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

as far as I can tell, hasn't even scratched the surface of how weird things are really going to be. The

reality, as often happens, the reality, I think, is is going to be much weirder than than anybody anticipated.

>> Hey Carl, how's it going?

>> Good. How are you? Welcome to our listeners to the Grow Everything podcast. You just had a very special

podcast. You just had a very special week. It was your birthday. What did you

week. It was your birthday. What did you do?

>> 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 Ayat, which is a Palestinian place. It's really delicious food. And

place. 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

King's Theater. And wow, Carl, it was so amazing. Besides Carara and Scott, they

amazing. Besides Carara and Scott, they had a special guest, Curtis Sivwa, who was a nominee for mayor of New York. I

would say, you know, Mum Donnie, incredible campaign, but I think Curtis Leewa staying in the race also helped.

But he is so funny. What a character.

You heard the podcast came out right afterwards.

>> Yeah. So, 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

incredible. It was incredible to hear Curtis. He's a character would have been

Curtis. He's a character would have been a crazy guy to be mayor, but the whole story of mayor and hearing about his history with the Guardian Angels, which

was this kind of like vigilante self-p policing group in the 70s and 80s in New York City, and they just talked 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. It sounds like a great

great birthday. It sounds like a great great way to spend the birthday.

>> Yeah. Yeah, it was wonderful. 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 and I'm like,

birthday card. I go in and I'm like, "Are people still saying happy birthday to me?" And they are. I was like, "Wow,

to me?" And they are. I was like, "Wow, I still have so many friends on Facebook." And I was like, "All right,

Facebook." And I was like, "All right, well, you had to give a shout out to all those people." Facebook near and dear to

those people." Facebook near and dear to my heart, but it wasn't my first social media. We had AOL instant messenger

media. We had AOL instant messenger first, MySpace before that, but Facebook still staying strong. So, thank you Facebook people.

>> Yeah, I'm trying to think of what was the profession. There was a professional

the profession. There was a professional network before LinkedIn took over and like there were virtual events because I worked during.com and like in the mid 2000s something showed up and they would

have a lot of events in New York. So, I

ended up meeting a lot of people through this kind of like hybrid thing. And I

think we're going back in that direction. I bet if like people started

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. Meta is so successful.

help. Meta is so successful.

>> Yes, absolutely. Absolutely. And then

you have a very exciting event and opportunity right around the corner.

Tell us about your TED talk.

>> Yeah, so I'm going to be speaking at TEDex at MIT as part of Planet Action.

is a three-day event and the guy who convenes this, John Werner, he's like the ultimate network creator and so all the talks really have to do about

sustainability and taking action on the planet and I'm one of the people who's going to be talking about biofabrication. I'll specifically be

biofabrication. 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 going to make it available very quickly, hopefully I won't flub it and you guys could see my video and give me notes.

>> 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.

>> 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. And

we just wanted to give a congrats to the McGill University team for winning the undergraduate grand prize. They

basically created a synthetic biology toolkit to control celltocell adhesion, which means really the way that cells communicate with each other. And then

the overgraduate grand prize, these are people who have already graduated from school. They're not undergrads. They're

school. They're not undergrads. They're

not in high school. They basically

develop a duckweedbased phytored remediation system to remove 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,

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. So congratulations to both

of those teams. Now, as part of your birthday week, Iram, you told me you went to the bookstore. What did you get?

>> Oh, yeah. So, I went to the bookstore and actually went for my son to pick up something new for him. And I was like, you know what? I'm going to have a day of relaxing and I want to read something like profound. And so, there was

like profound. And so, there was obviously tons of books being recommended. And I was like, I don't

recommended. And I was like, I don't really have much time. So, I take out my phone and take pictures of the bookshelf. And I take those pictures and

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 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 Cyberpunker 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.

>> Yeah, I'll just say I did read that book and I'm going to want to reread it because I don't really remember it, but when you said that to me, I don't know why it gave me I think it gave you and gave me both terraformer vibes.

>> So, you know, Annalie 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. Let me just rewind a bit to kind

talk. Let me just rewind a bit to kind of introduce this back in 2020. I don't

remember if we were like in the throws of the pandemic or or just into it, but xenobots were in the news because they're the world's first living robots

that could reproduce and they were called, you know, biobots or living robots made from these particular ones were made from frog skin cells. And

these organisms are capable of spontaneous behavior 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 Leaven who we're going to introduce. And I ended up seeing Mike

introduce. And I ended up seeing Mike Leaven give a talk at Dside New York maybe two years ago. And I was so blown away by his talk that right away I was like we got to 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.

>> Yeah. Yeah. First meeting with Michael Lean 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 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 stimulating convers conversations or workshops I've ever been to. I'm still

processing it. And you guys, when you hear this episode, you will still be processing it for days and weeks to come. It is going to blow your mind and

come. 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 Michael Leven. Dr.

Michael Leven is a Vanvar Bush Distinguished Professor of Biology at Tus University and director of the Allen Discovery Center. His background is in

Discovery Center. His background is in computer science and biology. What a

great intersection. And his group works at this intersection of developmental biophysics, computer science, and cognitive science. And he's primarily

cognitive science. And he's primarily interested in how intelligence can self-organize in a diverse range of natural,

engineered, or hybrid embodiment. Okay,

this is getting really interesting. So,

Professor Michael Leven has been developing this framework for recognizing and communicating with unconventional cognitive systems. And this is applied to collective

intelligence of cell groups undergoing morphagenesis. So, we're getting very

morphagenesis. So, we're getting very deep here. And these ideas really have

deep here. And these ideas really have allowed the Levan lab to develop new applications in birth defects, organ regeneration, and cancer suppression.

Um, his lab also produces synthetic life forms such as xenobots and anthropots, which you mentioned up top. Clearly, his

lab is doing such amazing things. So,

let's stop there and let Professor Mike Leven take it away.

Professor Mike Leven, 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 Abley workshop on computationally 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 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, >> thank you for having me. Yeah, it's nice to be here.

Sorry, I'm just ready to go.

>> Let's do it.

>> So, before we get into computationally designed 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 TUS University and you work at this intersection of developmental biology, cognition, robotics. So, for listeners

cognition, robotics. So, for listeners that are new to this field, what exactly do you study and how does bio robotics like fit into this picture? Yeah, that's

a great question. It's actually very difficult uh when people ask me what it is that I do. It's there's no obvious answer. My background is actually

answer. My background is actually computer science, philosophy of mind, engineering, what we do now. And so my lab at the Allen Discovery Center at Tus University and also um I'm a faculty at

the V institute at Harvard. We ask some very 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, computer science and biology come together. And then there's applications,

together. And then there's applications, right? Applications in birth defects and

right? Applications in birth defects and regenerative medicine and cancer, in robotics, all these kinds of things.

>> And your background, you said, you know, you come from computer science, but you also work in developmental biology. When

I saw you talk, you talked a lot about bio electricity in non-neural tissue.

That's right. 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?

>> From an extremely early age, and we're talking six, seven years old, I remember 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

things. And I was always fascinated by the differences and the commonalities between them. And as soon as you

between them. And as soon as you understand, I mean, developmental biology to me is sort of the queen of all the sciences, if you 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

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. Right? How

did we get here? How did this 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 it's all there. And

so being a computer scientist and interested in artificial intelligence and intelligence more broadly and nothing is more fitting to that than this self assembling a gentle material of life which puts itself together. Both

the body and the mind put themselves together that way.

>> 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 taken away from the workshop

and what you talk about throughout your work and your career is that know we have these collective of cells and they are decision-m 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. Like you don't necessarily need neurons to do cognitive tasks. So how does this whole like being

tasks. So how does this whole like being able to have cells 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?

>> So there are two very profound issues that you just raised and and we have to look at them separately. One is this notion of what I call a multiscale competency architecture. And this is the

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

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. So the parts don't have agendas

parts. So 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 that's how

we've been building so far. This

actually goes back to your previous point about the terminology, the agential materials. So in engineering

agential materials. So in engineering for thousands of years, we've been dealing with passive matter and this is wood and metal and 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 passive matter, the only thing it's really going to do is keep its shape. Ideally, that's pretty much

its shape. Ideally, that's pretty much all. And so if you're building with

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.

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,

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

our bodies. It actually goes well below that. Molecular networks inside of cells

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

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 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

that's one. And then the other thing you pointed out is this notion of decision-making. And so this is

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. What do I mean by that? It's really important to

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 and a lot of people will say wow you know look at the the whole solar system it must be this like 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 done specific experiments and these are not just observational. These are

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

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 zenobot or what 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

control theory. They might be behavioral science and training and things like that. They might be psychoanalysis and

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 if you want to convince a 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

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 less critical 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

in categories. I don't believe in category errors. What I believe in is

category errors. What I believe in is doing experiments. And that means that

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

works. It's a publicly observable research program. Everybody can see,

research program. Everybody can see, hey, look, everybody's been studying these molecular networks as if they were hardwired circuits. We've now applied

hardwired circuits. We've now applied the tools of Pavlovian conditioning, which previously was only applied to brainy organisms. And sure enough, apparently, they can learn associative tasks. when I make these claims, this

tasks. when I make these claims, this isn't a poetic, you know, wouldn't it be nice if if we said that this is actual experiment that you have to use.

>> 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 a regeneration

computers? What does a regeneration experiment look like? What does someone witness when they come in and walk around?

>> They see a lot of different things.

Partly, it's we've had lots of visitors who who come in for an hour or an afternoon and they expect to see the whole experiment. Some of these

whole experiment. Some of these experiments take weeks and so you have to kind of compress. So the story that I'm going to tell you is sort of compressed across let's say 10 days or two weeks. The first thing they're going

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,

automation. We have lots of fridges, freezers, PCR machines, all the kinds of molecular biology stuff that you would expect. We have some very unusual

expect. We have some very unusual things. We have machines that automate

things. We have machines that automate the training and testing of 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. So,

we've built a device for that. You're

also going to see this giant thing called Momot, which is a 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

animals. So you'll see you'll see tanks of pleneria and you'll see if you look in the incubator you'll see anthrobots which are little motile protoorganisms made of human cells. You'll see some

zenobots. You'll see on different days

zenobots. 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

embryos. You'll see the organisms and typically what you'll see is people the postocs and the staff scientists and the grad students in the lab. what they'll

typically be doing is standing in front of a bench, some type of equipment that they're using, and somewhere in there is perhaps the biological system. Now, now,

by the way, we also have a computational side of our lab. 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's 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

one, right? Which tells you that algae actually have the ability to notice patterns in their environment, aka intelligence. And so they prefer a

intelligence. And so they prefer a pattern over randomness. They can tell the difference. They actually don't like

the difference. They actually don't like randomness, right? So So it's

randomness, right? So So it's interesting when their expectations are subverted, they they don't like it. And

that just tells you that even algae have expectations. And so you would see that

expectations. And so you would see that somebody is taking down data and and looking at the experiments. That's what

it looks like.

>> 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

are leveraging. And one of the interesting things and I'd love for you to walk us through this experiment with the plenaria, the worms, >> and how it's just so bizarre. I 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 pleneria has two heads. So can

you walk us through that cuz it is so phenomenal.

>> Sure. 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 you can grow back any organ that you've lost or

that's injured, you want to deage, 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. Okay?

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 embryionic

first place? Groups of human embryionic cells typically make a human. They can

also make an anthrobot but but they typically make a human. And the question is how does the standard body architecture of a being where is it specified? Now the obvious thing that

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

the DNA. But there's something very very interesting and important going on here which is that we know and 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

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 then 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 like physiological process which

you can describe in certain ways as 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 simple

critical. It is not a hardwired simple the genome tells you what to build and you build it. That is not what this is.

The genome gives you problemsolving hardware. It gives you an incredible

hardware. It gives you an incredible reprogrammable navigating system that can meet certain goals even when circumstances change. And if those goals

circumstances change. And if those goals are unattainable, it'll meet other goals. that we can talk about where

goals. that we can talk about where those come from too. And so that is very important. The mapping between the

important. The mapping between the genome and the final result is full of problem solving intelligent competencies. That's critical for how

competencies. That's critical for how biio medicine is going to work out and the role of genetics and and all evolution all that stuff. So pleneria.

So so pleneria let me just introduce the model system. Pleneria are flatworms.

model system. Pleneria are flatworms. They are similar to our direct ancestor.

They're not like nematode worms or anything like that. They are very advanced. They have a central nervous

advanced. They have a central nervous system. They have a true brain. And they

system. They have a true brain. And they

have all the same neurotransmitters that you and I have. And they have a few fascinating properties. Again, like all

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 275 for something like that. And uh every 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.

>> Wild.

>> Yeah. Wild. The second thing is they are cancer resistant and they are immortal.

They do not age. So the asexual strains of pleneria that we work with have no lifespan limit. The worms that we have

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. They 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

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 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

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 mutation does not get inherited into your offspring. So basically only the

your offspring. So basically only the egg and sperm carry and everything else gets thrown away. Pleneria aren't like that. If you have a mutation in the

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 the cell gets

propagated into the future. Right? So

for this reason, their genome is a total mess. And we can talk about it's kind of

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, you know, and

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

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

that is. So, so I can tell you, but anyway, so so you got this pleneria and we ask the question, how does every piece of the worm know what it should build? That it should have one head and

build? That it should have one head and one tail, for example. How does it know that specifically? Because if you

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 head. But those cells were right neighbors until you cut them apart with your scalpel. They were right neighbors.

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 tail,

different fates? This one makes a tail, this one makes a head. Why would they do that? So that means that in order to

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 a head? Am I the head? Am I the tail?

a head? Am I the head? Am I the tail?

Like they have to have this kind of information exchange. So we started to

information exchange. So we started to wonder why that was. And I was interested for for a very long time in in bio electricity. Bio electricity,

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 of for them to join in electrical networks that's that

process information globally, right?

It's a collective. All of us are a collective intelligence. We are we are

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

come from? Surely it didn't just spring into existence. It had to have an

into existence. It had to have an evolutionary precursor. And it turns out

evolutionary precursor. And it turns out that the evolutionary precursor for all this is 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

threedimensional space. So I made the

threedimensional space. So I made the hypothesis and and of course there have been people the best one my my favorite is Harold Burr who was working in the 30s and 40s who basically already kind of foresaw that guy had a crystal ball

like you wouldn't believe he he foresaw some amazing things of the future hundred years that he had no way of proving but he could see them quite clearly somehow in his head and uh one of the things that people like Bur

talked about is this idea that some of this information might be biomectrical.

So back around uh now wow almost 25 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 you could

tissues. So for the first time 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 pleneria

system. And so we looked at the pleneria and we looked at 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 what's 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

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

like. How many heads does a correct flatworm have? We said okay we're going

flatworm have? We said okay we're going to rewrite that memory. It's like uh we're going to incept a new false memory into the system. This collective

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 learned 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 two head like this. It's it's this way, right? So they you know, two heads on

right? So they you know, two heads on either end. It's like a pull me push you

either end. It's like a pull me push you kind of thing from Dr. Doodle, right?

And these guys, they're incredible. And

the heads are properly sized and and and all of that. And so that showed a number of things. It showed first of all that

of things. It showed first of all 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

the genome. If you wanted to sequence those worms, you could sequence them all day long. You would have no idea that

day long. You would have no idea that they have two heads. That information is simply not in the genome. It's not

available. And and later if you want, we can talk about what isn't is not reachable by genomic technologies. This

is one great example where there's a fundamental feature. the the body plane

fundamental feature. the the body plane of this animal, the sensory motor architecture, everything is completely invisible to any kind of genetic profiling. That was the first piece to

profiling. That was the first piece to show that the biomectric pattern memory is readable. It's rewritable. So the so

is readable. It's rewritable. So the so the material is reprogrammable like any cognitive system, right? The memory

sticks, but you can rewrite, you know, 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

forever. As far as we could tell, we've now done it. You know, it's been 25 years we've been cutting these things.

They keep they they they're two-headed forever. And so now, this is really

forever. And so now, this is really important, too. The first two-headed

important, too. The first two-headed worms were seen around 1903, 1905, something like that. There are other ways of making two-headed worms. And the people did that well over 100 years ago.

As far as I know, no one had recut them for 120 years. Nobody had recut them. We

did it in 2009. 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

happen. Because the genome sets the shape and the genome hasn't been touched. When you cut off that ectopic

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.

>> So, wait. You're saying that these two-headed worms that appeared in 1903 or so, no one bothered to cut off a head to see whether or not they would grow another head or just turn into a normal worm.

>> Yeah. To my knowledge, I I have not come across. So, so we did it for the first

across. So, so we did it for the first time around 2008 2009 and we published it in 2010. To my knowledge, that had never been done.

>> Okay. And a couple 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.

>> What I mean is um 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

body. They accumulate mutations during the day. You know, the skin, the

the day. You know, the skin, the everything else is accumulating mutations. Your children do not inherit

mutations. Your children do not inherit those mutations, right? Because the body doesn't participate in the next regeneration. The only mutations that go

regeneration. 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. Pleneria don't have that

cleaner. Pleneria don't have that because the way at least the ones we study, there are sexual pleneria. We 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

regenerate. which means that the body continues the soma continues through the generations. So any mutation that didn't

generations. So any mutation that didn't kill the stem cell that it 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

the body. So they keep accumulating mutations. So for 400 million years this

mutations. So for 400 million years this thing's been accumulating mutations, right? So there are other things we

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

reproductive mode. And it actually has some some massive implications because learning to operate in an unreliable medium, which all biological material is unreliable material, but but pleneria

bodies are especially unreliable. It

means that your algorithm has to be incredible. If you know that your parts

incredible. If you know that your parts are kind of junky, it means that your algorithm and there's um Steve Frank uh pointed this out to me that there's a you know um it's a RAID arrays. These

are discs that you can have in your computer that basically mirror each other so that if there's something wrong with one disc, you don't lose the information. So Steve pointed out that

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 discs if you have this redundancy. The

redundancy is in software basically, right?

>> So here's what you have. You have

pleneria have this amazing capacity.

Part of it is biological, part of it who who knows to make the correct thing even when the hardware is pretty unreliable.

But we found a way to reprogram it.

>> And before we get into that, you guys did these experiments going on 15 years ago. Yeah.

ago. Yeah.

>> And where is this bioelectrical memory?

Where is that located?

>> So it's distributed throughout the entire worm, you can see it. We have

photographs and videos of it using voltage sensitive fluorescent dice. It

is basically located. If you wanted to ask where is the memory uh in the RAM of a computer located, where are 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 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 biomectric pattern we've we decoded we found others before Danny Adams and 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

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. It's an electrically excitable medium.

>> 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.

>> So, so just to be clear because a lot of people think we're using some sort of electrodes or something. Okay. So, there

are no fields, waves, frequencies, electromagnetic radiation. There are no

electromagnetic radiation. There are no electrodes, no zapping. We don't do any of that because our goal is this is this is not um some sort of weird external um

exogenous piece of physics that you now apply to the cells and get them to do something. That is not what this is. The

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

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.

>> Okay.

>> Okay. So we do we use all the tools of neuroscience which is basically ion channel drugs. So electrauticals

channel drugs. So electrauticals basically right ion channel drugs and I think something like the last time I looked that up this is something like 20% of all drugs are in fact ion channel drugs. So there's this massive toolkit

drugs. So there's this massive toolkit of already human approved in many cases um electrauticals that we can deploy in biomedical contexts. We can do all of

biomedical contexts. We can do all of these things and you know fix the brains and normalize tumors. We it's not because we're so smart. It's because we are hijacking an amazing cognitive glue

that is already there and that's this bioelectrical these biological pattern memories. We're just using that as an

memories. We're just using that as an interface to communicate to the system.

Can I I mean I want to ask a really stupid maybe woo woo question and I hope you don't mind like you know there's this whole thing where you know I think in Eastern medicine they talk about the

chi you go and you get acupuncture it's like to balance theqi >> when they talk about that is this the same as this bioelectricity that you guys are measuring. Yeah, it's it's not

a bad question at all. As far as I can tell, it is not the same thing, but I do think it's related. I'm not claiming to

be any kind of expert on GI or anything like that, but I will say that my suspicion is that the kinds of things they're talking about are other things that we also study in the lab that are a

little more subtle. There are patterns in I don't want to say of information because even that's that's not right but there are patterns in physiological spaces that are potential targets of

intervention and these patterns are not physical things. Think of um of a

physical things. Think of um of a soliton or a phonon or a or you know any kind of or or a hurricane for that matter, right? These are there's the

matter, right? These are there's the material or a vortex in water. There's

the material there the water molecules.

Those are physical things. And then

there's the pattern. Then there's the question of well is it going clockwise or counterclockwise? The molecules are

or counterclockwise? The molecules are still the same but it has information that the individual molecules don't have. So there are patterns in various

have. So there are patterns in various kinds of weird spaces metabolic spaces stress spaces and so on. there are

subtle patterns that are even more weird than than biological gradients. And I

suspect that what people mean when when they talk about that stuff in these alternative medicine implications. I

think that's what they're talking about, which certainly interfaces to the physics and the chem and the biochemistry of your cells through the bioelectrical interface, but they're not the same thing. I don't think they mean bioelectricity when they say that.

>> Yeah, that's wild.

>> So, I guess one of the things we can talk we talk about living systems talked about xenobots and mombot, right? This

is um something I would love for you to talk about because it starts getting to application areas and possible ways to use this discovery and this understanding that you've been acquiring. So can you tell a little bit

acquiring. So can you tell a little bit more about where we're headed with at least xenobots and mombot?

>> 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 kind of 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

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

these things. And so the robot scientist platform. And so ours we sort of

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 it will observe what happened. It

will revise its estimate of how well did I do and then do the next experiment.

And ideally the next versions will do it faster and and in higher throughput than human scientists. So if you decide you

human scientists. So if you decide you want to be so so bespoke robotics if you decide hey you know what I want a zenobot that goes around and collects um 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 what you will be able to say to this robot scientist platform. Hey go through a bunch of

platform. Hey go through a bunch of cycles of discovery and make me a zenabot that does xyz. Okay so that's the most obvious way of looking at it.

It's AI automation. Um,

okay. The second way of looking at it is is slightly slightly more unusual, which is this. I predicated this whole

is this. I predicated this whole conversation on this notion of an agential material. This means that you

agential material. This means that you are not trying to micromanage and force the material any more than we should be trying to micromanage chemical states in medical applications. What you're

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 literally. I this is not some poetic

literally. I this is not some poetic turn of phrase because aential 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 you're giving them, they're not going to do it. It's not the same as working with passive matter. So you need to figure out a way to communicate with

them and then you might also be able to exploit their competencies. For example,

in our work, we've shown how you use bioelectrical signaling to, for example, induce tadpoles to make an eye on their gut or in their tail. We don't have any idea how to make an eye. Eyes have that, you know, probably 30 different cell

types. Tens of thousands of genes have

types. Tens of thousands of genes have to turn on and we have no idea how to micromanage any of that. But what we do have is a highle prompt that says to the cells, build an eye. and they already know how to build an eye and it

propagates all the way down to the molecular biology that has to activate, right? So the other thing that this mom

right? So the other thing that this mom is doing is it is learning to navigate the space of possible morphologies, right, of of behavior shaping signals

and it's going to teach us to communicate with this material. So it is a translator interface. It is something that is helping us to convert our goals as engine as bioengineers to the goals

of the cellular collective. It's like um you can think about it as a you know kind of a bow tie. It's the it's the middle of that bow tie standing between us and this kind of alien material.

We're trying to communicate with this collective intelligence that lives in a very weird world that we're not used to visualizing this world of anatomical possibilities. And it's a translation

possibilities. And it's a translation device and it's trying to crack the possibilities of morphagenesis and ways for us to do that. So if you do that, that then of course goes way beyond useful living machines because that then

gets you to regenerative medicine. Once

we understand how to tell cells to build this or that, well then if you want a new limb or you want a new heart or you want a new eye, that's the road to that, right? Is is understand. Okay, that

right? Is is understand. Okay, that

wasn't weird enough. There's a third perspective here, which is the following. And this is this kind of goes

following. And this is this kind of goes a little bit beyond um standard engineering and bio medicine, and that has to do with our future as a species and the ethics of learning to live with

beings who are not like ourselves. Part

of what's going on here is that not just AIs. I mean, I'm sure you've seen all

AIs. I mean, I'm sure you've seen all the discussions about the status of AIS and all this, but but that isn't the difficult problem really. The difficult

problem we're all going to have are the cyborgs, which are the humans that are augmented in various ways, that have sensory motor substitution, that have new senses. Your your neighbor is going

new senses. Your your neighbor is going to have primary senses of the solar weather and the stock market. They're

going to feel them the way that you feel, you know, rough, you know, things and smell. What they're going to be

and smell. What they're going to be living in slightly different worlds.

They're going to have extra thumbs and someday tentacles and the 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 I'm a real organism. This is just a machine. Yeah,

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 zenobots is that there's never been selection to be a good zenobot. 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

there's never been the selection to be a good xenobot and to do all the things that zenobots do. We can talk about if you want where that actually comes from.

So trying to understand where do the goals of such beings come from? How do

we communicate with them? What how do we ethically relate to these kinds of things? And even the momot itself, I

things? And even the momot itself, I mean think about what the mombot is. It

itself is a really unusual alien intelligence where it has a mind and that's the AI component that entertains various possibilities and makes decisions about what it's going to do.

And it has a body. The body is this robotic thing. Instead of trundling

robotic thing. Instead of trundling around in three-dimensional space like like you know kitchen robots or whatever, this thing is navigating in anatomical space. The aectors that it

anatomical space. The aectors that it has are the zenobot cells. the way it navigates anatomical space is by moving around and making the zenobot cells do

new things, right? And so it's this weird hybrid um cyborg technology where it has AI, it has a physical robotics component, it has a aential aectors. So

you and I have them too and ours are under um pretty tight control, right?

Our arms and legs. But an octopus, right, octopus arms have a lot of autonomous activity. So an octopus brain

autonomous activity. So an octopus brain has a bunch of agential aectors where these tentacles, you know, they kind of do what you tell them, but they also do some of their own stuff. The momot has the same thing. You know, the end point

of their aectors are living cells that do all kinds of weird stuff. So that

whole thing is our first attempt to understand a novel partially constructed by us, partially constructed by nature, which are the the zenobots. A novel

being whose job it is to help us in the discovery of science. It's a colleague that's basically an alien, right? that

requires us to really update all of our mental constructs about what machines are, what AIs are, how we relate to them, what does it mean to to have an embodiment doesn't mean to be to be wheeling around 3D space. That's not

what embodiment. That's not all it means. So all of these things so there

means. So all of these things so there is this like layer to it that has to do with the ethics of relating to these beings as a mature species that understand something other than you know standard human and then we have this you know we have machines.

>> You just like blew my mind. I think

everyone's mind is just blown right now because this the whole concept of cyborgs and that we could just go we could just have a whole podcast about that because there are elements of that

happening now in terms of biohackers and people trying to enhance themselves like the human enhancement movement but to be able to do this on a more fundamental

way you know be able to regenerate arms and maybe create an extra arm I mean this was a really big part of this the workshop that I attended ended was that all right we're going to have these new organisms what are we going to name them

how are we going to classify them like how do we decide what comes first then when does decision-m stop and it's just these living organisms are evolving

themselves and there's a lot of ethics behind that what should come into life and not and the most beautiful thing I thought was that you related this to a painting and it was this painting of you

have to remind me but it was a man and then there was these animals and it is like, >> "Oh, it's Adam." Yeah. Yeah. It's Adam

naming the animals in the Garden of Eden. It's a It's based on the biblical

Eden. It's a It's based on the biblical story. Yeah.

story. Yeah.

>> Yeah. Yeah. And just Okay. They're

naming animals. That was the time where it's like, "Oh, 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 Yeah, horses and cows, but like there were taking them for granted, >> taking them for granted and not bringing them into our experience, our everyday experience of making things and living.

>> There's 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 like, oh my god, transhumanism and and all of that, right? A lot of a lot of people have the

right? A lot of a lot of people have the the reactions. I want to point out what

the reactions. I want to point out what this really is. I call this notion freedom of embodiment.

>> And roughly speaking, it means that in the future, you should have exactly the embodiment that you want. It means that not just, wow, I'd like a third, you

know, third hand or the kinds of ridiculous arguments that we have nowadays when people try to change themselves in various ways and everybody flips out about it. In the future, that stuff is going to be completely laughable. If you want gills because

laughable. If you want gills because you'd 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. I think that what we're looking at and again here's the important thing because people see that and they say, "Wow, maybe it's too much.

Maybe we shouldn't be messing with it."

Just want to point out I think in the future and not that long we're talking one two generations at most. I think if if things go well, which you know, who knows? But if things go well, the

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 you realize that, wow, so

back in those days, you step on a sharp stick and you get sepsis and die or you crack a tooth and then you got a toothache and it drives you insane because nobody understands what to do with a toothache and stuff like that.

How do they live like, you know, I remember thinking, my god, how could you live like that? Having, you know, no antibiotics, didn't know how to set bones, just like incredible, right? How

do you live like that? I think in the next couple of generations, you're going to have kids who are going to learn about us and they're going to say, "Hold on, let me understand this. You're

telling me that these people had to live in whatever body they were randomly given at birth with all of the possible birth defects, with whatever kinds of limitations of age, of IQ, of disease

susceptibilities, dumb viruses would take them out, you know, stupid bacterial infection. They just had to

bacterial infection. They just had to live like this in whatever body they they got handled." And they say, "Well, wait a minute. Surely at least somehow this was well thought out relative. No,

no cosmic rays like whatever cosmic ray hits your cells when you're an embryo that that's what decides it. Cosmic rays

>> just random. And then you have to stay like that because we were too stupid to fix it. So here's here's I think what's

fix it. So here's here's I think what's important. If you're freaked out about

important. If you're freaked out about making decisions about your own embodiment and the idea that you're going to have choices about what you make, just compare it to the status quo.

Because you know who's making those decisions for you right now? dumb cosmic

rays that have no interest and no no alignment with whatever your values are or anything like that. That's it. You

just whatever you're you're handed at birth, that's it and good luck to you.

So, I think that's really important to understand that what we have now with all our susceptibilities and our macular degeneration and and dementia, all this stuff that gets us after just as you sort of start to wise up and figure out, you know, hopefully a few things about

life, then suddenly suddenly, you know, you lower back pain and all this crazy stuff. All of that is a complete

stuff. All of that is a complete accident of various evolutionary forces that do not care about any of our hopes and dreams or any of the things that we're capable of. And for the first

time, we're getting an inkling that actually we might have some agency in this. I think future generations are

this. I think future generations are going to think it is amazing that we lived without it and more amazing that there were people who didn't see it and tried to prevent others from reaching.

>> Yeah. I I have a question. I want to stick to the biology question, but I'm kind of curious your point of view on how computation and hardware will change based on our what we're learning about

biology. Like I kind of have this

biology. Like I kind of have this feeling that the way we design chips, the way programs are designed, the way memory systems are designed, all will have to evolve because of how much more complicated biology is than than

computation. And I'm just kind of

computation. And I'm just kind of curious your point of view on it since you are working with these systems that are so complicated and engineering with them is going to require really creating a whole new field.

>> Yeah. Well, perhaps not shockingly, I have a very weird perspective on this that's different than the than the standard view of it. First, first the part that I absolutely agree with is that there are principles that biology

is teaching us that are completely different with how we design and engineer. And I'll just give you one

engineer. And I'll just give you one example of that and then we can talk about the complexity aspect of it. In

computer science, when you program, let's say you're programming in some high level language, you don't worry that your copper is getting too warm or that your registers are going to float off and be something else. What they

have is these abstraction layers that are designed to keep the data exactly where it is. So, they optimize for fidelity of the data. You have error correction codes and they're designed to isolate you from all the different weird

things that can go wrong. That's how we engineer. Biology does the exact

engineer. Biology does the exact opposite. Biology basically realizes in

opposite. Biology basically realizes in the functional sense that there's no hope of that. Biological material is ultimately unreliable. You never know

ultimately unreliable. You never know how many copies of anything you have.

You're constantly getting hacked by some system above you or some system within you or something. And so what biology does is optimize for salency of information, not for the fidelity of it.

Meaning that it's constantly improvising. You can call it

improvising. You can call it confabulation if you want. The way you and this goes back to my point that the genome is not a determinant of what's going to happen. It's a set of prompts

into to the hardware and the process of morphagenesis interprets the genome in various ways. And if you've got a human

various ways. And if you've got a human genome and can interpret it as a human under other circumstances, it'll decide to make an anthrobot. It's

improvisation. It's I've 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 at

every moment. And so every whatever two

every moment. And so every whatever two 300 milliseconds, you have access to the engrams that the past has left in your body, the memory traces. But you don't have access to that past. You have

memories from your past self, which are messages. They're messages from your

messages. They're messages from your past self. Every message has to be

past self. Every message has to be interpreted. You have to be in charge of

interpreted. You have to be in charge of interpreting them. Now, under normal

interpreting them. Now, under normal circumstances, you will probably interpret them the way the same way that your past self did, which means you have continuity, and it seems like, well, I've I've lived in the past because

you're interpreting those memories. But

because information gets lost, they get compressed into a representation, right?

General generalized and compressed. You

have the ability to interpret those memories in other ways. I'll give you just a very simple example. Butterfly,

caterpillar. So butterflies turn into caterpillars, completely different body.

They they dissolve their brain, build a new brain to be a caterpillar, from a caterpillar to a butterfly. If you train that caterpillar to recognize a certain color cue, and then crawl over and eat some leaves, it turns out that the

butterfly or moth remembers that information. Now, one question you could

information. Now, one question you could ask is how do you maintain that information when the brain is getting ripped up? But believe it or not, that's

ripped up? But believe it or not, that's not even the amazing part here. The

amazing part is that just think about this. The butterfly butterfly doesn't

this. The butterfly butterfly doesn't care anything about the leaves. It

doesn't eat leaves. It wants nectar. And

it doesn't crawl. It flies. Completely

different motor pattern. So those

memories don't just get preserved. They

get remapped. They get reinterpreted in a completely different way in a novel architecture. So what computers don't do

architecture. So what computers don't do is take responsibility for deciding what their memories mean. And I'm not saying they couldn't. We absolutely can and I'm

they couldn't. We absolutely can and I'm sure will build devices that do this.

I'm not trying to make some sort of sharp distinction between computers and biology. But right now, the way we've

biology. But right now, the way we've been doing it, you as the user decide what the memories mean. For biological

creatures, you have to do it yourself.

You have to decide what your memories mean. And so an architecture that's

mean. And so an architecture that's constantly improvising, not trying to maintain the memory as it is, but improvising. I'm going to tell the best

improvising. I'm going to tell the best story for the future that I can. And I

don't care if this is how past me interpreted this is. I I have to deal with this now. That is an architecture that's completely different. And so

that's one of many things that the biology is teaching us is that it's not about the fidelity of the information.

It's about the constant creative problem solving and the improvisation. So based

on that, you're right. And I think there are a lot of bio truly bio inpired technologies um that we can make based on that. But I want to I want to um say

on that. But I want to I want to um say something something kind of strange about the other part of what you said.

You emphasize complexity and the fact that biology is very complex. Biology is

obviously very complex and having multiple layers with competency certainly adds to that. There's

something important that happens in biology that is connected to this question of we know when we paid the computational cost of learning to be a good tadpole or a good human. It was

paid in the millions of years of evolution, right? That's when you know

evolution, right? That's when you know this selecting and and throwing away varants like that's when we pay the computational cost. When did we pay the

computational cost. When did we pay the computational cost of being a zenobot or an anthrobot? There's never been any

an anthrobot? There's never been any before. Why do they know how to do all

before. Why do they know how to do all these things? Where does that come from?

these things? Where does that come from?

There's a whole new area that that my group is opening up here in terms of asking these kinds of questions, things that have never been here before. Where

do those come from? And you might think that that kind of stuff, you can view it as a kind of free lunches in the physics sense as things you didn't have to pay for with computation. There are these free lunches that are all over biology.

And you might think that, okay, you need to be a complex biological creature to make the benefit of it. And that feeds a lot of people's desire to keep a strict

distinction between living things and machines. Okay. What I'm absolutely not

machines. Okay. What I'm absolutely not saying, let's be clear because I know I'm going to get hate mail over this. I

always do. What I'm not saying is that machine simple machine metaphors are appropriate to living things, to humans, to any of that. That is not what I'm saying. I'm saying the exact opposite. I

saying. I'm saying the exact opposite. I

am saying that it turns out that even dumb simple systems that look to us as if they are fully describable by algorithms and by the standard model of computer science where you have an

algorithm and the machine does exactly what the algorithm tells it to do.

Right? Even simple things like that are in fact the beneficiaries of some of the stuff that life taps into. I don't think just to kind of 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

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 the mechanistic paradigm that was supposed to apply to them. So there are some people that

them. So there are some people that think that we are all machines and we are all you know sort of the story of chemistry tells our whole story. There

are other people who will say no no no we 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 of the world which are

these dumb mechanical machines and at least they are fully describable by algorithms and they're boring and they do exist and and we are completely different from them and I'm saying this very strange third thing which is that

no actually it goes all the way down and that magic that exists in living forms is actually pervades as far as I can see almost everything maybe everything so

that's something else that's important that I don't think it's pure complexity as we think about building computers and machines and so on a lot of people have this computationalist uh perspective

where it's whatever intelligent uh whatever because of the algorithm it's running if we find the right algorithm be it a machine learning algorithm or some other kind of AI thing because of

that it gets to have all these incredible features and Maybe it will be immorally have moral status and so on.

My perspective now is it might but it won't be because of the algorithm. It's

going to be in spite of the algorithm.

Literally it's the spaces in between the algorithm that lets it do all the things that is interesting to us. Just like for us, do you and I have to obey the laws of biochemistry? Yeah, we do. But that's

of biochemistry? Yeah, we do. But that's

okay because in the spaces of those laws, there's tons of cool stuff that we can do. And it's the stuff in the spaces

can do. And it's the stuff in the spaces of those laws that makes us special. And

it appears that the amazing thing to me and this is what blows my mind on a daily basis is that that appears to go all the way down.

>> Yeah. I mean definitely mind-b blown and you're talking about like this can change everything name of the podcast grow everything. One of the things that

grow everything. One of the things that I would like to wrap the question part before we get into the fun quickfire question part is um tell us a little bit more about the application areas. Living

agental materials for example. There's a

lot in science fiction. There's in the book Terraformers, there's a bus that's alive and it has feelings and consciousness and but it also takes people around and it can talk to people.

We talked to Joshua Bongard, one of your partners, partners at Fondest Systems, talking about having a house that has an immune system.

>> Share a little bit in your wildest dream like and where do you start? Like where

do you start? I know you mentioned environmental cleanup. That's a great

environmental cleanup. That's a great admirable application to this, but tell us a little bit more about like where can we get started and you know what can the world look like with this?

>> Well, the first thing so I'm a big science fiction fan. I was raised on science fiction. I I love it. I I would

science fiction. I I love it. I I would just say that it's a very important mindexpanding tool when you're young to get into these things. But I think it hasn't even, as far as I can tell, hasn't even scratched the surface of how

weird things are really going to be. The

reality, as often happens, the reality, I think, is is going to be much weirder than than anybody anticipated. Where I

would like to start is first and foremost and I mean it doesn't matter because all of this will go in parallel I'm sure but to me one of my focus areas is biio medicine because I think the amount of suffering out there is

incredible and I'm not even a clinician okay I don't I don't treat patients but the emails I get every day for every one email that says frog skin is scary and you shouldn't be doing these things and

it's too much and whatever I get 10 emails that say what the hell is taking you so long I've got a spinal cord injury my kid has a birth defect, somebody else has cancer, somebody else has whatever. What's taking you so long?

has whatever. What's taking you so long?

When are we going to have some solutions to these things? And so I think, you know, this this the future of biio medicine is going to look a lot more like sematic psychiatry than it looks like chemistry. I think the goal is not

like chemistry. I think the goal is not going to be to manipulate and force specific biochemical states. I'll just

make this analogy that if you picture, you know how uh what programming looked like in the 40s and 50s, she's sitting there and pulling wires, right? This is

how you had to program in those days because you had to interact with the hardware. Nowadays, you don't get out

hardware. Nowadays, you don't get out your soldering iron every time you want to go from Photoshop to PowerPoint because you're right. You don't have to you don't go rewiring the thing because because we've learned that if you have a reprogrammable medium, there are high

levels of interaction where you can talk you can manipulate the memories it has.

You can give it signals. It has some degree of intelligence and decision-m whatever. biology is is like that on

whatever. biology is is like that on steroids that basically if you think that everything we can do is going to be reachable by communicating with the 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 competencies 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 an anatomical compiler so this is the idea is that you sit down you draw the plant animal biobot 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. That's

the 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

communicate. It's not a 3D printer. It's

a communications devices. It's

translated 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 are nice enabling technologies, but I don't think the 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

come from. It's going to come from the application of the cognitive sciences to the amazing material of which we're made. And so that means that birth

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 non-issue. There there will be some

non-issue. There there will be some other things that are left that are different, but all of those things are are going to be solved. That's where my emphasis is um to start with. And then

of course there's the environmental stuff to have agential sensors such that you're not deciding what you're going to measure about complex things like ecosystems or whatever but you but the front end of your sensor is itself a

living system that can tell you you could you're using it again as a translator 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.

>> Yeah just amazing. So, we always end these interviews with some quickfire questions. We try to keep them snappy. I

questions. We try to keep them snappy. I

am 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's one question you would ask a regenerating limb?

>> Uh, why didn't you think you could do it until we showed you how to do it?

>> Good one. Okay. What's the weirdest living organism you've ever created or imagined?

>> Wow. uh imagined is uh the weirdest thing we've created so far are sets of zenobots connected by an AI that sort of

functions as um like a corpus colossum between your two hemispheres to bind individual bots into a larger scale collective intelligence. So so it's an

collective intelligence. So so it's an AI interface between two biologicals allowing them to sort of fuse into a novel being.

>> Amazing. By the way, 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

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.

>> 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. They don't have the imagination

healthy. They don't have the imagination to go, you know, when you have kids and you're and there's something wrong with your kid, you're going to be running to the hospital. You're not going to be

the hospital. You're not going to be sending me nasty emails. You're going to be running to the hospital praying like hell that somebody over there figured something out. And guess where that

something out. And guess where that comes from? And so, as long as we have

comes from? And so, as long as we have children's hospitals and pediatric oncology and things like that, this work has to be, you know, it it has to be done. People just don't have the

done. People just don't have the imagination to understand what is at stake here when they complain about these things.

>> Okay, next uh quick take. One

bioelectric metaphor that your students quote back to you.

>> Well, they talk about collective intelligence a lot. I'll just uh I'll I'll I'll tell you know the another one that I really like. I've said recently that in this platonic space that

includes mathematical objects like numbers and things like that that you know the mathematicians study the low agency kinds of things like numbers and so on. But then there's also these high

so on. But then there's also these high agency forms that are basically kinds of minds right and one of the mathematicians in our symposium said how do you know they're low agency? Don't

you always say that you have to test these things? And I say touche. I You're

these things? And I say touche. I You're

right. I don't. For all I know, they might be. You're absolutely right. I We

might be. You're absolutely right. I We

should do experiments and we are now doing experiments.

>> Amazing. Amazing. Okay. You mentioned

that you love sci-fi and so we would love to know one science fiction story, book or film you feel gets the body mind problem right.

>> Okay. Uh I'll tell you the story. I'm a

little embarrassed in that I am not sure which story it is. I remember it distinctly, but I am 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

core. Okay, they live down in the Earth's core. They're incredibly dense.

Earth's core. They're incredibly dense.

They have a gammaray 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

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. It's just they're so dense compared

it. It's just they're so dense compared to us. We're like a thin um mist on top

to us. We're like a thin um 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 walking on is covered in like this gas that has all kinds of little patterns in it." And like, "Well, yeah, who cares?"

it." And 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 aential. They almost

are doing things. They're moving around.

They kind of keep together. For how

long?" Well, for about a hundred years.

Well, that's nothing interesting can happen on that scale. But no, no, but they seem to be doing things. It's

almost like they have goals and purpose and and the others say to him, "Look, we are real physical organisms, okay? We're

beings. Patterns in a gas can't have goals. They're just data effent patterns

goals. They're just data effent patterns through an excitable medium. They can't

be real." And so this is very important and this is a bunch of work that we're doing recently is trying to dissolve this notion between machines and data, between thoughts and thinkers, right?

It's a matter of perspective whether something is we are also patterns. We're

metabolic patterns temporary, right? 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 computer or whatever. How much mind blindness do we

whatever. How much mind blindness do we have that we are not good at recognizing these things? That to me is a very was a

these things? That to me is a very was a very profound story and I really wish I remember who who wrote it. I'm not sure they it had all this mindbody 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 the medium or an actual an actual agent.

>> Wow. Okay. Listeners, if you know what that story is, write to us. We'll give

you $50. I don't know.

>> That's that's the Easter egg.

If you had to pick one word for the next frontier of biology, would it be interfaces, agility, shape? What what

would it be and why?

>> If it had to be one word, I would say mind. Mind. Mind. Okay, that's a good

mind. Mind. Mind. Okay, that's a good one. Yeah, this I think that really

one. Yeah, this I think that really helps bring it all together.

Professor Mike Leven, this has been so amazing to have you on the Grow Everything podcast. You gave our

Everything podcast. You gave our listeners so much to think about, so much to process. I think people will have to listen to this episode three or four times. I know I will do that

four times. I know I will do that because there's a lot of gems that you've shared. Very poetic. I mean,

you've shared. Very poetic. I mean,

there's so many new terms that I come across. So, thank you so much for taking

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.

>> 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

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 the undergrads the grad students the posttos 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 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 >> the collective consciousness

>> yeah yeah exactly exactly >> amazing amazing >> exactly yeah thank you so much >> yeah thank you all right we'll talk to you soon again This episode is sponsored by Messaging

Lab. At Messaging Lab, we translate

Lab. At Messaging Lab, we translate complex science and economics into compelling business narratives, and we have done so for the most successful biotech forward companies across pharma,

agriculture, personal care, and beauty, materials, and the list goes on. We're

here to make sure your ideas not only get heard, but resonate with your audience. So, if it's time to amplify

audience. So, if it's time to amplify your company's voice and elevate your business, visit us at messaginglab.com.

Let's grow.

I mean, this episode was so deep and we barely scratched the surface of what we could cover with Mike. 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 and also to him because we barely got into it. I mean, I think If you're a fan of this stuff, you

should do some searches and listen to him. Like he was on the Lex Freedman

him. Like he was on the Lex Freedman podcast for like three hours and again still doesn't even cover all of the territory that he is exploring. What do

you think?

>> 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

notes. the terminology he uses, it makes it more accessible to me personally and I have to relisten to it a couple of times to see how these connections are

being made because there are such deep concepts here, right? This idea of xenobots for example, you know, when I went to the workshop, I learned about xenobots could mean two things, right?

Zeno is the scientific term for frogs, but also means other, you know, outside of xenophobia. So, we know about that.

of xenophobia. So, we know about that.

And you've learned a little bit about xenobots. So tell me a little bit more

xenobots. So tell me a little bit more from your perspective. What do you think that is?

>> Well, I like the term biiobotics. I

mean, there's also like a term out there, soft robots, which 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 of cells. And this idea of building like an

cells. And this idea of building like an anatomical compiler just really blew me away. I said it I think in the podcast

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. Like

that's really the kinds of things that Mike and his team are working towards.

So again, very deep thinker. I think I mentioned it or someone had told me he's probably someone who's going to get a Nobel Prize. his science and what he

Nobel Prize. his science and what he does is something people are going to be talking about everywhere all the time because it's just so deep.

>> Yeah. And it starts giving some explanation to phenomena. I mean like the whole part of the episode where we talked about the pleneria the worms how

they were able to create two heads on a worm. You know at first they're like you

worm. You know at first they're like you know bio electricity they're talking about that and I was like hm what does it actually look like in practice? And

then when he said electrauticals, I was like, damn. I was like, what is that?

like, damn. I was like, what is that?

And I'm like, it sounds like electruticals can really reset a lot of our biological systems to do what we needed to do or wanted to do if it's like, oh, we want to create another

head. I mean, I I'm getting too crazy

head. 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

different ways. And I think that's a very incredible useful tool especially for tissue regeneration.

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

>> Oh yeah, for sure. You know, there's certain things that we talked about just in terms of imagining things besides biomedical which is the need and for this because you're talking about tissue

regeneration, but the idea of creating a immune system for your house. So you

have like 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 and the thing is with this essential material 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

environment inside. That was one of the applications that really stuck with me.

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. No, I was just going

to say that like what Levan and his team are 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,

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 applications.

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.

>> 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 the rabbit hole of Michael Levan and his lab and all the work that they're doing. And so to be able to, you

they're doing. And so to be able to, you know, use your imagination is really great. And I I think this is also very

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.

>> What's sentient is life. It's biology,

right? to be able to combine the two.

And you know, we started talking about that computationally designed organisms, right? We did this crazy exercise during

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. Did it 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 wetwware and have something

but you at least know what the recipe is to create an organism. And so this is the unlock. You know, people like, oh,

the unlock. You know, people like, oh, you know, AI is going to 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, what people are using these, it's like smoking mirrors, right? It kind of gives you the impression that there's sentience and decision-m, but really,

it's Yeah. predictions. Exactly. That's

it's 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. This is the world that you listener live in by listening to the Gre 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

doorsteps. All you have to do is listen and stay tuned.

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

>> Yes, absolutely. Continue listening.

Follow us on YouTube, Instagram, all the things. We love talking to you. We love

things. We love talking to you. We love

talking about AI, biology, and all the things. So, that's a pod.

things. So, that's a pod.