Public Lecture: Artificial Life for Bigger & Safer Computing

Good evening and welcome to C4’s von Neumann Public Lecture Series in Complexity and Computation My name is Jessica Flack I’m the co-Director of the Center for Complexity and Collective Computation here in WID, or C4. Now, before introducing tonight’s speaker, Dave Ackley, a few words of thanks to WID’s IT and admin teams who helped organize this series. I’d also like to highlight that the next public lecture, which is on ‘Our Networked World’, will be given by Raisa d’Sousa, a physicist at UC Davis, and that’s November 5th, so please join us for that; it should be a fun lecture. And finally, a word about support for this series. Last year the lecture series was supported by the John Templeton Foundation and a generous gift from John Wiley, the former Chancellor of UW, and this year we are seeking new sources of funding, and I really mean that. If you are enjoying the series, please consider making a donation. Information about how to make a donation is up on these slides here and also can be found on the WID and C4 websites. And I really want to emphasize that only with your support will we be able to continue this series. So any kind of gift would be appreciated. Thank you so much. OK, so tonight’s lecture. While John von Neumann, the scientist for whom this series is named, is considered one of the founding figures in computing. In addition to his many accomplishments in mathematics and quantum mechanics, he developed one of the first computer architectures which could support complicated programs that could be developed and debugged relatively quickly. Now, this was many years ago, almost sixty years after its development, the John von Neumann architecture remains an important part of the design of modern computers. And the fact that it’s persisted for as long as it has is a testament of course to its quality but it also perhaps is an indication of what in the study of technological and and evolutionary innovation we call ‘lock in’ — inertia that prevents adaptation to a changed or changing enviroment even though that adaptation is critical for survival. The current challenges to machine development as of course I’m sure most of you know is CPU scalability and increasing sophistication of the programs and viruses used by hackers to gain unauthorized access to data and networks. Now, von Neumann recognized the limitations of his design, perhaps because he’s also the founder of the field of game theory, and the inventor of one of the first computer viruses. So he understood that robustness would eventually become a critical part of any architecture. But tonight, Dave Ackley is going to drill down into von Neumann’s prediction and tell us about how we might use ideas from living systems and the study of artificial life to improve the design, behavior, and robustness of our computers. Dave Ackley serves as associate professor of Computer Science at the University of New Mexico He’s got degrees from Tufts and Carnegie Mellon His work focused on neural networks and artificial life in the 1980s, encrypted and distributed social networks
in the 1990’s and biological approaches to computer science in the 2000s. Since 2008, he has focused on research and advocacy for robust, scalable computer architectures. I first heard him talk at a Complex Systems Summer School some years ago, where he spoke about homeostatic architectures for robust computing — or at least that’s how I remember it — It’s a talk that’s stuck with me over the years and influenced my own thinking about how nature computes, which is one of the things we work on in C4. So it’s a real pleasure to welcome Dave tonight. He’s one of the most wacky, creative and fun scientists around. Thank you. All right. All right. Jessica, thank you. Everybody, thanks for coming, this is great. This is really exciting. ‘Artificial life for bigger and safer computing’ I’ve been giving various versions of this talk for six years now, anyway, or even longer if you reach back. into the thing. You know, it sort of turns out you sort of back into having a point without even knowing about it, and I finally got around to admitting it. And I realized that there’s something that seems really obvious to me that does not seem sufficiently obvious to enough people. And so what I want to try to do, is try it out on y’all, tonight, and see if I can get you to get it. If you have a quick question I’d be happy to take it as we go through; we certainly can take questions at the end. I have a tendency to talk fairly long so experience says I should start with the conclusions. Here they are. The way we compute today the way your phone, your tablet, your PC computes today, is inherently and ultimately unscalable. It’s not going to be able to keep getting bigger and more powerful using the design that we’re currently using. Worse, it is unsecurable. The way we build computers today, the way we program computers today is essentially impossible to make them secure. It’s easy enough to blame the user ‘Did you update your viruses?’ It’s easy enough to blame the programmer ‘You made a bug’ But that misses the bigger point. The bigger point is: The architecture itself, the way the computer is designed, makes it virtually impossible to be correct, to be secure So I want to try to make that point The alternative, and it wouldn’t matter If things were really terrible it wouldn’t matter if there was no other choice But there is another choice If we are willing to look at living systems Everything from us down to bacteria perhaps beyond as kinds of computations, as kinds of machines, They manifest a very different architecture of computation from what we are using to build the machines that we use. We can have robust, indefinitely scalable computations if we model them in the way that living systems work viewed as computations. And if we do that, we will get computing systems which will be much bigger, which kind of excites me, but also much safer, which should excite everybody. So the action item I want to leave with all of you is to ask this question: Why are we racing to entrust our valuables to gullible idiots instead of fleshing out the alternative The computers that we have today in a very literal sense are gullible idiots. Perhaps, idiot savants, and that’s why we want to use
it but idiots nonetheless and gullible as the day is long. There isn’t a con that a computer won’t buy It doesn’t have to be that way. We accept it; we shouldn’t accept it. We are in crazy land. That’s the message. OK? Also at the conclusion we thank all the contributors These are some of the ones that we have. These are students and faculty that I work
with; the funding agencies; this semester I’m doing a seminar with undergrads and grads and we are trying to beat on this new model, beat on this alternate model of computation. That I hope all works well, I will demonstrate for you
tonight So we’re trying to learn the easiest things, we’re trying just to spring the biggest bear
traps To understand how these kind of models work. OK. Oh yeah, and if there’s any problems I get
to be responsible for that You can kind of get the idea about the ill-advised rants, I’ve already started doing that. OK So here’s what I want to do First I want to deliver on what Jessica mentioned in the introduction von Neumann’s prediction for the future of
computation why it hasn’t happened. In the story of two approaches to computation already alluded to. Second, I want to just quickly explain the meaning of life, in case anybody is not clear on that. And explain why the pagans had it right, and we’ve been sort of going wrong ever since then. Finally, well, next, computer science, computer architecture is political science. The way to understand why the architecture we have today is so messed
up is to think of it as a society think of the computer as a society, and say what kind of organizational system are we talking about here? If we were going to make an analogy between what the computer is doing and some kind of system that we would se among people, what kind of system would it be? The hint is: It’s not a system we want to live in. And then finally, I really want to save time to try to do some demos to see the beginnings, the absolute simplest, stupidest, lowest-level sorts of properties that come out of a different approach to doing computation. All right. Start at the top. I really used to hate John von Neumann. because he made this von Neumann machine. Which I realized intuitively had terrible
problems but I couldn’t exactly pin down why The whole idea is summed up right here. It’s a contract: Divide the world between the hardware people and the software people The hardware people take the unruly, nasty, noisy physical world, and turn it into logic. Turn it into nice square bits that go from one to the next according to mathematical rules. with absolute certainty Asterisk. Hardware turns physics into logic Software, on the other hand, turns logic into functions. And the story is hardware by itself is worthless it’s a doorstop. it’s a room heater. Hardware plus software has to do something that’s valuable enough to cause people to cough up enough
money to pay for the hardware and the software. That’s the computer industry. It’s worked great. The computer industry eight years ago, was accounting for 10% of world economy? By some measures? It hasn’t gotten less. The way that it works is called ‘serial determinism’. The way the computer works the von Neumann machine is, you do one thing at a time, serial. Step by step by step by step. Determinism means the outcome of each step depends only on the inputs that were there immediately prior. If you know the inputs, you know the output
for sure. If you know the new inputs, you know the output for sure. It’s deterministic. And that’s what allows you to program by making logical inferences. And it’s been this gigantic success. But as we know from the abstract, there was one guy who said this approach is going to fall down. And that was also von Neumann. And then I decided: I didn’t actually hate
him quite so much. Although this apology: The future ‘logic of automata’ — the future way computers work, will differ from the present system — that’s the von Neumann machine — in that the actual length of operations will have to be considered. That’s crazy. We have programs today You play a solitaire program It’s hundreds of billions and trillions of steps, one after the other, just to put the damn queen on the king. If any of those went wrong what would happen? Who knows? He’s saying we’re going to have to worry about the length of the program The longer the program is, the more likely something could go wrong. The operations will have to allowed to fail. That’s exactly what determinism doesn’t allow. He thought this would happen by the time computers got to the level of about 10,000 gates — he called them switching organisms — switching organs — sorry. We are now at something like a billion switching organs — gates — in everything. In your phone soon, in your watch. And we’re still not listening to his prediction. He offered a way to understand the alternative. Having to do with error. It’s the comparison between artificial machines and living systems. Natural organisms are designed to hide errors to make errors as harmless as possible to ride out the errors; keep on going heal up, later, if you can. Artificial automata, machines, computers are designed to make errors as distastrous as possible Why? Because if you’ve ever tried to debug a program If one thing has gone wrong, it’s hard enough to figure out the problem. But, if two things have gone wrong simultaneously, it’s virtually impossible to figure out what
went wrong. Now the possible combinations become astronomical. The only safe thing to do is to stop the insanity. The instant anything goes wrong. And that’s how computers work. And he’s saying we shouldn’t be that way. Our behavior is clearly overcaution generated by ignorance. I’m not going to go through this whole table. This is just kind of summing up the different kinds; the two sort of approaches we have to– where’s my mouse? — there it is — the finite approach is the von Neumann machine, the indefinite approach is the living systems
approach An algorithm, the key thing about an algorithm is that it’s finite. It has a bunch of step step step step, and then it ends, as opposed to a computational process that goes on indefinitely, reacting to things, taking input, producing output, and continuing to run. And so forth. I’ve made a nickname for it: The approach, the von Neumann machine approach; the reason it’s so enjoyable is that you are god. You are the Master Of The Universe. Nothing happens except what you decree. Change that bit! Stay the same! Yes sir! And that’s great work if you can get it The indefinite approach doesn’t work that
way. The indefinite approach there are other things
happening while you’re living. Sorry. The way you left it might not be the way it
is when you go back and look again. Member Of The Team vs Master Of The Universe. What I want to do, rather than go through
this in detail, is just take a few key ideas from the knowledge base of computing and understand how this duality this complementarity, between the way we traditionally look at computing, and the indefinitely scalable
approach, the living systems approach. Because they are deeply complementary. When I teach, in classes, we have a thing called ‘Define, Defend, and Attack’ The point is, you don’t actually understand
something unless you can define it, unless you can say something good about it, and unless you can point out a limitation
to it. There is a germ of truth in every idea seriously proposed. No idea captures it all. Define, defend, and attack: DDA. Deterministic hardware: We just talked about
it. Define:What is it? Programmable machines that guarantee 100%
predictable behavior. Well, what’s good about that? Programmers can focus exclusively on features and performance. They don’t have to worry about what happens if a step didn’t work. What’s the drawback? When the inevitable does go wrong, there’s no Plan B. The programmers focused exclusively on features and performance. They didn’t focus on error handling because the hardware guaranteed there’d be
no errors. ‘Absolutely’ isn’t quite right; there actually
is a Plan B — Welcome to Plan B. This is it. Something goes wrong, End the universe. That’s the only thing you can do. Because there’s no conceivable way to go forward once the guarantee has been violated. All right. Let’s do another one. Binary numbers. What are they? Binary numbers are like regular numbers — a hundred and twenty three — except each number just powers of two, so they’re all zeroes and ones. OK? Powers of two, so it’s just like powers of 10, if you only had one finger. What’s good about it? It’s the most efficient possible representation — digital representation — of a set of alternatives. Most efficient possible. Mathematically provably optimal. On the other hand, it’s the most error-sensitive possible representation of the set of alternatives. You can flip one bit in a typical computer; a typical representation of a binary number. You can flip one bit, and the answer will be off by two billion. Close! Yeah, yeah! That’s my income! These are not coincidental. These are flip sides of the same coin. The very thing that made it so efficient makes it so prone to error — to cause such great damage if an error occurs. It’s a duality. And yet, in computer science, in computer engineering, in the computer industry, we’ve really only ever focused on the defend. We’ve completely closed our eyes to the attack. All right, one more. The idea of ‘universal computation’. Backbone, brilliant theoretical breakthrough! Turing! von Neumann! The idea of dividing a machine into fixed hardware that does the same thing over and over again, and programmable flexible, modifiable software that allows you to change what the machine does. And, if you do it right.. Defend: We can arrange so that one particular machine, one single machine, is actually able to compute anything that can be computed. That’s the incredible theoretical result. That’s why the computation is universal. One machine compute anything that can be computed. Wow, that’s pretty good! But you should get the idea now. So what’s the attack? What’s the attack? A single machine.. — takes a single flaw — can be made to compute anything else. You get the ‘Shellshocked bug’ That’s one teeny little problem in code that’s in millions and millions of machines and all of a sudden, that machine is sending passwords and spam to Russia. Single flaw means the machine can now be reprogrammed, and that universality which was so good for us, is now equally bad for us. That’s the idea. That’s the duality that we haven’t recognized
enough, that’s put us into the situation that we are in now. OK. So, computer science, engineering, industry,
everything is incredibly invested in the efficient approach, the idea that all you have to do is make it be correct — that is to say, get the features the way you want — von Neumann’s prediction was expecting us to switch to emphasize robustness. So that even if we didn’t get the exact right answer, we’d get close. Or even if it didn’t work out quite right
the first time we would check our work! Computers don’t check their work. That would be stupid. Because the work has to come out the same
way. There’s a problem that an evolutionary system tends to drive out robustness. You could try to make the system that was really good. Everything was built like a tank, everything was done twelve times to be sure. Well, but then if there’s something — there’s a mutation, whatever it is. Or even just changes in the marketplace. If you say “Well I’m only going to do it eleven times.” That’s going to make it a little bit cheaper. And if you’re faced with one thing that works great, another thing that works great, that’s cheaper, which one are you going to
buy? You’re going to buy the cheaper one. So there is this problem, that even if you try to do right, there are these pressures eating away at you. Decaying away robustness. Getting you right to the edge where The thing’ll last until you don’t remember where you bought it, then bam. That sort of thing. Now that’s only true if we’re living in a stable environment. In times of war, in times of chaos, robustness comes into its own. Those little weenies made out of tin foil and soda straws they’re gone in the first wave. The original Betamax recorder, that weighs eighty pounds survives the nuclear blast. So the question is: How can we have a system be robust when even if we try to do it right, there’s going to be this relentless pressure to chip away at the robustness if it’s not being used. The answer, I suggest to you, is even if things are stable, if we are living in the land of plenty we may be able to afford some robustness along with our efficiency. Now, are we always living in the land of plenty? No. Are we usually living in the land of plenty? No. But. In computers, we are living in the land of
plenty. We’re gonna go from a billion gates to a billion and a quarter in the next generation, Intel doesn’t know what to do with the gates. Traditionally it would give them to Microsoft and say make your software twenty percent
slower but that game has kind of run out. Now we’ve got more transistors, we don’t know what to do with them. I know what to do with them. Let’s buy some robustness. All right. That’s my first story. Let’s talk about the meaning of life. This’ll be quick, because — yow I’m taking
a lot of time. This is, I just stole a couple of slides from one of the videos I’ve got on youtube; if this doesn’t make sense, that video won’t either. My field of research — I’ve got a lot, I kind of go from one to the other, but sort of the one I spent the most time in is called ‘Artificial Life’. People confuse that with ‘Artificial Intelligence’ It’s not the same thing. Artificial Life is about building systems — artificial systems — that somehow act like living systems. Which from one point of view, uh, excuse me? Wait? Life is supposed to be natural. So.. artificial life.. is like yes no, something
like that? We have to figure out a way to understand what life is that allows us to make it in more ways than one. The phrase ‘Artificial Life’ goes back to
Chris Langton Chris Langton, at LANL at the time, in 1987, And the definition that was offered was: The study of life as it could be, not just life as it is. And the idea is we could use computers to study life as it could be, by building models. That leaves us wondering OK, yeah, and what is this life that could
be something else? So let’s take a stab at it. Here’s the dictionary. And old dictionary. A public domain dictionary. The state of being which begins with generation, birth, germination, ends
with death any that happens in the middle. Yawn. What a horrible definition that is That’s utterly gutless. It’s the same as Monty Python’s Meaning of
Life. Never actually defines it, just shows what happens inside it. OK.. But we wanted a characterization that would allow us to look at other things and say ‘Is that life?’ Yes or no? Here’s a couple of definitions: A self-sustaining chemical system capable of undergoing Darwinian evolution. OK. A self-organized non-equilibrium system such that its processes are governed by a program, stored symbolically, and reproduce itself including the program. These things have some things in common — self-sustaining, self-organizing something about ‘self’ and something about systems. Gerald Joyce is a chemist; Lee Smolin is a
physicist. These things are very complicated definitions. I want to boil it down as simple as possible to try to get at the essence, what I think the essence of life is, which I want to offer
to you. My favorite definition I got from my dad There’s a surprising amount of truth to this I don’t know where it actually came from. But this is the definition I want to offer
you: Life is systems that dynamically preserve
pattern. That’s it. That’s it. Wherever you see a system that’s dynamically preserving its pattern, it’s working, it’s struggling, it’s consuming
energy to keep its pattern together: That is life. What do you think? It’s got a piece of the answer for sure. But we’re gonna have to admit that there are some systems that are gonna be kind of like dynamically preserved pattern, that we wouldn’t normally consider life. My favorite example is to imagine a little eddy in the stream. Walking down.. There’s a little water flowing, it makes a little whirlpool. It persists. The water circling around, circling, circling; It’s a pattern; it’s dynamically preserved. It’s actually sinking a tiny little bit of
energy of that stream, as the water goes around. But it’s so fragile. You hit one little pebble the thing is gone. According to this definition, we’re going to have to say that has a little bit of life. It’s a little bit life-like. And then things that can preserve their pattern in a much wider range of circumstances like people, or cockroaches, something unbelievably life-like are going to be more life-like than the eddy in the stream. and that’s the basis of Computational Paganism The way that we need to understand living
systems is not as Yes, you are alive, No, the fire in the forest is not alive, but in fact there’s a spectrum, and we’re going to chop off the spectrum at different places for different purposes. Is that unsatisfying, yes, to some degree
it is. Too bad. The distinction.. if you need a yes or no distinction, that’s on you. That’s not a property of the actual world
out there. OK? With computational paganism, we’re in good
shape. Now we can write computer programs, that have bit patterns that preserve themselves that copy themselves, that do whatever they want. And those things, by the principle, are going to have a degree of life to them. All right. Part Three. We’re doing okay. So computer archtecture is the basic design principles underlying a machine. Computer architecture, in an important sense, is just like real architecture. It has to do with the use of space and putting things near other things that need to be working together. It’s just tailored for the needs of a computer. In order to understand what I want to say here we have to think about what’s the right way to imagine what a computer is? What is a computer? How should we think about a computer? Here’s my computer here in my hotel room this morning How long it takes me to prepare a talk.. This is probably what people will tend to think a computer is. But really this is just its face, right? That’s like saying this is me. Which to some degree it’s true in terms of what expression I have, what emotions I probably have this is a good place to look, but if you wanted to understand how I’m gonna behave, what decisions I’m going to make, this is not the place to look. Where do we need to look? Well, we look inside. I did not tear open my machine, this is from Wikipedia. It’s a picture of a motherboard with all the chips and the fans to get rid of the heat We gotta look closer. Is it here? Is in one of these chips? Well we’re getting closer but it isn’t there. And those chips, those are weird things anyway. What if we went inside of those? You look at one of those chips, almost all of its just stupid plastic. The whole thing it’s just plastic to carry these tiny little wires away to get to the pins. The thing that’s making the decision has got to be further inside. Here’s a picture of a chip. A single chip. We can get a little closer. This is where we need to look to understand how computer architecture works. On this particular thing, I don’t know all the details of it, except this stuff up here is memory. The vast majority of the gates, in fact, of this thing is memory. Even though it’s a fairly small and old computer. It’s actually just a computer to help with the I/O of a bigger computer, but that’s a separate story. This stuff doesn’t change at all. Everything that’s up here is slaves, passive slaves. Your job is to remember ‘zero’. “I’m zero.” Your job is to remember ‘one’. You’re also a one. That’s it. That’s all they’re doing. And the control, the agency, the authority, the ability to make changes, is down in here, in the central processing unit. And that was the essence of the von Neumann machine architecture. A centralized place that made all of the decisions, and then this vast ocean of completely equivalent worker-bee drones that did nothing except remember what they were told, and cough it up when they were asked about it later. That’s called ‘random access memory’ RAM Buy a computer it’s got eight gigabytes of
RAM. That’s eight gigabytes of these pitiful slaves saying “Yes, zero”, “Yes, still zero”. If we were to live in here, would you like to be one of those little bits? It’d be a pretty dreary existence. It’s kind of like, you know, there’s this big drum going “Boom”, “Boom”, “Boom”, that’s telling everybody “What are you doing?” “What are you doing?” “I’m a zero” “I’m a one”. Then there’s this tiny little region where everything happens. The CPU. You get picked, you get up, you go down to the CPU, you get one added to you! And you get sent back to memory. That’s how it works. That’s what makes it work. Serial determinism. That’s also what makes it a terrible, impossible-to-make-secure device. Think about it. Every change that happens, in this machine, happens in one place, the CPU. It’s not like we have a mud room at one end of the house, and the living room at the other end of the house, and mom in the middle screaming at you if you still have your shoes on. There’s only one place where everything happens. The kitchen. The stuff that happens at the CPU are the instructions that correspond to our most trusted, deeply held, most personal internal stuff, and also the scum of whatever the internet dragged in. All of that stuff gets processed in the exact same spot. The exact same microscopic little bit of silicon, down here. So what happens if something goes wrong? What happens if there’s a bit flip? Worse, what happens if our software had a bug in it, and somebody knows about that bug. They can then switch the CPU from doing what it had been doing to doing essentially anything, in one step. Is it possible to write secure software, to make a secure system, with this sort of thing? In principle, sure. In a mathematical sense, sure. Just like in a mathematical sense you could stand an entire deck of cards end to end vertically and it could stay up. It could happen. Are you going to count on that for your financial information? That is really where we’re at. We’re taking more and more of our selves, our valuables, our lives, in some cases, and we’re putting them in control of machines like this, that have all of their decision
making power centralized. Computers might be all touchy-feely and happy and show us our Facebook friends but if you lived inside of one, it’s a nasty horrible fascist slave existence. The individual bits.. and you’re not one of you’re not part of the CPU, statistically speaking, and even if you were, you’re just one teeny little bit of the CPU. The political science on the inside of the machines we have today are a centrally controlled, centrally planned centrally run dictatorship. And, just like, well not just like Mussolini, in one way, it’s extremely efficient. There’s no redundancy. But on the other hand, if anything goes wrong, all bets are off. There isn’t anybody down there saying “Wait a minute! I’m not just holding a zero, I’m holding the important part of how much money we’ve got. It doesn’t make sense for me to be a one!” There’s none of that. There’s no individual agency. All there is, is, you passively do something, I told you what to do, Don’t Ask Why. That’s where we’re living. Every computer you’ve got is that. There is an alternative. We could think about what would it mean to give a little teeny bit of silicon, to give a little bit of computing power initiative, autonomy, agency. Could we even conceive of such a thing? Sure. It’s easy. We already gave it to the CPU. So, let’s just make the CPU really small, and make a whole bunch of them. And now, it’s like everybody in this room. It’s all blab blab blab blab blah, everybody’s thinking their own thoughts:”This guy’s kind of cool; sort of stupid.” Whatever it
is that they’re thinking, all at once. When we do that, there’s a ton of waste. We’re all thinking the same thing half the time. We could have gotten away with, just have Jessica think that for all of us. That’s the way it would have worked in the von Neumann machine. But instead, no. We all think it ourselves. Incredibly redundant. But that’s what makes it robust. If something god forbid, happens to Jessica, there’s plenty of other people thinking the thought. It doesn’t feel like redundancy when it’s me. When it’s you. When it’s the other guys, yeah maybe it does. We can get robustness, we can get scalability, we just have to figure out how to architect a computer to be something more like free market democratic capitalism. Bottom-up autonomy, with distributed agency. If we could do that.. Don’t you think it would be un-American not too? I ask you. But we’re not doing it yet. We’ve got a lot more that we need to do. So that’s the real point I want to leave you
with as far as before we actually talk about well, actually try to demo, try to see what this might actually be like. A computer is made of billions and billions of individual little gates connected by wires so that they can interact with each other. Each of those gates is actually capable of making a decision. An incredibly simple, incredibly tiny, but legitimate decision. Between ‘this stuff says I should be zero’, ‘this stuff says I should be one’. I think, mmm, ‘one’. Each bit of a computer is a non-linear element, that can take some inputs and then bend it. Make a decision. OK? That is an opportunity for each tiny little
bit of things to have a tiny little bit of agency. A tiny little bit of autonomy, independence. To use that tiny little bit of decision-making
power. And what von Neumann was telling us in error handling, was that we were just being gutless weenies by having our machines blue screen of death when anything goes wrong. Because we hadn’t done the work to give the machines a Plan B. We hadn’t done the work to say we could arrange computation so that we’re doing everything dozens of times, at least unless we get really, really strapped. And so if something goes wrong, who cares? Almost for sure it’s going to get washed out by all the other guys who are doing it right. You know how that story works out. That evolutionary problem of robustness going away. I don’t have to be robust, because someone else will be robust for me. So nobody is robust. How are we going to address that? We can build computers that are more like free market economies, that will do the work as many times as they can. They’ll do the work as many times as there are coffee shops on every block. One of them closes, you’ll never know. Did I walk a little further today? I don’t
know. OK. Let’s do a demo. Let’s suppose we could actually build computers that were organized this way. That we’re going to be able to make them as big as we want. How is that even possible? A piece of material is physical and finite. It can’t be as big as we want. I can want pretty big. Well how we’re going to do it is we’re going to break it down into a tile. We’re going to say: A rule for a computer to be satisfying — I feel your pain — I’ll try to be more interesting. To be acceptable, a design for a computer, is not something that just comes to the end and says that’s it. A design for a computer is a design for a tile that we can plug together we can fill space by tiling together these little individual pieces. That’s the design of a computer. And then, we can make the thing as big as we want. We just buy more tiles, plug them together, and the computer gets
bigger. We’re going to have a whole nother level of needs for our computations to be robust because I mean, you know, we might be plugging in stuff while they’re running. Or, you know, whups, I disconnected the whole south forty, and you know it’s going to have to let that stuff restart. So. Indefinitely scalable computer. An indefinitely scalable computer architecture is a rule for filling space with tiles, little
computers. And we’ve been working on this. Here’s our first one. This is the tile that we built in 2008 designed it, in 2009. It was actually briefly for sale, some people bought them, ha! No, some truly adventurous, really cool folks. Including some folks at NASA and so forth, as well as just hobbyists. This thing here the chip in the center, that’s a CPU. It’s basically a 2007 smartphone, like that. Which you wouldn’t spit on today. Seventy Megahertz. Thirty-two kilobytes of RAM. We’d paid a lot of money for them, cause we were buying them by the ones instead of the one millions. It’s got connectors to talk to the neighbors. They exchange power and ground and bits, they talk to each other, and so forth. So
you can plug these guys together. Here’s four of them plugged together with little connectors. See how it goes? You can hotplug them and they’ll all just start running. Here’s a schematic version of the same thing,
OK? This is meant to be four tiles. This is meant to be like this except sort of stood up Here’s one, here’s one, they’re talking to
each other in all directions. OK? Let’s take a look at them. All right. Hopefully you didn’t see a thing. These are our four tiles. They’re now being simulated on my laptop. They’re actually four separate tiles; we can show them separately, if we want, but it’s typically more convenient
to squeeze them together to hide the fact that they’re actually physically separate because they have communication channels, so that when something changes on one of them, they tell the next one, to try to maintain a consistent view of the whole thing. So let’s squeeze them back together again. All right. This is a small one. Let’s start with a bigger one first. All right. Here’s another one. This is five by three is fifteen tiles. OK? Now the idea is there’s a grid here. Each
of these teeny little squares here is one little
site that we can plop something down into and do a little computation. OK? And we’ve got this table of elements that we can grab from like a palette and paint with them. So let’s see, we’ll get our brush here. We’ll get this stuff, whatever this is, and we’ll plop one of these guys down. OK. And then we let it run. Whups, where did he go? Oh there he is, we just can’t.. let’s get rid of the background. There we go. OK. This thing that you see floating around here is an element called ‘Dreg’. It stands for ‘Dynamic Regulator’. And it was the first element I invented when I got this stuff going around 2010. The way Dreg works is this: When it’s his turn to go, he wakes up and looks North, South, East, or West — one direction, at random, and says: What is in there? And, when it looks in one of its neighboring squares, it’s either occupied or its empty. If it’s occupied, he throws a random number, and maybe erases whatever’s there. Doesn’t check and see if it’s something important. Doesn’t check to see if it’s one of a kind. Doesn’t check to see if it’s in the middle of an important computation. If its number comes up, pop, erases it. On the other hand, if the spot that it encounters is already empty, it throws a random number, different odds, and creates a ‘Res’, and you can see, we’ve got seven Res in the world now; those are the brown ones. And two Dreg, those are the little gray ones. So the basic rule is: If it’s occupied, throw
a random number and maybe erase it; if it’s empty throw a random number and maybe create a Res. The only exceptions are: If it’s empty, there’s a very low probability of creating another Dreg. So the Dreg will reproduce itself, with very low odds. And what happens is the world gradually fills up with this mix of Res, which stands for ‘Resource’ atom — like manna. It’s like the fundamental goo that you can make anything want out of, and these Dregs which, on the one hand, are the source all manna, and on the other
hand, they are the source of random death. And this, Dreg, is a canonical example of
how we beat that evolutionary tradeoff, of losing robustness over time. We make the source of resources be the source of destruction. We don’t wait for a cosmic ray to come hit the memory and flip a bit. We don’t wait for an attacker to finish with J.P. Morgan and come after us. We attack ourselves, continually, to stay on our toes. If we got rid of all the attackers, we’d get
rid of all the resources — it’s the same guy. So this is gradually filling up. I’ve got
— this is like a cooking show — I’ve got a
version that’s already gone on a little further. So this is what it looks like a little bit
later. The world is filled up with about thirty-five, forty percent of something — mostly Res, some Dreg. Now we can do other stuff with
this. And I’d like to see if we can do it here for
you. Let’s see, so I’m going to make myself an
‘Em’ atom, That stands for ‘Emitter’. Let’s get the grid
down here. All right. So I’m going to plop an Emitter
down there. OK. And those blue things that it just started popping out are Data. This is a thing which is now emitting Data items, which for our
purposes is random numbers, into the grid. Now at the other side we’re going to get these
guys. See and these are Consumers. They will pull Data out of the grid. So put a couple of those guys down there. And you notice, the consumers and the emitters spread vertically all by themselves. That’s a hallmark how robust computations work. They rebuild themselves from a seed. You don’t have to make the whole thing yourself. You build a seed. All right. And then finally, I’m going to take one of these guys, the ‘Sr’. This is a sorting element. And I’ll plop him right in the middle. Can you see it here? There we go. The way these red guys work — the Sorters
— yeah, now we’re going. The first thing that they do, when it’s their turn to go, they look around them and say is there any Res in the area? And if there’s any resources, they convert the Res into Sorters. So they reproduce themselves opportunistically, — uh oh, there we go — based on the availability of Res. And after they’ve done that, they look to their stage right and say: Is there any Data item there, and if so, is the number inside him bigger or smaller than the number I’ve got inside of me. If it’s bigger, then I’m going to try to move it from my left to my right, and put it below me. If
it’s smaller I’m going to try to move it from my left to my right and put it above me. And
then as the final step I take whatever the number was on that Data item I just moved, and I
make my threshold be that number. So what each of these Sorter guys is doing
is a little quantum step of sorting. They’re
not actually sorting a bunch of numbers, but they’re making it a little better. Bigger
numbers are a little lower, smaller numbers are a
little higher, and they’ve moved from the right, where the emitters are, towards the left, where the consumers are. OK? We have an alternate way that we can draw
these. Let’s put these up. We can color them instead of blue for data and red for sorter,
we can color them according to their value, their numerical value. We get a picture like
this. The lighter colors, the whiter colors, the
brighter colors are small; the darker colors, the blacker
colors are big. And what do you see? On the right hand end, near the emitters, it’s a hash of all different colors, ’cause
the numbers are random, but as we move across the array the numbers get more and more laminar, and by the time we get to probably two-thirds of the way through, something like that, the numbers are actually fairly
well sorted. And now the sorters are making these very fine distinctions, 830 thousand vs 840
thousand, and so on. So much so, that once the numbers get to the left hand side, when they get to the consumers, the consumers pull them off. So the Consumers, what they do, is they look around them and if they see any Data items, which I don’t really see many here, they should be coming through every so often, they yank ’em out, and they score them. Because they know the numbers are going from one to a million, so if the numbers are really random, there
ought to be about one-thirty-second of numbers from
one to a million here, the next thirty-second
here, and then all the way at the bottom, like that. And if you do that, the Consumer guys can work together and tell us how well the sorting is going, and the answer is: It’s going okay. Is it perfect sorting algorithm? Absolutely
not. These guys are wandering around, whups. Sometimes it’s a little easy to see the stuff flowing
if you look at it from a distance. There’s Dreg in there. Dreg is erasing some of the Data! Bad luck! That input does never appear in
the output. Better get used to it. But it does quite well. It does pretty well. And in fact it’s not possible to solve this sorting problem perfectly because what if you get unlucky and just the random numbers all happen to be big for a second — they’re going to get put to the wrong place. It’s
not its fault. But what this thing is, is unbelievably robust. We can flip a whole bunch of bits at random throughout the thing, you can’t even see it. We can blow a big old hole in it somewhere. No problem! You see the data starts piling up leading
edge of the thing, cause there aren’t any sorters to pull it through, but that’s okay. The Dreg diffuse in, build Res, the Sorters
diffuse in behind it: The machine rebuilds itself. The machine is in a perpetual state of constructing itself, so when damage occurs, it heals. Regular old computer. Totally different way to think about computation. The key, well, there’s a bunch of keys. But one of the keys is: We made this assumption of geometry. That input was left, output was right, small was up, big was down. By making a geometric assumption on space, that allowed each of the individual Sorters to have agency. “I know how to make things better.” This guy should go from here, he’d be better off there, and he’d be better off
up, he’d be better off down. And then, we just need to have enough of them and the job gets
done. So this — it’s not an algorithm — this process, I called the “Demon Horde Sort.” It’s like Maxwell’s Demon. Each little guy’s
making a decision.. you get enough of them, you do computational work. And we’ve explored this in a lot of different contexts. You can use this not just for sorting, you could use this for routing packets, for
example, if you could have knowledge about these guys want to go this way, these guys want
to go that way, throw them into the grid. If you’re worried about robustness, throw three or four copies in the grid, it’s okay. A very different way to compute. All right. Let’s umm, this has recovered fairly
well. You can still see, it’s kind of a little,
it’s got a bit of a traffic jam up there. All right I want to show you one more thing. Let’s go to the smaller world, here, for it, ’cause it’s a little bit easier. Once you have this indefinitely scalable computing
fabric where you can just plug it together you can pick elements as you wish. Get the grid going. And furthermore, if you’re a programmer, you can create new
elements that have new properties that do new things that you want to do. And I want to show one
other one that no one’s seen this before, this has just been invented, since our seminar started,
called ‘Xg’, which stands for ‘Generalized Crystal’. And the idea is this: This diamond shape here is how big, when it’s a time for a guy to go, this is how much he can see of the world around him. This is his ‘neighborhood’. It’s four steps Manhattan distance — city
blocks — in any direction. OK? Which is tiny, from the point of view eight gigabytes, but it’s huge from the point of view a ‘Cellular
Automata’, which is sort of the thing that this kind of competes with. So what we can do with Generalized Crystal
is we can say: Well, if you’re a generalized crystal, you want, you want to see yourself, okay,
that’s good. But let’s say you want to see a guy there,
and a guy there, and a guy there, and a guy there. OK? And if you see that, you’re happy. And if you don’t see that — let’s make a guy — whups, that’s it.. There he is, so we made a guy. If we let time start to run.. He’s not that happy ’cause he would like to see four guys
around him, and he doesn’t see it. If there was resources
there, he’s empowered to crystallize them into more
of him. So let’s throw in a little Res, just for fun. Get rid of the background. OK so there’s a
little Res. And it builds more crystal. Now if we go in here and we build a guy who’s out of position, that’s gonna piss him
off, and in fact what happened in that case was he recognized he was inconsistent with his
surroundings and he decayed back to Resource. Which then got snapped up by another guy. OK? So, if we make a bunch of these guys, they’ll sort themselves out, and they ended
up decaying to resources when they were inconsistent. OK? And these guys, the Res will eventually drift out to the edges and build the crystal. And that’s very nice; it’s fine. And, the thing is, it’s all incredibly general. You know, we could make a crystal that’s got a bit of skew to it, for example. Like this. And now we get.. it’s having a little trouble
there, kind of fighting against two different shifts for the crystal, but they sorted it out. OK? Very nice. These have a lot of the properties of crystals in a very crude sort of categorical sense. But this is a new, digital, media. We can
do anything we want, here. Suppose we did something like this: I want to have a guy, and I want there to be a guy right above me, and that’s it. Now, that’s a problem. If we apply the rule to the one guy, we’re going to have to apply the rule to the guy above him
as well. And then he’s going to be unhappy because he wants a guy above him and not a guy below
him. Crystal like this can’t happen in nature, in a single material — a single element. What happens? Well, we make a guy, he’s fine. Make another guy.. What happened? The guy who was high, behind, decayed, and got soaked up by the guy’s in front, and now we get these guys stacking up, and, what they’re doing is they’re stacking up until
they are so close — they get as close as possible without being able to see each other. Because the instant they come — this is called the ‘event window’ — the instant one guy come in, intrudes into the event window, that’s inconsistent, that’s terrible: Somebody decays. But as long as they stay outside the event window, out of sight, everything’s good. So we get these guys who head north. You can do this kind of thing in actual physical
reality with doping in crystals, by making multiple
layers of different stuff that have different properties. But here we can explore it directly. Now, I don’t know if it will happen here right
now. One of the things that I didn’t realize — I
didn’t expect about this — we’ve had lots of surprises in the class already, is – these guys, they act like a ‘Res pump’. They gather all the resources up to the top of the screen, because
— you can see it — it’s kind of sparse down
at the bottom, and it’s very concentrated. Because when they’re down at the bottom, they’re far
apart, and they can soak them up to try to make crystal, and then the crystal rises up, and they get inconsistent with their neighbors, they decay back to Res. It’s an active, dynamic Res pump. Who knew? Try one more. Suppose we have a guy like this. Now, this is symmetric, but it’s inconsistent
because once again, when this guy gets a turn he wants someone above him and below him, he’s not going to have it. We get one guy, no problem. We get two guys, no problem. We get three guys, now they say, well no, I don’t
want to be here; he decays; someone else picks
him up.. And what we’re getting is we’re getting random walk. Brownian motion in one dimension. And, it’s fine. And one of the students in
the class said, Well, what happens if you make it in
both directions? Do you get Brownian motion in two dimensions? I said “Well, we don’t know. Let’s try it.” So we’ll try something like that. And we make one guy, two guys, three guys, and it’s kind of like Brownan motion in two dimensions. But this construction has an interesting additional property. Let’s put some Res in here. When it runs into the Res, it’s gonna try to build itself out larger. But there’s no consistent way to do it. And it reproduces. Every so often it’ll leave a pair there, that’ll get soaked up by others. Simplest, stupidest, one particular material. This is the thin edge of the wedge, of a new world of computing possibilities. How are we going to make this play iTunes songs? I have no idea. And you know what? I don’t care. I want to understand how we make the basics of robust computing happen in models like this. OK. I’ve ran over my time, so let me finish
up. This is called the Vickers’ Cross. This particular one. All right. Let me finish with this. I totally appreciate this scary cool poster that the WID guys made. But it bothered me a little bit, the subtext. That security is about bad people. You can tell: He’s got a hoodie. Right? And ’cause I hope you can understand, having sat with me for an hour, that this is not where I think the blame for computer security lies. Yes, this guy is taking advantage. But, come on! We’re not even trying. Our computers.. one mistake, the CPU.. control goes to somebody else, they can do anything they want. So I tried to make up a response to this, it didn’t come out too well, but maybe you can get the idea. It’s sort of a cartoon. The fundamental security problem is not the people. It’s not the programmers. It’s the computer that never saw a con he didn’t buy. We have to figure out how to make computers be savvy. And we can do it. We do it by beginning with distributed agency. Pushing it out to the leaves, so there’s no single place to take the machine over. Thank you so much for listening. Anybody dare? (Offscreen) I’m just curious on your system
there, what if somebody were to introduce a send-me-your-information crystal that would go around and — Sure — that kind of thing. There’s.. the vulnerability.. the risk is
always there. We can’t eliminate the risk, because we need to have it actually do work. Work for us can turn into work for somebody else. The way we mitigate the risk is we make a huge distinction between the ‘periodic table of the elements’ — that’s the actual code
— and that we control very carefully. It’s stored
in non-volatile memory. You have to use extra separate channels of information to
convey it. So if there’s no element that will steal your information, then there’s no way it can happen. If, on the other hand, you can take a bunch of existing little elements and put them together well then, you’d potentially have a problem. But what we then have is, we have the fact the
whole thing is distributed. So unlike what we have today, where all you got to do is take over the CPU and you got the keys to the kingdom, whatever it is that is going to do bad to
us is gonna have to fight a land war, tile by tile by tile by tile, first to get
in and reach the data, and then figure out how to exfil it, given that we have this world that is inherently noisy, so the attacker cannot know exactly what it’s gonna find inside, as opposed to the machines we have today. I’ve baffled everybody. Got one here. (Offscreen) You mentioned how you don’t see how this would play something like iTunes or files or — perhaps I was exaggerating
— or storage. What kinds of applications do you see as real-world purpose? In the near term what this is best suited
for is signal processing tasks. Where you have
a continuous flow of data coming in and you have to make decisions about it.. you have to
reduce the data, you have to integrate it.. and if you screw up a little bit it’s not the end
of the world because the sensors are going to be reporting data again immediately. And, you know, I hate to think about warlike applications, but that’s a place where robustness is valuable. So, I could imagine that sort of thing. Signal processing tasks, targeting tasks, that kind of stuff. The fantasy is that these tiles, down the years, if you get a bullet through your computer, it’ll start working a little less great; the targeting’ll be off by a tenth of a degree, something like
that but if it bugs you too much, you can go over to the radio and scoop a bunch of its
stuff out and pack it into the targeting system
and it’ll fix it up. Like that. We’ll see. It’s the earliest days. (Dave Krakauer) It’s a related question actually. And it has to do with: So you’re building up, as you say, you can somehow put the periodic table behind a firewall — try to, yeah — but in
the end, once you’ve explored the natural history of
these devices — yes — and you have an ontology
that allows you to construct a protofunctional alphabet — yes — then you’re going to have
to interface with the human mind using a programming language that’s familiar to them. And there’s your vulnerability again. That question — in other words — that question
didn’t arrive where I thought it was going to arrive — OK, well interpret it any way you
like — All right, let me take it one way and then you tell me the other part. What we hope to do in principle is the fact that we can create
new elements here is what we need to do research. ‘Cause we don’t know what the really powerful, effective, minimal set of elements would be.
If in the future, which is where I thought you
were going, we figured it out — you need 256 elements 60 that do this, 30 that do that, and bam
bam bam, Then, we could start building tiles that are
no longer reprogrammable. That all they can do is those 256 functions. And then, the obvious hole, the functionality hole is closed. The ability to do magic — to change the laws
of physics — disappears from the universe at that point. In exchange, presumably those atoms are pretty powerful and you can compose them and do sort of things, so — how does it work with the people? (Dave Krakauer) In the sense that you’re going to need to learn the grammar to operate — you a person?
— you a person, so I’m thinking of it. And that presumably, by virtue of the constraints of our own mind — yeah — sort of serial nature of our own thought, will impose a vulnerability in terms of the language that we use to interface with your hardware. All right. I think.. All the stuff that I’ve shown you here is all two dimensional, right. And that bugs a lot of people, mathematicians, geometers, stuff like that; they say the world is three dimensional and so forth, and I say ‘Yeah, you’re right, but in fact for manufacturing systems it’s really nice to have the third dimension available to construct the thing, to maintain the thing, and so on. That said, we could take a model like this
and start stacking them up.. and I have this fantasy, that we build a tablet computer in
effect, out of a few hundred layers of this stuff, that has sensors on the bottom, processing layer after layer and layer, and little LEDs on top. And it looks like an unbelievable iPad. This kind of organic. whole thing pointed at something, seeing it, processing it, displaying it to you The top three layers, all they’re doing is working to get themselves into a configuration to represent the letter ‘E’. So that when they light up, you’ll see pictures. So the interface problem doesn’t have to be any different than what we already have, to
people. and the hope is, you know, the machines are going to have to come to us, certainly, the expectation is there now. (Offscreen) One of the contrasts I thought you were drawing between old-fashioned computing and your style of computing was that in the old ways, things were deterministic, but the systems you just described every bit as deterministic as anything. They actually aren’t. And even this stupid little simulator, is actually non-deterministic. This is a nerdy answer, but, because each of these tiles is being run by a different thread of execution, and thread scheduling even in computers today is non-deterministic. There’s no guarantee which one is actually going to go next, which makes debugging this stuff really fun. So, no. Like that. And again, these things have pseudorandom number generators built into them, which we believe are not really predictable, so it’s almost as good as being non-deterministic. Really the key part of serial determinism is that the programmer assumes, from the time the program starts, it just builds more and more and more assumptions. That value is 0, this one is 1, this is one bigger than it was before, and
so on, and the layers of assumptions become teeteringly gigantic, so much so that if any bit flips, pop, who knows? Because we build this system with noise at a very fundamental layer, you can’t do that. You have to keep your chains of reasoning short. And that’s what von Neumann was telling us we needed to do, so hopefully it’ll lead us right to
the end, to robustness. (Offscreen) Dave, one final question here. So, a lot of people think it’s actually not just the like deterministic nature, and like
the kind of stacking nature you were talking about, but the reliance on state in the computation. Go ahead, not just determinism, but.. The reliance on state, so like, setting memory
values instead of relying on like the functional
approach to programming where you have processes that complete with any given inputs? So I was wondering about how this like relates to that approach to computer security? I have a perennial fight with one of my colleagues who’s a huge functional programming fan, believes it’s the answer to everything, and, so I have to kind of needle him about how, well, you know, nice little monad you got there
buddy, sort of hiding a pile of state behind that,
aren’t you? So I’m sorry for nerding out.. The shorter answer is: State isn’t going away, the question
is Number 1: How much are you relying on? and Number 2: What happens if it’s corrupt? So it just goes back to the Plan B problem. If people, I mean, we could take machines.. There’s nothing wrong with a von Neumann machine — here’s my slogan — There’s nothing wrong with a von Neumann machine that can’t be fixed by making it be small and insignificant part of a bigger system. If we had tons and tons of von Neumann machines then we’re going to have to be able to shoot them out and keep on going. Like that. So state is going to be there, the question is how much do you depend on it? (Jessica Flack) All right, well thank you
all for coming, please join me in thanking Dave.

13 thoughts on “Public Lecture: Artificial Life for Bigger & Safer Computing

  1. Dave thank you for sharing these new ideas. I watched all of your youtube videos and enjoyed them very much. Your new way of looking at architecture is very refreshing and I'd like to explore. I was curious to know if you had more details on Von Neumann's "chains of operations comment"?

    Thanks again!

  2. Very interesting and inspiring. But I have some problem understanding.
    Lets say we build this computer out of 4 peaces, 4 separate cpus as was in your example (right?) and then there are these dots or elements that move inside of these cpus, so as I understand depending on which cpu it is located then that cpu is executing that cell? So cells on same cpu are on the same thread? Or its like 4 core cpu and each cell has its own thread and it just doesn matter where the cell is?

  3. Dave, what are your thoughts on the effects of such movements as test-driven development? Making sure while building the software that things (mostly) can't go wrong?
    Thanks in advance 🙂

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