 Today I want to talk about artificial life, something I've worked on for a lot of years and it sort of underlies a lot of research I've done more recently. Very idea of artificial life is kind of a weird because we normally think of life as natural, nature as full of life, but we need to come up with a definition broad enough so that we can see other kinds of things as being life or life like. And in fact that's the distinction that we'll talk about immediately after. The weak claim is that these systems are merely life-like, the strong claim is that these systems are actually life. We'll take a look at some demos and then try to wrap up as quick as we can. Here's a dictionary definition, the state of being which begins with generation, birth or germination ends with death, anything that happens in the middle. Many people have taken cracks at coming up with such a definition, here's two, a self-sustaining chemical system capable of undergoing Darwinian evolution, a self-organized non-equilibrium system such that it's processed and governed by a program, stored symbolically and reproduced itself. Now both of these talk about systems, they both talk about them doing something on their own, sustaining, organizing, they have slightly different emphases, Gerald Joyce is a chemist, perhaps not surprisingly, Lee Smallan is a physicist, life is a system that dynamically preserves pattern. Now you might think you'd be able to preserve a pattern without being dynamic about it, like Mount Rushmore, you just make it big and you make it out of rock and the pattern will persist, the pattern will be preserved, but in fact even the mountains come down eventually and every fall the National Park Service gets out there and repairs it, so the larger system of Mount Rushmore plus people are dynamically preserving that pattern, but the lifelike part of it is really the people more than Mount Rushmore. Okay, so this is clearly extremely, I mean even a little eddy whirlpool in a stream, that's going to have a little bit of life according to this view and then it's a spectrum. So now we can build computers, computer programs, whatever, that will display this kind of property that will dynamically preserve their pattern. What are those computer systems that we build? Well that depends on the stance that we want to take to them. We could take the weak claim and say that this computational system is a model of some living system. And when we do that, our goal is to understand something about life or understand something about whatever system we're claiming it's a model of and ideally we'd like to be able to make predictions about whatever it is that it's a model of. Okay, let's take a look at an example. Alright, here is a model of the world, everything. It's been running for a while, here a day, 2400 and something. What it is is a 100 by 100 grid with mountains all around it that nothing can get through and spaces inside that can hold things. You may see these little flashes every so often. Those are I'm imagining lightning bolts, any given time step, any given square has some probability of just zapping whatever is there. So how do we fight against that? We fight against that by reproduction. We fight against that by building a system that can make copies of itself. So here is such a thing. This is little green square is like a plant that just grows under the influence of sunlight or some resource that's generally available and it goes from 1 to 2, now it's 4, well one of them got zapped, it would have gone 4, oh we lost our standing guy, okay. He wasn't very satisfying anyway, he was Mount Rushmore. Mount Rushmore just got lightning hit it. On the other hand it's very, very, very unlikely at this point that enough lightning is going to hit all of these little type P plant guys to wipe them out. Their pattern is being preserved by copying and that's the basic living trick. We start out with one guy, then we hit 2, then we hit 4 and it's often used as an example of an exponential growth process, 1 times 2 is 4 times 2 is 2 times 2 is 4 and so on. It's worth noting that this growth process that we're watching right here, this is not actually growing exponentially, why not? Well, because very quickly the plants are not limited by their ability to reproduce the way I program these guys, they can reproduce fast. They're limited by the ability to find empty space to put an offspring in. It's about amplification, it's about reproduction and it's doing great. Obviously we could have other things show up in this world like in particular a herbivore, whoa, so what happened there, we sort of missed it, it was so quick here. Let's try it again, we'll stick in a herbivore and he eats, he reproduces, they reproduce 200, 300, 400, reproducing very rapidly and basically we play a game of just sweeping across it, eating everything and once again they will all start to death. So the herbivores are chasing around after the plants, the plants are getting eaten but then the herbivores are having famines and starving, plants are re-growing. This is quite stable for as long as I've been watching it as I've been setting up this video. Let's take a look at another example. This plant has some genetic information that tells what kind of color he wants to be. This is sort of a pale blue-green and now we're going through the exponential growth phase 1, 2, 4 which will quickly give way to a quadratic growth phase in this case because our world is two-dimensional so the periphery only grows that fast. The difference here is that every time one of these plants has an offspring, the offspring's color may shift slightly. You may already be able to see that off on one side here. The color is a little different. If we let it speed up, it should become more apparent. We've got an evolving herbivore, let's get one of him going and the idea with him is he has a particular color that he'd most like to eat so the main color of his face, this guy would sort of like to eat a sort of light cream color so the food that's around him, the plants that are around him, not his favorite food, that means when he tries to interact with one and tries to eat it, he won't succeed as often. That's the way I've made this work. But once again, when you have a kid, the kid's preference for what color and what amount of saturation, how intense the color, slightly modified compared to yours. These guys are starting to reproduce and spread a little bit. Unlike the original herbivores which just ate everything, these guys are more selective. It seems like here anyway they kind of like the greenish stuff and they're sort of leaving the blue or purple or stuff behind and as a result, the green stuff is starting to disappear from the world and the blue and purple or stuff is filling in the empty space left behind. We now have an evolutionary system where the plants are evolving under the pressure of the herbivores and the herbivores in turn are evolving under the pressure of the food sources provided by the plants. Okay, so what's going to happen in the long run here? Maybe we should let this cook for a while and talk about some other stuff and then come back and see what's happened here. One of the terrible problems with the weak claim of artificial life is that it's really hard to make a specific, accurate model of an actual living system because actual living systems are so sort of gnarly. They kind of depend on all sorts of different things and because they have that amplification step, because they have that reproductive step with a system that has amplification small differences at one time become big differences later on. They can become big differences and as a result, the small details of the living system may in fact make a big difference how the system works over time. Okay, well if we're not talking about the weak claim that this is a computer program which is just a model as we've been talking about all through this class then the alternative is making the strong claim. The strong claim is that a computational system can be, literally be a living system and that we need to bend the definition of living system and or computational system until that can actually be true. The difference between taking the strong claim and the weak claim again is a matter of emphasis. With the strong claim, we don't necessarily care about modeling any particular system. It's the difference between trying to model flight by making bird wings and modeling flight by making jet engines and aluminum wings. With the strong claim for artificial life, the focus is just on building systems that actually do something that potentially is useful or at least useful to somebody. So, do we have examples of this? Artificial systems that are out in the world doing work that are reproducing, you know? Well, here's one example. This is data from 2001, kind of old now from UCSD showing the infections of the code red computer virus. And we can watch it over time. At midnight, there was a few hundred by noon. Well, by 6, 7 a.m., there's thousands. By the afternoon, there's tens of thousands. By the evening, there's hundreds of thousands all over the globe. Since then, there have been many other sorts of these things out in the wild getting transmitted by internet or floppy disks or picture frames, all sorts of things, USB keys. And they actually do stuff. They may do stuff just to computers. The Stuxnet virus worked its way through communication channels, reproducing itself, found its target and destroyed stuff in the actual world. So, I think there's some pretty strong basis for the strong claim that computational systems can be living systems. Since I think I am a computational system, I need the strong claim. You do too. Or there's a new area called living technology, which is focused specifically on the strong claim. It's saying we can build living systems that are life-like in some way. We can build them out of hardware. That's like robots and stuff like that. We can build them purely out of software. That's the kind of stuff that we just saw here. We can build them out of wet wear, that sort of carbon-based chemistry, water solvents, the stuff that we are made of. And hopefully, we can do something useful with them for society. It's difficult to find really decent examples of using reproducing systems for good, for the overall benefit of society. I think that's got to change. Life fills space wherever there's enough resources to support it, and that means it can be there when the extraordinary event just happens to happen somewhere. And...