep. What you're, what you're looking for is what is the width

• 1:26:37Krishna Choudhary?

  of that Gaussian when the height is half the, the peak height? Okay. And what this tells you is something about the noise in the data gathering process. Now, I told you this, this, um, this telescope, right? It's traversing the night sky. Yes. And it's being controlled by a human being. Well, there's gonna be a little bit of noise in how I control that telescope as it tracks. Right? So even a stationary point of light is gonna have a little bit of jitter, right? As I move this telescope, the telescope is gonna not totally track it completely. And even a point of light, like a star, that is definitely stationary. Yes. Over the 50 minutes at least. Yes.

• 1:27:22Krishna Choudhary?

  You know, it might be moving over like several years, but like over 50 minutes, it's stationary. That's gonna have a little spread in my photographic plate. And what he noticed was these transients, the ones that, the ones that sort of appear and disappear, they've got a, a spread that's way smaller than the stars. Okay? Yeah. And that, to him, was a telltale sign that this is a defect. Because if it was, if it was a light source, then the light source should have the same sort of error that the stars do. Because they're all light. Right? Does that make sense? It does, it does. Right? Like, all of the other things have the same sort of spread because it's from the same jitter of the telescope. It's uniform across the system. Right? Yeah, yeah, yeah. That's, it's a, it's a key thing.

• 1:28:07Krishna Choudhary?

  Yeah. Now, the contention is, the, the sort of counter argument is, well, If it's truly transient, meaning like it only like lit up for a minute or a fraction of a second, and it had a bright signature, then only one part of that 50 minute exposure is going to have that light come on, and then it's going to go away. So then, of course, the full with a half max is going to be way shorter because it's not going to have time to accumulate that noise.

• 1:28:37Lester Nare?

  Yes.

• 1:28:38Krishna Choudhary?

  Right? But now we're here, we're at a degeneracy problem, right? Because a genuinely short-lived astronomical event, right? Something that happens for a fraction of a second is going to look identical to a plate artifact.

• 1:28:52Lester Nare?

  Got it.

• 1:28:53Krishna Choudhary?

  You know?

• 1:28:54Lester Nare?

  Yeah.

• 1:28:54Krishna Choudhary?

  It's like you're looking for, you're looking for a short-lived astronomical event, but that's going to look exact, you know?

• 1:29:00Lester Nare?

  Yeah, yeah. How do you distinguish between what is being argued as a plate artifact versus a true

• 1:29:07Krishna Choudhary?

  Yeah.

• 1:29:07Lester Nare?

  Uh, short-lived transient event? Because it's hard to distinguish between those two.

• 1:29:14Krishna Choudhary?

  Yeah, yeah, yeah. The, the, the measure that we're using is degenerate to discerning between one or the other. Does that make sense?

• 1:29:24Lester Nare?

  It's, it's, it's one of the two.

• 1:29:25Krishna Choudhary?

  Yeah.

• 1:29:25Lester Nare?

  But

• 1:29:26Krishna Choudhary?

  But like the measure that we have, which is this full with a half max is like, it's going to be the same for both.

• 1:29:30Lester Nare?

  Got it.

• 1:29:31Krishna Choudhary?

  Right? So, so which one is it?

• 1:29:33Lester Nare?

  Yes.

• 1:29:34Krishna Choudhary?

  And that's why there was a necessity for an independent line of evidence.

• 1:29:38Lester Nare?

  Okay.

• 1:29:38Krishna Choudhary?

  And this is where we're getting into the two papers that we have now.

• 1:29:41Lester Nare?

  Okay.

• 1:29:41Krishna Choudhary?

  Okay?

• 1:29:41Lester Nare?

  Okay.

• 1:29:42Krishna Choudhary?

  So it's, it's this, it's this challenge of, well, it could be a defect or it could be a truly transient object. Which one is it?

• 1:29:50Lester Nare?

  And the, the, the interesting note here, just as a, a caveat or a point of order is there, there was a emphasis on this being, you know, the Palomar Sky Survey, which happened in the 50s, was pre-Sputnik.

• 1:30:06Krishna Choudhary?

  Yes.

• 1:30:07Lester Nare?

  Uh, Sky Survey. And nowadays, when we talk about transient light artifacts, it's

• 1:30:17Lester Nare?

  Starlink, its other satellites, other whatever, Chinese classified platforms. But there's a lot of stuff.

• 1:30:26Krishna Choudhary?

  Yeah.

• 1:30:27Lester Nare?

  That we have now put up there.

• 1:30:29Krishna Choudhary?

  Up there.

• 1:30:30Lester Nare?

  And so it is much easier nowadays to say, oh, it's X, Y, or Z.

• 1:30:36Krishna Choudhary?

  Yep, yep.

• 1:30:37Lester Nare?

  One of the interesting aspects of why this distinguishing between whether it's a plate artifact or not, is if it's not, we're seeing these transient light anomalies at a time period where we.

• 1:30:54Krishna Choudhary?

  We at least didn't put anything up there, right? On purpose.

• 1:30:58Lester Nare?

  On purpose.

• 1:30:58Krishna Choudhary?

  Right? It's, this is from November 1949 to December 1958.

• 1:31:03Lester Nare?

  Which.

• 1:31:03Krishna Choudhary?

  This is before Spudnik.

• 1:31:05Lester Nare?

  Which could be interesting.

• 1:31:06Krishna Choudhary?

  Yes.

• 1:31:06Lester Nare?

  Because of the fact that it can't just be pointed to being Starlink passing by.

• 1:31:12Krishna Choudhary?

  Exactly, exactly, exactly. And so now that is where we're getting into the two papers that have come out from the Vasco project, okay? What they've done is with the first paper, they're doing a spatial arrangement on individual plates, and they're arguing about the transients, how they look on individual plates, and where they are in the night sky in terms of when they were, when these photographic plates were taken, where was the earth's shadow? And if we can do some kind of statistical comparison and really show that these are stuff that's in the night sky and not photographic plate defects.

• 1:31:53Krishna Choudhary?

  And then there's a second paper that talks about temporal correlations in time between the stuff that we see in the photographic plates and stuff like nuclear tests and UAP sightings, which we'll get to. So, study one. This is the alignment hypothesis. This is Villarreal and others in 2025 in PASP.

• 1:32:16Lester Nare?

  Yes.

• 1:32:18Krishna Choudhary?

  The paper is aligned multiple transient events in the first Palomar sky survey. The hypothesis is that these multiple point-like transients are aligned along a straight line on the same plate, which is very unlikely by chance, and they could be real physical objects, and that is the argument that they're making, okay? So they've got a catalog of about 300,000 short-duration transients from this Palomar survey that we just went into.

• 1:32:43Lester Nare?

  Yes.

• 1:32:43Krishna Choudhary?

  Okay? And the first thing that they do is they ask, how many of these things are aligned? They're in a line, okay? And here we've got a photo of plate five. This is candidate five. There's five different thingies that are all in a line. Okay. And they use this thing, the statistical framework from Edmonds and George, published in 1985. And it calculates how often you would expect these things to happen by chance.

• 1:33:16Lester Nare?

  Okay.

• 1:33:17Krishna Choudhary?

  Okay. And they find a sigma of 3.9 that this is not chance. Okay, 3.9 is, it's pretty high. Okay, it's not like five sigma.

• 1:33:28Lester Nare?

  Which we've talked about in our previous episode about the strongest evidence yet.

• 1:33:35Krishna Choudhary?

  Yeah.

• 1:33:35Lester Nare?

  For life on an exoplanet, which was three sigma.

• 1:33:39Krishna Choudhary?

  Which was three sigma, this is 3.9, it's higher, but it's still not five sigma.

• 1:33:42Lester Nare?

  Sure.

• 1:33:43Krishna Choudhary?

  Right? But it's kinda interesting. Okay. And this is just candidate five. There's other candidates that also have around three sigma. Right? And they go through this in their paper. That's actually not personally what I found the most interesting. The alignment is like, I don't know, by chance. I don't really know how to reason about that in my head, about how often to do things by chance. And also the framework, the statistical framework by Edmonds and George, it relies on a bunch of different things, like the number of objects in the sky, the assumption about the density of objects, how high they are, like all this other kind of stuff, right? The Earth's shadow test is, I think, a decisive diagnostic. So here's the logic.

• 1:34:28Krishna Choudhary?

  You've got the sun, you've got the Earth, and the Earth casts a shadow on the rest of space, right? When the moon goes in the Earth's shadow, that's how you get a lunar eclipse. So.

• 1:34:41Krishna Choudhary?

  If we were to think that all of these things are plate defects, all of these transients are plate defects and not actual objects in the sky that are reflecting sunlight, then if they are plate defects, the number of things that are happening in Earth's shadow should be the same as outside Earth's shadow. Right. Because it's not dependent on whether the thingy, Right. whether the patch of sky I'm looking at is in Earth's shadow or not. Right. If, on the other hand, these objects are reflecting the light from the sun, then there should be fewer transients when I'm looking in Earth's shadow. Right. Than when I'm not. Right.

• 1:35:25Krishna Choudhary?

  Okay, and I know the coordinate of the spot in the sky where I'm looking, right? Because each of these photographic plates has a right ascension and declination, which is our latitude and longitude in the sky. We have our time of day. So from that, I can calculate which patch of the photographic plate is in Earth's shadow and which patch is not. I do have to do an assumption based on like how high I think these objects are. But they make an argument, which I actually don't quite fully understand, but they make an argument that these objects are probably at geosynchronous orbit, which is 42,000 kilometers.

• 1:36:07Krishna Choudhary?

  And at that altitude, I can calculate which objects, which part of my photographic plate is going to be in Earth's shadow and which part is not, right? And then from that, I get an expected number of 1,200 objects, but I only observed 349. So that's a pretty statistically significant sample. And this is where, I remember when you first came to me with the story, you said 22 sigma significance, right? The statistical significance. I was like, what are you talking about? This is what they're talking about. Right. They're talking about that like the 349 is so far away from 1223, it's 22 sigma away. They actually did a more realistic comparison,

• 1:36:52Krishna Choudhary?

  where what they did was they looked at the actual sky, because... This was actually kind of cool. So the photographic plates in Palomar didn't uniformly sample the entire night sky.

• 1:37:07Lester Nare?

  Right.

• 1:37:08Krishna Choudhary?

  The 22 sigma comes from the assumption that the photographic plates are completely uniformly sampling the night sky. On the other hand, some patches were looked at more than others, right?

• 1:37:18Lester Nare?

  Right.

• 1:37:18Krishna Choudhary?

  And some patches were completely missed. So what they did was, they did a more realistic simulation where they said, okay, suppose I only looked at those patches and I randomly generated objects. How often would I see defects versus not defects and all that other kind of stuff? And there they get a lower significance, but it's still a lot. 7.6 sigma. That sits better with me. Okay?

• 1:37:41Lester Nare?

  Yeah.

• 1:37:42Krishna Choudhary?

  7.6?

• 1:37:43Lester Nare?

  Yeah.

• 1:37:43Krishna Choudhary?

  Seems more reasonable. And the benchmarking that they did kind of made more sense to me, right? Because they're, they're taking into account this prior distribution of like where the telescope was looking rather than an assumption that, oh, it just looked everywhere. Well, it didn't, right?

• 1:37:59Lester Nare?

  It's not the Verarubin Observatory.

• 1:38:01Krishna Choudhary?

  Right, right. It's not just like, yeah, yeah, this is the 50s, right?

• 1:38:05Lester Nare?

  Yeah.

• 1:38:05Krishna Choudhary?

  It took us 80 years to get to the Verarubin. So, so that's the first, that's the first story. The second story, which is study number two, this is published in Nature Scientific Reports, and this is correlations with the atomic age, okay? Transience in the Palomar Observatory Sky Survey may be associated with nuclear testing and reports of unidentified anomalous phenomenon. This is, it's got UAP in the title.

• 1:38:35Lester Nare?

  It's got UAP in the title.

• 1:38:36Krishna Choudhary?

  Bro.

• 1:38:36Lester Nare?

  In, in, in a Nature sub-journal.

• 1:38:38Krishna Choudhary?

  In a Nature sub-journal, Springer, Springer publication.

• 1:38:41Lester Nare?

  Which is a, a significant thing in and of itself, given the aversion to doing anything related to this subject.

• 1:38:51Krishna Choudhary?

  Yeah.

• 1:38:51Lester Nare?

  From most peer-reviewed journals.

• 1:38:53Krishna Choudhary?

  Yeah, I mean, I was pretty surprised myself.

• 1:38:55Lester Nare?

  Yeah, when you were like, because I brought it up, you're like, well, what journal was it? And I was like, oh, and you're like, wait, really?

• 1:38:59Krishna Choudhary?

  Wait, Nature, yeah. Oh, fine. I guess if it's Springer, we can do it.

• 1:39:04Lester Nare?

  We'll cover it if it's Springer.

• 1:39:05Krishna Choudhary?

  Yeah, we'll cover it. Yeah. So. Here what they're doing is they're doing a more of a time sensitive approach. Okay, they're looking at the rate of the transients. Yep. That appear in those photographic plates that I talked about. Yes. And they're trying to correlate it with dates of above ground nuclear weapons tests. Yes. And daily counts of UAP reports. Okay, so the the timeline is again 1949 to 1957. They get a bunch of transient count. So for every single day, they've got photographic plates and they count how many transients were found on each day. Each day also gets a binary flag on whether it's within a day of a nuclear test. Or not. Right.

• 1:39:50Krishna Choudhary?

  And back then, I think only three countries were doing nuclear tests. The Soviets, the US and Great Britain. And then there's also daily UAP reports from the UFO CAT database. And these three things are what they're trying to relate. Yeah. They use something called the generalized linear model. I'm very familiar with something called the generalized linear model because my PhD had a lot of that. Basically, these kinds of models are what you want to do when things are not just simply linearly correlated to one another. In my PhD specifically, I was trying to correlate the spiking of neurons in terms

• 1:40:31Krishna Choudhary?

  of many different behavioral motifs, like which way he's looking, how fast he's running, where he is. All of these things can have varied responses that are not necessarily like, oh, if I'm looking this way, I just have more. Maybe it's like a weird response, right? Where only if he's looking this way, there's going to be more. So all of these like different multimodal effects can be captured by a generalized linear model. And that's what they did. It can also simulate weird distributions that aren't like just like uniformly distributed or Gaussian distributed. If you've got like a binomial distribution, which is like, like coin flips. Yep. If you've got, you know, did I see something or did I not? Things like that. It's very good at modeling those kinds of things. Right?

• 1:41:15Krishna Choudhary?

  So there was a correlation to nuclear testing. So the transience occurrence in these photographic plates was 45% more likely within a three day nuclear test window. Okay. And the main thing was it was 68% higher likelihood on a day after the test. Right? It's pretty significant. It's pretty significant. There's also a correlation plot that they do with UAP reports where using that generalized linear model analysis, 8.5% increase in transience for every additional independent UAP report on the same day. I don't really care for that as much. Like, UAP reports to me are like, I don't know, humans are fooled very easily and UAP reports are just humans being like, I saw something that was crazy.

• 1:42:02Krishna Choudhary?

  There's, I can't think of whether there are tests that I could do to see if this would work. Like, for example, what if you, like, assume that, like, on average, half of the UAP reports were nonsense? If you took out half, would the correlation still be there? If you did a bunch of samples where you took out half and you looked for correlation, would there be some samples where the correlation would be, like, way higher? You know? Yeah. Like, that's something that we used to do in my neuroscience lab and PhD is like, how sure are we that this variable is correlated to this variable? Where if I were to just take out a subset of the data, if the correlation still persists, it's a more robust finding, right? Yep.

• 1:42:47Krishna Choudhary?

  And given the, given the shape of that plot, I'm not sure if I believe it. But the, the, the nuclear testing thing is just like, that's just there, right? Yeah. So, so, so those are the two reports. There's one report in PASP, that's physical evidence. You've got the Earth's shadow test, that a substantial fraction of the transients are sunlit objects, right? And then, and then you've got the scientific reports, that's circumstantial behavioral evidence that says that the timing of these transients is affected by nuclear tests, right? There are people who, obviously,

• 1:43:31Krishna Choudhary?

  Are skeptics, right?

• 1:43:32Lester Nare?

  As, as we also saw with the same paper I just brought up earlier about the strongest evidence yet.

• 1:43:38Krishna Choudhary?

  Yeah.

• 1:43:38Lester Nare?

  That came immediately with follow ups from multiple institutions that pointed to why their modeling was not sufficiently robust.

• 1:43:46Krishna Choudhary?

  Yeah, like a, as of now, I haven't seen any follow up scientific papers. Because all of this, all of this data is publicly available. And I actually saw on the, on the two papers, it's, it's readily available for people to look at. So I encourage anyone to, to do their own analysis, actually.

• 1:44:06Lester Nare?

  I, I want to make a funny note. On the paper that I keep bringing up, within two weeks, because it was, was it?

• 1:44:13Krishna Choudhary?

  It was a week, dude.

• 1:44:14Lester Nare?

  It was a week.

• 1:44:14Krishna Choudhary?

  Yeah.

• 1:44:14Lester Nare?

  Within a week, because it was out of Cambridge Oxford, one of the two.

• 1:44:17Krishna Choudhary?

  Yeah, yeah. Cambridge had the original paper and then the guy at Oxford was like, nah.

• 1:44:20Lester Nare?

  And within a week, Oxford came out with the rebuttal.

• 1:44:22Krishna Choudhary?

  Yeah.

• 1:44:23Lester Nare?

  And then multiple came out. It's been more than a week.

• 1:44:26Krishna Choudhary?

  Yeah.

• 1:44:27Lester Nare?

  It is getting, this paper is getting quite a bit of just, obviously, a lot of the science media is, has, is doing a variety of different coverage, but there's not yet necessarily been follows as quickly.

• 1:44:38Krishna Choudhary?

  Yeah.

• 1:44:39Lester Nare?

  There's a number of reasons that could be true.

• 1:44:40Krishna Choudhary?

  Yeah, I mean, one of them, I think the big one is probably the, it's not an active scientific field, like exoplanets and exoplanet researchers. There's, everyone's trying to make a name for themselves, right? They've got a career to look after. And so this one is not like, something that, you know, okay, so you proved this wrong. It's not going to like, give you a leg up and ask for physical career.

• 1:45:01Lester Nare?

  There's no, there's no incentive for people who are looking to climb a ladder.

• 1:45:04Krishna Choudhary?

  Mm hmm.

• 1:45:04Lester Nare?

  To do a follow up on it.

• 1:45:05Krishna Choudhary?

  Yeah, yeah, yeah, yeah. So, so that could be one of the reasons. But in any case, the Scientific American actually had an article about it that I was reading, and they showed some rebuttals, right? So there's the Hamley argument, which is from the University of Edinburgh. He's an expert, Nigel Hamley, he's an expert in photographic plate analysis. And his main contention was that full width at half max, right? The fact that these transients are so pinpointy and precise compared to the stars that are kind of spread out because of all the noise from the detectors. And There's skepticism that, you know, this is a classic signature of an emulsion or a flaw or a plate defect.

• 1:45:50Krishna Choudhary?

  And it's not a celestial object because a celestial object would have this kind of noise. And one of the crucial things that he says is the ultimate arbiter is actually just the physical evidence. The plates are still around. We can take those original glass plates and examine them under a microscope and not examine the digital scans. And under a microscope is going to be very obvious whether this is a defect of the silver just like being kind of weird at that point, or if it's a genuine astrophysical source. So I think, I think that's, you know, he's, he's proposing a way to

• 1:46:26Lester Nare?

  To make it go away.

• 1:46:27Krishna Choudhary?

  To make it go away. I, I think that's good.

• 1:46:30Lester Nare?

  So if you're interested in doing microscopic study of the digital plates and you would like to collaborate, just in the comments below.

• 1:46:39Krishna Choudhary?

  Yeah, let us know.

• 1:46:40Lester Nare?

  Let us know. But, and we already talked just previously about even in the paper itself, a means by which that is not necessarily the likely outcome. However, you can remove it from the table. Yes, the earth shadow point. You can still remove it entirely from being the cudgel that you're seeing being put out. Cause a lot of times in the coverage of it, people say, oh, it's probably plate defects, without actually explaining

• 1:47:08Krishna Choudhary?

  Yeah.

• 1:47:09Lester Nare?

  How the paper points to why it's not plate defects.

• 1:47:12Krishna Choudhary?

  Yeah, yeah, yeah, yeah.

• 1:47:13Lester Nare?

  Which is kind of the issue.

• 1:47:14Krishna Choudhary?

  The earth shadow thing is like pretty, pretty substantive, I think.

• 1:47:18Lester Nare?

  And so however, it can, the, the beauty, the beauty of us still having the original plates is that it's falsifiable.

• 1:47:27Krishna Choudhary?

  Yes, exactly. There's another one that's proposed by Sean Kirkpatrick.

• 1:47:33Lester Nare?

  Ooh, Dr. Jean Kirkpatrick.

• 1:47:36Krishna Choudhary?

  Former Pentagon AARO head.

• 1:47:40Lester Nare?

  We, we like to call it Arrow.

• 1:47:41Krishna Choudhary?

  Arrow, okay, Arrowhead. They, I guess that's like a anomaly office or something?

• 1:47:47Lester Nare?

  The, that's the All Domain Anomaly Resolution Office.

• 1:47:50Krishna Choudhary?

  Oh, okay, got it.

• 1:47:51Lester Nare?

  That was created in 2022 as a result of Congress pushing on the executive branch to like, give them more information about, again, because Military and intelligence officers were coming to Congress saying, this is happening and we, we, we need help in dealing with this and we're not getting the support we need.

• 1:48:08Krishna Choudhary?

  I see, okay.

• 1:48:09Lester Nare?

  That was, that was the impetus for why the office was established and its mandate was twofold, which was unfortunate because it was basically a mandate that, that had conflating outcomes. One was, we want you, Arrow, to take data from the Department of Defense, all of the combatant commands, the intelligence community, intake it and then analyze what we're seeing as these UAP reports and if it's conventional, send it to the office that exists to deal with it and if it's unconventional, figure out what we need to do to figure out what, what it is.

• 1:48:42Krishna Choudhary?

  Okay.

• 1:48:43Lester Nare?

  That was one of its mandates.

• 1:48:44Krishna Choudhary?

  Yeah.

• 1:48:45Lester Nare?

  The other mandate was a public communications mandate to help to demystify this issue to the public by creating public education materials and the, and the third. The problem with that is you're asking, you know, the Department of Defense, which is not a public communications office, to do public communications. There's a reason why NASA is its own agency.

• 1:49:09Krishna Choudhary?

  Yeah.

• 1:49:09Lester Nare?

  And you don't have NASA running out of the DOD because you, we would never get anything out of

• 1:49:14Krishna Choudhary?

  Yeah.

• 1:49:15Lester Nare?

  that structure. So there's sort of a conflate, there's, there's a friction between the two thing, the two things Congress asked it to do, which is why a lot of the public is frustrated because they are not very forthcoming with, they have thousands of these reports, we've basically seen none of it.

• 1:49:30Krishna Choudhary?

  Yeah, okay. Well, he, he, he put his, Sean Kirkpatrick, he, he gave his two cents, which I thought was actually quite good, which was, he said, you know, there's nuclear detonation and clearly this thing is correlated with nuclear detonation. Now, when you have these high altitude nuclear detonations, you know, you can have radiation, particulate.

• 1:49:52Krishna Choudhary?

  Stuff that goes away and it can create transient luminous events like Chernkov radiation or even reflective metallic junk like the the stuff that made the nuclears like, you know, there's going to be stuff that gets thrown out and that junk could reflect light in the way that we're seeing in these Palomar sky surveys.

• 1:50:12Lester Nare?

  This is interesting because I was actually, I was just at the Seoul conference in Lake Missouri, Italy this past week where Dr. Beersch's Villarreal presented these two papers. And one of the, there was a Q and A section where one of the people actually asked about the Chernkov radiation as a solution to the problem set. Someone can clip this and put the response. I don't think it's public yet. I didn't, I didn't write it down, but she did have a response to that issue.

• 1:50:42Krishna Choudhary?

  Yeah.

• 1:50:43Lester Nare?

  Which I unfortunately can't replicate in this context, but that was, that was brought up by someone in the audience as the like obvious next step in the progression.

• 1:50:52Krishna Choudhary?

  Yeah.

• 1:50:52Lester Nare?

  As, as an answer to it.

• 1:50:54Krishna Choudhary?

  Yeah, I can imagine like, I mean, for Chernkov radiation, it would probably be like streaky rather than single point sources. But, you know, if you have like metallic junk.

• 1:51:06Lester Nare?

  Then it's that.

• 1:51:07Krishna Choudhary?

  Then that, that could like, just like, you know, as it's turning, like create that reflective. So to me, the metallic junk is, is a, a better explanation than Chirkov radiation or ionization because those have very characteristic.

• 1:51:21Lester Nare?

  Streaks.

• 1:51:22Krishna Choudhary?

  Signatures on photographic plates. But one of the challenges that Sean Kirkpatrick said was, you know, we've got GPS satellites now. Why don't we try to reproduce these results with the GPS satellites? I think it would, I think it's harder said than done because GPS, there are so many satellites now.

• 1:51:43Lester Nare?

  Yeah.

• 1:51:44Krishna Choudhary?

  Right? It's not just like, you're gonna have so many streaks.

• 1:51:46Lester Nare?

  Yeah.

• 1:51:47Krishna Choudhary?

  So for a 50 minute exposure, you're just gonna get completely wrecked by Starlink.

• 1:51:53Lester Nare?

  Right.

• 1:51:53Krishna Choudhary?

  There's another proposition by Elliot Gillum at the SETI Institute, Search for Extraterrestrial Intelligence. He said, you know, meteors traveling directly towards the telescope could appear stationary dots, not streaks. It's viable, but like now you got, it's traveling directly at the telescope, like.

• 1:52:15Lester Nare?

  At that, that volume level and simultaneous.

• 1:52:17Krishna Choudhary?

  Yeah, yeah, yeah. It's like simultaneous, multiple aligned.

• 1:52:21Lester Nare?

  A little challenge.

• 1:52:22Krishna Choudhary?

  Yeah, it's a little challenging, given the numbers.

• 1:52:26Krishna Choudhary?

  There's a Princeton astrophysicist, Robert Lupton, who has this look elsewhere effect. He's basically saying that, you know, in a massive data set with thousands of plates, millions of images, finding a few apparent alignments by chance is not that surprising. And you can't actually use the Edmonds and George formula to do that kind of statistical significance to figure out whether stuff isn't aligned, because there's so many things that are in a line, you know, you're you, you can easily fool yourself into thinking that the thing that is in a line is there for some reason, right? And the probabilities need to be corrected for the vast number of stuff that's out there.

• 1:53:06Lester Nare?

  This goes back to your not looking at the alignment as the interesting part.

• 1:53:09Krishna Choudhary?

  Yeah, yeah, yeah. And I think there's a I'm I need a little bit more convincing on the alignment being something that's interesting.

• 1:53:17Lester Nare?

  Okay.

• 1:53:17Krishna Choudhary?

  Right?

• 1:53:19Krishna Choudhary?

  But, I mean, the debate is there, you know, 7.6 sigma deficit of transience in the Umbra. That's not something that I think we can ignore.

• 1:53:31Krishna Choudhary?

  I think it forces a shift from are any of these transients real to, okay, given a significant fraction of them are probably real sunlit objects, what are they? Okay.

• 1:53:44Krishna Choudhary?

  It's it's it's certainly like giving me pause. I'll be honest.

• 1:53:49Lester Nare?

  Which I want to say for the audience, I've been working this guy for a long time. So to be giving him pause.

• 1:53:56Krishna Choudhary?

  Yeah.

• 1:53:56Lester Nare?

  Is is.

• 1:53:57Krishna Choudhary?

  It's like, yeah.

• 1:53:58Lester Nare?

  That's statistically significant.

• 1:54:00Krishna Choudhary?

  Yeah, yeah, yeah, yeah, yeah. You probably have, like, the number of times you've tried.

• 1:54:05Lester Nare?

  I'm like, nah.

• 1:54:06Krishna Choudhary?

  I've tried so hard, but but I I thought you'd find this interesting, even just from the experimental design perspective, the clever approach of trying to do a study like this in a way that's compelling. Yeah. I mean, I mean, clearly, like, the team.

• 1:54:23Krishna Choudhary?

  The team in the PASP paper, at least, the one in the Proceedings of the Pacific Astronomical Society, that one, that one has done its job of like, you know, going through the proper science and things like that. The time stuff, I'm a little less excited about, but the one with the Earth's shadow thing, that's just like solid calculation, you know?

• 1:54:49Lester Nare?

  Good math.

• 1:54:49Krishna Choudhary?

  Yeah, that's just like good math. There is like, there was one that was kind of interesting. So Candidate Five, the five transients systematically in alignment, that happened on July 22nd, 1952.

• 1:55:03Lester Nare?

  July 27th.

• 1:55:04Krishna Choudhary?

  27th. Oh, yeah, July 27th, 1952. And they mentioned that this coincides with the most intense weekend of 1952 Washington DC UFO flap, which I hadn't heard about, but I looked it up and we've got a photo showing like the, the, the recorded number of, of incidents that were reported. And apparently like there were visual sightings, fighter jets were scrambled, there was a press conference from the Pentagon. So I didn't even know that this thing happened.

• 1:55:38Lester Nare?

  So this is one of the things.

• 1:55:39Krishna Choudhary?

  That's weird, bro.

• 1:55:40Lester Nare?

  This is one of the things that's so interesting.

• 1:55:41Krishna Choudhary?

  Why would they do that?

• 1:55:46Lester Nare?

  It's, it's so interesting because there's, again, this is why when I started at the beginning of this, there's this whole legacy of this subject that exists. That's very dense and there's a lot of information there. And, you know, unless you have an entry point, you won't know it's there, but you know, we've, UFO flaps are consistent and persistent throughout the history, not only of the US but global society. And it, it is interesting to see it, how it cyclically comes back up. But that was Candidate Five.

• 1:56:23Krishna Choudhary?

  Yeah. And that was the one that was like super aligned. I mean, I still think it's prob, it's probably coincidence, but it's, it's an interesting coincidence. I'll say that. The other, the other thing they, they do at the end of the, at the end of the PASP paper. Is they look at 3D simulations in Blender and show how these reflective objects, if they're like slowly processing something that has a shape like that, could create the kind of signature that we see where like randomly it's at exactly the right angle, where it's reflecting the sun into the Palomar telescope and then it goes away real quick. So then you get a really sharp, you know, image spec in your photographic plate.

• 1:57:08Krishna Choudhary?

  It's speculative at best. I think, I think, um, I think it's certainly interesting. I think, I think the Earth's shadow thing is the one that like is really like sticking with me. Right? That calculation is quite nice. And the way that they do the, the way, the way that they do the controls, where they simulate a random subset in that photographic plate and say, okay, how many are going to be in Earth's shadow versus not like, yeah, that's, seems to be the way that I used to do, like, you know, these bootstrap

• 1:57:38Lester Nare?

  Yep.

• 1:57:39Krishna Choudhary?

  Things to try to figure out what the significance of my result was. So, yeah, I thought it was, I thought it was pretty cool. I think next steps would be to just look at the photographic plate. Make sure that it's not a defect.

• 1:57:49Lester Nare?

  Take, take a look at the plate.

• 1:57:51Krishna Choudhary?

  Yeah.

• 1:57:51Lester Nare?

  Not defect. And then from there, if proven to be the case, now we can have the next stage of the conversation.

• 1:57:57Krishna Choudhary?

  Yeah. Which is like, okay, like, you know, with modern surveys, perhaps there's a way to get an idea of how often this is happening in Vera Rubin. Vera Rubin's, um, completely public, right? All of the data is public like immediately after it's taken.

• 1:58:12Lester Nare?

  And it's up and running right now.

• 1:58:13Krishna Choudhary?

  Yeah. Yeah. So we have already three months of data from the Vera Rubin.

• 1:58:17Lester Nare?

  If you've not already listened, take a look back to our previous episode where we covered the Vera Rubin Observatory, why it matters. It's another Sky survey, but it is just light years in terms of the structure and substance of how it works and what we talked about, the data, the immediate data availability.

• 1:58:36Krishna Choudhary?

  Yeah.

• 1:58:36Lester Nare?

  No embargo, da, da, da.

• 1:58:38Krishna Choudhary?

  Yeah.

• 1:58:39Lester Nare?

  Again, some people be like, well, you know, government's gonna like, try to like wipe away their classified projects that are.

• 1:58:44Krishna Choudhary?

  No, but this is just, I mean, and it's also, it's been, I think, let's say three months.

• 1:58:50Lester Nare?

  Yeah.

• 1:58:50Krishna Choudhary?

  Three months, meaning 100 days, meaning there's 30 photos of the entire night sky already.

• 1:58:55Lester Nare?

  Right.

• 1:58:55Krishna Choudhary?

  Right?

• 1:58:56Lester Nare?

  Right.

• 1:58:56Krishna Choudhary?

  Palomar took years for one photo of the entire night sky. This already has 30.

• 1:59:01Lester Nare?

  It's an incredible, the largest camera.

• 1:59:05Krishna Choudhary?

  Yeah. The largest camera in the world.

• 1:59:06Lester Nare?

  In the world.

• 1:59:09Lester Nare?

  Creating the biggest amount of data.

• 1:59:11Krishna Choudhary?

  Yeah.

• 1:59:11Lester Nare?

  And one of the most incredible data processing and distribution platforms.

• 1:59:16Krishna Choudhary?

  Yeah.

• 1:59:17Lester Nare?

  It's it's just an incredible. So the the point here being there are things that can be drawn from this study in terms of now looking at us having this larger, much more robust data set and applying some of those thoughts and learnings to it in addition to continuing to validate and replicate using the old, the, the, the plates from the Palomar Sky Survey as well. Like, there's multiple avenues of exploration here around this idea of these transient light artifacts that are occurring. I'm, I really was excited to just get your perspective on this.

• 1:59:58Krishna Choudhary?

  Yeah, I thought, I thought it was, I, I thought it was a cool set of papers. Yeah.

• 2:00:01Lester Nare?

  And, and.

• 2:00:02Krishna Choudhary?

  Definitely the P A S P one I, I liked.

• 2:00:03Lester Nare?

  And just also talking about it from first principles. Again.

• 2:00:07Krishna Choudhary?

  Yeah, I think it's important to understand because, because this is historical data. We're not used to analyzing that, right? And the analog nature of it. It's not digital, right? You're not sticking a CCD in the back of a telescope. This is, at the end of the day, a chemical reaction that's giving us something. So it's important to understand the whole chain of custody.

• 2:00:29Lester Nare?

  Right.

• 2:00:29Krishna Choudhary?

  From photon to pixel.

• 2:00:32Lester Nare?

  Right, right. And how, how do we even get to pixel from photon? An incredible set of stories this week. We did our lucky number three. We started with our generative AI for cancer detection out of University of Toronto. Again, sorry again about the loss, but you're gonna save the lives, gonna save the lives of millions of people.

• 2:00:52Krishna Choudhary?

  Yeah, that was great.

• 2:00:54Lester Nare?

  With, with that, that great, great story, followed up by our super story on the retinal wireless retinal chip. We're making blind people see again.

• 2:01:03Krishna Choudhary?

  Yeah.

• 2:01:04Lester Nare?

  Not.

• 2:01:04Krishna Choudhary?

  Wireless.

• 2:01:05Lester Nare?

  Restoring.

• 2:01:06Krishna Choudhary?

  Yeah, restoring.

• 2:01:07Lester Nare?

  Not stopping, not slowing down, restoring.

• 2:01:10Krishna Choudhary?

  Yeah.

• 2:01:11Lester Nare?

  And, and we ended with our interesting possibility of data related to what have now been renamed U A P, formerly known as UFOs, and these transient, pre-Sputnik transients in the Palomar Sky Survey. I'm very interested to see people apply some kind of similar.

• 2:01:31Lester Nare?

  Methodology to the Verarubin data sets. They're there, they're publicly available. You're interested in working on that? Please shoot us a DM. Uh, we can make the connections. Uh, episode 15. We're continuing to cook.

• 2:01:45Krishna Choudhary?

  Mm hmm.

• 2:01:46Lester Nare?

  Uh, unbelievable engagement on just, I, I still can't believe people care about science as much as they do.

• 2:01:52Krishna Choudhary?

  Yeah. Uh, I love to see it.

• 2:01:54Lester Nare?

  It's, you, you do love to see it. Science is important. We are not covering, I know people are begging us to cover 3I Atlas.

• 2:02:02Krishna Choudhary?

  Yeah.

• 2:02:02Lester Nare?

  There's no data. The government shut down. There's no data for us to cover. Uh, however, Europa, Europa Clipper is in the tail.

• 2:02:10Krishna Choudhary?

  Yeah.

• 2:02:11Lester Nare?

  Right now.

• 2:02:11Krishna Choudhary?

  As we speak.

• 2:02:12Lester Nare?

  Right now.

• 2:02:13Krishna Choudhary?

  Yeah.

• 2:02:13Lester Nare?

  But we can't do anything.

• 2:02:15Krishna Choudhary?

  Yeah.

• 2:02:15Lester Nare?

  Because there's nothing to talk about.

• 2:02:17Krishna Choudhary?

  The ESA and, um, the, the guys around Mars have taken photographs of 3I Atlas as it went across. It's, it's reached, it's gone through the closest point of approach from the sun. And now it's on the other side of the sun. So we can actually see it again from the earth. It's on its way to Jupiter. But we have no data that we can talk about.

• 2:02:38Lester Nare?

  So until we get the data, we won't cover it.

• 2:02:41Krishna Choudhary?

  Yeah.

• 2:02:42Lester Nare?

  I know that Avi Loeb has talked about the, uh, non-gravitational acceleration, which can have a prosaic explanation, but we can't know whether it is that prosaic commentary tale or not. Thingy or not, until we get the data.

• 2:02:58Krishna Choudhary?

  Yeah.

• 2:02:58Lester Nare?

  So we, we hear you guys. You want us to cover 3I Atlas. Uh, open up the government, give us the data and we will talk about it.

• 2:03:06Krishna Choudhary?

  Yeah.

• 2:03:06Lester Nare?

  Uh, I am your host, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Choudhary. This is From First Principles. We'll see y'all next week.