Simulating Evolution: Playing God or the Next Frontier?

Danny Crichton:
Hello and welcome to Securities, an audio and video podcast, plus a newsletter focused on science, technology, finance, and the human condition. I'm your host, Danny Crichton. And today we're talking about artificial life or A-life. Artificial life encompasses a wide spectrum of computational approaches to life with consciousness and sentience, and we'll scope it more and talk about where the frontiers of research are in today's episode. Joining me today we have Lux scientist and resident Samuel Arbesman, as well as special guest, Olaf Witkowski. Olaf has an extensive biography. One of those you really have to wonder if there wasn't a medley of six artificial life forms all rolled into one person. But most notably for our purposes, he is the director of research at Cross Labs, an AI research institute in Kyoto, where he leads fundamental research and artificial life and ethical AI. And he is also the current president of the International Society for Artificial Life. Olaf, welcome to the show.

Olaf Witkowski:
Thank you for having me.

Danny Crichton:
So Olaf, let's start with you. I'd love to just get a general sense of the idea of artificial life. What is it? And how does it differ from the much more commonly discussed artificial intelligence?

Olaf Witkowski:
Well, the way I see it is that artificial life is larger than artificial intelligence. I guess you could say that a reasonable definition is that it's life as it could be, as opposed to life as we know it. And it englobes a difference diverse forms of intelligences that are possible in the universe. And that we can possibly build from scratch by understanding their fundamental mechanisms.

Danny Crichton:
And what's interesting in this field today? So when you look at life as it could be, I think of exobiology, I think of life in outer space. I think of life on computers. I think of that episode of Star Trek: The Next Generation, where there are two-dimensional life forms and they're scanning it at the wrong edge and so they can't see it. That was probably season five or something like that. But obviously life as it could be is such an open-ended, open concept problem. How do you break up that research field into different areas?

Olaf Witkowski:
Yeah, I guess it did that naturally. The questions emerged by trying out, mostly it's a community of builders. So all the scientists are working on their own models, they're building them with different pieces. Mostly right now it's software, but we also use some wetware, chemistry and also hardware, electronics. But also weird materials that you never heard about. It's just about replicating the properties that we see out there in living systems and trying to bring that inside within models that we can poke at and understand better how they work.

Danny Crichton:
And Sam, you've been involved in artificial life and I feel like you're interested in everything. But one of your interests has been artificial life. So I'm curious, how did you fall into this category? How did you start learning about it and what interests you today in the field?

Samuel Arbesman:
Yeah, that's a really good question. I think I got into it well, I was very interested in the origins of life, and when it comes to the origins of life, the question becomes, okay, how do you move from chemistry to biology? And then what does it actually mean to go from chemistry to biology? What is special about biology? And then alongside that, I've just been very interested in about the ideas of evolutionary biology, but then also taking the algorithm of evolution, which is this process of natural selection across the population and embodying it within a computer, within software. So that's kind of the things I've been very interested in. So of course, since people started using genetic algorithms or genetic programming, we've made great advances in this idea of evolutionary computation.
At this point now, it's a very powerful optimization technique, which is essentially what evolution is. It's this process of finding solutions to problems. In this case, in real evolution, it's like finding a solution to, okay, how can we get this species to be the best bit or reproduce the best? And I'm probably anthropomorphizing evolution a little bit, I don't know how to think about that. But yeah, yeah, in terms of the different things right now, I would say one thing that is really exciting to me within the field of artificial life right now is this system called Lenia or Lenia. And the idea behind it is it's actually a modification or kind of like a fancier version of John Horton Conway's Game of Life, which was I guess in the '60s or '70s, which is this cellular automaton.
This idea of like okay, you have a grid of squares that can be on or off and they change from one time step to the next, according to certain rules. And it turns out with the very simple set of rules, you end up getting these really complex patterns that emerge from just the simple rules. And so many people have been playing with this and there's lots of interesting things there. And then more recently, Lenia is saying, okay, let's make it smooth. Let's make it like continuous time, continuous space. And it turns out if you do that, you end up being able to create... There are these, I guess organisms and species that pop out from the rules and the pattern and... Or am I describing Lenia properly? There's a lot of really interesting features of it, so Bert Chan, he's the one who developed this. But yeah, I think he found over 100 different persistent pattern organisms essentially in Lenia.

Olaf Witkowski:
Yeah, we can definitely show those on the podcast or mentioned some videos, those are very pretty to look at. It's really beautiful, from very simple rules, what can emerge. And really, like you said, Sam, this came from very early on, maybe as far as John von Neumann's first self-replicating machines, his cellular automator, very simple creatures emerging from a very simple modest computer. But there were many evolutions based off that. And now we have something that is much more complex. I would say that the first phase of any of those simulation is that you start having something that maintains itself, a pattern that self-corrects, self repairs through time. So it maintains a sort of identity as you watch it evolve. And then yeah, the next step is really the evolution of it. So they sometimes can be selected for, and that's super interesting.
So you can have all the evolutionary dynamics that we still study until now and there are new things we can discover with that. And yeah, this is not the only one. There are so many forms of artificial living systems now. I guess Carl's Sims was those kind of embodied robotics early on in the mid '90s, and you have Tierra that are much more like the cellular automata, but replicators that are trying to invade your PC basically. That's also mid '90s or late '90s with Tom Ray's Tierra. You have Avida is a very similar idea by Charles and Chris and others at Michigan, yeah. So many of those things, yeah, just it's amazing that those organisms can light of it, like viruses maintain themselves, just take advantage of the resources are in there to maintain their own patterns. But then they start evolving and that's purely amazing, what they can achieve.

Danny Crichton:
So when we think about cellular autonomous, I think of Conway's Game of Life. He does use the term life and this is a, for those who aren't familiar with it, is a very simple rules-based where if there's sort of, oh God, now I'm going to just trap myself in having to explain cellular automata.

Samuel Arbesman:
So the cells remains alive, I think if it has two or three neighbors or maybe three or four neighbors. If it has less than that, it dies. And if it has more or if it has a specific number and it's not alive, then it becomes alive. And if it's too high, too dense, it also dies. That's very hand wavy, but approximate [inaudible], yeah.

Danny Crichton:
Exactly. So if you think of a square and a nine by nine, the square in the middle is the cell, and I recall it's two to the eighth power. So you have eight cells nearby and there's just different rules based on which of the cells that'd be on and off, and there's a lot of delightful patterns that come out of that. Now the question I have is when we think about artificial life, does that meet the bar? When we define precisely what's the minimum life form, if you will, in the artificial sense? Does Conway's cellular automata, does that count as artificial life in the modern way we approach the field?

Olaf Witkowski:
Yeah, it's a very tricky question. I think the language is there, is confusing too, because it's Game of Life. And actually, those cells are just on and off and life is not there yet when they're on or off. But we call them alive, so it's causing extra confusion there. But yeah, so those can turn on and off and then you can have amazing things that can appear, that are patterns that are maintaining themselves and propelling themselves through the things. And those are called gliders. And you have whole families and classes of species like that, that can do amazing things. But just propelling yourself and changing yourself slightly through different states where they cycle through states, that's already amazing. They sense their environment, and through their own behavior. And yeah, like you said, there's a circle around them, their sensorium that they are able to perceive and can react to.
And even more complex creatures like that have a problem, is that if they encounter anything weird, they are going to disappear. They explode. They can't maintain themselves beyond some kind of level of error correction. If the error is too big, they can't correct it and they just die off. I can't correct for this. It's much like humans. But those are very primitive. It's just about maintaining the pattern. What they evolve into is they start making small memories, they start maintaining more and more memory about their environment. They can model, at some point, if there is enough memory and they have the mechanisms that go with it, they can model their environments and react, predict the environment, which is much of what we humans do. We try to have laws of physics and science in order to predict better our environments and react to it and do amazing things. And that's what we see appears in those very simplistic model. And that's really exciting.

Danny Crichton:
When I walked in here, I was thinking like cyborgs and artificial life in this way. But what I'm getting at is more of a Turing machine, which is you're starting to think about actual primitives and very definitional foundational objects that allow you to define life and non-life very tightly. So to pull out a couple of pieces that you just said, memory or sense of self. So even though these are little dots that are literally scanning, in Conway's you're calling them gliders, but in Conway's Game of Life, there are certain rules that allow this object to move across the screen. And in that case, there's the sense of identity, there's a sense of being.
And even though it's just a couple of bits, we're starting to get a very small lens into what is the most primitive life form you could possibly have. Obviously there's more and more. But from my perspective, I think what's interesting here is you have this sense of memory, the sense of genetic or evolution. So there's sense of here is my identity and it can change over time. And then you call it a sensorium. But this idea of understanding I'm in an environment of context and that influences my outcomes. Did we cover the main facets of artificial life, like memory, some sort of genetic evolution aspect, some sort of sensorium or environmental component? What else is there that would be primitive for artificial life? Are we missing anything?

Olaf Witkowski:
Well, I guess there is the next steps that the creature can go into that is very exciting. So real quick, this could be learning and different ways of learning about your environment. And the exciting thing is that you didn't encode anything. All of this creature is just, you keep it like you keep a cup of tea, you just watch it. And at some point something becomes intelligent inside. How exciting is that? And it can become so intelligent, it can model the world and do the same things as we do. And essentially what happened through evolution. We were studying really this pattern of learning and learning to learn. And possibly patterns that keep learning forever. And it's not very easy to program. So even the best computer scientists, best algorithms nowadays stop learning at some point. They keep improving, but at some point they plateau and they become boring. They achieve their objective function and now they're not creating anything new. This creativity is something that is very central in artificial life and has real potential compared to other types of avenues, I would say that is-

Samuel Arbesman:
And I think that also distinguishes between, I was talking about, I mentioned evolution is this kind of optimization algorithm and it is that, but you're also speaking about evolution as the raw materials for open-endedness. And I think that open-endedness is a very, very interesting aspect of artificial life. And this is also one of the current fields and the current avenues of research that people are spending a lot of time with.

Danny Crichton:
Olaf, I jumped so far ahead, so we sort of have an understanding of artificial life, some of these fundamentals and the building blocks. How did you get into this field? Before we jump into all the complexities of modern world. What drew you into it? How did you stumble upon it? This isn't the kind of thing that I went to middle school and they teach an artificial life class.

Olaf Witkowski:
Yeah, it is not. And it should be. Maybe we'll talk about that. But yeah, I really want to really talk about A-life. And it would have been so fun as a kid if I could have played with those models, I would have had eternal fun just learning new patterns and trying out new things. And there's so much you can do with just this very simple kind of simulation. It is just code. Kids can play with that and achieve amazing things. But the way I guess I got in there was, I guess I'm super excited by language or languages. I learned a lot of languages as a kid. It was my main hobby.
And then I guess I was so excited about how come we have those languages are so complex. And how did that happen? So I guess I tried to program my own. There was Eliza back then or other variants of that. You had one with MATLAB. You played with that, tried to code your own. But yeah, it was very difficult to achieve something that would be natural, that you would have in the machine with rules, emerging from rules possibly that you could treat as a friend and partner, that would respond to you and you would learn from each other. So it sounds like the kid that wants to build a friend, it's much like that. It's I guess when you're a scientist, it's...
And I guess I just met people who were interested in the same questions about nature of cognition, minds, science, ethics and the artificial life community was a very exciting one when I met them. I guess I went through both routes. I went into academia, but at the same time I had a bunch of ventures that were doing this kind of science and machine learning kind of stuff in yeah, research. But I guess my main passion is this kind of universal, yeah, what are universal languages? And yeah, what are invariants in language in any universe that you would simulate? What are Asian languages? So what's universal about communication that would reoccur again if you went ahead and reran the tape of life again? So those kind of questions.

Danny Crichton:
When you think about the community for artificial life, is it a space that's growing? Are there more and more people interested? Is it shrinking, growing, changing, evolving itself? Because in some ways any community, an organization, a sort of organizing of life in of itself, how's the community doing today before we jump into all the complex challenges today?

Olaf Witkowski:
Yeah, maybe Sam can say what he thinks too. But I have this feeling that a bit like my name Olaf was very difficult for where I live for people to remember. All of a sudden there was a movie that had a character recently for kids, and all of a sudden everybody knows it's not exactly me. But yeah, it's easier to introduce myself like that.

Danny Crichton:
That would be, I assume Frozen.

Olaf Witkowski:
Exactly, yeah, yeah, yeah. [inaudible].

Danny Crichton:
Olaf the snowman, yeah, yeah, yeah. There we go.

Olaf Witkowski:
That's me. Letting it go, yeah, very zen attitude. But I guess it's the same with AI, that developed so much and has so much, I guess what is it? Friction, I guess congruence with artificial living systems kind of concept and theorems that now that the language models are a thing, large language models, you can explain that to anyone, to elderly people. You can say, "Okay, yeah, it's like that except..." And you can start from that. So people are excited about AI and now it gets easier to go into that. And in my view, artificial life is really this next step or actually many diverse different steps for artificial intelligence. So that's my view of how it has evolved recently. Sam, what do you think?

Samuel Arbesman:
Oh, certainly, and there was this early growth in the '80s and 90s and I feel like parallel to the AI boom and bust, there was this period where it was not as hot and not as many people were working on it. There were a number of people still carrying the torch of A-life and it continued. And then I do think, yeah, with this resurgence of generative AI and more people getting excited about AI, they're beginning to think, okay, yeah, where can I go next with it? How do I think even more broadly about life? Not just intelligence is very important and very interesting, but that's a subset of all the behaviors of life. And so I'm hopeful that it will continue growing. But it definitely has had ups and downs similar to AI as well.

Olaf Witkowski:
Yeah, I was just going to say, there is so much interest I see in my students. They're very excited about A-life right now, and I see yeah, a growth in that with current advances, rather than being interested in something else. I think it's growing very nicely.

Danny Crichton:
Well, let's dive into 2023. So here we are, we're in the height of the AI craze, lots of money, lots of people, lots of talent showing up. Thinking about generative AI and all these new technologies. First of all, what's differentiation between artificial intelligence and artificial life? You said at the beginning it's much more wide and wide angled. Is it also seeing the surge right now? And what are the issues that people are worried about today in the artificial life community? Both from the research side and the ethics side.

Olaf Witkowski:
Right, yeah. The ethics side is a monster of its own, very interesting. I think artificial life has real tools for that. In general, the difference between A-life and AI, I really think it's larger. So AI, its goal is to model human intelligence. That's it. So you simulate human intelligence. That's the goal we have in AI. So I work a lot in AI, so we try to help the industry and society here. And yeah, you can build tools. But what you're doing is really you're trying to take some stuff that humans can do really well and you're trying to do that instead of them or help them extend that. What you're trying to do with A-life is in a way includes that, but it's really not only human. You look at diverse intelligences. That's the main thing we do. Diverse types of algorithms that can achieve similar properties, but also diverse media.
So you wouldn't build it only with, it works really well with GPUs now. So you can use GPUs and Linea, oh right, and other Swarm simulations that we do like Swarm intelligence that uses that. But you don't have to. You can simulate it with artificial chemistry, you can simulate it with actually... Or yeah, different substrates, mixed hybrid systems, which is some of the research we do. How do you make those systems collaborate to different media? We're talking about putting basically wood and water together and trying to make computation out of that instead of just one of them. And they both compute. A longer talk about that. But yeah, I think this hybridity and the diversity is really present as something we think about in unconventional ways in artificial life. And AI is into whatever works best right now because you have to achieve, you have those standards, which is excellent, I like it. But you lose some of the heterogeneity of the discovery of computation, which we are more focused on.
One addition that leads us to the ethical point, I think is what you talked about before. You asked me about the sensorium, the umwelt around the glider, around this artificial agent, how they perceive reality. I think A-life is interested, because of its history also in cognitive science, neuro phenomenology, phenomenology, study of subjective experience and so on. Studying the first golems, and what are the implications philosophically of those? We really study not only the creature, the artificial creature from an objective point of view. So you are God, looking at your creature that you created on your PC. But we also are interested in the subjective experience of being inside the simulation, in the cellular automaton, like being within those running simulations and looking at the glider from the inside. Or which is the third point of view, being the glider. And you can actually study those concepts mathematically. It's not easy.
It sounds easy when I explain it like this maybe, but actually we don't have formal... What is it? A formal framing characterization of gliders. It seems easy, you can just point at it, but it's magical. You can point at it, but it's not characterizable in an easy way. It's so mathematically thrilling. I can't express enough the excitement about this. You can point at the agent, but you can't mathematically write down how exciting is that? And yeah, I think this subjective perspective leads into, yeah, what is it like to be this artificial being that you can create? And yeah, sure, the glider is a bit simplistic, but you can create fairly complex things. And I think artificial life gives you the tools to think about that very deeply. And because you have everything in front of you, you have all the ingredients. So it's now about thinking deeply. Yeah, what is the math of the philosophy? What is it the mapping between the math and the philosophy now?

Danny Crichton:
And Sam, you and I were on a recent episode of the podcast and we were talking about Blindsight, a science fiction book. And one of the things you corrected me on was this distinction between intelligence and consciousness, and specifically my sense of emotion, the fact that emotes indicates intelligence. And you were like, no, no, that's actually our perception of intelligence. The fact that you feel like it's more likely to be intelligent just because of a certain action does not actually prove anything mathematically. And so when I think about this glider situation with some artificial life, I think of something that's very similar.
Like here's this agent that's moving across the screen and moving through a space. I perceive it as intelligent because it seems to be moving around in the same way that the security camera and the office moves around. And it seems slightly intelligent, someone's watching me in a panopticon. But the question I have is that life? Is that just our giving it an name in the same way that Conway gave his Game of Life the name of Life? Are we looking at this and going, "God, there's something intelligent here just happening on its own, this consciousness,"? Or is it the golem as you were talking about. It's an algorithm, it's running, it does things, creates unique fractal patterns. But we're seeing signal out of noise that doesn't really exist.

Samuel Arbesman:
It's a good question. I think first of all, just to define life, what is life? That is very far from a trivial task. It's like does it encompass reproduction? Does it encompass metabolism? And I'm not even talking about life as it could be. I'm talking about how do we distinguish between just some weird chemical reaction and something like a cell. What is the difference? And then of course then you have all the edge cases of what's a virus? And I agree with you that I think in the same way that we're predisposed to notice faces and objects and clouds or to think that something moving across a screen has intelligence, we notice things that feel lifelike.
And so I think we always have to be careful not trying to impute too much onto whatever we see. And so I think it's less about like, oh, this thing that I made is alive and more about this system that I've developed can help me to understand and interrogate these following specific attributes that I think are important for life. And going back, I think Olaf mentioned this earlier, a lot of artificial life systems, they're almost like toy models. They're saying, okay, let's take the messiness of life that we see and strip it down to the bones. And say, okay, here's the things that we care about. I.
T might not be alive, that we're creating, but it's going to give us insight into what is life and how does life work and how does evolution work or whatever they are. How does emergence occur? And so it's less about, and sometimes I think some people are actually trying to build life within a computer and that's really interesting. But I would say many other times and possibly even much more common is about, okay, what are the features of life that I really want to understand? And then just trying to build a system to understand that. So it's less about, okay, is this thing that I'm looking at, is it truly alive? Is it not? It's an interesting question, but it's often kind of like, it might be the wrong question when it comes to artificial life. Olaf, is that a reasonable way of thinking about this?

Olaf Witkowski:
I think it makes perfect sense. It's such a difficult question to define life. So many people have tried that. I think Schrödinger's definition of self-assembly against nature's tendency to what is it? Yeah, towards disorder or entropy-

Samuel Arbesman:
Or he said that the genetic information would have to be part of some aperiodic crystal, which basically he predicted there would be something like DNA, even though this was before-

Olaf Witkowski:
That's truly amazing. It's hard to graph the genius of [inaudible] crystal way before-

Samuel Arbesman:
Yeah, he was amazing.

Olaf Witkowski:
... that's right. So yeah, there was the idea that preceded exactly right, the computational concept preceded the discovery of the actual thing. And that's exactly what artificial life is attempting to do here. It's looking at properties instead of looking at, there were plenty of other definitions. So each NASA assembly we have, there's someone who is going to say something slightly different. Early on, Jay Joyce was all about the chemistry, self-sustaining chemicals that are able to achieve evolutionary dynamics or something like that. And every time there is something, but then it's only chemistry. How about this other medium and so on. So you might be looking at only for [inaudible] probes and all those maybe machinery or maybe different types of mechanics.
I think yeah, when you look at that, it's better not to say living systems or it's okay to point at things and say it's life. But you want to really say start from the properties and see where you get. And that's what we do. So it's really bottom up kind of perspective. I might say something controversial here, but I think consciousness is the opposite. It's easier to define because everybody has one. I know everybody is alive, but we don't understand what that means. And experience, you have an experience. So at least where we start from. And if anything, science is intersubjective and starts from this experience. So as I said, this is controversial. But life, it's much harder and so is intelligence in many ways it's broken down. There's no such thing as intelligence. It's really about the ability to do this, that different types of learning, representation, ability to achieve different types of symbols, self-reference, universals of communication. We have to break that down to understand it and that makes the most sense, yeah.

Danny Crichton:
Well if you're a philosopher of consciousness, please don't email me. Or if you do, we'll try to respond or something. I don't actually know. So life-

Olaf Witkowski:
[inaudible] very difficult.

Danny Crichton:
... I enjoy this. Exactly. This will be the one that has the most angry mails. But let's move on to some of the ethical issues because I think you're getting at some of these philosophical challenges, some of these ethical issues. And certainly in the artificial life community, I think this has to have some of the most open-ended challenging almost black box, and probably in some ways unanswerable questions around life and what is the meaning of this? And particularly if you have it on a computer, I think of was something as basic as like I just pulled the plug in the computer, am I destroying all the life that is on it right now? What is happening? But when you think about 2023, what are some of the top two or three ethical challenges that the community of artificial life is thinking about, wrestling with, et cetera?

Olaf Witkowski:
I think it's as tricky as ever, but it's more visible now with the advent of the technologies we were just talking about before, that seem very close now. I don't think they're as close as some people say, but definitely it's getting much more real. People can see how automation may happen in the next few years. And of course, yeah, you can replicate a lot of what we are as living systems or human systems. And like Sam said, I think it's very interesting that we tend to attribute qualities of life, qualities of even intelligence and definitely even consciousness for some, creating big debates, to stuff that don't have them. And I look at maybe a toy or maybe an imaginary friend and maybe I can personify that entity very easily. We tend to do that. And we are a social species. It does make sense in many ways actually. Part of the community studies that.
But yeah, so what are the tools that allow us to study that and talk about it? There are many traps in thinking about those things and their implications in terms of ethics. But I think it's also important that as we become hybrid beings, and I think with those things that we are holding on our hands every second of the day right now, we become augmented and we are hybrid beings. Maybe we transfer in much like some thought experiments. We replace some of our parts more and more by the prosthetics that correspond to them. And that's also something that A-life is very interested in because as you do that, you might have a change drastically in your embodiment, your valve that we're talking about, the sensorium.
And you might ask, yeah, how are those things recognized? If I break someone's phone, am I breaking someone's body? Am I hurting them directly? Maybe that relates to the sensation of pain. It's painful to break my phone. I feel the actual pain. And some of that may be real. There are illusions of agency, there are illusions of subjective, various feelings that are very real. I feel them as real, and yet they are made from scratch. So what are the limitations of that? I think this is what we're exploring I think in the ethics of artificial life right now. And yeah, the extreme of that is ethics of robots. But we look at the whole spectrum really, of hybrid living systems.

Danny Crichton:
Okay, I want to zoom in a little bit on this. So obviously there's a history of cybernetics that goes back decades in the '60s, of human augmentation that turned into the cyberpunkian world, I think of Neuromancer, William Gibson, et cetera. Lots of science fiction is built around either adding appendages or uploading the human consciousness. When you get into artificial life, is it all under the sun is relevant here? Is it that certain categories, like you're talking about adding a part, or if I slough my body and replace it with a cloud-based installation and I become one with the algorithm, is that still within the ken of artificial life?

Olaf Witkowski:
I guess the center of artificial life is building entities that are autonomous from scratch and studying those properties. Now yeah, what happens when you build something that attaches itself to something else? There is actually a big part of the community of artificial life, A-lifers that actually concentrate on that. And it's everyone who studies cooperation. The emergence of cooperation systems that communicate, and what we are looking at is really the emergence of symbiosis in those systems, and it is very simple. So when you have those little creatures in the simplest kind of simulations you can get, you'll have some pieces that might be eaten by other pieces or will combine at some point and start helping each other.
Maybe not at first, but in the end, the trees in which they evolve, involve also some kind of loops, so they sort of merge back. And that's exactly what happened with real biological evolution. But here you can study it in the box, which is one of the big advantages of A-life. You can look at it and invent information, theoretic measures, so some math that you can point at it or some glasses that you can put on to look at it and could be different mathematical tools. But that allows you to see those mechanisms in another way. And one of them is cooperation. And it's such a big question, yeah, how come we care for each other? Yeah, how come there is this emergence of being nice basically in nature or in those simulations that you see? What's the emergence of norms? And yeah, you can simulate those effects. They're fairly complex within the box. I think that's a fabulous tool.

Danny Crichton:
And let me ask you, when you think about, so we're migrating from the individual artificial life, individuality, the autonomy of an individual into, we call it civilizations, a social life, connecting individual entities together. I'm curious both on monoculture, so bring the exact same kind of entity into harmony with each other. So how they interact and then different forms of artificial life, different types of agents, different types of entities, and how they interact. Are we learning any lessons there in terms of from the A-life community or is it mostly focused on that individual agent?

Olaf Witkowski:
No. Yeah, as I said, I think there are a lot of people in A-life that do study, Swarms are the simplest ones. Early on, I guess it was mid late '80s, Craig Reynolds. And then other people have encoded behaviors of simply they're called bird-oids, so the short version is boids. So there are little robots that fly in 3D and yeah, with very simple rules actually, only three rules. So they have cohesion, a sort of gravity, they also avoid each other a little bit. And then they try to align themselves with each other. Only three things, a bit like the game of Go, is so simple and has a lot of emerging properties that are amazing. Same thing, boids have so much interesting things happening in those simple simulations. With those three rules, you manage to simulate Swarms in amazing movies like Batman Returns, I think, and other things later on.
Are very realistic, and also you can dynamically see how those patterns can maintain memories. How exciting is that? They don't have none of those boids, those agents flying around have internal memories and yet they can remember things by combining their behaviors together into patterns that maintain something through time. How exciting is that? Yeah, I think that's the first level of the simulation. But then you can add memory and all sort of things happen, like they hold hands and do things together or they forage together because they can share whatever they have to digest, enzymes and so on. You can simulate cells, you can simulate larger herds of animals. And that's yeah, see how cooperation, also communication from signaling to a bit more complex language can occur within your simulation.
And like you said, you can also simulate human societies and people do exactly that. During the pandemic, a lot of simulations, multi-agent simulations helped us predict very complex dynamics. And what would be, for example, research at my lab, trying to predict what's the best way to behave in terms of isolation. So is it better to stay home for a week and then go out once? Or is it better to distribute that? Yeah, so what's the right distribution of things? Like birds or things like that can be simulated. You can simulate best behaviors in society and so on. It's really fascinating, very powerful. Again, I don't know if there may be other example. I know you-

Danny Crichton:
I think Olaf might be the most excited person, except for Sam, on this podcast in terms of your enthusiasm for so many different parts of science. I want to finish up here because we've already covered a lot of ground. We've covered the definitions of artificial life. We've talked about some examples, some ethical issues, some of the piece of evolution and emergence of cooperation. Given how broad this subject is, how does someone join the community? How do people learn about this? Where do they connect to? Where do folks start? How do they get onboarded into this community today?

Olaf Witkowski:
It's pretty easy. So we are pretty present online these days. So you can join one of the sub communities very easily. If you're into say Linea, you can join one of those channels or there are mailing lists. I would say just look up the International Society for Artificial Life website. And then from there, you have a lot of links to a lot of things happening and come to the conference. I think that's the best experience you can have. I know that students are super excited the first time they get to the conference. I definitely was. And you can see what many people are demonstrating. You'll see simulations, robots, even chemistry.
And there are tutorials there. I think the most fun part of this is that you can very easily get your hands dirty. And you have your laptop, you download the code immediately and start doing it with people around you and discuss about, "Oh yeah, look, this pattern, I haven't seen it before. Have you seen this? How does that work?" And you have all the specialists. It's a very tight community and they're very, very open to new ideas. I think it's super fun really, to join those. And this year it's in Sapporo, just here in Japan. Yeah, exciting place to visit. So if you have nothing to do, it's actually next week. [inaudible] this one? Yeah, yeah, join us.

Danny Crichton:
Sapporo, the winter city of Japan, visiting in July. I have not been there, but it is a gorgeous place. But Olaf, we've covered a lot of ground. Sam, we've covered a lot of ground. So much more to talk about in A-life. We have to have you back on the program at some point. Thank to you both for joining us.

Samuel Arbesman:
Thank you-

Olaf Witkowski:
Thank you.

‍