156. When Matter Makes Decisions: Michael Levin on the Intelligence of Form

Erum Hey Carl, how's it going?

Karl Good. How are you erum? Welcome to our listeners, to the Grow Everything podcast. You just had a very special week. It was your birthday. What did you do?

Erum Well, I spent the day at the spa, which you do when you're 42. That's my age. Hello everyone. and it was incredible. I spent the whole day there and then I met my husband for dinner at a place called iat, which is a Palestinian place.

Erum It's really delicious food and. Right after that, we went to go see the Pivot Podcast, live in Brooklyn, in the King's Theater and Wow. Carl, it was so amazing. Besides Kara and Scott, they had a special guest, Curtis Sliwa, who was a nominee for Mayor of New York. I would say. [00:01:00] Momani incredible campaign, but I think first Lee West saying the race also helped but he is so funny.

Erum What a character. You heard the podcast has came out right afterwards.

Karl Yeah. So I. we don't normally push other people's podcasts, but we do talk about Karen Scott on The Pivot podcast. And I was looking forward to hearing that episode and I thought it was incredible. It was incredible to hear Curtis, he's a character, would've been a crazy guy to be mayor, but the whole story of mayor and hearing what his history with the Guardian Angels, which was this kind of like, vigilante self-policing group in the seventies and eighties in New York City.

Karl About the money that was throwing at him. But the episode is incredible and while I was listening to it, I kept thinking of you being in the audience and how great that must have been. but yeah, what a great birthday. Sounds like a great, great way to spend the birthday.

Erum Yeah. Yeah. It was wonderful.

Erum And, one of the things that like happens on my birthday is like, I always dust off my Facebook app to see it's like my birthday card. I go in [00:02:00] and I'm like. people still saying Happy Birthday to me? And they are. I was like, wow, I still have so many friends on Facebook.

Erum And I was like, all right, well you had to give us a shout out to all those people. Facebook near and dear to my heart, but, it wasn't my first social media. we had a a OL instant messenger first, MySpace before that, but Facebook's still staying strong, so. Thank you Facebook people.

Karl Yeah. I'm trying to think of what was the professional, there was a professional network before, LinkedIn took over and like there were virtual events because worked during and like in the 2000 something showed up and a lot of events in New York. So I ended up meeting a lot of people through, this kind of like hybrid thing.

Karl And I think we're going back in that direction. I bet if like. People started using Facebook more as a way to do in real life events, which I'm sure they do. I think it would kind of help them with their success, but they don't need help. Yeah. Meta is so successful.

Erum Yes, absolutely. Absolutely.

Erum And then you have a very exciting event and opportunity right around the corner. Tell us about [00:03:00] your TED Talk.

Erum Yeah. So I'm gonna be speaking at TEDx at MIT. It's part of Planet Action. It's a three day event, and, the guy who convenes this, John Werner, he is like the ultimate network creator. And so all the talks really have to do about sustainability and taking action on the planet.

Karl And I'm one of the people who's gonna be talking about bio fabrication. I'll specifically be talking about cities, the use of garbage as a infrastructure, builder, as waste, as value. something that we talk a lot about here on the podcast and, once it's available, because I'm sure they're gonna make it available very quickly.

Karl I won't flub it and you guys could see my video and gimme notes.

Erum Oh gosh, you're a professional. I can't wait to see it. a lot of friends of the pod will also be with you, which I'm excited to hear them talk as well alongside you.

Karl Yeah. So we thought that today we'd give a quick shout out. two weeks ago was the International Genetically Engineered Machine competition, which the big summit takes place in Paris. we [00:04:00] just wanted to give a congrats to the McGill University team for winning the undergraduate grand prize.

Karl They basically created a synthetic biology toolkit to control cell to cell adhesion, which means really the way that cells communicate with each other, and then the over graduate grand prize. These are people who have already graduated from school. They're not undergrads, they're not in high school. They basically developed a duckweed based Fido remediation system.

Karl Excess nutrients from ecosystems. So why is excess nutrients a problem? Because when you have too much nutrients in the water, you end up with really weird growth. I think like with red tide. which is like this crazy, invasion of algae blooms. These kinds of things happen when there's too much nutrition in a system.

Karl So congratulations to both of those teams. Now, as part of your birthday week, Erum, you told me you went to the bookstore. What did you get?

Erum Oh, yeah, so I went to the bookstore [00:05:00] and actually went for my son to pick up something new for him. And I was like, you know what? I'm gonna have a day of relaxing and I wanna read something like profound.

Erum And so there was obviously tons of books being recommended, and I was like, I don't only have much time, so I take out my phone and take pictures of the bookshelf. And I take those pictures and put them into Chachi pt and I say, based on what you know about me, which book should I read on a Day of Chillaxing?

Erum And, and it recommended, the Mountain and the Sea, and it's described as a futuristic eco punk, or cyberpunk thriller, which I know you'd love it because you know, you're Carl Cyberpunk Schmeer.

Erum It is your middle name and it is a spec, speculative fiction about octopus intelligence, which is. A great teaser for what we're about to talk about today. You guys are in for a treat.

Karl Yeah. I'll just say I did read that book and I'm gonna wanna reread it because I don't really remember it. but when you said that to me, I don't know why it [00:06:00] gave me, I think it gave you and gave me both Terraform vibes. Mm-hmm. So, you know, Anna Lee knew it's his book about terraforming a planet, that has a living transportation system. So yes, this is a perfect segue into today's talk. Lemme just rewind a bit to kind of introduce this back in 2020, I don't remember if we were like in the throes of the pandemic or before just into it, but Xeno bots were in the news because they're the world's first living robots that could reproduce.

Karl And they were called, you know, bio bots or living robots made from, these particular ones were made from frog skin cells. These organisms, are capable of spontaneous behaviors such as navigating mazes and despite the fact that they had no nervous system or brain, and one of the people behind, this paper was Professor Mike Levin who we're gonna introduce.

Karl And I ended up seeing Mike Levin, give a talk at D side New York maybe two years ago. And I [00:07:00] was so blown away by his talk that right away I was like, we gotta get him on the podcast. And then subsequently. His team at Fauna Systems invited us to participate in the computationally designed workshop last July, and you attended that and you've talked about that a little bit.

Erum Yeah. Yeah. First meeting with Michael Levin and his colleagues. We just had a talk amongst us. I couldn't believe what I was hearing, and you guys will be, you guys will understand what I'm talking about in just a few minutes. and they invited us to this workshop in Vermont at the University of Vermont to really talk about a new discipline of computationally designed organisms.

Erum And it's this very interesting convergence of disciplines. And, you know, you couldn't go. So I had gone in your place and I was just one of the most intellectually simulating conversations or workshops I've ever been to. I'm still processing it. and you guys, when you hear [00:08:00] this episode, you will still be processing it for days and weeks to come.

Erum it is going to. Blow your mind and bust the paradigm wide open when it comes to biology. So I think with that we should introduce Mike Levin.

Erum Dr. Michael Levin is the Vannevar Busch Distinguished Professor of Biology at Tufts University and director of the Allen Discovery Center.

Erum His background is in computer science and biology. What a great intersection. And his group works at this intersection of the developmental biophysics, computer science and cognitive science, and he's primarily interested in how intelligence can self organize in a diverse. Range of natural engineered or hybrid embodiments.

Erum Okay, this is getting really interesting. So, professor Michael Levin has been developing this framework for recognizing and communicating with unconventional cognitive systems, and this is applied to collective [00:09:00] intelligence. Cell groups undergoing morphogenesis. So we're getting very deep here.

Erum And these ideas really have allowed the Levin lab to develop new applications in birth defects, organ regeneration, and cancer suppression. Um, his lab also produces synthetic life forms such as Xena bots and anthrop bots, which you mentioned up top.

Erum clearly his lab is doing such amazing things. So let's stop there and let Professor Mike Levin take it away.

Erum Professor Mike Levin. Welcome to the Grow Everything podcast. I'm so excited to have you on. We have so much to talk about, your work, your expertise. I had the pleasure of meeting you and attending the workshop on Designed Organisms, which was the most intellectually stimulating workshop I've ever been to. There was so many disciplines coming together to explore life, and I was just blown away by it all. [00:10:00] And this is why we need to have you on, because this is going to change the way everything could be made. So let's get started. Yeah.

Michael Thank you for having me. Yeah. It's nice to be here.

Erum It's super exciting.

Erum I'm just ready to go.

Erum Let's do it.

Erum So before we get into computational design organisms, let's talk a little bit about your area of expertise, your research, what you're doing today. So you lead the Allen Discovery Center at Tufts University. And you work at this intersection of developmental biology, cognition, robotics. So listeners that are new to this field, what exactly do you study and how does Biorobotics fit into this picture?

Michael Yeah, that's a great question. It's actually very difficult when people ask me what it is that I do. there's no obvious answer. My background is actually computer science, philosophy of mind engineering. what we do

Michael now and so my lab at the Allen Discovery Center at Tufts University, and also I'm a faculty at the Institute at Harvard. We ask some very [00:11:00] fundamental questions about what embodied intelligence is, and what does it mean to be a mind embodied in this, physical universe and how minds arise, how they scale, how they transform these kinds of questions. But the interesting thing is that as you get answers to those kinds of questions, they lead to new technologies. And so it's kind of a really amazing place where deep philosophical issues, cognitive science, behavioral science, science and biology come together. And then there's applications, right? Applications in birth defects and regenerative medicine and cancer in robotics. all these kinds of things.

Karl And your background, you said you come from computer science, but you also work in developmental biology. when I saw you talk, you talked a lot about bioeleectricity in non-neural tissue.

Michael That's right.

Karl What was the thing that made you shift from classic biology or classical biology to really decoding the software of, biology or the software of life?

Michael from an extremely early age and we're talking six, seven years old. remember of [00:12:00] fooling around with, constructor sets and, taking apart electrical things and, and also being outside and observing caterpillars and insects and eggs and things. And I was always fascinated by the differences and the commonalities between them. And as soon as you understand, developmental biology to me, is sort of the queen of all the sciences. If want to say it that way. Because, because there you see the answer to all of the profound questions right there in front of your eyes. You go from an egg, from a single cell that, looks like it's the province of physics and chemistry. And then there's this slow, gradual process, and eventually you end up with something that is the subject of psychoanalysis. friendship, love, whatever. So how did we get here? how did the system traverse all of those different disciplines? And whatever it's doing has the answer to where we come from, where we might go, what information is, what complexity is. It's all there. And so being a computer scientist and interested in artificial intelligence and intelligence more broadly, nothing is more fitting to that than this, self-assembling, agential [00:13:00] material of life which puts itself together both the body and the mind. Put themselves together that way.

Erum Wow. Yeah. Self assembling agential material that we can just break that word down into so many different parts. But a lot of what I take away from the workshop and what you talk about throughout your work and your career is that, you know, we have these collective of cells and they are decision making networks. So how do these cells decide to replicate and specialize into different parts of the body? And we talked about like they have little brains without neurons. They don't necessarily need neurons to do cognition. tasks. So how does this whole like being able to have cells just decide things like how do you see cognition in tissues. And what does that change about how we think of regeneration or cancer or just how cells operate.

Michael so there are two very profound issues [00:14:00] that you just raised. And we have to look at them separately. One is this notion what I call a multiscale competency architecture. And this is the idea that living material, not necessarily the way we build computers and technology now, although I think we can and we will, but the way we build things right now, there's typically one level. So here's a robot of some sort or an AI that has intelligence at the top level, but it's made typically of dumb parts. The parts don't have agendas of their own. This is why you've never heard of robot cancer. Okay, robots don't get cancer because the parts don't have agendas of their own. And in the absence of being held together by this high level, virtual governor that sits at the top, the parts don't run off and do different things, right. So, so how we've been building so far. this actually goes back to your previous point about terminology, the general materials. So in engineering for thousands of years we've been dealing with passive matter. And this is wood and metal and things like that and that material, as an engineer, what you need to ask yourself is how much can I count on the material to do versus what I need to manage? So if you're dealing with [00:15:00] passive matter, the only thing that's really going to do is keep its shape. Ideally, that's pretty much all. And so if you're building with Legos, you need to put every Lego where it goes. It's on you as the engineer. The material is passive. Since then in recent decades, there's been fascinating work in active matter and then computational matter. But in biology, you've got something different. And this is agential materials and agential matter which you have to engineer with very, very differently than, all of these others is this notion that at every level of organization, so it actually goes below cells. The reason I'm bringing this up is that the cells are not the first unit of intelligence in our bodies. It actually goes well below that. Molecular networks inside of cells themselves have at least several different kinds of learning capacity, including Pavlovian conditioning. And this is just the molecular networks themselves. So at every level of organization in biology, you have subsystems that have competencies. They have goals, they have competencies. They have certain abilities to navigate specific problem spaces, not just this three dimensional space that we navigate with muscles and nerves, but the space of possible gene [00:16:00] expression, space of possible anatomy, space of physiological states, metabolic states. So they navigate all of these different things. And each level is literally in charge of aligning all of its parts towards a higher level goal that the parts don't know anything about. And so that is sort of the secret sauce of biology is that causation and intelligence propagates all the way down. And so, so that's one. And then the other. thing you pointed out is this notion of decision making. And so this is important to get right because people often get stuck on this. What do we mean When we say that cells are making decisions, they're storing memories. They're solving problems. do I mean by that? It's really important to understand that this is not a philosophical or linguistic project. What I am not doing is saying, look how complex and beautiful this is. Let's just say it's intelligent, okay? people do do that. And, you know, a lot of people will say, wow, you know, look at the whole solar system. It must be this global mind. Maybe, maybe. But you can't just redefine terms because you feel like it. what do these things mean? What they mean is that you've [00:17:00] done specific experiments. And these are not just observational. These are perturbational functional experiments to see how well do the concepts of behavioral and cognitive science apply somewhere else. So this is not you know, metaphorical or wishful thinking. This is a very specific research agenda. It's an empirical question. If you say to me this system, and it might be a cell or an organ or a tissue or Xenobot or whatever it is, if you say to me that this system has a degree of intelligence, what you're really saying is I have a certain set of tools. Those tools might be hardware rewiring, they might be cybernetics, they might be control theory, they might be behavioral science and training and things like that. They might be psychoanalysis and things like that. I have a set of tools. I'm going to apply those tools to the system, and then we all get to find out how well that worked out for you. Okay. So so this is an empirical project to find to get this right. Because there are two ways to get it wrong. One way to get it wrong is to do a sort of cheap animism where you say, oh, look, everything just has intelligence, and that's great. But you want to convince a [00:18:00] mechanical clock to do something and you're going to do it with rational arguments, that's not going to work. so there's a real mismatch, right, in that sense. But you can also make a mistake in the opposite direction. You can make another kind of error, which I think is far more common these days, even though people only talk about the first one. You know, scientists are very worried, oh my God, we're going to anthropomorphize molecules and things like that. That's actually far, for advancement in science. What actually is holding us back is the other side, where people make an assumption, which they treat as either fact or some sort of philosophical conclusion, where they say, these are cells only, brainy organisms. Or maybe humans can really be intelligent. And if you try to say that the cells are doing whatever you say they're doing, it's a category error. It's a philosophical mistake. I don't believe in categories. I don't believe in category errors. What I believe in is doing experiments. And that means that we can take tools, well-developed tools of behavioral science, apply them to other systems, and find out how well that works. It's a publicly observable research program. Everybody can see, hey, look, everybody's been studying these molecular networks as if they were hardwired [00:19:00] circuits. We've now applied the tools of Pavlovian conditioning, which previously was only applied brainy organisms. And sure enough, apparently they can learn associative tasks. when I make these claims. This isn't a poetic, you know, wouldn't it be nice if we said that this is actual experiment that you have to use.

Karl So on a very sensory level, what does that look like? If someone walked into your lab, what are they going to see? Are they going to see living constructs? Are they going to see a lot of computers? What does the, know, regeneration experiment look like? What does someone witness when they come in and walk around

Michael They're going to see a lot of different things. partly it's we've had lots of visitors who come in for an hour or an afternoon, and they expect to see the whole experiment. Some of these experiments take weeks. And so you have to kind of compress the story that I'm going to tell you, sort of compressed across, let's say, ten days or two weeks. the first thing they're going to see is lots of equipment. So we have lots of microscopy, we have lots of automation, we have, lots of fridges and freezers, PCR machines, all the kinds of molecular biology stuff that you would expect. We have some very unusual things. We have [00:20:00] machines that automate the training and testing various model systems. So for example, if you want to know whether your tadpoles that only have an eye on their back, whether they can see out of that eye, you need to do visual training experiments. And we've built a device for that. You're also going to see this giant thing called Mombot, which is basically a robot scientist colleague whose job it is to navigate the space of possible Xenobots and things like that. We can talk about that later. So you'll see some of that stuff Then you'll see the animals. So you'll see you'll see tanks of Planaria. And you'll see if you look in the incubator, you'll see, anthrobots which are little motile proto organisms made of human cells. You'll see some xenobots, you'll see, on different days, you might see slime molds. We've had ants in the lab. We've had chicken embryos. You'll see the organisms. And typically what you'll see is, people, the postdocs and staff scientists and the grad students in the lab. What they'll typically be doing is, uh, standing in front of a bench, some type of equipment that they're using. And somewhere in there is, perhaps biological system now. Now, by the way, we also have a computational side of our lab. [00:21:00] About a third to a quarter of our lab does, computational kinds of work. So they're sitting at computers, and you might see. So, so, for example, one experiment you might see is that, someone has algae in a little container, and the computer is providing the algae with, very patterned regular stimuli on one end, but random stimuli on the other end. And you'll see that the algae actually go to the, predictable one. Right. Which tells you that algae actually have the ability to notice patterns in their environment, aka intelligence. And so they prefer a pattern over randomness. They can tell the difference. They actually don't like randomness. Right. So so it's interesting, when their expectations are subverted, they don't like it. And that just tells you that even algae have expectations. And so you would see that somebody is taking down data and looking at the experiments. That's, what it looks like.

Erum Wow. Yeah. Again, I had a chance to go, up to, University of Vermont and I was able to see a few of these parts of the experiment and parts of the computational framework that you guys are leveraging. And one of the interesting things, and I'd love for you to walk us through [00:22:00] this experiment with the planaria, the worms, and how it's just so bizarre. And think you do a better job talking about first of all, why you decided to set this experiment up in such a way that there's one. One Planaria has two heads. So can you walk us through that because it is so phenomenal?

Michael Sure.

Michael So this goes all the way back to a very fundamental question. If you want to let's say we're after, radical regenerative medicine where, where you can grow back any organ that you've lost or that's injured. You want to de-age you want to fix birth defects, normalize tumors. All of that becomes possible. If you can say to a bunch of cells, you should make this rather than that. If you can tell cells what to do, all of those problems go away. And so that raises the obvious question how did groups of cells know what to do in the first place? Groups of human embryonic cells typically make a human. They can also make an anthrobot, but they typically make a human. and the question is, how does the standard body [00:23:00] architecture of a being, where is it specified? Now, the obvious thing that most people nowadays would say is, well, it's in the genome. It's in the DNA. This is why, you know, dogs have puppies, and cats have kittens because it's in the DNA. but there's something very, very interesting and important going on here, which is that know and we've known this for a very long time, but even more recently, now that we can read actual DNA, we know that the DNA is not directly specifying the large scale structure of your body. There's nothing in the DNA that reads out as how many eyes you're going to have. How many fingers? Where do these fingers go? There's nothing like that in the DNA. The DNA specifies proteins, it specifies and some timing information about when those proteins show up. But basically it specifies the molecular hardware of the body. Now cells having access to that hardware. then there's this physiological process which you can describe in certain ways as software because it is reprogrammable. then they make decisions about what they're going to build. And one of the most fascinating things in the field, and we can talk about examples, is the incredible plasticity of that process. Because it is not and this is absolutely critical. It is not a hardwired [00:24:00] simple. The genome tells you what to build and you build it. That is not what this is. The genome gives you problem solving hardware. It gives you an incredible reprogrammable, navigating system that can meet certain goals even when circumstances change. And if those goals are unattainable, it'll meet other goals we can talk about where those come from too. And so that is very important. The mapping between the genome and the final result is full of problem solving intelligent competencies. that's critical for how biomedicine is going to work out in the role of genetics and all the evolution, all that stuff. so planaria. So so Planaria, let me just introduce the model system. Planaria are flatworms. They are similar to our direct ancestors. They're not like nematode worms or anything like that. They are very advanced. They have a central nervous system. They have a true brain. and they have all this same neurotransmitters that you and I have, and they have a few fascinating properties. again, like all of the mysteries of medicine are hidden in this one little worm. first of all, you can cut the worm into pieces. The record, I think, is like two hundred and seventy five or something like that. And every [00:25:00] piece makes a perfect little worm. Okay, so they're highly, highly regenerative. They regenerate every part of their body. That's the first thing.

Karl Wild.

Michael Yeah. While the second thing is they are cancer resistant and they are immortal. They do not age. So the asexual strains of planaria that we work with have no lifespan limit. The worms that we have in our lab are in direct physical continuity with animals that were here half a billion years ago. don't age. So now you think about an animal like that and you might say, wow, that's amazing. So probably it has an incredibly pristine genome. We've been told that the genome is really important for everything the body does. So surely these animals, which are at the peak of regenerative capacity, cancer resistance, anti-aging, they must have an amazing genome. Their genome is incredibly junky, for a particular reason. The reason is the way they reproduce. They tear themselves in half and they regenerate. you and I and other species that go through sexual reproduction, we throw away the soma at every event. Right? So the body, if you have a mutation in your body, that does not get inherited into your offspring. So [00:26:00] basically only the egg and sperm carry and everything else gets thrown away. Planaria aren't like that. If you have a mutation in the body, anything that doesn't kill the stem cell when the thing divides is just going to get replicated into filling in the two offspring. And so any mutation doesn't kill the cell gets propagated into the future. So for this reason their genome is a total mess. And we can talk about it's kind of a scandal as to what biology courses have you ever had that would tell you that? Oh yeah. The animal with the best, morphological fidelity and robustness and anti-aging is going to be the one with the messiest genome. Like, that's not at all what we learn. Right. And so we can talk about why I can tell you finally, I think I understand why that is as of the last couple of years, I think we know why that is so so I can tell you. But anyway, so so you got this, planaria. And we asked the question, how does every piece of the worm know what it should build, that it should have one head and one tail, for example. How does it know that specifically because if you imagine you have a worm and you cut it down the middle, the back end of the front half is going to make a tail. The front end of the back half is going to make a [00:27:00] head. But those cells were right neighbors until you cut them apart with your scalpel. They were right neighbors. Why do these cells that were sitting next to each other have radically different fates? This one makes a tailless one makes a head. Why would they do that? so that means that in order to make that decision, the cells have to talk to the other cells to figure out which way am I facing? is there already ahead? Am I the head? Am I the tail? Like, they have to have this kind of information exchange. so we started to wonder why that was. And I was interested for, for a very long time in Bioelectricity everyone's familiar with it because it is the cognitive glue that makes you more than the sum of your neurons. You have a bunch of neurons in your head, and the reason that you know things that your neurons don't know, and you have goals that no individual neuron has, is because of this ability for them to join in electrical networks that process information globally. Right. It's a collective. All of us are a collective intelligence. We are. We are all collective intelligence. So we said, okay, where did that amazing capacity come from? Surely it didn't just spring into existence, it had to have an evolutionary precursor. And it turns out that the evolutionary precursor for all this is [00:28:00] a very ancient ability of all cells, to get together in groups and make collective decisions about what structures they're going to build. So the way that groups of cells navigate anatomical space, meaning the space of all possible shapes that you could possibly be is by reading information, processing it, and making decisions as a collective, just like we do in three dimensional space. So, I made the hypothesis. And of course there have been people the best one, my favorite is Harold Burr, who was working in the thirties, and 40s, who basically already kind of foresaw that guy had a crystal ball like you wouldn't believe. He foresaw some amazing things of the future. One hundred years that he had no way of proving. But he could see them quite clearly. Somehow, in his head. And one of the things that people like Burt talked about is this idea that some of this information might be bioelectrical. So back around now. Wow. Almost twenty five years ago, my group developed the first molecular tools for reading and writing electrical states out of non-neural tissues. So for the first time [00:29:00] you could look at cells, embryos, whatever, and pretend they were a brain. Look at all the electrical signals. Try to understand what do they encode? What are the memories? What are the goal states? What decisions is this thing making? Just like neuroscience, but outside of the nervous system. And so we looked at the planaria and we looked at intact worms, and we looked at the pieces. And what we noticed was that there was an electrical pattern and it was not too hard to decode. You could see it said one head, one tail. That's what the pattern said. and so then when you see something like that, then you have to do the functional experiment and say, okay, what if I rewrite the pattern. And so again, just to say happening here is you have genomically specified hardware, which has a bunch of ion channels and other things in these cells that allow them to keep an electrical potential. And then you're looking and what you're seeing is a memory, you're seeing an electrically encoded memory. And the memory is of what a correct flatworm is supposed to look like. How many heads does a correct flatworm have? We say, okay, we're going to rewrite that memory. it's like we're going to incept a a new false memory into the system, this collective [00:30:00] intelligence. We're going to now rewrite that memory and tell it that, no, actually, a proper worm should have two heads. And so we learn to do that. And when you do that, what we found is that, sure enough, the pieces make two heads. Amazing. in fact. And it's not a fork to head like this. It's this way. Right. So they, you know, two, two heads on, either end. it's like a pull me push you kind of, thing from Doctor Doolittle. Right? and these guys, They're incredible and the heads are properly sized and all of that. And so that showed a number of things. It showed, that the number of heads in a flatworm is not nailed down by the genetics. Because here's the cool part. We didn't touch the genome. If you wanted to sequence those worms, you could sequence them all day long. You'd have no idea that they have two heads. That information is simply not in the genome. It's not available. And later, if you want, we can talk about what is and is not reachable by genomic technologies. This is one great example where there's a fundamental feature, the body plan of this animal, the sensory motor architecture. Everything is completely invisible to any kind of genetic profiling. that was the first piece to show that the bioelectric pattern memory is readable. It's rewritable. So the [00:31:00] so the material is reprogrammable like any cognitive system. Right. The memory sticks. But you can rewrite you can form new memories. here's the kicker. we recut those worms because a good memory should be persistent. And so we recut those worms and the two headed worms gave rise to new two headed worms forever. As far as we could tell, we've now done it. It's been twenty five years. We've been cutting these things. They keep the two headed forever. And so now this is really important to, The first two headed worms were seen around nineteen oh three, nineteen oh five, something like that. There are other ways of making two headed worms. And the people did that well over one hundred years ago. As far as I know, no one had recut them one hundred and twenty years. Nobody had recut them. We did it in two thousand and nine. No, no one recut them. Why? I think it's because everybody thought it was obvious what was going to happen, because the genome sets the shape and the genome hasn't been touched. When you cut off that ectopic second head, of course it's going to go back to normal. What else would it do? this is a normal worm. And nobody actually checked as far as I understand.

Karl So [00:32:00] what you're saying that these two headed worms that appeared in nineteen oh three or so, no one bothered to cut off a head to see whether or not they would grow another head, or just turn it into a normal worm.

Michael Yeah, to my knowledge, I have not come across. So we did it for the first time around two thousand and eight, two thousand and nine, and we published it in twenty ten. To my knowledge, that had never been done.

Karl Okay. And a couple of follow up questions. can you just explain to our readers or listeners, when you say that the genome is very junky, just so that someone who doesn't know what that means can understand.

Michael What I mean is the normal process of sexual reproduction. one of the nice things it does is it keeps the genome clean because as we walk around, so you have you have billions of cells in your body. They accumulate mutations during the day, you know, the skin, the everything else is accumulating mutations. Your children do not inherit those mutations, right. Because the body doesn't participate in the next regeneration. The the only mutations that go through are mutations that are made in the sperm and egg. And so that has the function of keeping the genome much cleaner. Planaria [00:33:00] don't have that because. the way at least the ones we study there are sexual planaria we don't study those. The ones we work with, the way they reproduce is they tear themselves in half and then they regenerate, which means that the body continues. The soma continues through the generations. So any mutation that didn't kill the stem cell that it found itself in is going to be replicated as half the worm has to regrow the rest of the body. So they keep accumulating mutations. So for four hundred million years this thing's been accumulating mutations, right. so there are other things we could talk about in terms of the genomics and repeat elements and other other stuff. But they accumulate this stuff is because of this unusual, unusual reproductive mode. and it actually has some, some massive implications, because learning to operate in an unreliable medium, which biological material is unreliable material. But but planaria bodies are especially unreliable. It means that your algorithm has to be incredible. If you know that your parts are kind of junky, it means that your algorithm and there's a. Steve Frank pointed this out to me, that there's a, you know, um, it's raid [00:34:00] arrays. these are disks that you can have in your computer that basically mirror each other so that if there's something wrong with one disk, you don't lose the information. so Steve pointed out that as Raid arrays became more and more popular, the actual quality of the hardware went down. Because you don't need to have great disks if you have this redundancy. The redundancy is in software basically. Right. So, here's what you have. You have Planaria have this amazing, capacity. Part of it is bioelectrical part of it, who knows? to make the correct thing even when the hardware is pretty unreliable. but we found a way. to reprogram it.

Karl And before we get into that, you guys did these experiments, going on fifteen years ago And where is this Bioelectrical memory? Where is that located?

Michael So it's distributed throughout the entire worm. You can see it. We have photographs and videos of it using voltage sensitive fluorescent dyes. it is basically located, you wanted to ask where is the memory in the Ram of a computer located, where our memory patterns in the brain located, it's exactly the same thing. There is no one specific tiny location. It is distributed over the tissue, and there is a pattern of [00:35:00] voltage which you can see with your own eyes. Through the microscope, you can see it. There is a pattern of voltage that says one head, one tail. And we've since this was not the first bioelectric pattern, we've we decoded. We found others before. Danny Adams in my group discovered the electric face, which tells the embryo what the face should look like and where the eyes should go and where the mouth should go. And we found patterns for limbs and for brains. And this is how we fix brain defects, is by sharpening up this nice pattern that tells the brain where it should be and what size it should be. These are pattern memories that you can see directly in the in the tissue. Because it is an excitable medium. It's an electrically excitable medium.

Erum Yeah. so you see the pattern in order to make the pattern, you're pretty much zapping the cells in a certain pattern in order to influence it to match, to create the head or the tail.

Michael It's a great, it's a great question because [00:36:00] weird, exogenous piece of physics that you now apply to the cells and get them to do something. That is not what this is. The cells are already using these electrical signals to communicate with each other. And all we're doing is hacking that interface. We are doing exactly the same thing that the cells and the way you do that is by opening and closing existing ion channels.

Karl Okay.

Michael Okay. So we do we use all the tools of neuroscience which is basically ion channel drugs. So electroceuticals basically. Right. ion channel drugs. And I think something like the last time I looked it up, something like twenty percent of all drugs are in fact ion channel drugs. And so they're already they're [00:37:00] already in your body, but [00:38:00] they're not used to rewire this sort of pattern

Karl memory.

Karl Wow. Yeah.

Erum So this is going into a little bit of robotics and computation. And I would love to spend some time on this because I think this is going to be the most profound part for my listeners to understand the possible ways to use this discovery and this understanding that you've been acquiring. [00:39:00] So can you tell a little bit more about, where we're headed with at least Xenobots and Mom bot.

Michael Yeah, let me do it in three separate layers. Because I think there are three ways to look at this. the most obvious way and the most straightforward way is the following. what we would really love to have is synthetic living machines for useful environment, making repairs and things. Hydroponics is a million, applications. And so in order to do that, our goal is to speed up discovery of how it is that you build these things. And so the robot scientist platform. And so ours we sort of jokingly call it Mamba, is that it is a robot scientist platform that has an AI component that will facilitate it to do experiments using frog cells to build certain kinds of self-autonomous Biobots like Xenobots, it will observe what happened. It will revise its estimate how well did I do and then do the next experiment. And ideally the next versions will do it faster and in higher throughput [00:40:00] than human scientists. So if you decide you want to so, so bespoke robotics, if you decide, hey, you know what, I want to send a bot that goes around and collects this type of toxin from the environment, or I want something that burrows into this particular kind of pores in this material and pulls out whatever will be able to do that.

Michael So is there a. Is there a distinction, then, between Xenobots and the other things? Well, the Xenobots are the result of the process. Right. The Xenobots are a synthetic living machine that the robot scientist is helping us design. Mamba is the scientist. Xenobots are the experiment subject. Okay. And then so that's the first level. The second level, which is what I'm interested in, is sort of slightly, slightly more, unusual, which is this I predicated this whole conversation on this notion of an essential material. This means that you are not trying to micromanage and force the material any more than should be trying to micromanage chemical states in medical applications. What you're actually doing is communicating. You're communicating to the material. And what you should be discovering are sets of behavior shaping stimuli. What stimuli do I need to give these frog cells to convince the collective? And I mean this [00:41:00] literally, this is not, some poetic turn of phrase because agential materials, unlike passive matter, they sometimes refuse to do what you want them to do. That's the whole point is they have prior memories and they have agendas. And if you're not convincing with the stimuli.

Michael you're giving them, they're not going to do it it's not the same as working with passive matter interesting.

Michael Yeah. Right. [00:42:00] It won't. It won't work. So so understanding that interaction. And then the third level is the most interesting to me, which is which is this. You know, these experiments show that the space of possibilities [00:43:00] is vast. It is not just about what evolution has happened to select The space of possibilities are all going to get filled and trying to understand what does it mean when these silly binary distinctions that people try to keep up. Oh, I'm a real organism. This is just a machine. Yeah. Those things are not going to survive the next decades. And trying to understand how do we relate to beings that are very different than us? One of the interesting things about Xenobots is that there's never [00:44:00] been selection to be a good xenobiotic. we are the products of an evolutionary stream. We've been selected for certain kinds of shapes and behaviors and so on as humans. There's no evolutionary history for the Xenobots. The cells have been taken out of a frog embryo that's been selected to be a frog. And they are dropped into a novel context where they have to be an individual. And so they revert to an ancestral capacity to be an individual.

Michael Fascinating.

Michael Yeah, right. And so it's a very it's a very interesting idea of where does the identity of this organism come from?

Michael Yeah. And just to talk about this last part, the ethical part of creating life. And it's not even creating life, but, you know, enhancing life or [00:45:00] moving [00:46:00]

Erum into different forms and when does decision making stop? And it's just these living organisms are evolving themselves. [00:47:00] And there's a lot of ethics behind that. what should come into life not. And the most beautiful thing I, thought was that you related this to a painting, and it was this painting of you remind me. But it was a man. And then there was these animals and it was like.

Michael Oh, it's Adam. Yeah, yeah, it's Adam naming the animals in the Garden of Eden. It's a it's based on a biblical story.

Michael Yeah,

Erum yeah, yeah. And just. Okay, they're naming animals that was the, the time where it's like, ah, humans are amazing and animals are other, but it's no, we could work with them and, you know, shame on us for not even working with the animals that we, you know. Yeah, horses and cows. But like there were.

Erum Taking them for granted.

Erum Granted. And not bringing them into our experience, our everyday experience of making things and living,

Michael something really important here because, as we start to talk about human augmentation and all of that, a lot of people get freaked out and they're like, oh my God, transhumanism and all of that, right? A lot, a lot of people are like, well, you know, should we be doing that?

Michael Should we be doing that? yeah, should we? Yeah. And so, you know, the way I look at it of our bodies to meet our functional goals, is a basic freedom. And so if you decide, you know what, [00:48:00]  like to spend half the year underwater, you'll have gills. If you want to live on some other planet in some other way, great. think that what we're looking at again, here's the important thing, because people see that and they say, well, maybe it's too much. Maybe we shouldn't be messing with it. I just want to point out, think in the future and not that long, we're talking one or two generations at most. I think if things go well, which, you know, who knows. but if things go well, the children of the future, you know how nowadays, when you're in school for the first time, you learn about prehistoric man and you look at, you know, the, the art they made and the things they did and all of that. We are going to be those prehistoric people, because the children of the future are going to look back on us and say, oh my God, imagine those people, they were they were enslaved to the bodies that they were born with. They didn't have the freedom to change their own bodies.

Michael Wow. Yeah. The next evolution of being. Exactly. Exactly.

Michael Wow.

Michael And this is not. This is not about, again, a lot of people make these kind or technology. This is actual ability to intervene on [00:49:00] this ancient cognitive layer of our bodies to guide them to new shapes and functions.

Michael Yeah. And I love how you mentioned that the genome is not the destiny of your body's shape. Like, it's not a hardwired thing, but it's more like a suggestion, right? [00:50:00] I, I mean, I think that's true in general. I, I think that genome is not

Michael [00:51:00] [00:52:00] of what's going to happen. It's a set of prompts into the hardware and the process of Is of morphogenesis, interprets the genome in various ways. And if you've got a human genome, it can interpret it as a human. [00:53:00] Under other circumstances, it'll decide to make an anthropoid. It's improvisation. It's been given a memory. Right. here, it's interesting to say another thing. none of us have access to the past. You don't know anything about the past. What you have access to are the memory traces at every moment. So every whatever two or three hundred milliseconds, you have access to the n grams that the past has left in your body, the memory traces. But you don't have access to that past. And so that is a really profound philosophical thing.

Michael Fascinating.

Michael Yeah. And I think it's true all the way up and down the hierarchy. And so that means that for example, the memory of what a correct face should look like, that memory is not directly in the genome. The genome is, is a set of prompts, but the memory itself is stored electrically in the collective of cells in that tissue. So. So that means you can rewrite the memory without touching the genome. You can change what the thing's trying to build. You can go from a normal face to a frog face without touching the genome. It's amazing.

Michael That's amazing.

Michael That's right. It's a memory. It's a stored pattern memory that you can write to.

Michael And you've related this to the way we build technology, and, you know, we have these sort of dumb parts our technologies today, in the future they can [00:54:00] be living [00:55:00] technologies. But you did say that you're not entirely sure if we are building any dumb technologies.

Michael [00:56:00] Yeah. I'll [00:57:00] pull it all together into one sort of crazy statement. I'm not sure there are any dumb machines anywhere. I'm not sure it's possible. We have now done experiments with extremely simple, minimal things, and I see them already, going beyond themechanistic paradigm that was supposed to apply to them. So, there are some, people that, think that, we are all machines and we are all sort of the story of chemistry tells our whole story. There are other people who will say, no, no, are magic and special. And there's a particular kind of this is the organicist view. Like there's a particular kind of magic that exists in living things, but it's okay because there is a corner [00:58:00] of the world which are these dumb mechanical machines, and at least they are fully describable by algorithms, and they're not special. I think that corner is going to shrink to zero.

Michael I think you're right. Yeah. Yeah. Right. I think so.

Michael So it's very exciting what you're doing. And I know you've covered a lot on this topic. And I just want to ask one more question to tie this back to the anatomy compiler. I believe you call it an anatomical compiler. Yes. Yeah. Right.

Michael So where does this leave the world of medicine in the future? And if we are to [00:59:00] [01:00:00]

Erum where do we go from here, knowing all this? Well, I think that the future of medicine is going to move away from where

Michael it's focused now. I think that for, for, for a while now, there [01:01:00] has been this focus on molecular, on molecular The idea [01:02:00] everything we can do is going to be reachable by communicating with a molecular level. You're leaving so much on the table because all of the higher levels of organization have their own intelligence, they have their own and that is what we're going to be communicating with. And so I think that we're going to reach something that I call anatomical compiler. So this is the idea is that you sit down, you draw the plant animal, bio bot,organ, whatever that you want. And what it does is compile that down into a set of stimuli that have to be given to cells to get them to build whatever it is that you want them to build. end game. That's what we're going to have when we really understand how to communicate. It's not a 3D printer, it's a communications device. It's a translator from your goals to the goals of the cells. So when we are able to do this and this is not about genetics or genomics or stem cells, all of those nice enabling technologies. But I don't think the kind of revolution that everybody's talking about that we need to have. That's not where it's going to come from. It's going to come from the application of the cognitive [01:03:00] sciences to the amazing material of which we're made. And so that means that birth defects, traumatic injury, aging, degenerative disease, cancer, all of that kind of stuff is going to be a nonissue. There will be some other things that are left that are different. but all of those things are are going to be solved. And so lost the higher level government goal, and they've gone down to their lowest level. That's what we want to avoid. We don't want to make things that just mindlessly replicate. That's what we have now. What we want is we want things that have higher level goals. So Xenobots have an agenda, but their agenda is to minimize stress, to live in a benign environment, to protect themselves from damage. That is the agenda of every living system. And so if we can learn to give them goals, then they become controllable, because now we can manipulate the relationship between where they are and where they want to be. We can manipulate the path they will take to get from A to B. But when a system does not have goals, you can only push it, like pushing a passive object. And when you stop pushing it, it just stops. You can't rely on it to keep going. So I think that the goal is to increase the goal capacity and the control capacity.

Michael Yeah. And that's what makes the difference between a dumb technology and an organism with an agenda. That's right. That's we've realized is that those two are not really a difference in kind. They are a difference in degree. And what we're talking about is the scale of the self that the intelligence is trying to protect. So in the case of cancer, it's just the cell itself. In the case of, you know, a normal body, it's the whole body, the entire body. In the case of a social insect colony, it's the entire colony. So the size of the self is what is being rewired.

Michael Wow. Yeah. It just keeps getting deeper and deeper. That's right.

Michael And I guess that leads to another question about, you know, there's a lot of things to consider when you're thinking about the human mind and its intelligence. And what are we learning about the intelligence that the cells have, that the mind doesn't, or the human mind doesn't? Is there anything that we're learning about that, that can make the human mind better, in a sense?

Michael Yeah. So that's a fantastic question. And I think that we are learning a few key things. First of all, the way our minds, and when I say our minds, I mean our nervous system based mind. The way that mind scales up is by using very, very large and very fast neural networks. The cells of our bodies scale up by using a massive parallelism of cells, each of which is doing its own cognitive work. And so the lessons we are learning here are ways to distribute the computational load over a massive number of tiny agents that each have their own mini mind. And that works extremely well. And in fact, that's what we want to do with a lot of our AI systems anyway. Now we try to make them very centralized. We make one massive, very smart thing, but that's going to be very brittle. You're going to lose the robustness. And so that is one lesson is that the robustness is going to come from a multi-scale architecture. The second lesson is that there are whole cognitive domains that we haven't been talking about. We are constantly talking about our cognitive domains, which is three dimensional space, time, social interaction. The cellular mind is navigating an entirely different cognitive domain. It is navigating the space of anatomy, the space of gene expression, the space of physiological states. We need to learn how to access those kinds of cognitive spaces. And this relates to the third point, which is that we have a massive mind blindness. We are very bad at recognizing intelligence in other embodiments. If it doesn't have two eyes and a mouth and legs, we have a very hard time seeing the intelligence in it. And so if we can overcome that mind blindness, then we can see that the cognitive field is much wider than we thought.

Michael Fascinating.

Michael And in relation to that, I mean, the future is now in a lot of ways. And as you're talking about all of these future problems that you're solving, are you also in the lab and in your research looking at ways to mitigate some of the crises that we're facing now, like climate change, for example? Absolutely. Absolutely. So a lot of the projects are about using the bio bots for, for environmental cleanup. They are about creating biological materials that are better for construction, that are fully biodegradable, that are fully self repairing, self constructing, self monitoring. So imagine a building material that can tell you when it's under stress, that can tell you, oh, I need a repair here. That can tell you whether there's a problem, that you have a building material that is an active sensing node that uses to tell you like, is this system under stress? what should we do? What's the issue. so environmental applications of course at some point space exploration no doubt. all of these things are downstream of this.

Karl Yeah. Just amazing. So we always end these interviews with some quick fire questions. We try to keep them snappy. And I will alternate them, and you can take as long as you want to answer them. why don't I kick it off? If cells could talk, what would they say?

Karl They would say, finally, somebody is asking the right questions. Wow.

Karl Wow.

Karl What's the most unusual animal that you've worked with? It's a three way tie. It's the, it's the [01:04:00] planaria with the two heads. It's the xenobots that we've constructed. And it's the little frog faces that we've made on the bodies of the tadpoles, where we

Michael can

Erum tell the tadpole, no, no, you don't need a head here. We just need a little face, a little tiny face. It's a little tiny face that has two eyes and a mouth.

Erum What's the

Michael best book you've read recently?

Michael So, you the best

Karl book I read recently,

Karl but I love this emphasis that like, for every one person saying, you shouldn't be doing this, you get 10 people saying, why is it taking so long? that's just amazing to me. And it just shows to me, it says what people really care about, you know, as opposed to like, being against these technologies.

Michael well that's the funny part is I can always tell the people who are writing to me, who are complaining about this? First of all, they tend to be young and healthy.

Michael [01:05:00] I'm [01:06:00] not sure actually who wrote it or what the title is. I have a guess, But I'm not sure. a bunch of creatures come up from the Earth's core. Okay. They live down in the Earth's core. They're incredibly dense. They have gamma ray vision. They're just super dense. They come up to the surface of the Earth. What do they see? Well, all of this stuff that's solid to us as far as they're concerned. It's a thin gas. It's a thin plasma. None of this is solid to them. they're walking through our stuff. They don't even see it. just they're so dense compared to us. We're like. thin mist on top of the thing. And one of them is a scientist and he's watching this gas and he says, he says, hey, did you guys know that this, planet that we're sort of, [01:07:00] walking on is covered in, like, this gas that has all kinds of little patterns in it. And I'm like, well, yeah, who cares? He's like, no, no. But I'm watching these patterns and they kind of look like they're almost essential. They almost are doing things. they're moving around. They kind of keep together. For how long? Well, for about a hundred years. And then they kind of dissolve back into the mist. And he's like, I wonder, I wonder if those things are alive.

Michael Wow. That's a great book. Yeah, right. And so it was just this very, very, very brief story. It's a short story. And it you realize We are metabolic patterns, temporary. We hang together for some amount of time. We do things. And so that raises an obvious question. When we see other patterns in media, and we assume that it's a dumb, low agency pattern that just sort of propagates, whether it's waves or data going through a [01:08:00] computer or whatever, how much mind blindness do we have that we are not good at recognizing these things?

Michael Fascinating. that to me is a very was a very profound story. And I really wish I remember who wrote it and I'm not sure that it had all this mind body stuff that I sort of painted onto it just now. I think it was different, but, just this idea of it's a matter of perspective as to whether you are a mere pattern in a medium or...

Erum That's why we have you on, because you're adding the layers, professor.

Erum You're adding the context. Right. Exactly.

Erum If you could have dinner with one scientist, living or dead, who would it be?

Erum I would say, William James, because he's a fantastic writer, a fantastic philosopher, and he understood the interplay between what we call physical systems and mental systems, and that all of these distinctions If you could be any animal, what would it be?

Erum I would be a planaria. Amazing.

Erum And I'd be a two-headed one. So you could go both ways.

Erum Yeah. Right. Exactly. And you're immortal.

Erum That's right. And you don't age. Wow.

Erum Where do you see yourself in five years?

Erum Five years, I hope to have finished the software for the anatomical compiler.

Erum So it's coming soon.

Erum I hope so. I hope so. It's been a long project. I think we're very close.

Erum Amazing. And what is one myth about biology that you wish to debunk?

Erum That everything interesting is reducible to genes. What is your favorite quote?

Erum My favorite quote is by, I believe it's William James. all [01:09:00] of that because there's a lot of gems that you've shared. Very poetic. I mean, there's so many new terms that I come across. So thank you so much for taking the time to speak with us today. We'll have to have you back on as things progress.

Michael Thank you for the conversation. I'm happy to come on. And I guess just one last thing to leave you with is for everybody to understand is that it isn't just me sitting here coming up with these concepts. There have been hundreds of people in our lab over the years whose hard work makes this possible. Everything I say to you now is based on very difficult, painstaking experiments that have been done by the students, the undergrads, the grad students and postdocs in our lab. it is all of that work that this is based on. And so it needs to be remembered that you know, I'm kind of the face of it, telling you all these things. But it's based on, on several decades now of, people doing really, really amazing work in my group. And I just, I want to make sure that's recognized.

Erum The collective consciousness.

Michael Yeah. Yeah. Exactly, exactly.

Erum [01:10:00] Amazing, amazing. Thank

Michael you so much.

Erum Yeah. Thank you. All right. We'll talk...

Karl Iam, I mean, this episode was so deep and we barely scratched the surface of what we could cover with Mike.

Karl And after we finished the interview, he said, really enjoyed the conversation. Let's talk again soon, which I think was a compliment to us. Also to him, because we barely got into it. I mean, I [01:11:00] think, if you're a fan of this stuff, you should do some, searches and listen to him like he was on the Lex Friedman podcast for like three hours, and again, still doesn't even cover all of the territory that he, is exploring.

Karl What do you think?

Erum Yeah, I'm still processing it. Wow. Like, it's, it's so deep. I had taken so many notes. The terminology he uses, it makes it more accessible to me personally. and. I have to re-listen to it a couple times to see how these connections are being made because there are such deep concepts here, right?

Erum this idea of Xena Botts, for example. you know, when I went to the workshop, I learned about, Xena Botts could mean two things, right? Xeno is, the scientific term for, frogs, but also means other, you know, outside the xenophobia. so we know about that.

Erum and you've learned a little bit about Xena Botts. So tell me a little bit more from your perspective, what do you think that is?

Karl well I like the term, bio robotics. Mm-hmm. I mean, there's also like a term out there, soft robots, which [01:12:00] is a robots made from squishy, wet stuff. but the fact that they've been studying this, it just gives them this incredible platform for really understanding, like you said, at the top, collective intelligence themselves. And this idea of building like an anatomical compiler just really blew me away. I said it I think in the podcast, or I was thinking about it, where some dude in a science fiction show loses his arm and they put like a bioreactor on his arm so that he can regrow it.

Karl Um. Like, that's really the kinds of things that Mike and his team are working towards. So, again, very deep thinker. I think I've mentioned it or someone had told me he's probably someone who's gonna get a Nobel Prize, his science. And what he does is something people are gonna be talking about everywhere, all the time because it's just so deep.

Erum Yeah. And starts giving some ex explanation to phenomena. I mean, like the whole part of the. Episode where we talked about the plin, the worms. Yeah. how, they were able to create two heads on a worm. you know, at [01:13:00] first, they're like, no bioelectricity, they're talking about that.

Erum And I was like, Hmm. what does it actually look like in practice? And then when he said, electroceuticals, I was like, damn. I was like, what? Is that, and I'm like, it sounds like Electroceuticals can really reset a lot of, our biological systems to do what we needed to do or want it to do if it's like, oh, we wanna create another head.

Erum I mean, I, I'm getting too crazy here, but, I thought that, his work it shows results, you know, like to be able to, coax cells to differentiate in. Different ways, and I think that's, a very incredible, useful tool, especially for tissue regeneration.

Karl When you were at the workshop on computationally designed organisms in Vermont, did you feel like there were commercial applications being discussed?

Erum Oh yeah, for sure. you know, there's, Certain things that we talked about just in terms of imagining things besides a biomedical, which is the need.

Erum And for this, because you're talking about tissue regeneration, but the idea of creating, [01:14:00] a immune system for your house. So you have like, like, oh wow, this a gentle material that your house is made out of that can help. keep pathogens at bay from the outside, help clean air. So it's like you have a healthy air system inside.

Erum And the thing is, with this essential materials that's able to make decisions to say, okay, well no, I don't want these pathogens or this, pollution to come inside. or I'm able to use it and process it differently to make a more healthy environment inside. that was one of the applications that really stuck with me.

Erum of course there's other applications such as environmental remediation to be able to sense where there's pollution and then do something about it or even just sense it so that, you can know where it is and use another solution to do targeted cleaning versus just, blanket cleaning some area.

Karl No, I was just gonna say that. What Levin and his team are [01:15:00] doing is so science fiction that even thinking of what those applications requires, like a next level of imagination. And I just feel like more, again, this is one of those places where we need more really imaginative people to understand what these things are so they can come up with the application.

Karl So these things could be programmed to do those things because this stuff is so deep and so interesting, and there's so many possibilities with them. That it really could be world changing. and I just am so happy that we were able to have Mike on because I think exposing more people to what they're doing and that kind of thinking will enable there to be more applications developed that really, you know, like I said, could be world changing.

Erum Yeah, absolutely. That's why we love that we have designers listening to this podcast, people from other disciplines and other professions. To be able to know how these tools are used. And of course we have links in our show notes to take you down that rabbit hole of Michael Levin in his lab and all the work [01:16:00] that they're doing.

Erum And so to be able to, you know, use your imagination is really great. And I, I think this is also very interesting because we live at a time where we're talking about ai, which a lot of AI that's being applied are like LLMs or machine learning. They're not like pure ai where it's like sentient intelligence.

Erum What sentient is life. It's biology, right? to be able to combine the two, and you know, we started talking about that computationally, design organisms, right? We did this crazy exercise during this workshop where, we had to download, a virtual computer onto our laptops. So we had like an environment where we were able to change different parameters and it simulated how, different molecules can emerge and interact.

Erum did for a very, uh, short amount of time. So I didn't really design an organism per se, but I saw like, what the makings were of this software, and then obviously to translate that into wetware and have something. But you at least know what the [01:17:00] recipe is to create an organism.

Erum and so this is the unlock, you know, people are like, oh, you know, AI's gonna come. I mean, AI. It's dumb. the different parts of a computer are dumb. You know what I mean? there's no agency, even a gentle ai, when people are using these like, it's like smoke and mirrors, right?

Erum It kind of gives you the impression that there's sentient and decision making, but really it's, programs. Predictions. Yeah, predictions. Exactly. That's probably a better way of saying it. The hybrid between AI and biology is going to be so fascinating, and this is the world that we live in.

Erum This is the world that you listener live in by listening to the GR Everything podcast. We're bringing this to you people. We're bringing the future right to your doorsteps. All you have to do is listen and stay tuned.

Karl There you go. I think that's a great way to end the episode.

Erum Yes, episode. Absolutely.

Erum Continue listening. Follow us on YouTube, Instagram, all the things we love talking to you. We love talking about AI, biology, and all the things. So that's a pod.[01:18:00]