The Grand Quest To Simulate Life - EP 57 Ed Boyden

Suppose you did simulate a brain, doesn't have subjective experiences. Would it be conscious? Of course, the core issue in some ways is that we can't really measure consciousness. Even now, you don't know for sure if I'm conscious. There's no meter you aim at my head and you have three out of four or whatever units of consciousness right now. Yeah, so in the lab, you're going to start to wonder, is there a way to measure consciousness? And a couple of ideas. One is that if you have a simulation of a brain, right now in neuroscience, we mostly focus on behavior, outwardly manifested in the end movements. I move my mouth to speak, I walk out the door, there are these observables that are governed. But if I'm sitting here daydreaming all morning and I never tell anybody about it and I never change my behavior based on that, there's no way to know that that happened. So one of my depopses is that if we have the simulation of the brain, we can look under the hood and if there's some pattern of activity that doesn't really link to behavior anyway, but we can watch it and simulate it and you know, probe at it, we can ask ourselves what kind of information processing is happening in that circuit. Ed Boydner. Hi, how are you? I'm good. How are you? Good. Good. Good. Awesome. Thank you. I wanted to talk to you for so many years. I feel like, I'm running around the world talking to all these interesting people and very often someone, the phrase that comes out of their mouth is, "I worked in the Ed Boydner lab for some of the most interesting people I run into." And it seems to happen the most with you and George Churches. Oh, yeah. Wow, interesting. Yeah, you've got this litany of fascinating students and then obviously have followed your work. Oh, great. Great. For people who don't know your research and your work, I'll just frame it a little bit and then please correct me if I get anything wrong. But yeah, I'm so pleased to be a legendary scientist researcher. You've done such incredible work, especially around the brain and how we get to know about the brain or what we know about the brain. We are at the McGovern Brain Institute. You're also a professor at MIT and then you are affiliated with or have co-founded numerous startups. Is that fair? Yeah. So far, I missed. No, sounds good. Okay. And this is funny. Pat McGovern, he was my first boss. Oh, wow. Swarov. I worked at IDG back in the day. Wow. Cover reporter. So cool. Pat? I did. Yeah. Yeah. Pat and Laurie were very involved. I bet that many times in the early days and of course Pat sadly passed away. But Laurie still comes to the meetings. Yeah. Yeah. Yeah. He used to, so I worked at IDG, the publishing side of what he did. I guess I'll make his money really. And every Christmas Pat would come by and hand you your Christmas bonus in person to like all, I don't know how many thousands of employees he had. Yeah, he would travel the whole world and then it was obviously a very nice gesture. It got slightly comical at times because somebody would brief him about who he was about to meet. And I was on the wrong end one time when he crossed wires. He confused two of us. And he would kind of recount your achievements for the year. And I just had to nod. And he was like talking about some of the hearts. Wow. Well, so great to hear that. I mean, it's all about the people at the end and I think that's really awesome. I don't know. It is. And like obviously I'm still talking about it. So it's an impression. Yeah. That's great. Okay, well there's a million things I want to talk to you about just about Pat McGovern related. If you, you know, when I do my research for these shows, I mean, again and again, the thing that kept getting called out was that you co-invented optogenetics, which helped us understand how the brain works in profound new ways that we couldn't before and that you developed expansion microscopy, which is kind of swelling. Hearts of the body and the brains that we can understand them better. I was curious though, you know, when you're reflecting on your work. Yeah, one of the things that really stand out to you during your career. Yeah. Well, I guess for me it's these are all eight puzzle pieces, but there is an ultimate goal. And the goal is really, I trained as a physicist and engineer before becoming a biologist. You know, can we understand a biological system? The same way that an engineer would completely understand a system that they built. Of course, the problem with biology is we didn't build it and it's also really messy. But I think great inspiration from physics. In physics, they've hit what you might call the ground truth, the list of the parts and how they work together. And on the surface of the earth, you know, quantum mechanics is, you know, more or less that that gives you lasers and microchips and cell phones and the internet and all sorts of stuff, right? Can we have sort of the microchip moment for biology? Now in contrast to physics, where there's like a couple of things and a couple of ways they work together and biology, there's thousands of things, right? Genes, gene products, biomolecules. So the overarching vision is really, can we just understand all of that? Can we map all the parts and how they work together? And my dream is that we can make a simulation in a computer of a living thing. You know, could you see all the parts working together and maybe even pinpoint where in that network you could treat a disease or, you know, maybe someday, this is obviously in the future, could you run a clinical trial in a computer? And so you mentioned optogenetics where we can control signals with light and then we have expansion where we can map the building blocks of life and there's a third branch of technology we've recently been putting out, which is how to image many things at the same time in a living system. But the way I think about it, these are three legs of a tripod and my hope is that we can integrate them and collect the right kind of data that you can use it to make simulations of living things. And that's what you feel like you've been charging after your entire career. Did you set out with this goal in mind? Yeah, in the end. When I switch from physics, I guess I always was very intrigued by the intersection of science and philosophy. And so my first lab experience was in an origins of life, they were trying to create DNA out of clay, which of course didn't work or you would have heard about it, but it was still a very educational time. I left home really young. It was like 14 when I started that job. And so eventually I came around to the brain as a sort of philosophical, practical intersection. But when I dream, I really understand the human condition. Why do we do what we do and feel what we feel? But the practical angle that pays the bills that keeps things going and is also very important to me is can we understand these complex biological systems in terms of their parts? We will pause the genius that I am no doubt interviewing right now to tell you briefly about Brex. Brex is obviously the flagship sponsor of the Core Memory Podcast. In our video series, but more than that, we actually use Brex to run our company. They have returned countless hours to all the members on our team by automating our expenses, automating some of our counting. And they have returned money to us by using their high yield savings accounts made a huge difference to our company. And it will make a huge difference to your company to go to Brex.com/corememory to learn more. 35,000 plus companies are running on Brex. Give it a try. You will not regret it. Since you brought it up, I was going to save it for a little bit, but you were talking about when you were 14 working on the origins of life. I mean, when anyone reads about your biography, one of these things that stands out is for all appearance. It looks like a prodigy as a kid and started at MIT, I think it's at 16. And got a triple degree plus a master's degree by the time you were 19. I think what was it? Electrical engineering. I did a two bachelor's in electrical and physics. I did a master's degree in electrical engineering. Okay. And you grew up in Plano, in Dallas. That's right. Okay. And so I'm always fascinated by people that excel at such an early age. I mean, I don't know. I mean, what was childhood? Did you have some sense that you were a bit different from the other kids pretty early on? Well, I had a very thoughtful childhood. My mother stayed at home and we would talk about science. We would run little experiments in the kitchen. My dad, he's retired. He was a bandage rec consultant. And so it was a very thoughtful, full-home environment. We talked a lot about problems. They had to solve them and things like that. I was probably 12 years old. I went to a science fair and I had my project, which I think was something like microwaving a bean in the microwave or something. It would happen to it. But then at the science fair, other people had real projects. They were able to get cancer genes and all sorts of stuff. And I was like, wow, I need to get to a place where I can do this. And so I just got really hard working on ambitious and I started taking summer courses at a local community college. The State of Texas has this really quirky and interesting and amazing program called TAMS. Texas Academy of Math and Science, and people skipped the last two years of high school and go right to college. And so I ended up skipping the last, I guess, four years, I guess more or less, and went at 14. But it's on the University of North Texas campus, and you get to do real scientific work, lab work, your side-by-side with college students, and it was a lot of fun. So you were at home school before you did that? No, no. I went to the regular public school. Oh, you did. Okay. Okay. And then, I mean, you mentioned the science fair. 12, didn't I mean, didn't you end up winning the science fair fairly quickly? Oh, there was a later year where I did a math project because math, you don't need to lab to do it. So I was very intrigued by geometry and the rules of geometry and did a project that I won't think with the current lens, I would be proud of. But, you know, as a 12 or 13-year-old, and I said 13 by then, I was pretty excited that you could do mathematics. And so math was one of my first loves, was that you could analyze things and think about them. But then quickly, the question became, what can you do about reality, right? Tangible things, the world. Can we make it a better place? And you left home at 14 to go to the school. Yeah. So 14 went to this boarding school, which was on the University of North Texas campus, and they picked 150 kids from the whole state, and they all moved there, and it's a very interesting program. You were the youngest one. I think up to that point, I was the youngest. Yeah. So, yeah, is that strange or exciting all the above? I thought it was a lot of fun. Yeah. I had a great time there. I made lots of good friends there. It was a fantastic environment for doing science, and it was only half an hour from home. I mean, Texas is a big state, so some people had a long commute, but it was not so far away from home that I would be, you know, too distant from the family. Yeah. And I always picture it's got, you know, just being a young kid off on your own, especially when you came to MIT at 16, as you live in the dorms, where did your parents move up there? No, no, I lived in Baker House. Yeah. Same floor with, had roommates, the whole diner. Yeah. Yeah. And that was not, that wasn't strange hanging out with all these things. Well, it's hard to deal with strange or not strange, because I have no other reference than my own life, I guess. But for me, I was in a state of constant curiosity. I guess for a long time, I had this frame of mind that, you know, I was much younger than everybody, so I was here to learn from them. And so when I came to MIT, I was in a continued, I was actually studying a focus on chemistry when I was back in Texas. And I thought, all right, I'm going to continue majoring in chemistry. And then, this was 1995, and like the whole internet thing was coming out then, you know, web browsers were just exploding that very here. And so everybody in my floor was majoring in computer science. So I was like, OK, I guess I'll go along with that. Yeah. So switching to physics and electrical engineering and computer science, which I don't regret, because, you know, that turned out to be so useful, as you know. Yeah. And you coded or anything before that? I did. Yeah. I guess I was very interested in mathematics beforehand, but I also used computer code to solve math problems. And so that was my entry point into computer science. OK. OK. So if we go back to your central quest to understand the body almost, this is this engineering problem. I mean, there's a bunch of things that I've reported on where we've made stabs at doing this. The connect home is this effort to image the brain on this level where you can see individual neurons and synapses. And, you know, it's been a struggle. I mean, yeah. Yeah. So we just finished the fly. It seems like it could take many years and ridiculous amount of computing power to start doing a human brain on the same level. I think the most we've done is like a cubic centimeter or something like that of the human brain so far. Well, this spring though, a group in Austria showed that our, this expansion method that our group invented can be used to get connect home information. And importantly, because it's a light microscope technique, you could also get molecular information. So the way that I think about the brain is, so the connect home course is the wire and diagram of the brain, the shapes of the cells, if you will. But in the end, we're made of biomolecules and biomolecules worked by touching and when two molecules touched, they can cut, bind or change shape. You know, and of course, there's for the case of the brain, electric fields as well. But so I want to not more than just the connect home. I also want the molecules, right? The molecules that make the cells do what they do and that in disease states are the things that go wrong. And so this expansion method that we built looks like it can give you not just connect to mix, but also the molecules along those wires and so forth. And one of our former students, Andrew Payne, is running this nonprofit, that's called an FRO focused research organization called E11 where they're adding in bar coding as well, basically we have tallie the cells apart. And what I want to make is, I'm really obsessed with the idea of ground truth. What are the fundamental building blocks and how they work together? And physics, of course, you have the particles and forces and biology, we have the biomolecules and how they interact. So if we can get that, we should be able to make a simulation of life. Okay. I mean, this is my question where I'm going. Andrew is actually, he's coming on the show in a couple weeks. I'm in the E11 and it's fascinating. Yeah. Actually they sort of inject brains with the virus to put all these barcodes in so you can map the wires almost automatically, I guess, for a lack of a better term. But yeah, okay. So, you know, we've been on this quest to map the brain, whether you pick a neuron or a molecule. And it's been this long arduous path. It's been expensive. We've had like entire nation states trying to fund these efforts. Where would you say we're at and if we really want to get this complete picture of the mind, I don't know, when do you think this might happen? Yeah. Well, so this expansion method that we developed. It's going out of works. Sure, yeah. Yeah. So, I guess for literally 300 years, the way biologists have imaged cells is with some kind of lens, right? So a microscope was invented hundreds of years ago. It's how cells were discovered in the first place, about 150 years ago, neurons were discovered in the brain. But light has a finite size or wavelength. And so you can't see really tiny things. So, many people tried to cut with tricks, super-resolution microscopes. Use clever tricks to break that so-called diffraction limit, the electron microscopy, x-ray microscopy. You can use particles that have smaller wavelengths, basically. But they're all tricky in some way, right? You know, x-ray and electron imaging can be quite expensive. And they also struggle to give you molecular info. And light microscopy methods that break that resolution limit can be quite slow and difficult to do 3D imaging. So, in our group, we thought we have lots of critical thinking and creativity skills that I like to think of as our learnable and teachable skills. And so we started thinking, like, what's the most opposite thing we could do rather than zoom in? And we thought, let's just make it bigger. So we could take a brain or any biological specimen for that matter, and we immerse it in a solution of what are called moderns, basically little building blocks, and they self-assemble into polymers, basically a dense spider web like mesh. And that's basically a mesh of polymer that's a lot like the stuff in baby diapers. We said that it winds its way through the bio specimen. So, if you do it just right, and you process the sample just right, like softening it up a little bit, when you add water, the baby diapers swells, but it makes the brain, or whatever it's embedded in, bigger. And what I think is kind of the surprising discovery we made is that that expansion process is even. In fact, with some colleagues in Germany who we've been collaborating with, it looks like that the expansion process might be precise nearly down to atomic scale. And so the idea of mapping all the building blocks of life throughout an entire system is no longer a science fiction. It sounds like it might be within grasp in the near future. Yeah, I mean, because I kind of cut you off to explain that technique, but when you say in the near future, because even if I talk to E11, any of the connectomics people, they still present something of a journey. And I don't, I'm not looking for like a precise year, but I mean, you think where this is within what, 5, 10, 15, 20 years or? Well, I mean, let's take a single cell. If we were to map all the proteins and nucleic acids and the major macromolecules of a single cell, and if we could simulate that cell, you know, basically if some biomolecules aren't touching, they're diffusing around. We know the mathematics of that. If two biomolecules are touching, then you know, with alpha-fold and other methods of simulating molecular contacts, you can imagine making a model of that. You might be able to make a model of a cell. So these numbers that I'm going to say now are just made up, but you know, I think it's very intriguing to think of whether you could try to make a model of the cell. And maybe a small brain like that of the worm, C. elegans, in the next couple years, let's say three to five years would be ambitious, but interesting to shoot for. Now if you show that that can be done, then what happens of course is that more and more people will adopt the technology. We all share all of our tools freely and optogenetics and expansion are being used by thousands of groups each. So if we have a way of converting a biological system into a computer simulation, I'm sure there'll be a similar kind of growth afterwards that can be hard to predict. But my hope is that if we can show a proof of concept with a small brain or a small cell or maybe a bit of both, then we do also some work on small fish as well. See your finish? level zebrafish and the worm sea elegans, and then also very small cells are interesting as well. If we show that's possible, then I think it'll be irresistible for many of us at the community to give it a try for different purposes. If you can simulate an immune cell, could you understand how it works? Can you simulate a cancer cell and find its weakest spot? That kind of thing. Okay, but what you're. Okay. I feel like we've been on the same path with the central connect homework from the sea elegans to the fly, to the human, and it has still been this slow process. I think I. Well, those are arguing that you're more optimistic that things maybe go faster with these new techniques. Yeah, yeah. So, two thoughts. One, of course, is that with electric and cross-cappy, which is how the flight connect home and the worm connect home and so forth were done. Again, you see the shapes of the cells in exquisite detail, but it's really hard to identify the specific molecules, right? And you want the molecules. The way that brain cells work, they generate these electrical pulses. You have ion channels, you have receptors, you have transmitters. You really want to know where they are. And so, my hope is that with our method of expansion, because it lets you label those, we can help pinpoint them. And you're going to get. And the second is scale. So when you expand something, now you can use much, much less expensive optics to image it. One, tantalizing thought is, if you expand something big enough, if you chop it into sections, and then use like dirt cheap optics, maybe even modified kinds of optics, the kind that you find on a cell phone to image the brain. So if you can make sort of it asymptotically zero-close microscope, and then you use commodity chemicals to do the modification, maybe we could democratize the ability to do extremely high volume scalable nano-imaging. Okay, because with the connect home we've been doing these slices of the brain and each slice has to be image, pretty much the best imaging techniques we have. And it's this manual labor part of this, that's part of the reason it's so slow and different hope. I think that's part of it. Yeah. We did a collaborative paper many years ago with a couple other groups where basically it took a hacked webcam, and then we expanded some bacteria. We showed that we could image bacteria with the hacked webcam. So this is not out of the realm of what has already been envisioned. Okay. And you hit on it a little bit in the very beginning, but okay, with the connect home one downside has been that it's you're getting this image of the brain in a fixed moment in time. It's not a living brain, it's just a snapshot of whatever was happening. And so there's people who argue you can get tons of information even from that snapshot and it's fascinating. And then there's some people who had it down on how valuable this could be because you don't have the activity. If we use your techniques to get this much more detailed picture of a brain, what do you think this unlocks? Yeah. Well, so if you have a map of the static organization of molecules of a brain, of course, one can then look at the molecules and then using the principles of chemistry, which are well known, you could try to simulate what happens next, right? And so again, molecules that are fusing around, you can simulate that if they're bound in this post-alpha-fold world, you could try to simulate that interface. But knowing the ongoing activity is also really helpful, right? Like right now, there must be millions of these MacBooks that you have all over the Earth. But right now, they're probably all doing somewhat different things, right? So knowing the ongoing activity is important because all these MacBooks have the same wiring more or less, but you know, their ongoing dynamics can be quite different. And so the other two technologies we've been building, which are the imaging of signals in living cells and then the optogenic control of living cells, I think are still very important. So my dream experiment would be, sorry, with a small brain, like a fish or a worm, let's image all the activity as many molecular signals as we can, use optogenetics to perturb those cells and look at what happens. And then at the end of the day, preserve the specimen, expand it and make a map of the molecules. Those three datasets, I think, could be combined to make models even better than if we use the technologies alone from each other. And so when you say this is your dream experiment, is it something, is that how you're spending your days trying to create that experiment? Yeah, yeah. I think that, yeah, we've got two goals. What is to advance each of the individual technologies to bring them to their limits of performance and then the other is to integrate them so we can get these kinds of integrated structure and function data. That's okay. And then, I mean, my question about what you get at the end of this, I guess it's sort of a philosophical one. I mean, on one hand, you're getting just this immensely useful tool to understand how our body works and to study it. We're also at this era of AI, hundreds of billions, trillions of dollars are being funneled into creating an artificial intelligence. I keep seeing overlap between these fields. This brings up a million philosophical questions about simulating artificial brains while we're trying to understand how our brain works. And one of the things I was most interested in talking to you was your take on the convergence of these worlds and the interplay between all of this. Oh, yeah, yeah. Oh, it's so intriguing. I guess a couple of thoughts. One, of course, is that if we can simulate a brain, would you be able to develop interesting kinds of AI that act more like brains? For like large language models, for example, they're very good language, but every week there seems to be some example showing that they're not really thinking the way that we do because they'll make some error that a five-year-old would not make, right? And so maybe they kind of solve language, but do they solve thought, probably not at same. And so if we can understand how the brain works though, enough detail that lets you simulate it, maybe that does help you understand what thinking or feeling or other things are. But that brings us to a second big question, which is almost philosophical, which is, so as you did simulate a brain, does it have subjective experiences? Like even now, you don't know for sure if I'm conscious, right? There's no meter you aim at my head and, aha, you have three out of four or whatever units of consciousness right now. Yeah, so in the long run, I started wondering, is there a way to measure consciousness? They're daydreaming all morning and I never tell anybody about it and I never change my behavior based on that. There's no way to know that that happened. So one of my depopses is that if we have the simulation of the brain, we can look under the hood and if there's some pattern of activity that doesn't really link to behavior anyway, but we can watch it and simulate it and probe at it, we can ask ourselves, what kind of information processing is happening in that circuit? In that? Sorry, go ahead. So maybe a theory emerges out of what a subjective experience might be. In the end, of course, in the current stage of history, you still have to test it with informed consent human volunteers who maybe there's ways of stimulating the brain with electric fields or other modalities that let you activate brain cells, even non-invasively in people. And so maybe you want to ask a question about whether that pattern that we saw in the model, does it actually change a subjective experience? So the simulation you're talking about is the one that's based off this new era of brain data that you're hoping to get. And so you would be creating a replica of the brain? More level. Yeah. And so then getting it to a level where we can watch its genuine activity and like probe a brain in ways we can't because people don't want us running around with electrodes out of their head all day. Yeah. And then how excited or not, do you get with the progress you see in the AI fields? There's people who would want to match the software brain versus the flash brain and look for overlap or have our AI models based even more on our more complex models of the brain to make them sort of more aligned with humans. I don't know if this is stuff that you get interested in at all. I have to have people about it. Yeah. Because a lot of people in AI are concerned about things like AI safety and what if you create something that you cannot control and so forth. Yeah. I do think that there are some interesting arguments for simulating brains in support of that. One of course is that if you understand what ethics is, like what is our brain doing when we make an ethical judgment or an empathetic judgment or socially beneficial judgment, could we capitulate that in some way? The second thing of course is problem solving. So again, LLMs are pretty good at certain things. But there are hardly new ideas that solve a problem. Humans, I think, still have vastly the upper hand on that for a lot of real world problems anyway. Maybe not contrived problems or specific problems that are defined in like competition spaces or forth. But if you want to have creativity, if you want to have like a factor problem solving, there are things that the human brain does that we might not even understand at all. I mean, somebody is walking down the street and an idea clops into their heads. Where it is? We don't know. And the history of science is full of people, you know, Einstein daydreaming or, you know, the chemists who thought of the benzene ring that appeared to him in a dream when he thought of a snake swallowed its tail. You know, there's all these sort of irrational subconscious stings that are so important for human creativity. And yet maybe we haven't understood any of them completely, maybe even partially, in terms of how the process is occurring our brains. And finally, I think there is a hope among some that, you know, rather than having AIs over here and humans over here, we'll find ways to work together. And I think for that to happen, we kind of have to understand a bit about how our brains work, right? Because if we enter information in a way that is completely incompatible or has unpredictable effects, right? And you know, every neural therapy out there to my knowledge has the, you know, decent chance of causing side effects, at least in some people, right? You know, because the brain is a network, you stimulate one part of the brain, it's going to activate other parts of the brain. And so I really want to have machines and brains talk to each other in a really meaningful way. I think we need to learn more about the brain. I mean, the hard part of neuroengineering, I think, is often the neuro part. You know, we've built, you know, as a community, electrodes and non-invasive machines to deliver magnetic fields and ultrasound, the list goes on and on. But where do you aim that energy? Where do you record information? What do you do with it? These are open questions. You seem kind of not terribly impressed with how LLMs. Well, it's maybe because I'm a scientist looking for, in my own life, I guess, how we could generate really creative ideas. And so do you think about really big ideas in biology, for example, PCR, CRISPR, things that really changed the game? Has an LLM built anything of that caliber? Or maybe he's just sitting too high a bar, but I'm very intrigued about how we can make creativity more learnable and teachable and to boost it. And so if we could build AI's that have that capability, it'd be very exciting. But don't get me wrong. I mean, I do see LLMs doing amazing things, lots of other domains. Hello, geniuses. Let me tell you about E1 ventures. They are venture capital firm in Silicon Valley, a long time supporter of this podcast and a long time supporter of big, fantastic world changing ideas. If you have such an idea, hit up E1 ventures or send me a note and I'll put you in touch with E1 ventures. It's quite a deal for listening to this podcast. Thank you again, as always, to E1 ventures for their support. Do you spend a fair amount of time using AI and/or digging in to how they work? Let's see. Using AI, we use AI routinely for things that I think it's good at. So if we want to analyze something in terms of physical principles or equations, it's pretty good at mature sciences like physics. We can run, hey, here's this material, what's it going to do? And it can look at coefficients and make calculations and so forth. When it comes to chemistry, which is a bit more nebulous, then I have asked AI to help me design chemical reactions and so forth. And there, I think, now we're already in a much more nebulous space. I think humans might still have the upper hand for doing really creative chemistry for sure. That's what I was going to curious about. And then biology, part of the problem with biology is that the literature has a lot of hidden structure, a lot of which has not even written down sometimes, right? Maybe a group bought this chemical from a company and the company went out of business five years later. Now, 25 years later, somebody wants to replicate that study, but you can't buy that kind of cl any more. And that kind of thing won't be written down in the scientific literature. Who knows? Maybe somebody even bought the brand of that company, and they're selling something with the same name, but it's very different, right? And so I think the problem with biology is that there's a lot of unwritten metadata, a lot of unwritten wisdom. One group did it in their lab had their air conditioning blasting and the temperature was a little bit lower, another group, and maybe a country where they have less air conditioning, you know, had their rooms a higher temperature, maybe the reaction went differently. But again, people don't write down the temperature of their room, typically, right? Yeah, a scientific paper. Yeah. Do you have like, Chanchibitir, Claude, open on your laptop on a day-to-day basis? I think I might be using just Google Gemini a lot because it's able to, I feel like it's very integrated with different Google search functions that are just familiar to how I think. Yeah. And, but MIT now does have a Chanchibitir subscription for faculty where it won't use the information to train the model, and so I'm playing with that as well. Yeah, I guess. I was curious because I keep wrote across these 20-year-olds what I'm reporting on stories and they are, I feel like they're sitting with their laptop while I'm talking to them, they're just using it in ways that, like, I use AI, you know, a bit, and every day for some purpose. But I mean, they have like seven or eight AI apps, so they're like talking to their laptop. It's just, it's, it reminds me of, you were talking about the rise of the consumer internet sort of like, what era to the next? Now you're just using your computer in a different way than the generation before. And I don't know, I was trying to put myself in your shoes a bit because you're doing such high-level science. And, yeah. I was kind of curious if you had adopted where I see sort of, where I feel like this next generation is going with this stuff. Yeah. Well, one of my four students, Sam Rodriguez, he started a thing called Future House, another FRO. The other way I built AI scientists and to be able to, you know, analyze the literature and draw conclusions and make design experiments and so forth. And other entities are also trying to design AI scientists of different kinds. So it is a very active area of research. But it seems like they have big, great advances. It also says that there's a long way to go in terms of like really revolutionizing science, like a drastically out of left-field idea. And maybe it's because of the way that machine learning works out where they get a corpus of data and are trained on it. And they're great to kind of interpolate and extrapolate. But doing like really out of distribution predictions and seems like no currently, does that super, super, super well. Do you, last day I question, I don't know. Do you, you know, where do you fall on the, going off what you just said? There's tons of people, I like in Silicon Valley. You know, if you talk to like Larry Page or, you know, people who are knowledgeable about this, right in the thick of it, there's, it's easy to find ones who would say those limitations you talk about in the next three years are blown away for sure. Super intelligence is here. And then those same people usually are the most optimistic, I find, about where this leads. It doesn't strike me as something that's like consuming you as although you're thinking about, but do you have a tank on where this is heading in the relatively near future? Well, it's not really not my core expertise. It's a much more of a biologist, but it does seem like people are trying to scale to larger and larger models. People are trying to tweak the architectures and build networks of agents of them and so forth. But, but again, it's not my core expertise. I don't, if it's like a radically different architecture proposed. That's what got me thinking, like maybe a little bit of brain data could go a long way. And, you know, if you take a simple motif and scale it up, you know, with Google or OpenAI or whatever scale, now it starts to exhibit interesting and urgent behaviors. Well, what if we learn more about the brain and we could then try to scale that up? Yeah, I know. One second, worth a try. This is why I wanted to ask you, I just really do have a better handle on the complexity of the body and the brain, and are obviously clever enough to kind of compare that against what the LLM's are. I just really do have a better handle maybe on. And so, because clearly one of the arguments sometimes is, as you mentioned, you just throw enough GPUs, maybe consciousness just arrives out of enough complexity from throwing enough compute, it's something in connections. And I don't know, I felt like you might have an intuitive sense to whether you believe something like that or not. I don't know, I tend to be very data driven. Like, I would love to have a measure consciousness. I want to be able to model it. I think we need to be very quantitative and detailed about characterization. Yeah. Okay. And if it's the history of other sciences, that's how it has been, too, right? You know, centuries of astronomical data, then made couplers laws, and then Newton came along and then distilled them further and made comparisons of things on Earth and so forth, and then we got Newton's laws. But, and people love to talk about how theory driven physics is, but it started with centuries of data. So, if that's physics, what hope do we have to do biology with that lots of data? And the right kind of data, too. That's why this idea of ground truth, the fundamental building blocks and how they work together, I think, has been such a driving force for our research. And we'll note for listeners of the podcast, if they hear some noise. I think people are washing, washing lab equipment above us. I think, yeah, there is a dishwasher room overhead, I believe. So, if that's what you hear, it's just science. - I'm gonna take him place. - I guess you do this relaxing, wonderful soundtracks. (laughing) - I wanna shift gears a little bit to brain computer interface stuff, which is an area that I'm just deeply into. I know, I remember when I was reporting on kernel in the earlier, I think it was the first time I ever talked to this. - That's right. - You were an advisor, I think, to Brian and Strader. - Yeah, I was one of the science experts. - Right, so they were trying to build that kind of external. They started internal, and then ended up, did an external BCI. You also have, you have a startup that, or you're affiliated with the startup, I talk to the other day that does an employee at the kind of sits in the bone matter of the cell. - Intercostals? - Right, right, right. - Yeah, yeah, yeah. - I hope that company going as well. - And so that's not fully going into the brain, but it's sitting in this layer of the skull, and it's for mental health. It's in modulating, I don't, maybe I'm sure you'll do a better job. - Oh, yeah, yeah. Well, the core idea is, you know, you can have non-abasive devices, but they're outside your scalp, and that can be quite inconvenient. And then you can put wires in the brain, but that can also damage your displaced brain tissue. And so our idea was, what if you had a sort of a hybrid? You had a minimally invasive device, so it would never enter the brain, or displace your damaged brain tissue, but it also wouldn't be sticking out. And so you can imagine the sort of, you know, inside the skull, sort of speak implant, and then you can deliver electric fields. - Yeah, so there's a surgical team and an engineering team that is exploring topics like, yeah, could you go after depression or other conditions? - Yeah, and so, you know, in this field, you have so many different approaches. I mean, Neuralink is in some ways the most drastic, I guess, being the most invasive, and then all the way up to things that are totally external. You've got things that are in blood vessels. - Yeah. I mean, when you, and this is moved pretty rapidly, you have the Utah Array, which was the main tool of, for researchers doing this type of work for 20, 25 years, and it was an invasive device, but it was very hard to use yet to be in a hospital or a research setting, and then, you know, over these last 12 years. All this venture capital money is ported in this. We have all these different approaches. There's, you know, each one has pros and cons. This must be kind of fascinating to you to see all this activity, and then, do you tend to favor one approach over the other? I'm curious what you make when you look at the BCI field. - Well, since there's no ultimate theory of the brain, what matters most as data, is it working? Our group has put out a couple of inventions, which are being used in human patients or human subjects. So, optogenetics, where we borrow molecules from plants, put them into brain cells, the molecules convert light to electricity, and then you can use light to control the brain. Very widespread in use in animal neurosciences, study of the brain, but a few summers ago, a brave team in Europe did put one of our molecules into the human eye for blindness, and there now I think a couple dozen people walking around, it's not approved by any regulatory body, but with at least a partial restoration, a functional vision. So, you lost the-- - More how it works. - Sure. - Yeah, so blindness specifically, or optogenetics in general? - Let's explain optogenetics through the blindness example. - Okay, yeah. - All right. Yeah, so millions of people have lost the photoreceptors. These are cells in the eye that capture light and translate that into signals that brain can understand. So electrical signals. If somebody has this, then there's not a whole lot you can do for most of the patients unfortunately. So, with optogenetics, we take molecules from single-celled algae microbes and so forth, and what these microbes have are proteins. And these proteins convert sunlight into electrical current, like little solar panels, basically. And what we did with optogenetics, as we took the gene that encodes for this protein, put the gene into a brain cell, and now if you shine light on the brain cell, you can activate the brain cell too. 'Cause that little solar panel converts your light from your LED or laser into electrical activity. So, back to the eye. The photoreceptors cells have died off, but the rest of the eye is still there, right? You can take the gene that encodes for one of these light activated proteins, put it in with gene therapy vectors, which there are many, including many, they're already used in people. The gene therapy vector will deliver the gene for light activated protein to one of the spared cells of the eye in the retina back. Those cells become light sensitive, they were not beforehand. And now you basically have, biologically speaking, install a camera in your eye. So, these people wear goggles that take light from the world and project it onto the area of the eye that has the molecule, but they can recognize lines of a crosswalk or doors on a hallway, they can see household objects. It's not perfect vision, but it's a pretty significant restoration of functional vision. - Is this the stuff that science corporation, the French company they acquired, or it's different? - I don't know what the status is on business-wise. There is a French company called Junsite, which was the company that licensed the molecule from us. They were the first to put these molecules into people if they go prior to them. - The memory is failing, I feel like it was called Prisma, maybe. - They're moving multiple companies, yeah. So Junsite was the first, and now there are a bunch of other ones that are going after different targets or using different molecules, but there was a French team that did, December 2022, think, was the first person who had these options put into the eye. And then in the BCI field, BCI field writ large, all of this technology right now is being used for people who are suffering from usually quite dire conditions, like blindness, ALS paralysis. - Yeah. - Yeah. - I keep getting both excited about where all this is going and then sometimes a bit muted in that you almost want to see more. - Yeah, so I guess this was part of my, when you're looking, I don't know, you could pick Nirlink if you want, but, or any, well, you know, okay. In terms of really restoring somebody to, somebody like an ALS patient, being able to get their powers of speech back to where they could communicate like they could before they had their ALS. How excited are you about the power of this technology? - It's very interesting. I mean, I've seen a couple examples recently where by reading their activity out, even with older technologies, though with enough machine learning to try to interpret it, you can make very interesting predictions of what spoken words the person was intending and so forth. So on the one hand, I think, at the phenomenological level with enough data and machine learning, it's quite possible to have a lot of restoration of different kinds of functions. I think if we really want to understand like the depths of cognition and emotion and be able to build brain-mesh interfaces that have arbitrarily useful functions, then that's where the understanding of the brain would be wonderful to greatly increase. That's one reason why we've, although we spent the first decade of the group building lots of brain-mesh interfaces, we also developed a technology for non-invasive focusing of electric fields into the brain, which also got spun out as a company. - Is it conscious though? - No. - This is called a temporal interference and the company is in a zero called TI solution. - Okay. - Yeah. So basically brain cells are non-linear low-pass systems, which is a fancy way of saying, they act like old-tash and AM radios. And so if you deliver multiple high frequencies to the brain, we can kind of endorse them, the same way that broadcasting across the city, the radio waves go right over your head, you would even know they're happening, right? But where the different frequencies collide, what a non-linear low-pass filter does, what an old-fashioned AM radio does, is basically subtract off that high frequency carrier and then you get the voice, or whatever, in the case of the AM radio, in the case of neurotivity, we get whatever signal we want to deliver. But to make it long story short, basically it's a way of having multiple high frequency fields delivered to the outside of the brain and then where they collide, you get information delivery. And so that's one technique that we built that is capable of non-invasive, but deep stimulation of the brain. So the point where I make though is that, yeah, there are these different tools. How do we use them? You know, what are the best at? Right now, the only way to know is to try. There's no grand theory, so we must be empirical about it. But my hope is if we can make really good models of the brain, you can be like, wait a second. This region, and those are these two other regions, you actually talk to each other this way we didn't know about. But if we stimulate the three regions with these three different patterns, could we restore this function that could be quite complicated and maybe we don't even understand it at all currently? But now because of the precision of our knowledge, we can dial in something that helps somebody and release their suffering. Yeah, well, I feel like I keep trying to get you to rank things and put dates. and predictions on stuff. I mean, why would my, all the stuff that I cover, I just really were having to some strange new world where humans and machines really are merging in some dramatic way. When I hear you talk, I feel like my timeline in my head is probably too quick. - Well, it depends. I mean, if you want to have a general strategy for any kind of brain augmentation, I think that's somewhat far off. But if you record from a specific region and your goals are very specific, already several groups, I've been marveling at the videos and so forth, can read out neural activity and do things like generate language or control a computer cursor, right? - Yeah. - And that seems to be like a very well posed problem. Now, does it work in the real world when the environment is rapidly changing? I mean, you've looked at like even different field like self-driving cars, where in the desert, the DARPA Grand Challenge, the DARPA Crossed the Desert, we saw the long time ago, getting it to work in a busy city with kids and pets and so forth running around, you could argue that still is not completely solved, right? And so, I think one of the big questions for brain-machine interfaces is, what do they look like when they're put into the busy, complicated world? And you could argue that nobody's really put a, on mass, a large set of people with brain-machine interfaces out into the real world, at least not to my knowledge. And we have ability to read out data with optometrhex, you can control the brain as far as inputting data. As far as I'm aware, I mean, we don't seem terribly far along, unsublieable, yeah, putting information in. - Well, I mean, so, after genetics and the eye, there's several dozen patients. So that's the entry information into the eye. I think if any of that gets approved, if that people might start wondering about putting it into the brain, but I think a lot of people are kind of waiting to see what happens, yeah. Because if a lot of people are doing the eye, and the eye, of course, is much better understood than the brain, let's see how it goes. But yeah, if we could, imagine a holographic projector that would aim light in 3D patterns, and you can then make brain cells sensitive to light, and also you know enough neuroscience that if I stimulate these cells, I know it's gonna happen, because remember the brain is this complex network, I can be locally stimulating, but still influencing things all over the brain. So it's important to be able to predict that, I think. - But that would be generating, what if, like, 'cause I'm sort of talking about putting almost like an idea, a thought in, 'cause aren't you describing more, like a reaction in the brain? - Well, that's very broad idea of holographic projection. So if you go to a very good holographic projector, and so one of our collaborators, Valentin and Miliani in Paris, was one of the key pioneers of this, you know, a hologram is basically a 3D sculpture of light, right? So you should be able to give every cell its own recipe, its own information, right? So if you do that with enough fidelity, you should be able to enter arbitrarily complicated information into the brain, is my hypothesis? Of course you have to build such a machine, but optics is a field where, you know, with enough engineering, you know, one could imagine anything eventually coming within grasp. And so, yeah, can you enter an idea or a concept with a holographic projector and optogenetics, maybe? - Have you looked at the bio-hybrid stuff Max Hodak is doing? - Tell me more, I don't know about that. - He co-founded Nurelink and then went off to do science cooperation, and they have, they're used IPSCs to create lab-made neurons that live in the. - Oh, this guy, yeah, I love it. - Yeah, they kind of hold out on a skull, put the gel next to the hole and the human-made neurons start to form axons and dendrites down into the actual brain. - Yeah, they're very intriguing. - Yeah, I mean, what are you bringing that? - Okay, I think it really boils down to what does the data show? If you can get, you know, consistent connections, if they're stable, if you're not getting side effects, where, you know, you know, neurons are getting wired up with the wrong way, or, you know, then it's intriguing. But yeah, it boils down to the data, right? 'Cause you don't have a theory of neural development yet, but it is a very intriguing idea. - I just, I know that you approach some of the stuff philosophically. Like, what do you think humans and machines are like in 30 years? - Yeah. Well, if we can really build good models of small things the next three to five years, like we were saying earlier. And now, in the same way that with earlier tools, we spread it freely, so that everybody's modeling biology in this way. Maybe we do understand the brain so deeply, and I'm just gonna make this number up, but what if, you know, within 10 to 15 years, we have good models of mammalian brains and maybe human brains. But if you understand what thoughts really are, if you're gonna understand what emotions really are, maybe we could try to, you know, augment them in certain ways. The thing about augmentation is that you really want to know what you're doing, right? Because if you augment something and then, but you've introduced a side effect that, you know, causes something negative later, then that can be caused for regret, right? So I think we want to find ways to deeply understand what we're doing to the brain if we're gonna go beyond, you know, the most pressing medical needs for sure. - Well, I give that a thought. Humans trying to keep up with AIs or merging with AIs or evolving the species as this flesh, computing hybrid. Are these things you think about, are these things you think should happen or inevitable? - Well, I think at some points inevitable, all the question is when, is it now 50 years, 500 years or some other time point? My guess is it probably will happen in decades, but here's the thing. I think we want to understand things very deeply before that point. You know, there's what the old Silicon Valley is saying, move fast and break things. You might not want that if it's your brain, right? And so my hope is that if we can understand the brain rapidly enough, right? Let's say models of small brains in three to five years, understand the human brain deeply in 10 to 15, again, these are just made up numbers, but they're not entirely unreasonable. Then you can imagine putting the field in a very firm foundation, right? Okay, here's what you want to do. If you want to help somebody with this, but not mess up these other 17 things. And so I think if we could do it right, there could be a lot of benefits of technologies that help us understand ourselves or improve ourselves in some way. That said, if you look at the history of biotechnology and maybe neuroengineering in particular, when we don't understand something, there are often other side effects that result in something not working out well. I mean, there's so many examples, right? Like, I don't know, merke-baited viox, the pain drug. It had side effects in a small percentage of people and they pulled it off the market, right? I do some work at the Alzheimer's field and there's lots of examples of Alzheimer's drugs where it's been a long, hard slot, right? And if they have a side effect, then you have to take this drug for years, right? It's a chronic disease. Will people do that? So I think there's a lot of questions that are about, like, what do we really do it? What really work? - Yeah. - And so for that, I think it does make sense to put it on a well-post footing. Make sure we really understand what we're doing. - Yeah, okay. You and George Church, I both see you involved with lots of startups, but then still heavily affiliated with your respective research institutions. - Yeah, I'm always interested in how you make the decision of staying in a place like MIT versus just going online, not start up and chasing some giant idea full time all the time, everything. - Yeah. - Well, I feel every problem has a natural home. Some things go well at academia where you have open collaboration and you can publish and you can look at certain time horizons. You know, you start up, we'll have to have a shorter time horizon, you'll be much more focused, less certain dipty, perhaps. And then a couple of our group members developed this FRO focused research organization concept and Adam Marlestor on his San Marigracus and so forth. And so maybe that also fills in a gap where he's something more scalable than academia, but not as profit-making as a startup. So that's great. For me, because I am just, you know, the risk in biology is so high, right? You can work for a decade and you can spend billions of dollars and then your therapy can fail with a side effect that, you know, in some part of the body you've never thought about, right? And that happens all the time. And so the way I think about it is we need to remove the risk from biology. And I mentioned this idea earlier about the microtrip moment, you know, this moment where, you know, after the microtrip, you can be a drop out and start Apple, Facebook and Microsoft, right? The risk of science that is represented by the microtrip as far as physics goes is a huge reduction of science, right? And a biology, can we get to that microtrip moment? Can we get to the point where, you know, a college dropout could start, you know, a company that cures a disease? We're not there yet, to my knowledge. But my hope is that if we can make simulations of living things and how any kid in high school or college could, you know, use a simulation to try to go after a problem. You know, maybe that is what causes the true democratization of biology. - You have this reputation in your lab as this reputation. for people having quite a bit of intellectual freedom, the ability to pursue their curiosity, not terribly, sort of rigid and self-serving to your particular interest. Like it's just a conscious decision that you made when you started this is just a reflection of your personality. I didn't know if you had some management genius wisdom to pass down. - I mean, the things we're doing in the lab are things I'm interested in. I guess I feel that my role, you know, is not just to let people do random things, but neither to micro-manage. I'm here to persuade, look, okay, here's this idea. If it's great, let's talk about it. Let's deconstruct it. You know, is it gonna solve the problem? Is there a path we can realize? And so it's more of a process than a rigid set of rules. But if we have an idea, whether it's from me or from a student or, you know, so forth, and we can show to ourselves that it is likely to work. We show to ourselves that it will give us what we want in terms of solving the problem. You know, and there are some heuristics that I like. I love to build technology. It's very inexpensive to use. So both optogenetics and expansion across could be, anybody can do it. Does it really do believe in democratizing science? You know, then we can do it. So in fact, I'm starting to write a blog where I want to talk more about like our problem solving methods. As we posted a couple of articles about how to generate ideas or how do you, you know, think about what to learn in order to solve a problem and so forth. And so I would like to just write down a lot of our problem solving methods that get them out there. But I think if we apply those methods, then it's very easy for a group member and myself to be aligned 'cause we will converge upon something that ideally is extremely impactful, but also is very practical. - This is a big question. I guess what do you make of the state of US science in this particular moment in time with all kinds of interesting political and sort of nation-state level competitive things going on? - Yeah, yeah. No, I mean science is being very disrupted with mass cancellations and reductions in national institutes of health and national science foundation and so forth. Priorities, yeah, I do think it has a real risk. And I think the real risk has already happened. I've heard of national competitiveness in science. I've had people, you know, and the group who've gone to other countries who have been unable to continue on, funding of course is always an issue. Yeah, it is quite worrisome. And especially at this moment where it felt like we were poised to, you know, like with advances at AI, like we said earlier, with advances in ground truth and at biology, you know, it felt like many fields were poised to really go to the next level. So in terms of timing as well as, you know, both the state of funding as well as the general freedom of people to be and do things. It's just quite a quite disruptive state. Where do the students tend to go, ones that leave? Well, let's see. Well, my group, like many, is very international. And so, yeah, many people, you know, if they have a visa issue or something, we'll try to go to their home country or other countries and so forth. But it's pretty chaotic, too. I don't know if there's like, all we can do is try to support our group and help people, you know, just as people. But one of my hopes is, well, is there maybe a new model that emerges of international labs working together? I don't know, I didn't really prepare a detailed thought on that topic. But I'm thinking a lot more about like, you know, in an earlier time of crisis, right, with like the Cold War and mathematicians and physicists started talking across, you know, lines of political distance and, you know, maybe that helped with some reaching of the wheels and communication and so forth, is, you know, whether it be the pendulum swing the other way and now maybe there will be more international collaboration in science. I don't know, I don't know. Yeah, because it, well, I mean, it would be nice if like the China and the US could collaborate all these fields and push the world forward together. That doesn't seem to be the way things are trending. I guess this is one reason also I wanted to start writing this blog and writing more for the public and so forth, which is, it seems that, you know, of course, if somebody has Alzheimer's disease or terminal cancer or something like that, the disease does not care what nation you're from or what political party you are. It's just gonna try to kill you. And so, you know, is there a common enemy here, you know, is there a way to tell it story in a way that motivates us to go after, you know, the things that are causing so much suffering? And I feel like that story, you know, maybe there, maybe if fresh voices can tackle it, maybe there's a way of telling that story that basically we realize, wow, you know, there's a lot of suffering here that we are just, you know, giving up the ground on. You were, you got this prolific career and you started so young chasing big ideas. Is it harder as you get older to find the big ideas? - Well, I mean, I feel like the big idea that I had was sort of near the beginning, which is the idea of could we convert biology into physics? Could we solve the ground truth? And the basic thinking from day one was, if you could understand a complex biological system in terms of its parts and how they work together, that is the answer. You know, you should be able to see all the parts, control them and make a model of them. And my very first biology experience, and I was still undergrad here, was trying to make a little model of a circuit in the songboard brain. You know, how does the songboard clock out the syllables of a song? And I made a model that kind of explained the data, but how can you test it, right? And so that motivated me to go down the direction of building tools and really getting into the molecular, you know, nitty gritty, I guess. But then, I don't know, it seems like often, the idea that solves the problem almost has this sort of feeling of inevitability about it. But suddenly, we have to go through what I call constructive failure to get there. On your example. So we had the idea, the first idea for expansion in 2007, a long time ago. But we didn't know that it was an important idea at the time. And then in 2012, 2011, 2012 or so, two fantastic grad students in the group were trying to do nano-imaging with super-resolution microscopy. And we built, and also electrically microscopy, classical techniques. And it was hard, you know? And then even if it did work, it's slow and expensive. How would you ever scan a whole brain? And that's where we kind of learned a secret about the field, which is, wow, nano-imaging is really hard. If you want to image anything, that's like a big biological structure. And so then we mothballed those projects and started doing expansion. So I think in some ways, it's almost like a process of elimination. Let's carve away, what was it? What Michelangelo's like, carve away all the parts of the marble and the sculpture is simply what's left behind. You know, very often we'll fail as we try things. And then as we eliminate the different possibilities, there's kind of a nucleus defined by the failure of us in some ways. But in a constructive way, it helps what to do. - When I talk about these models and simulating things, it just makes me think, I don't know. Are you one of these people that thinks we live in a simulation? - Oh. (laughing) It's a good question. I'm not sure. I mean, the laws of physics and the laws of science are very intriguingly concise. And there's so many interesting coincidences. You know, like if this parameter was a little bit bigger, or a little bit smaller, then the universe would be totally unrecognizable. That said, I guess again, I'm very data-driven. I would love just to analyze reality until we have some clue about what it is. But I don't feel like I have a strong opinion, but whether it is one extreme of total atheistic nature of total chaos versus a contrived simulation or something in between. I don't know, I feel like I'm very data-driven. I would like to confront some evidence that one or more of these possibilities are the case. I don't feel a strong belief for opinion. - You said, hmm, like you hadn't thought about that before, but I'm sure you must have. - Well, I have wondered about it a bit. And when I was a physics student, I was very intrigued by just how much structure the universe has, right? Like it's kind of weird if you think about it, that there are lots of physics. There's only a couple of them, right? And there's only a couple of things, like electrons, protons, neutrons, that's it, right? And then a couple of forces. And then if you're big enough, then you wear a gravity. But for most things, you know, you can kind of neglect gravity from any chemical and physical things. But then if you do the equations, you get the periodicity of the elements, right? And I got carbon and nitrogen and all that. And then take carbon. Carbon is this interesting atom. Forms these long chains. You can get DNA, you can get proteins. Northern atom seems to do that, right? Silicon is right under carbon. Silicon doesn't do those things. And that was their Silicon life somewhere in the universe. So anyway, there are all these interesting coincidences there. Is it evidence for simulation? Is it evidence for religion? Is it evidence of just, you know, something you'll call it the unprofit principle that, you know, There's lots of chaos, and we just happen to be here, and we notice it because it's the only universe where we can exist to notice things. But yeah, I don't know. I would love to see if we can discover evidence for one or another. And I'm a speaking suspicion that this is why investigating consciousness is so interesting, right? We don't have any measurement device or theory, which helps us understand why this bit of matter feels its feelings, has subjective experiences, and this other bit of matter presumably, of course, we can't prove it, does not. And yet it's so essential to everything, right? If you have a robot vacuum cleaner, nobody thinks twice about throwing it into the trash or recycling it when it breaks, but, you know, with beings with subjective experiences, pets and humans is so forth, deserve justice and also are held accountable. And so it's very important, even though we don't have a scientific grasp of where it is. - Yeah, yeah, well, I got to ask most of my big questions that I wanted to ask you and I'm just being cognizant of time, it's just an absolute treat to actually get that. Sit with you for a bit and chat. - Yeah, that was a lot of fun. - Yeah, let's say I'm touched. We've got a couple of years, we're gonna be hopefully really exciting. - Yeah, I mean, you went over so many things and you talked about your research, but is there a particular thing we should keep on our eye on that you're excited about that you're working on at the moment? - Well, I'm very intrigued by this idea that you could map all of the building blocks of life to the point where you could simulate it. I think that would be a real game changer. - Yeah. - And just in the last year, some papers that were coming out from us and collaborators and others would suggest that you might be able to map the building blocks of life. So that's very, very intriguing. And if we could do that, this idea of converting biology into basically physics with computer science running on top of it, that would be a big game. - Yeah, absolutely. Like what happens in these, I was just walking down the hallways. I mean, is this where you do, you would work on a day-to-day basis? - Yeah, yeah, our group has much of the second floor of the McGovern Wing of this building. And yeah, here we do things like expand brains. We build microscopes to scan things. We work on up to genetic molecules that are building optics to do the holography that I mentioned earlier. - Okay, so just pushing all those different fronts forward all the time. - Yeah, I think it is three lines of a tripod, right? We're gonna image lots of things, we're gonna turn lots of things and then make a map of the structure of the things and then integrate that to a model. - Okay. - So most of the products in the lab are directly on the lines of one of those three things or opportunistically related to them. - Okay, okay. Well, Ed, thank you so much. We really appreciate it. - Okay, so a lot of fun. - Okay, awesome. (upbeat music) - The Cormary Podcast is hosted by me, Ashley Vance, and or Kylie Robison, or both of us together. It is produced by me and David Nicholson. Our theme song is by James Mercer and John Sortland and the show is edited. Always by the John Sortland. Thank you so much to Brex, anyone, Ventures, for all your support. Thank you most of all to everybody for listening, for watching, we love you. Please leave us a like, a review, a subscribe. All those tremendous things. Thank you, and we'll see you again.