The following is a rough transcript which has not been revised by The Jim Rutt Show or Bruce Damer. Please check with us before using any quotations from this transcript. Thank you.
Jim: Today’s guest is Bruce Damer. He is the chief scientist of the Biota Institute, and he’s a research associate in biomolecular engineering at UC Santa Cruz. And he’s an astrobiologist working on the science of life’s origins and the future for sustainable paths for humanity. You can find out a lot more about Bruce and his work at biota.org. Welcome, Bruce.
Bruce: Thank you, Jim. Good to be back.
Jim: Yeah, it’s great to have you back. In fact, this episode is essentially going to be an expansion and continuation of some of the themes we talked about in our recent EP 167, where Bruce went into some considerable detail on his theories of the origins of life. And it’s really an excellent episode. I relistened to some of it in preparing for today’s episode, and I go, damn, Bruce really drilled it. He presented it very well. So if you’re interested in origins of life, check out EP 167. And today we’re going to take his theories and look at some of the process that got to the theory or some of the conceptual engines. Maybe we can say near the core of the theory, and see what that has to say about some other things. But before we get into those implications, let’s start with a recap, if you would, Bruce, of kind of the core of your origin of life hypothesis.
Bruce: Well, here’s a brief recap for the audience, just a couple of minutes for 125, 150 years ago, Charles Darwin wrote a letter to his friend Hooker, a fellow named Hooker in the Victorian English style. Whereas Hooker asked him, “What do you think about where life began and how?” And Darwin wrote that he felt that it potentially could have begun in a warm little pond somewhere with all sorts of phosphoric salts, electricity, heat, energy, et cetera, that a protein compound would form and become more complex, basically. And so in that 1871 little paragraph, Charles Darwin gave us, in a sense, the greatest insight on one of the top three questions in science. How did life begin?
And for about 125 of those years, our colleagues back to Oparin and JB Haldane in the 1920s, and then the Miller-Urey experiments of the 1950s, and onward up we’re putting the pieces together. And then there was a discovery of mid-ocean vents in the late ’70s. And one of our other colleagues suggested, well, perhaps because there are disc equilibria, there’s hot water coming out, it has a different pH that could be highly alkaline versus an acidic sea, and there could be little mineral membranes that perhaps life could start there in this equilibria. And why do you need this equilibria? You need an engine to drive chemistry. So if things are just floating around in water, they get together pretty rarely. But if there’s a barrier which energy can cross, you can get all kinds of work done chemically.
But that proposal really hasn’t panned out. And for one particular reason, the water, too much water’s around so that if you form a bond in the deep ocean, for example, it’ll pretty much break. And not only that, the structures will come apart, but the materials just disperse into the cool of the ocean around these vent chimneys. And so it was never really a plausible proposal for how to get organic compounds to become more complex as Charles Darwin had suggested in 1871. So our field started to look back into the warm little ponds, and instead of having just a warm little ponds as sort of a puddle sitting there, we have an entire science of hydrothermal fields. And hydrothermal fields are like Yellowstone, all those bubbling regularly blasting off geysers that are filling and refilling volumes, that creates an engine of its own. That creates thermodynamic disequilibrium as well.
And so my colleague, Dave Deemer, started trudging through the snow in the late ’70s to a place called Bumpass Hell in Northern California. And it was called Bumpass Hell because a Mr. Bumpass in the 19th century was trekking people up there, and it’s a volcanic field full of fumarole events and bubbling hot springs. He fell through and burned his legs so badly it had to be amputated. And because he was such a tough 19th century type of a fellow, he survived and led a long life, Mr. Bumpass. And so Dave was looking for ways to get the chemistry for life’s origins, the chemistry that Charles Darwin was pointing to, to work in these settings. And recently through a lot of discoveries, our group at UC Santa Cruz, and many, many other colleagues around the world, it’s all coming together.
So for example, what Dave discovered is if you have wet dry cycling, think of your bathtub draining and drying down, you get a concentrated mixture of soap bubbles and all kinds of organics as the bathtub dries down or your jacuzzi, and it leaves a ring around the edge. And that ring is where the chemical magic can happen. It’s a factory to create what are called polymers. There’s long chains of building blocks. Some of them are like proteins made up of amino acids. Some of them are made up of nucleotides, which form polymers like RNA and DNA. And so all of those things can form through wet and dry cycling phases. So the water has to go away for a period of time because you can’t form the polymer in the presence of a lot of water.
And then when you fill your bathtub up, it buds off not just the polymers go back into the bathtub, but they’re surrounded by a compartments of lipid, which is a membranous compound that’s similar to every cell in your body, it’s made up of a phospho lipid. And so these bud off into the bathtub like soap bubbles. And those soap bubbles either pop or they do not, and that’s the beginning of selection. So as each time your bathtub fills and refills buds off these little membranous compartments full of the polymers of life or the pre-polymers of life, it creates a selection engine of creation that can cycle and cycle and cycle and select function out of those polymers. So out of a random background sequence, you get polymers that form pores, polymers that do Stuart Kauffman’s auto catalytic sets, polymers that do structural things.
Out of the trillions of roulette wheel spins of these protocells, you can get the emergence of a, well, what we call a protocell, a thing that’s on the way to life. And so that’s where the science has come or many, many other supporting pieces of evidence such as the organics to drive all this don’t have to be fabricated. They’re dropping in from space. They’re coming in from a dusty early solar system accretion disc full of amino acids and the building blocks of membranes, landing in these warm little pools that then can cycle.
And UV chemistry can serve a role to create the nucleotides from their source materials. And so on and on it goes. So this has now gone from a conjectural science to an experimental science with experiments not only happening in the laboratories around the world, but out in real hot spring analogs from Rotorua, New Zealand to Yellowstone, to Russia, Iceland, et cetera. Teams are going out doing prebiotic origin of life, protocell type chemistry at the edges of hot springs. And that’s where we find ourselves.
Jim: Yeah, that’s good. Thank you very much. Good job. We’ll like to just make sure for the audience’s sake, that while this is one of the leading theories, it’s not the only one. You did allude to the Mid Ocean Ridge theory that a significant number of people still adhere to, I believe, and have various mechanisms that they described. And there was for a while a theory about clays, again, in a similar kind of cycling mode where clays themselves could form physical structures that could act like catalysts, et cetera. I don’t hear about that one so much, but there are others. And so this is a definite contender and it seems to be on the rise, but it may not be the answer, but we’ll know one of these days. But let’s do deem it to be true. Let’s say it’s true, Bruce, and his collaborators have got it, what does that hypothesis do to change how we see how life then subsequently evolved?
Bruce: That’s the question of the hour or of the two hours here. What are the implications of this? And one of them emerged when I was Googling around about four years ago when we were writing our core hypothesis paper. Because I was thinking to myself, wait a minute, the protocells that are cycling through our little pool, when the pool dries down, they form this sludge, this aggregate. It just makes sense. Your bathtub ring starts as a kind of sludge at the bottom of your bathtub. And I thought, you know what, that sludge protects the protocells. They get inside it and they’re protected from all kinds of things like sheer forces in the water, and maybe they have a more stable pH. Maybe they can be connected into a network almost because. Things that gather together generally survive longer.
Humans that are alone in the wilderness don’t survive really, typically, but when we’re in groups and we form communities the whole is greater than the sum of the parts, as the old expression. And so I sort of started googling around, this is like a niche. An ecosystem niche is something that is built by active organisms. So a beaver builds a beaver dam and creates a pond and creates a niche that actually makes it better for the beavers. It forms ponds, it supports fish and wildlife and birds and things like that. So niche construction is a real thing. And so I literally Googled niche construction, found a guy named John Odling-Smee, very English gentleman based at University of Oxford, who was the father of niche construction theory, had written the first book about it with colleagues, got in touch with him, met him. And we’ve been collaborating now for five years and we’re currently, I’m helping him put together a new book on niche construction theory.
And what we are proposing for evolutionary biology is something quite new, that niches came first, i.e., that the protocells have to be in community or in some sort of collective aggregate in order to be secure to undergo evolutionary changes. And that, in fact, the origin of life was not the origin of a common ancestor in competition with other potential common ancestors. It was a communal aggregate, call it in microbiology terms, a consortium. The consortium model of microbial communities was primal. And that a niche emerged first into which protocells basically transited from non-life to animate matter. And that this niche was very complex and very nuanced. It was able to support, it was an engine of creation, if you will, or an engine of emergence. The progenitor we’re calling, this niche.
And so that this unit is a new discovery or it’s a new proposal that we can test in the lab every day as we do protocell experiments, bringing the hot spring into laboratory simulation chambers, for example, and growing the bathtub ring, which is the progenitor environment, which is the niche construction, the proto niche construction, which yields life. And so where this affects the view of the long evolution of life, back to your question, is that if niches came first, they’re primal and everything’s consortia within a niche.
So the emergence of this [inaudible 00:13:10] filled hormone infused, internetworked electric message passing genes everywhere smush that is the planet earth, is the primordial soup into which life emerge, and still emerges, and we ought to pay more attention to it. And that Gaia, if Gaia is a thing, it’s a common niche first. And that the origin of life was not in the beginning, not strictly competitive. It was mostly collaborative, mostly about a sharing network about network relations. So that’s where some of the ideas of the 21st century can come in even in AI.
Jim: Very good. Yeah, that was good. So let’s now go to what we talked about. I don’t remember if we talked actually on the last podcast in any detail, but I know we talked about it in the post game conversation, which I often have. And that is that kind of beneath your theory of origin of life and then life’s ongoing evolution in this kind of co-evolution of life and its niches, you’ve discovered something perhaps more general, what you call the probability interaction memory process. And you’ve helped that up as a potential fairly general purpose engine of emergence. Why don’t you give us a fair bit on the PIM process model?
Bruce: I’m certainly happy to do that. So Jim, one day, I was looking down the barrel of a microscope at these passively assembled protocells, which we had stained. So we stained them so that we could use fluorescent imaging to tell whether or not there were polymers in there. And these were polymers of RNA. And as we watched them move around under the cover slip, Dave and I noticed that if we had had two different microscopes going, one had no polymers and one had polymers, and the ones that had polymers stayed stable and were moving, even when we rehydrated the solution, they were stable for at least 10 times the amount of time than the non polymer bearing little lipid compartments. Which is kind of fall apart, they bleb off, but they don’t stay stable. They don’t create a network.
And it occurred to me at that time that what you’re looking at in this microscopic movie is an engine that is doing something. And I did some thought experiments as is my normal practice, and I said, “Well, what are the parts of this engine that are working that we can see?” And so the thought experiment went deductively as follows. What is the difference between those polymers as free floating in solution in the universe somewhere? And when they get crowded together and trapped in these membranous capsules we call protocells. And I realize, well, that does one thing that increases the probability that they’re going to get together and do something chemically. If you’re crowded together, you are in a probability engine that makes unlikely outcomes happen.
So we’re crowded together right now in this podcast, and unlikely things could occur because we’re close even though you’re on the east coast, I’m on the west coast, et cetera. But we’re engaging in a almost protocell probability increasing crowding exercise. And I thought, okay, fine, you have to get things together to get them to react as just a general principle. And then the next part of the thought experiment was, well, what happens when those compartments themselves crowd together? They crowd together in this aggregate sludge, if you will, and you can have trillions of them in compartments next to each other. And because the membranes allow materials to pass back and forth, something amazing happens in that sludge. A reaction over here in one of the compartments can diffuse products throughout this aggregate to another compartment, which has a different process that picks it up, does something else with it, and passes it on, passes it back, or passes it to another place. And you get the spontaneous emergent of interconnection of an internetworked system.
And that’s new in the universe. The universe doesn’t do these discreet point, node point network graphs very well. I mean you don’t find it in geology, you don’t find it in stellar furnaces. The universe just doesn’t do discreet networking. And so I realized I interconnection was the second property we were seeing down the barrel of the microscope. And then what came was the proposal that in a cycling system of protocells moving around, getting dried down, getting squeezed together, the polymers getting mixed together, and resynthesizing then getting butted off again back to that gelatinous crowded phase that something could emerge in that system where you have probability shaping, you have interconnection, you could get memory.
On top of that stack, you could get the first templates that might emerge and get selected for that can make copies of things like Stuart Kauffman’s auto catalytic processes. And that when memory emerges inside probability shaping, inside interconnection, internetworking, you now have a takeoff point because the little memory modules say, “Make a copy of this for the next round. And do it good, make a bunch of them. And then let’s try to make a copy of a different thing for the next round.” And you keep cycling, now you have the takeoff point of life because in the universe, think of it as like a two cycle engine, on/off motorcycle engines or lawnmower engines. The universe just gets stuff together and blows it up, gets it together and blows it up.
But there’s no memory that tells one star how to make the next star. The next star comes out of the cloud of matter blown off by stars that preceded it. But that’s not instructions, that’s not blueprints. But as soon as you get the little templates in the PIM model of protocell evolution, you now have a blueprint template linear polymer that tells you what to do. And the takeoff point is going to be rapid in those systems. You get what in chemistry is called amplification, and it can happen with catalyst, it happens extremely rapidly. So PIM seem to be the three ingredients working together, crowd stuff together.
So it happens then share the results in interconnection and then build those results into templates through memory and do it again and do it again and do it again. And what we’re doing here on the Jim Rutt Show is crowded together, you and me, we’re doing internetworking, we’re doing a whole lot of symbolic passing with our voices. And we’re recording the show for the next show or for the next conversation you have or I have, we’re doing PIM. So PIM, in a sense, could be universal. It could be a completely general principle of emergency and culture and from life’s origins.
Jim: That’s really good. Let me play it back at you a little bit, make sure I got it really clear in my head and for the audience as well. So when we talk about probability in the origins of life sense, it’s concentration, holding things together. So the probability of things colliding goes up to the point where other things can happen. So first you got to increase the interaction rate because actually if we move from biology to let’s say, the internet, we may not have physical concentration, but we may have some other form of concentration. Does that make sense? Do we got that right so far?
Bruce: That makes sense. And it’s not just concentration say, of if you put all your chemicals into a cup, you might get them concentrated enough to start reactions. But if they’re in little compartments that are, in our case, cell-sized, the reactions not only happen more frequently, but they’re contained. They’re not lost into the bulk. So it’s concentration within a compartment that matters, and that boundary lets things in and out.
Jim: That’s the next piece. And this is actually our game B social chain stuff, interestingly, talks a fair bit about social constructs as semi-permeable membranes. And semi-permeable is the magic word because semi-permeable membrane lets certain things in and certain things out, but other things it excludes from leaving and other things it excludes from coming in. And so this is your next piece of your PIM models, I understood it, is that in the biological sphere, these membranes evolve to not just be rigid and impenetrable, but to become semi-permeable so that messages, in essence, could come in, in this case chemicals, could come in and out, but selectively. And with different selectivity in to out than out to in. Is that approximately correct?
Bruce: That’s correct. And what happens at the origin of life is initially everything is passive because in a living cell you have pores, they’re energy-driven, active selecting pumps in your cell boundaries. In the origin of life, in the prebiotic progenate world, you have these passive disruptions of membranes that might let smaller or medium-sized molecules in, but they’re really not that selective. It’s kind of like a sponge soaking stuff up and then letting stuff go. But the sponge is still pulling stuff in, but it’s not very selective, it’s not very efficient, and life makes that efficient. And as you say, social networks like rules of the game and joining this group are blah, blah, blah. And this is the conversations we’ll accept and this is our coda, if you will. So semi-permeability is a powerful concept that is universal, I think, in biology, in protobiology, and it’s in culture.
Jim: And then if you abstract that up, it basically is if we have a concept, an abstract concept of a container, let’s call it a membrane, and we have protocols that penetrate the membrane in two directions. And the protocols can either be symmetrical or non-symmetrical, right? A protocol could allow inbound messages and outbound messages that are identical, or it could be asymmetrical where an inbound protocol may be quite different than an outbound protocol. Is that getting closer to a general description?
Bruce: So here’s a really good example of it is COVID. The way that the spike proteins work to trick the cell into allowing the COVID virus to dump its contents into the cell, that’s a one way. And then the COVID virus tricks the cell into making tons of copies of itself that then can get out the other way. So the inbound and the outbound allow the amplification of the COVID virus and people can get sick and die from that. But a trillion, trillion other processes are going on at the same time. And in a sense, the intelligence of the cell, a big chunk of it emerges or exists right on that membrane, right in the boundary. That’s where the wars, the battles are fought in your body every day.
Jim: Gotcha, gotcha. Now let’s take this idea of a general model for ratcheting up of emergence and apply it to some other discipline. What would be your choice of something you’d like to apply the PIM model, too?
Bruce: Well, here’s a good one for you. How about the health of society or the health of civilization itself? So for example, picture civilization in which people get together in sort of high trust environments, they form these units, whether they be companies or churches or villages, where they have healthy communication, where the communication’s clear. Like in a farming village, if you need to go and get your horses shod, you need to have someone you can reliably say, “I need a type one horseshoe.” Or you have to determine are these horses’ hooves healthy or not. And you get a clear answer from your ironmonger, and then you get a clear relationship to shod the horses if the horses need to be shod.
And then you have a clear way to record the transaction. So while it’s four shoes, and this is what the rate is, and this is an agreed upon thing. And so you pay the ironmonger in whatever coin you have, and you have a healthy community so that those horses can go out and do work and make food for the village, and et cetera. But what if in that village, communication had broken down? So that when you go into the village you’re not trusted, and you don’t trust the ironmonger. Or you can’t even get together with the ironmonger. The ironmonger is never available so you can never get into that concentrated high probability environment to have the conversation. And what if the knowledge about horseshoeing has been lost, and there’s sort of a junior ironmonger who doesn’t know how to do it? And so the very skill and practice has been lost.
Then you have a place that’s going south very fast that in the ratcheting game of Darwinian selection, that village is not going to be around for long. People are going to have to, they’re going to run out of food and they’re going to have to migrate on. But that holds for the whole of our civilization. So if we corrupt our data, our engineering data, our health data, our science data, our climate data, and we create distrust between individuals, communication breaks down, that’s I, the I in PIM, and then the memories are no longer trustable, that’s the M. Guess what happens? The P, which is the probability that you’re going to survive and make progress, crashes it’s pulled down pretty quickly without the I and the M. So it’s a core kind of a thing. The health of all three of these algorithms, the core algorithm running together.
Jim: And we think about this, this also gives us a big design space for systems. We can invent changes in the permeability of membranes, we can change the scope of the membranes. We can invent new classes of membranes. For instance, if we look at modern late phase financialized capitalism and compare it to earlier stages of human civilization, we have most humans being broken down to the nuclear family or the individual as the main locus of their attention. And then we have interactions with two anonymous systems, one called the market and one called the government. And most of us get our sustenance and a fair bit of our status and everything else from those two kind of abstract transaction machines that are lacking in much of a human touch.
While if we look back in time, in fact, not that far back in time, even as recently as 1850, most people lived in face-to-face communities famously around the Dunbar number in size. And their security and their sustenance came from their community as a whole. There really weren’t homeless people living on the streets of English villages in 1850. Now, there were on the streets of London in 1850 where the rules of financialized capitalism were starting to apply. But it wasn’t true in the villages where if somebody was down on their luck, somebody would take them in. If somebody was, let’s say, mentally deficient, some job would be invented for them, like going to the pump and bringing water and carrying it over to the blacksmith, something that was in their capacity to give them a role that gave dignity to their life.
And so what could maybe say, tell me if I’m right here or wrong, say wrong, is that in one design, the membrane is around the face-to-face community. And a lot of stuff happens within the face-to-face community. There is interaction outed in, but it’s at a lesser rate than in the new model, the financialized capitalism model where the membrane is around our nuclear family. And there’s just a very large rate of interactions in and out with the market and the government and of course, with other individuals. But there really isn’t the strong sense of this face-to-face community membrane. Does that make any sense?
Bruce: That actually does, Jim, and when you think about it, in a lot of circumstances, and I’ve been through this in my life many times where the membrane collapsed down just around me. So I moved to Prague at one point. I was doing work overseas, and I was pretty alone. And even when you’re a student, I mean you have a community around you, but sometimes your family’s distant and your friends come and go and people are moving constantly, and the membranes around the individual. So perhaps social media coming, people reached out to fill that need of community, of course, through social media, and tried to draw around them connection or the I, interconnection, such that they would develop a community of practice and memory and story, M, which could be memory, but it could also be meaning.
We talk about game B, and the meaning crisis memory, the health of a memory is also the search for meaning. And so then the probability of you being healthy and finding your way in life has increased. So I think it’s an apt metaphor. So people trying to climb back out of the loss of that village, despite their advantages of being in a technological civilization with capital and with the order stuff and it comes tomorrow, all that. But there’s been a lot that’s been lost. And perhaps our search is to try to get back into equilibrium with PIM.
Jim: Indeed. And the social networks that we have today, at least the leading ones, do not seem to have been designed or evolved their way towards a specific process of filling the need that face-to-face community used to have. They were essentially sort of just designed by happenstance and frozen accident, et cetera. For instance, Facebook has a limit of “5,000 friends”. Obviously, nobody actually has 5,000 friends, and work by things like Robin Dunbar, who I had on the podcast, really interesting episode. He suggest there are actually networks that humans have that are around the 5,000 number, but it’s the names that you could vaguely put a reference to. So it includes famous politicians, famous celebrities, sports stars, at least of the teams you follow, et cetera. Your friends from elementary school who you haven’t seen since you were nine, that is your world of 5,000.
And should that be the first class membrane, if we’re actually thinking about how to fill the needs that we have lost for human well-being and meaning, probably not. Maybe it’s part of a series of concentric circles, but the lack of actually thinking about these things from a principled design perspective kind of has left us in an odd state, right. Talk about that a little bit.
Bruce: Yeah, I think you’re absolutely right and that we think that we know who we are as creatures medically. So we have a hole in the heart, we can actually get surgery for it. We don’t have to sort of suffer a terrible reduced life so that the physiology, the mechanisms of our bodies, we’re kind of getting better and better at. Diet, scientific medicine, genomics, all these sorts of things. But perhaps we’re getting a D on understanding our thriving within community. We’re really not there. The idea of this movement constantly reoccurs, I mean since the ’60s and ’70s, the idea of reinventing the village or somehow bringing the village back. But it’s really hard to do if you’re living in a 50-story apartment tower in Shanghai. What does village mean there? What does village mean in modern life?
And we have gatherings here at Ancient Oaks Farm and we’re trying to sort of edge our way back toward that. But people are very, very pulled apart and very fractured. Even in a mountain community, people are overworked and they have all these stresses against them. But when you come together around food and around the producing of food, and this is what my wife Catherine Lucas is working on, the idea of breaking bread and sharing in food and producing, creating nutrition for each other and for children and for a community, I think that that’s a movement that could have real legs around it because people just love that home cooked meals.
If you’re harried and you’re working over the hill in Silicon Valley, coming home to somebody that Catherine calls a heartologist, who is the central core of a community that hosts people and produces high quality, thriving nutrition for them, it could be a real thing. And of course, this is what we had when we had farming communities. We always went over to the neighbor’s house. And I remember in the ’60s in suburbia we used to do that. And it all kind of went away by the ’80s.
Jim: Yeah, that was funny. I was talking to a friend about that, how the neighborhood parties that our parents had… I was born in 1953, so I am kind of an early-ish baby boomer, and parents were classic GI generation folks. And those folks, none of them ever hired a plumber, for instance. Somebody in the neighborhood sort of knew something about plumbing. And even my famously mechanically incompetent father got fairly decent at replacing a water line in a sink or something like that. And I remember the little suburban track houses we lived in 960 square feet, well built though. But one exception was the driveways. For whatever reason, the guy skimped on them, and they were little one car, small, tiny, little driveways, and they all cracked up.
And one summer, the various fathers, none of them could afford to pay some contractor to fix the driveways, rounded up the older kids. At that point, I was about 13, so I was one of the older kids, and we went around and replaced, I don’t know, 10 driveways one summer. And then of course, afterwards the group of fathers got together and sat around picnic tables and drank beer and told World War II stories for about nine hours. So there was something quite different from this transactional, getting everything from the marketplace via transactions. And that was as recently as say, 1965. So not exactly ancient history, at least not to old farts like you and I.
Bruce: Right. So perhaps that is a way to apply PIM to create healthy communities and raise a healthy next generation, perhaps. Perhaps PIM is a gear in the cog mechanism of game B.
Jim: Yeah, I would say, as I’ve been thinking about it, and I did do some thinking about it prepping for this episode, that it may be a underutilized lens to do the kind of thinking that people like game B and plenty of other people are doing. Because as you say, it turns out it’s really hard to build a village. The hippies in the ’60s and the early ’70s also proved the mean life to death for a hippie commune was like two-and-a-half years. There’s a very tiny number that survived for a long time. I think the farm in Tennessee is still going. There’s a few others, but not many. And so this time around if we actually use some principles, maybe we’ll be better off and perhaps PIM ought to be one of those principles.
Bruce: Yeah, I think it’s one of the Copernican potential implications of the work on origin of life.
Jim: Yeah, I do think it is interesting. I’m not quite yet sure. I’m willing to say it’s Copernican, but it may just be that I don’t understand it well enough. So now let’s move on to some other areas where you think that the PIM model can actually sort of be really important in making some progress. One we’ve sort of hinted out a couple of times so far in this episode, I think we talked about it a little bit last time, is AI. What does PIM have to say about the search, particularly for artificial general intelligence, the big kahuna, which may be summoning the demon, but nonetheless, I think if you woke them up at three o’clock in the morning, most people in AI, that’s what they really want to do.
Bruce: If the listeners go back to Ben Goertzel’s interview on Current 72, not just a few weeks ago, they’ll hear him talk about the three routes to AGI, artificial general intelligence, and essay that he published I think over the summer, right, Jim?
Jim: I think so, yeah. Recently at least.
Bruce: And I think there was the cognitive approach, the neuroscience or the brain approach. And then there was something he added at the end, which was the sort of chemical soup algorithmic chemistry approach. And from my understanding of Ben’s reasoning is that when you actually break things down, if you’re looking at a brain, and you want to model the brain, you can use a cognitive model that’s somehow abstracted from neurons. It’s just what is learning and is it in modular or is it in interplay? And this is not my expertise, but the cognitive approach is largely the work of AGI and their work on Open Cog to build the hyper on version of Open Cog, which is what singularity net and everybody’s doing right now or they’re sort of ramping up to do.
But then there’s the brain model where, well, we should model how that brain actually works, how neurons work for real, or at least to some level of detail, and try to mimic actual brains to get to a general learning system that isn’t narrow. Like say for instance, recognizing cats and images, which is a deep neural net job, a narrow AI job. It’s surprisingly successful at doing it, but these are very narrow little buckets. Even something like chat GPT, it’s a sort of a narrow bucket. It’s not a general learning system, which is what Ben is calling for. But then what he concluded was that if you’re going to model neurons and all their subtleness, not just neurons but glial cells and all of the potentially even quantum effects that are going on in the brain.
Jim: Yeah, neuromodulators, I mean it’s an amazingly literally complex system, right?
Bruce: Yeah. So if you’re going to do that, you might as well look at modeling chemistry itself. Now of course, there’s an entire field that does predictive models for how proteins would fold in distributed way. My work for my PhD was to build a system called the evolution grid or evo grid. And this was back in way back in 2009 to 2011. And that was a virtual chemical system to study the emergence of how bonds form in a soup, the Evo Grid Project. And we got a little bit of a ways along. We found something what we call the stochastic hill climbing optimization. And this is another general little principle that we could add to PIM.
So if PIM is an engine that allows things to ratchet up, a term I know you like, to ratchet up complexity and hold onto it, if you take a little PIM engine that’s climbing, and Stuart Kauffman, our mutual friend talks about complexity landscapes as being like mountain ranges, so if you have a rugged landscape, the hills and mountains just go up and down and up and down, and they’re not connected by any ridges. So if you’re going to walk up, try to get up the tallest mountain, you have to go up some pretty tall mountains and then all the way back down to the deepest valleys before you get to climb that tall one. It’s a hard slog.
But if the mountains have ridges between them and the mountain range sort of goes up on a general kind of a curve, you can climb up to that tallest peak by optimizing your selection of ridges, connecting peaks. And this is what mountain climbers know, but this is called stochastic hill climbing in terms of evolution. Because if you get up on top of a little mountain and you say, “You know what? I use these crampons to get up to this mountain, I’m just going to use these same crampons and try to find a higher way up.” And you might find that no, you have to use a different technique. You actually have to leave the mountain, walk down, and do a random walk until you find what seems like a ridge to get to the next mountain. You have to try that ridge.
So you have to be suboptimal, you have to give up all your gains or a bunch of your gains to go back down, try to find a path to the next mountain that goes up, and that’s called stochastic hill climbing because you’re saying, well just pick this route here. Say if you were a blind mountaineer, you couldn’t see the next mountain, you didn’t have a way to predict it. You would just go around until you felt the landscape was shaped to where it was a ridge that you could go out and try to find a next way up. And so in a sense, evolution and maybe even pre-evolution, the complexity formation back to cosmo genesises is all stochastic hill climbing.
And that’s what I wanted to ask the question in my PhD work was, is there a formalism that carries from cosmo genesises through biogenesis all the way into culture, consciousness, et cetera? And I think PIM is what comes in when life starts, but stochastic hill climbing I think is a general purpose. And so how does all of this tie into AGI?That’s the question of the second hour here, right?
Jim: For a while at least. So let’s hear it.
Bruce: So this is a guess. This is what we would call hand waving. I sent my evo grid thesis and PIM to Ben Goertzel, and he actually wrote the math for PIM. This was about four or five years ago, and it was a start. And then we started having meetings at Google. Our little team here went down to Google’s AI group. What they were looking for was a way out of the cul-de-sac of deep neural networks. That we have to train every single one of these back propagation, old school, but now very effective deep neural networks, we have to retrain them on data over and over again to get them to do new jobs. And because we’re Google, we have a lot of data so we can train them to do lots of different things, but it’s still a manual process. That these deep neural networks are not jumping across the chasm to find the next mountain to do the hill climb to work on a new problem. They’re not general.
And so how do we do that? And the general ideas came was like, okay, let’s take PIM as the core algorithm, something that isn’t a back prop network. It’s a generalized network that can form patterns and test against patterns and do pattern matching. And when I met Ben down here a couple of years ago, he came down to the property with his family, we sat out on lawn chairs just out in our meadow here. And he told me about Open Cog 2.0, and how they were going to do graph-based systems rather than deep neural networks with feedback. They’re going to do graph-based, nodal-based pattern recognition systems to try to push toward AGI. A little bit how the brain is wired. Definitely not how current narrow AI is done.
So then we kind of came to this idea that, let’s put it all together. So you’ve got a graph-based system that should be able to pattern and learn, and then you have a whole lot of hill climbing you need to do to scale ridges of learning. What you need is something like a clump of protocells. So a clump of protocells are little chemical engines that are little algorithmic chemical soups that each are being stressed, they’re being tested like you can go forward if you can stay together and you don’t blow up in solution. Therefore, your constituent parts, polymers are doing jobs to help you stay together, to help you amplify, to help you replicate for the next generation. Those are learning machines. Those protocells individually are learning certain things because they’re learning it through this selection process on a massive scale.
But those protocells as a group, as a clump, which we call the progenote, are a learning system. They’re learning how to be in a warm little pond four billion years ago, fed by sources of sunlight, heat energy, organics coming in. They’re learning how to form a bathtub ring that can grow and adapt. So there’s a clue there in our hand waving conjecture that the a progenote, protocell system is a general learning system in a complex environment because those progenote masses get washed downstream into other pools. They can dry down as films and get blown by wind and land in another pool.
And those other pools might be more dilute, they might have more organics, they might be under a rock where they’re shielded from UV, they may be cycling every 30 minutes instead every of every hour. So the clock rate is different. So if you see this landscape of progenote protocell masses in different environments, exchanging material back and forth across a landscape, that’s a learning system, that’s an AGI of the Hadean, of the early earth. It’s learning by templating itself onto the landscape, going through stresses, and it’s responding to the stresses through an early form of evolution. And that perhaps this is a model for AGI itself.
Jim: Talk about that a little bit more, because very low level functionality, we have lots of tools for achieving, as you mentioned, the current blossoming of the surprisingly simple-minded deep learning models that have taken us quite a long way. And I’ve been playing the last few days with chat GPT like everybody else on earth, and it’s pretty damn amazing. But it is quite the other extreme from starting with small little pieces and growing up. It’s a massive 180 billion node static compilation of essentially a gigantic body of text.
One could imagine in the PIM insight starting, getting a little bootstrappy thing at the bottom. But one of the hard problems in evolutionary computing and evolution more generally is how does emergence occur in these kinds of systems and how are hard problems attacked by building up essentially hierarchies of or networks of processing. Any thoughts on the relevance of the PIM model to that, particularly in an AGI context, if that was clear enough? If not, ask me some questions and I’ll try to clarify.
Bruce: Yeah, I think we’re groping a little bit in the dark here because it’s such a big and bilious problem. What is emergence? So what we have to actually do is to propose specific scenarios. So for example, you could write a software kind of derivative of Chris Langton’s original work and work of Tierra by Tom Ray, this is all back in the ’90s. And of course work, at SFI and many, many Alife researchers. One of the things that Tom Ray said toward the end of his work on Tierra, we were all up at a conference in Banff Canada where we climbed to the Burgess Shale to actually crack open shale and look at these, came explosion creatures that were made famous by Stephen Jay Gould’s book A Wonderful Life, this is in 1997, is the conference site I put together.
And one of the things that Tom told me on the way down the mountain after going to the Burgess Shale, he said, “What we need to do next in all of our simulations for artificial life is to create cells. Because we’re doing bare naked informational molecules.” Tierra was strings of genes, it was like digital genes, competing and replicating and viruses would emerge in the system. He said, “We have to make an environment that’s more like a cell, more like organelles, that’s highly complex, that’s highly, it’s full of wiggles and wobbles. It doesn’t use procedural algorithms that uses combinatorial wiggles to get everything together.” And that was the beginning of the thinking around PIM actually, that that would be the next step for Alife to do.
And so when I was working on this, trying to do an artificial chemistry in 2009 to ’11, I realized the compute is not there. The compute power is not there. And I think you talked about this a little bit with Ben, that massively parallel hardware would be needed to do this. So I have a proposal, an interim proposal for how to test this idea that virtual chemical soups could lead us to AGI, which is to do something that I’m calling a Genesis engine, which is a hybrid between real chemical soups and an AI engine that tracks them. So consider if you built a chamber in your laboratory that has injectors in it, that real injectors are injecting real chemicals into the system. Real little mineral dishes that have basically simulating a hot springing conditions pump full of CO2 so it simulates the early earth, doesn’t have free oxygen to break things down.
And then you literally wet dry cycle the system, create your little protocell slurries from meteoritic sources, from sources that are plausibly coming in on the early earth, you could crank up. We can do this on a frying pan on a kitchen stove in a morning. We can create these protocellular masses from the building blocks in their billions to trillions. So we can do this in the lab. Then what you do is the protocells are formed, they’re cycled through selection barriers, and then a little channel pumps them out of the pool through a cell sorter. And the cell sorter might have fluorescence in it. It might split the cells into certain types. The empty ones go off here, the full ones go off here, it stresses them and then it does real-time gene sequencing on the polymers within them.
The real-time gene sequencing data pours into the AI, into the AI that is trying to model what is going on, trying to make predictions about what is going on. It gets all this gene data, it runs a quick computation saying, “Well, those short little templates are templates,” or, “Those short little things are like proto ribozymes that can do certain jobs.” We found one. We found a ribozyme that can do this little widget job that emerged completely from random in the soup. What we’re going to do now is that AI then talks to a chemical computer that can create new sequenced polymers, and inject them into the system, the physical system, and it makes more of that same ribozyme.
And a ribozyme is a little polymer that can do jobs. It can fold into a job, it might be able to template and make a copy of something. So ribozymes are important in the origin of life so that the chemical, basically synthetic biology system cranks out more of those same things, puts it back into the hot spring chamber, and then makes a prediction like this could happen. We get more stable protocells or it’s going to compete more with such and such. Then the cell sorter clicks in and you test your prediction back into the AI and the AI learns from the chemistry. The chemistry runs most of the computing because the computers can’t do that kind of predictive molecular dynamics at any speed on that scale.
And then the whole system is a back and forth. It grows up, it learns from itself. And there is some of the AI making predictions, and this is how the free energy principle of how brains are constantly making predictions and testing the predictions, how intelligence and cognition works itself. Maybe we could borrow from that and put that into this in this engine, and see that if this engine can grow up, it can ratchet itself up where the chemistry becomes more sophisticated. The protocells are more robust, but the AI is learning from the chemistry, using the chemistry as a pool, like a kind of reservoir computing in chemicals to learn about how chemicals can evolve, but then helping pitch it in a certain direction and test hypothesis.
Jim: Yeah, actually that would be a pretty good fit for Open Cogs, surprisingly enough. I mean, that’s the kind of thing, Open Cog is actually good at, pattern matching, pattern discovery, et cetera. Did you and Ben have a useful collaboration discussing that idea?
Bruce: I think that the collaboration’s actually potentially emerging right here on the Jim Rutt show.
Jim: I’ve fooled around with Open Cog on and off since 2014. It’s its own peculiar thing and finding good problems that are a good fit for it. Sometimes a little tricky, but this strikes me as one that might be interesting. So I would suggest you reach back out to him and see if he thinks there’s a role in using the new… Well, actually it’s not yet done, but the hyperion version of Open Cog. Its ability to find patterns at any kind of level is one of its use case strengths, actually. And even better to do it probabilistically. It doesn’t have to find crisp. Yes, this is definitely true answers, but yeah, this could probably be a useful pattern in this system of other patterns, et cetera.
So probably worth the conversation. All right, well that’s given us a little bit of a flavor of the intersection between PIM and AI. Though I still think there needs to be a lot more work in how PIM leads to emergence and to networks that manifest general intelligence. That’s still a long way off. And so I think maybe you have a start here, but there’s still a lot to be done. Would you agree with that?
Bruce: Totally agree with that.
Jim: Yep. And I have thought a little bit about this artificial chemistry thing since I had that conversation with Ben. And if we add PIM to it basically says, oh, you better start thinking early on about the containers and the protocols and the messaging, et cetera, and not just have the artificial particles interact by rules. You got to have some structure added to them. So I think that might be the biggest takeaway in my own mind from this conversation. So let’s move on to another topic. Regular listeners to the Jim Rutt Show know that I couldn’t resist this one, and that’s the Fermi Paradox.
We did talk a little bit about it in the previous episode, but now that we have a more open-ended implications thereof conversation where we can go a little deeper on a smaller number of topics, tell me what your insights from your work so far tell you about the Fermi Paradox. And also, interestingly, we may be on the verge of actually getting some data that’s useful in plugging back in to the Drake equation, but just take it from the top. Your work, Fermi Paradox, and more generally life elsewhere. What do you think your work is pointing towards?
Bruce: Enrico Fermi I guess posed this question, was it in the late ’50s? Where are they? If the universe is so large and there are probably planets around stars, which we know, where are they?
Jim: Yeah, famously this is at least the story, the version I heard it was at Los Alamos and there were a bunch of young physicists nattering away about life elsewhere and playing with the Drake equation. The Drake equation, for the audience that doesn’t remember, is a series of multiplicative terms that says how many stars, how many of them have planets? How many of them are suitable for the evolution of life? How many of them did life evolve on? How many of them did it become intelligent or technologically capable? And how long did they exist? I think it may be a couple other terms. And they were just yammering away the way young physicists do at lunch.
And we were coming up with numbers like, oh, got to be at least 20,000 intelligent civilizations in the Milky Way alone. And Enrico Fermi, of course, one of the more legendary physicists came by and said, “Where are they?” And that’s the paradox. Some formulations of the plugging terms into the Drake equations give you large numbers. And it used to be common for people to just think it. Well, I confess, when I was a nerdy 14-year-old, I said, “Oh, that got to be tens of thousands of civilizations out there.” But we see no sign of them. So anyway, back to you.
Bruce: We were recently down actually at the SETI Institute, which is the search for extraterrestrial intelligence co-founded by Frank Drake. It’s been around for about 30, 35 years, and it was his memorial service. So he passed away a couple of months ago. And so we were all talking about the Drake equation. And on the wall, as you walk into the SETI Institute here in Mountain View, the equation is written, it’s kind of their scripture in a way. And one of the terms is FL, F sub L, which is the proportion of planets that life can get started on. And that’s actually what we’re focused on. And Dave and I published a paper with our graduate student, Francesca Cary, in the July issue of Astrobiology Journal, introducing a new word, urability, the ability for a planet to start life.
Not just habitability, where if liquid water was present life as we know it could possibly be sustained. Urability is different. You need different parameters, if you will, to get life going on an herbal world. So that’s one little piece that we’re adding in just as these tens of thousands of exoplanets are being discovered and people are asking, “Well, could life start on a super hot world like a hot Jupiter? Or could it start on a world that is completely enveloped in ice as we see in the moons of Solidus or Europa.” All the moons in Europa have tens of kilometers of ice shell and then an ocean clearly below that. And maybe there’s a little bit of hydrothermal activity, but could life start there?
So we created this urability framework with two dozen factors that we believe have to be present to get life going. But it’s not just getting life up to microbes, which may actually be hard, harder than we know. But after you get to living microbes, microbial communities, which there’s probably very few paths to life, because you have to do all the things we were talking about an hour ago, which is you have to have semi-permeable membranes and you have to form polymers. No polymers, no life. Polymers are best formed around carbon compounds. They’re best formed in liquid water at a certain range. They probably can’t form to do any kind of functions at tightened conditions where you could form polymers from tholins, another compound. But you can’t get information storage out of them and you can’t get function out of them. They can’t fold and do jobs.
So it may be that there is one route to life, this is just a proposal, if there’s one route to life. So you have a life bearing world, an FL world. But if it was like Mars, it lost its atmosphere, lost its oceans, lost the surface habitability, and the surface of Mars is sterilizing to life now. So life couldn’t survive at the surface and it can’t get started again on Mars. So how many of these worlds that start life can sustain life for long enough to get to complex life? The proposal that I came up with last time was it’s vanishingly small. It’s incredibly small.
That great filter is truly a great filter, and the rare earth that we are potentially on is potentially really rare as Brownley and Ward have written about years ago. And so there we have it. You need to have something that basically runs the PIM ratchet for four billion years reliably and through the reliable catastrophes that make the PIM ratchet stronger and jack the system up to oxygen, respirating, high energy, multi-cellular organisms. It’s not a given that that path is available because planets effectively can die out from underneath their own biospheres.
Jim: Yeah. Then as you point out, even though they may have once been able to initiate life, as in the case of Mars, when it dies out, there’s no ability to restart because the conditions have changed. All right, let’s go on. I asked the question last time, and I don’t think you actually answered it last time, so I’m going to answer it again. We were talking about some of the interesting things, the conversation just kind of just went. And that is we may be in a place where some external data may help us pick between the ocean vents and the warm pool model. And I would propose it like this, that if we find life that is not congruent with the earth life, so it’s truly different life and it might not be right, it might be the same life that’s kind of just moved around in the solar system.
And in a world as you described, under 10 miles of ice and no obvious mechanism for warm little pool that evaporates and refills model, then that would be a stroke against the warm pool model. On the other hand, finding life on Mars would at least be not opposed to the warm pool model because particularly a colder, more distant from the sun that’s smaller planet also may have had less water, may have had more land mass and may have had more opportunities for pools. It doesn’t necessarily rule out the hot vents model, but it’s at least supportive or not contradictory to the warm pool model. Would you agree with that?
Bruce: Definitely. And in fact, Chris McKay, who’s a planetary scientist at NASA here at NASA Ames, he has a quite famous quad chart that… And a quad chart is a government kind of a researcher’s way to say, well, if we find life on Mars and it’s genetically similar, and I use the same genetic code, then that’s a common origin between Earth and Mars. So life could have started on Mars and been blasted out into space in rocks, and just like that famous meteorite back in the mid ’90s, landed on earth and inoculated the earth. So that could be a common origin. If we find something that’s vastly different in terms of a genetic code between Earth and Mars, it argues for two distinct origins.
And the same thing would be true with Enceladus, for example. So missions are now being designed and instruments are being designed to fly through the plumes that are coming out off of in Enceladus’ South Pole to look for biomarkers, to look for things that are breakdown products of a living system to look for fossil lipids for example, or short strands of nucleic acids, which would argue that these things are just large enough and complex enough that they can’t be made by geochemical processes. They have to be sourced in life.
Now of course, if they find those products, they won’t necessarily be able to have a complete bug, a complete microbe that they can then break it down and say, well maybe Enceladus was inoculated by Mar’s life, for example. And it’s just life started elsewhere. It started in a rocky world with warm little ponds and then it can spread to an icy moon as long as the impact rock delivers that cargo right into that ocean. And then those microbes are free, adapted to live in the energy sources available in the ocean. So if we got a bug from Enceladus or Europa and it was a vastly different chemistry, then we really could argue that life can start on an icy moon or in an ocean, all ocean world. So those are definitely, that is there’s your quad chart there, and you just have to run the missions and the future will tell.
Jim: And of course the other thing that we’re just on the verge of getting are atmospheric component measures of exoplanets. Talk about what that is for the audience’s sake. And then what implications that might have on origins of life and Fermi Paradox questions.
Bruce: So if you can see starlight, the star of the planet’s orbiting, if you can see it through the atmosphere, you can get a spectrum and you might be able to say, for instance, certainly detect CO2, but what if you could detect methane? Does that suggest that methanogens, little bugs that eat and produce methane, are present on that world? Well, the challenge of course you’ve got is that there are methane whiffs that have been detected on Mars, but it’s not clear if they’re from microbes living deep in the rocks or if there are geochemical source. It’s suggestive. It’ll give us hints and clues, but it may be difficult to using atmospheric imaging to determine once and for all if there’s life on a particular planet somewhere.
Jim: And what about free oxygen? Some people argue that if we see any significant amount of free oxygen in an exoplanet atmosphere, kind of hard to figure out how that happened without life in the mix.
Bruce: It possibly would require life, but I keep seeing articles like sources of free oxygen on model exoplanet. They’re geological sources.
Jim: So that may not help us much. Is there anything that could be strongly helpful in these questions from observing exoplanet atmospheres? So I suppose we saw no oxygen and no methane anywhere. Would that tell us anything?
Bruce: Again, it’s not conclusive. It might hint in a certain direction that not only getting life started to the microbe stage, but getting it all the way to plants, where plants are just oxygen generating factories much more than a microbial map, which was the dominant form of life on earth for 90% of its history. But it produces sort of whiffs of oxygen, gradually converting, also oxidizing all that iron out of the oceans. And so when you get all the way to plants, you get this huge uptick. And of course, complex eukaryotic cell-based organisms need an oxygen source to drive their citri acid, Krebs cycle. Their mitochondria need it or their chloroplasts need it.
So it’s like a one-two thing again. It has to go hand in hand, but it took earth, what was it? I mean the first plants are 400 million years ago or something like that that started in the fresh water environment. Some of those proto plants are still around, plant 0.1. But to get to that, to get to that proto plant is a major bunch of probabilistic fortitude to get to plants, to get to multicellularity. Could we detect a micro bearing world? Could we detect the chemistry of a micro bearing world? Or do we have to go back to just saying, well we can get hints at this, but really what we’re looking for is a techno signature, a signature of an advanced technology that maybe has built a shell around its parent star to collect all the energy, a Dyson sphere. Maybe that’s the best shot we’ve got.
Jim: Or of course, with seti.org and the Breakthrough Institute are doing, they’re still looking for signaling modalities like radio or lasers, et cetera.
Jim: But so far, no obvious signals. Very interesting, right?
Bruce: If we are alone, if say, for instance, urability and a working model that’s a plausible model for how life gets started gives us the numbers of how challenging it may be to get from protocells to living cells. It might give us the chilling news that we are extremely rare. And in our time and space in this one galaxy, in this one little quadrant, we may be it or we may be it for the galaxy. And now you’ve gone there many times, Jim, with this, and what does that give us back that we may be it for our neighborhood?
Jim: I think that’s huge. I think that’s huge, right? It’s funny you say it’s chilling. I put it on the opposite side that it’s exciting. People ask me, “What’s the meaning of life?” I go, “I don’t know.” But if I had to say what’s the mission of life on earth? You kind of go up to the point where we are now, the stupidest possible general intelligence or damn close to that, just barely over line. And yet we can see that we could get to anywhere in the galaxy within a million years let’s say, right? And if we chose to bring the galaxy to life, we could do that. And wouldn’t that be exciting? Because then we could run the evolutionary experiments over billions of years, countless times, or we could even cheat and we could inoculate likely planets with eukaryotes, for instance.
And of course, there’s some origins of life people that they kind of just do the infinite regress where they still have an origin of life problem. But it’s further back in time, which is that life on earth may have been literally seated by some other life or at least big life components from elsewhere in the universe. And wouldn’t that be interesting for humanity’s medium term? Let’s say the next billion years is to go out and do these experiments. And if we blow it by destroying ourselves or we don’t even… We’re not going to destroy life on earth. We don’t yet have the capacity to do that. Maybe we could do it with a really, really bad nanotech, self-replicating machine.
But bacteria are going to soldier on, generally speaking, irrespective of what stupid ass shit humans do. But if we were to destroy technological civilization, it would be a huge and sad give up on this ability to bring the universe to life. That’s one fork on the Fermi Paradox resolution. Nobody else out there. Our duty to make the universe more interesting. And of course, the other fork, and we won’t know this for some time if we keep getting negative results, it’s always hard to say how many negatives do you need before it becomes an accepted operational given that it’s negative.
But if it’s positive that there are again, let’s say, we tune in the right frequency at the right day on the right time, and holy shit, there’s the chatter, they’re out there, then we have an entirely different path forward for humanity, which is to probably first stop and listen for a while and find out what’s going on out in the galaxy. Is it hostile? Is it cooperative? Is it indifferent to us? Has biological life been superseded by silicon life everywhere? It may well do us some good, does just listen for 10,000 years. And then depending on what we learn, we may have to defend ourselves or we may have to reach out. We may want to reach out if we conclude that they’re cooperative and friendly and have things to offer and we have things to offer them.
Or it may turn out that it’s turned into all silicon life who are essentially doing nothing but playing go with each other in perpetuity across the universe, in which case we just ignore them. And then go back to route number one, which is to see what our own human destiny is in the universe though maybe with a warning to be even more careful about runaway silicon beings, something like that. So again, that’s why I don’t get any chill, or at least not a negative chill from this idea that we could be alone. It’s actually kind of exciting.
Bruce: I would agree, Jim. And I get warmed up by the following reality, that despite our doom scrolling nature, that we think the world’s about to go off the cliff in a handcart, it’s actually not the case. I mean, we’re smarter, better, more adaptive. We have strong PIM in certain circumstances and our response to things we’re innovative beyond our ancestors’ wildest dreams. And that if the game B type thinking can become more prevalent, the long-term thinking, we will have a long future.
And I’ll give you one little warmup that happened to me about three, four years ago, because what I haven’t talked about here on the podcast is my work in space in mission simulation and design. But one of the things that happened, ironically with the SETI Institute astronomer at a conference back in 2014, I was working out a way how to take asteroids and turn them into resource hubs, get volatiles off them so you can create fuels and consumables because asteroids can have lots of ice on them. They can have all kinds of volatiles to get minerals off of them so you can do construction and space and to turn them into biospheres as well. And it all came down to one thing, encapsulation in a semi-permeable membrane.
And while at this conference in 2014, I met Peter Jenniskens, the SETI astronomer whose job it is to track asteroids as they come into the atmosphere and go and pick up the pieces. He’s a world expert in this. And I showed him the design that I had with a balloon structure going over the asteroid and a little stand that was sort of connect to it. I said, “You know what? If we can heat this interior, we can get volatiles to come off.”
And he turned to me and he said, “That’ll never work. That’ll never work.” And I didn’t know who he was at the time, and it turns out he’s a meteor astronomer and I thought, I’m busted. My whole design process of how to do this is out the window. And he said, “No, let’s go to lunch.” So we went to a fish restaurant in Sunnyvale and over a bowl of clam chowder, when we finished the bowl of clam chowder, he said, “I figured out how to make it work.”
Jim: I love it.
Bruce: We go out to the asteroid, we enclosed it in our balloon fabric, we seal it at the end, we seal it in, and then we introduce a controlling gas like helium. We pumped the gas into a 10th of an atmosphere. Asteroids rotating, it’ll hit the friction of the gas and slow down and stop. We can use waves of gas through the same pump system to turn the asteroid and to put force onto it, put like a one Newton force and start changing its orbit. While we fire, our ion dries out the back end into space to keep up with it. We don’t ever have to touch it. It’s a touchless system.
And then we can heat the interior, we can extract the volatiles into tanks, and create fueling stations. We can extract metals with the carbon oil, gas. We can do 3D printing inside the enclosure of nickel and iron. And my idea was let’s actually melt the asteroid down to where it’s a rocky core and a liquid surrounding globule, and introduce life, and create a small world. Create a biosphere that can create food stuffs, and we can also mobilize and move these things around. And we realize maybe we’ve solved all the three major problems to get to sustainable presence in space, and moving a pathway for life to move off of the earth.
And I did a TEDx talk on it. We did publications. I’m speaking to all kinds of people about this as a viable… People, our teams are working on the seal enclosure part of it now. But the warm fuzzy feeling came to me a couple of years ago when I woke up here at Ancient Oaks and thought the problem I’m working on, the origin of life, is the same principle as the problem I was working on with the extension of life into the universe, which was it’s all about membraneous enclosures to create new chemistries, to create selection, to create… So if you had a spacecraft out at Pluto imaging the solar system over the next 500 years, the solar system would turn into the warm little pond of Charles Darwin.
There were hundreds of thousands of little worldlets, liquid worldlets, gassier worldlets, biological chemical worldlets in bands all around the star because they’re being powered by sunlight hitting the external part of the enclosure and they’re powered by solar power. And then they’re moving around, and that they enable life to erupt from a home world into space sustainably. And it’s the same principle as what was happening four billion years ago in those little ponds. It’s membraneous enclosures, cycles. And it’s the same material, it’s the same organics that we’re using and water on the asteroids that seeded life in that first place. And I got to this point where like, oh my God, we’re on the verge of discovering these core principles, not just PIM or stochastic hill climbing, but this idea of how to expand life itself through common core principles that we’ve learned about predicting and trying to figure out how life itself started in these little pools.
Jim: Interestingly, one additional application for those is we could seed those little vesicles with artificial life that might be too dangerous to let loose on a planet, and let it have its way with an asteroid and see what it comes up with.
Bruce: Good point.
Jim: We want to be really careful messing with that stuff here on earth. But if it’s at a self-contained vesicle and it’s going to take it many, many hundreds of millions of years to develop space flight nuclear weapons, even if it’s lucky, right. So we don’t have to worry about them anytime soon. Let them do their thing out there and maybe they’ll produce some very, very interesting results in a relatively safe way. Well, those relatively optimistic notes, let’s wrap it up. I want to, again, thank Bruce for another very freewheeling, open-ended, and interesting conversation.
Bruce: You’re so welcome, Jim. It’s always a pleasure.