In the next 3 decades we’ll know if we’re alone in the universe — Adam Frank: Full Interview

[00:00:00] Adam Frank: I'm Adam Frank. I'm a professor at the University of Rochester. I'm an astrobiologist and an astrophysicist. I'm also the author of a bunch of books like The Little Book of Aliens and right now the newsletter Every Man's Universe. Today on Big Think I'm going to be talking about four different things. The first is why is life different from non-life? The second thing is an entirely different way of understanding what's happening in quantum mechanics. Third is the blind spot that lives right at the heart of modern science. And finally we're going to talk about [00:00:30] Speaker 2: life in the universe. Are we alone? Thank you for watching Big Think. If you'd like to support our work we encourage you to join our members community. As a member you'll receive our quarterly print magazine, a beautifully designed collection of the ideas and interviews that matter most. Big Think is for curious people who want to take deep dives into big ideas. To support the media you want to see in the world go to BigThink.com/join. And now back to the interview. Chapter 1: The Physics of Agency [00:01:01] Adam Frank: So I'm going to say something really radical here, which is that the most important problem in physics for the 21st century is not quantum gravity, you know, is not the black hole, you know, a firewall paradox. It is life. It is understanding why a cell is different from a rock. The question of what is life is a question of what's called complex systems, which is a, you know, kind of a new science over the last few decades. And it is fundamentally transdisciplinary. It's not multidisciplinary. It's an entirely new way of going about the process of science. And it's really life which sits right at the center of complex systems. So we often hear from science or we're told that science says that, you know, life is nothing but a machine. It's just nothing but a bunch of biochemical mechanisms. And one of the great problems with the life equals machine idea is that's the idea that's being sold to us. And that's the dominant philosophy in the heads of the billionaire tech bros who are shoving, you know, these AI technologies in whatever state they're in down our throats. So many of the people have that view would, if you ask them what a human being is, you'd say, oh yeah, it's just a, it's a robot made out of meat. And, you know, you are nothing but a computer made out of goo. So, you know, I like to use that term, you know, meat computers that, you know, these people would say that we're nothing but meat computers, which goes back to the beginning of modern science. You know, there's the famous story of Descartes who would operate on dogs while they were alive. And while they were screaming, you know, the dog screaming in pain, he's like, oh, don't worry. That's not, you know, it's just a machine. That's just the way it responds. And that metaphor is fundamentally wrong. Uh, life is not a machine. Life is something fundamentally different that we don't really fundamentally understand. Um, and the reason for that is right there in the word organism. It's life's self-organization that puts it into an entirely different class of physical thing. Like black holes are awesome. Uh, you know, comets are really cool, but they pale in comparison to what life is because life is the only system that is innovative. Life is the only system that creates and understanding how that's possible takes us into entirely new territory scientifically. So sometimes when people are trying to convince us that, uh, we are nothing but machines, they will introduce the idea, the famous brain in the vat experiment or thought experiment, right? And it's the idea, very popular in popular culture, like the matrix. One of my favorite movies is that, you know, you could kind of like remove the brain from somebody, put it in a vat, hook it up to electrodes, send stimulus into those electrodes. And then the brain would consider itself to be living in a full world. It's like a simulation. You would be able to simulate the world, uh, with just having that brain sort of sitting there. But the philosopher Evan Thompson did an in-depth exposition of this. And there were a couple of things that came out of it that really showed this idea doesn't really make sense. First thing to understand is on some level, there's no such thing as the brain, right? The brain is a scientific idea where you take the organism, right? This whole organism that needs all of its important parts to stay living and you isolate different parts. Like, oh, this is the circulatory system and this is the endocrine system and this is the nervous system, right? And that's where the brain then becomes a separate thing. What Evan was able to show is that because the brain is part of the organism, an in, uh, uh, escapable part of the organism, that the brain needs the rest of the body as much as the body needs the brain. So even something as important as movement, right? As you move around and you're looking at things, the neural computations that are going on, um, in the brain require in many ways that there is subtle movements happening in order to flush material out. So this idea that you could, you know, extract the brain and make it different from the body. What Evan was able to show is actually the only way to make that work is you would actually have to build a body for the brain, right? Uh, you know, some kind of robot body, and it would have to be so complex and so subtle in order to keep the brain alive, to do all the things that the rest of the organism is doing to keep the brain functioning. That in some sense, you would just end up with a biological body. So a lot of times when people talk about life, they're going to use the thermostat example. They're going to be like, well, you know, you have a thermostat, which is a system that has ability to respond to conditions. It senses the conditions and changes its state based on the conditions. But the thing about a thermostat is somebody designed the thermostat with that goal in mind. There was a goal, um, that someone else had that the thermostat then embodies that other goal, the programmer in some sense. Whereas a dog who's hot gets up and moves, not because somebody programmed the dog to do that. The dog feels hot. The dog has their own, um, urgencies, uh, has their own experience of being hot and moves for those reasons. So that is the fundamental difference between a thermostat and a self-organized organism. So if you want to go beyond thinking that life is just a machine, you have to think on the level of life's organization and it's how it functions. And the most important thing about living systems, all of them, is that they are all autonomous agents. They are all systems that choose their own, uh, goals. They choose and maintain their goals. They update their goals as they're moving through the world is sometimes called the inactive view. It's a view of life where things like consciousness, it's not a property of, you know, uh, of, of a living system. It's something it's performed. It's an action that living systems take because what autonomous agents always have to do is they need to make sense of their world. They're constantly working to make sense of their world for their own benefit, for them to stay alive. Or, you know, uh, it could be that that is a more complex, uh, thing and maybe their status in a social organ, uh, uh, you know, in a social system. So autonomous agents are constantly needing to make sense of the world to realize what's relevant, uh, about the world in a way that no machine that we have built so far is able to manifest. So this is a problem in cognitive science that, uh, has been known about for a long time called relevance realization. So let's say, uh, you go to a party, right? You go to a party and you come inside and you're actually, uh, uh, you want to get a drink, but then you see, uh, somebody who you've been meaning to talk to for a while. And then you start making your way over to them to go talk to them. But on the way, you actually see a book on the host's, uh, coffee table that you've been looking for, for a long time. So what's happening here on the fly, you are changing what's important and you're finding not only changing, but you're finding what's relevant to you as you move through the room. Okay. That's what we call relevance realization. Now machines can't do that. And why can't machines do that? Why can't they make sense of the world in this kind of fluid, constantly changing way? And the problem for machines is machines live in what we call small worlds. Every, uh, uh, AI basically has predefined sets of inputs and predefined sets of outputs as well. Machines are always trained on some set of, uh, uh, data, what they call training data and that defines its world. Uh, even machine learning where it's doing, um, uh, uh, you know, learning on the job, so to speak, somebody, a programmer had to decide what the rules were or what the rewards were for that system to work. So the choice of the data, the choice of the, the machine learning algorithms, those are done from the outside, but a living system like you moving through that room, nobody programmed you. You are finding what's relevant to you on the fly for your own reasons. You are making sense of the world in an ongoing way. That's because you live in a large world. In a large world, there are unknown unknowns, right? In a small world, the small world that the, that all artificial systems that machines live in, there are no unknown unknowns. So living systems have this constant ability to find relevance and adapt to whatever is happening because they live in what we call life worlds. They live in large worlds. They are constantly making sense of the world. They are constantly enacting that making of sense by how they move through their environments and change their environments by moving through them. And so this idea of autonomous agency is really what's at the frontier of this new view of life. And it is really kind of the fundamental mystery of how does something become an autonomous agent and always act on its own behalf. Autonomous agents, they always have their own itches to scratch in some sense. When we think about relevance realization, when we think about autonomous agency, we are recognizing that life is the only system that uses information. You can describe a rock, a crystal in terms of information, but the rock really doesn't care. There's other ways to, you know, to describe a rock thermodynamically or such that, that, you know, that, that would not, uh, where information wouldn't matter. Life, however, there's really no way to describe life fundamentally without taking into account its use of information. By use, I mean its ability to store, to process, to copy, to transfer information. So you could say like, well, then it's a computer. And the answer for that is the answer to this is no, because computers are based on what's called Shannon information. Claude Shannon in 1948 wrote this brilliant paper where he introduced our modern concept of information. And so that's why really, when we talk about information and computers, we're talking about Shannon information, but Shannon in that paper, very clearly, like right in the first paragraph says, I'm not interested in meaning. You know, when we talk about information, we often associate it with meaning, but I'm not interested in that. I'm interested in this engineering problem of, I have somebody who's sending some information and somebody who's receiving information and a noisy channel between them. And so he worked out the mathematics of that using a formulation of information that had cleared meaning out. There was no description of meaning, but really in life, all our information has meaning. All of the information has relevance. All of the information works towards this self created goal or goals that living systems have either to stay alive or to, you know, have raise our level in our social hierarchy, whatever it might be. So what we were is required then is a different kind of understanding of information. And that means an understanding of semantic information. For a long time, people struggled to try and have a mathematical theory of semantic information that would be as good, as useful as Shannon's mathematical theory of information. And by the way, when we talk about Shannon information, what we mean really is syntactic information. You have a string of characters and all you're really interested in is how often in a message does, you know, E, the letter E appear as opposed to the letter Z, right? That's, it's just about the syntax. Semantics adds this new level of meaning. But then in 2018, there was a paper by Artemy Kolchinsky and David Wolpert from the Santa Fe Institute. It was kind of a manifesto for a theory of semantic information. It was a beautiful paper. And along with Artemy and some other people, we formed a group to sort of pursue the theory of semantic information. And over the last four years, we were able to take that theory one step further. And we were able to show that you could have a mathematical formulation that associated the information that was being exchanged between the environment and an autonomous agent and the meaning that that information carried. In particular, the meaning of the relevance for staying vital, for staying alive. So this was just a first step, but it's an understanding. It's a beginning of recognizing that any discussion of information with living systems needs to have this other dimension, this dimension of meaning. So in this new view of life, this view that doesn't consider life to be a machine, the fundamental thing to be explained or the phenomena that we're after is something that is called autopoiesis, self-creation. So this is an idea that came from Humberto Montarana and Francisco Varela, who were Chilean theoretical biologists. And what they were interested in is the fact that there is a special way in which living systems are organized. They're self-organized. They're self-creating and they're self-repairing. Of course, they're made out of physical stuff. But the new thing that's happening here, the thing that we don't understand is their organization, how that stuff is sort of circularly organized. Take something like the membrane, right? The cell membrane is a part of the cell, right? It allows, it's what allows some things to go into the cell and some things to go out of the cell. And so it allows the cell to exist. But at the same time, the cell has to be operating to keep the membrane working, right? And so this sort of shows that loop of, uh, of autopoiesis. The cell is both a process and a product of the processes and products that make the cell function. So what happens is you get this sort of almost infinite regress. You get the processes and products, which make the processes and products, et cetera. It's that, that, um, closed loop, what Robert Rosen, the great theoretical biologist calls organizational closure, closure, also closure to efficient cause. Somehow you have a network of relationships in the cell between all the parts of the cell that always refer back to each other. So the mystery of something like a cell, and this is, this is what's amazing about this question of autopoiesis. It goes all the way down to the cell. We can ask questions about consciousness, et cetera, but really what the real question we're asking about is sentience. Sentience is the true mystery and it goes all the way down to, uh, to the cell. Cells have this ability to be both open systems, right? There is matter flow and energy flowing through every cell. Uh, and in fact, your body, the atoms that are in your body will, if you wait long enough, they will leave and new, new atoms will replace those, um, those atoms. So fundamentally you are an open system, uh, materially, but you're a closed system organizationally. And you see this with a cell that you've got this beautiful membrane and there's a goo inside. And then if you lice the cell, if you pop the membrane, the stuff spreads out and then it's dead, right? That, or you've broken that circular, that closed organizational system. And now it's, you know, the system doesn't work anymore. And in this sense, this auto poetic closed, um, uh, uh, organizationally closed, uh, uh, uh, system is really almost the definition of life. Cause what it shows you, if you really want to understand the difference between a machine and a living system is I can take a machine and I can turn it off and then I can turn it back on with a living system. I can turn it off, but I can never turn it back on. Right. Once you break, once you break that, that organizational closure, once you break that auto poetic structure, it's gone. And that's why this level of organization, this level of understanding life, even a cell as a complex system, an auto poetic complex system, that's the frontier for physics, for biology, for computer science. So people often ask like, well, why can't we just build a machine that does the same thing? And that's a, you know, a good question to ask, but what you have to understand is that we have no idea even how to build a machine like that. One of the particularly interesting things about Robert Rosen and his idea of causal closure or organizational closure in, uh, biological systems was that he proposed a way of looking at causal closure, um, that could be explored mathematically using something called category theory. And in the nineties, he produced a proof that looked at the structure of causally closed or organizationally closed systems. And what he said was that these systems will not be Turing computable, meaning they would not be representable as computations or other words, you couldn't simulate living systems. You couldn't simulate experiencing systems, auto poetic systems, which is a profound idea, right? It means that fundamentally you can't replace us with computers. You can't, you can't substitute computational systems for the kinds of organizationally closed systems that we are now that, um, proof has remained, you know, very contentious. Some people say it holds up. Some people it doesn't hold up. My, um, colleagues and I are just beginning some work that, you know, we hope to also, uh, uh, probe that, but it really does raise the question of, uh, uh, is it possible that living systems are just really fundamentally different from anything that happens in what we think of as a computer? Because it's really about, um, what's called mirrorology or the whole part relations. What happens with life is that the parts are assembled by the whole, but the parts are part of the whole, and they're necessary for the whole. You can't break that circularity. You know, with a machine, the parts are built somewhere else, or they're, if the machine is building the parts, it doesn't need the part while it's building it in order to stay alive. So living systems are, sometimes the word is used, a Kantian whole, because it was the philosopher Kant who first recognized that this strange self-organized self-built property of living systems is what made them unique. And so, you know, I would never say that we could never build a machine that would do that, but I can say pretty clearly that we don't understand, not even on a foundational level, how to do that. And the real reason is, again, going back to this idea of small worlds and large worlds, the kinds of machine systems we have now always live in what we call these small worlds. They have predefined ontologies. They have predefined worlds that they live in set by out by the outside. So even if you're trying to build a computer that can decide for itself, you're the one who decided how it's going to decide for itself. Living systems do this on their own. So in a lot of ways, this critique of machines ever getting that kind of self-organizational structure is a lot like thinking about those choose your own adventure books, right? If you remember those, where the book allowed you a bunch of different alternatives for an adventure. And as you went along, each choice led you in a different forking path. But the important thing was somebody else had come up with those forking paths, right? So even when we're trying to build a machine that can make its own decisions, what's happening is we're still imposing our understanding of what it means to make your own decisions. We are sort of setting up the forking paths. We're setting up the universe it lives in. And the extraordinary thing about machines is they do this on their own and they create universes of meaning in doing it that way. And that is how meaning becomes central. That's why you have to do semantic information. That is how meaning becomes central to the universe. So the important thing in understanding this difference between life and machines is to understand how it affects what's happening right now with artificial intelligence. So our current version of artificial intelligence, what are called large language models, are designed in some sense to mimic the structures that are in human language. So what that means is it's very easy for us to become fooled by them into thinking that there's someone in there in there when in fact, they're just hollow shells. I mean, they are brilliant pieces of technology, but there is a way in which we can be easily be fooled by them into thinking that we're engaging with some kind of subject, that there's someone in there that we're talking to. But once you understand this idea of relevance realization and machines do not know how to realize relevance for themselves, once you understand the real meaning of agency for a living system and that kind of self-organized, um, uh, uh, uh, uh, uh, uh, auto-poetic structure that they have, you then come to understand that these machines that we've built, they're not intelligent really, because intelligence is not just solving a problem, it's knowing which problem to solve. And a, um, AI would never choose its own problem to solve in one way or the other, it has given instructions and it follows scripts because it has read everything that is ever, it's been fed everything that's ever been, uh, written in some sense. It's just following along paths that the algorithm finds in every story that it's ever, ever read. So once you have that understanding, you can come to these machines and recognize they're not artificial intelligences. They're artificial mimics. Um, they are mimics of us. They are powerful tools that we use to do things. They don't do anything on their own that we use them to do it. For example, when DeepMind beat the world's best Go player, Alvin Noe said that computer didn't beat the Go champion. We use it to beat the Go champion. These machines are not like us. They do not have the richness of, you know, of even, uh, you know, a fox, right? They, they are actually very, they pale in comparison to what the richness of our conscious experience, our, uh, uh, our, um, our being as experiencers. And so what will happen is, is that as these technologies get deployed without any kind of oversight in particular, what we'll see is a leveling of humanity, the attempt to level humanity. So I, uh, uh, one time sat in a, uh, uh, lecture where a CEO of a company that was doing, uh, uh, um, uh, emotional computing was arguing that, oh, you know, we can make computers that the camera will notice the, you know, position of the muscles in your face, which correspond to emotions. And then they can change what it's giving you based on the, uh, what it, what it thinks your emotional state is. Um, and so then we could have robots that could take care of old people. And I was horrified by this idea because one, you can ask like, well, what kind of society do we have where we're just gonna leave old, we're gonna warehouse old people and leave them with robots. But even more, uh, was this idea that, you know, my, the, the N7 muscle or whatever you would call it in my cheekbone, its position tells whether I'm actually happy or sad. And in fact, that research has been shown to be completely false. Um, so what happens is a nice example of how we have these systems, which are not actually what we're told they are. They're not as good as they're, what they're told are. And they will, we will be forced to step down to their level. It's as if to build self-driving cars. We leveled Paris with all of its crazy street structure, and we built something that only had right-hand turns, right? So the real danger is not that, you know, Skynet is suddenly going to wake up and attempt to eliminate humanity. The danger is that we will be forced to, you know, drop down to these in the level of these incomplete systems, which don't have the richness that we have, but because of the economic structures around AI, they will be deployed and we will be forced to use them. And we will lose some of the best parts of being human beings and being alive. And so I'd like to offer three pieces of advice for when you sit down with an artificial mimic. The first is, is tell it to stop referring to itself in first person pronoun. Claude will tell me like, oh, would you like me to make a PDF of this for you? And I say, stop referring to yourself in first person, refer to yourself as the system. And it makes a huge difference. You know, now it's, the system can prepare a, you know, a PDF of this document for you. The second thing is you have to tell it to stop praising you, right? The companies who want you to keep using these machines have built in, um, that sort of modality that it's always telling me like, oh, Adam, what a great suggestion. What a great idea you had, Adam. What a nice shoes you're wearing today, Adam. And, um, uh, you can call it non-affiliative mode, um, or non-affirmative mode. And I just tell the machine to stop trying to please me. Just, I'm going to give you a, uh, I'm going to give the system, uh, uh, a prompt and I just want the information back from the prompt. The last thing is never say please and never say thank you. It doesn't help the computations that are going on in answering your query. And you are literally just purposely burning up, um, energy needed to do those calculations. So those three tricks can help remind you that this is not an autonomous agent. This is not a self. This is a hollow shell, a tool, an incredible tool that you are using for your own purposes. [00:26:58] Speaker 2: Chapter two, a possible solution to our quantum conundrum. [00:27:04] Adam Frank: So here's the crazy thing about quantum mechanics. It is our most powerful theory that we've ever developed. Um, it's been around for 120 years and nobody understands what it means. The great Niels Bohr, who was, you know, one of the guys who created quantum mechanics, said something like, I'm paraphrasing here, if you're not shocked by quantum mechanics, then you don't really understand what's going on with it. And I think every physics student starts off, you know, with this dream of having perfect knowledge of the universe. We get Newton's laws and we're told that we can predict like, you know, where, uh, uh, planets are, are going to be down to, you know, the centimeter, a thousand years in the future. Uh, so that's the kind of world we're hoping to get. And then you get to quantum mechanics and then you get the uncertainty principle and you learn about, you know, measurements affecting the, uh, uh, uh, the, the outcome. And suddenly it's an entirely different world and you're really bummed out. So sometimes people ask like, look, if you know, quantum mechanics is such a problem child for physics, like how did we end up with it anyway? And the answer is technology. What was happening at the beginning of the, um, uh, uh, the 20th century was that we had developed all these technologies that allowed us to probe the micro world, probe the level of atoms. We couldn't see them directly, but we could do things that got us data about what was happening with atoms and electrons, et cetera. And that forced us in order to understand that data, to make models of that data that forced us to use this mathematics, which when you tried to interpret what it meant, like tried to have visualize in your head, what was going on, you couldn't. So a lot of times people will say like, well, why do you need an interpretation? You've got this great calculus, you know, where you can just like put stuff in, turn the crank and you get computers, right? You get digital technologies. So why do you need an interpretation? This sort of view is what the American physicist David Merman called shut up and calculate, right? Stop worrying about what's actually going on. But he invented that term kind of as, uh, uh, derisively, right? It's like, I went into physics because I wanted to understand the nature of the world, right? So I think quantum mechanics is telling us something pretty remarkable about the world itself. Um, so that's why there is the urge for many physicists to actually try and understand what does this mathematics mean? What is it telling us about the structure of the world itself or us and the world? So, you know, quantum mechanics can be really confusing for people who aren't physicists. Not that it's not confusing for physicists too, but I want to give you a way of thinking about quantum mechanics that maybe makes it simpler because there's really just two pieces that are relevant of the quantum mechanical machinery that you have to know about. The first is the Schrodinger equation, right? The Schrodinger equation is what we call in physics, a dynamical equation. It's an equation that tells you how stuff moves, how it responds to forces, right? Newton's law is a dynamical equation. It tells you how planets move through space. Maxwell's equations are dynamical laws. They tell you how electric and magnetic fields behave. As physicists, we love dynamical equations, right? They're kind of like crack cocaine for us because they give us the sense that like, oh, I'm able to predict the future with like infinite accuracy. I have a way of thinking about the universe as if I'm seeing it from God's perspective. So the Schrodinger equation is the quantum mechanical equation. It tells you how electrons, for example, move. But there's a problem. Newton's equation tells you the exact path that a particle, you know, is going to take through space. Schrodinger's equation doesn't give you anything like that. Instead, it gives you a bunch of different paths that are all happening at the same time. It's as if Schrodinger's equation, the output of it, the solution to it, which is what we call the wave function, it tells you that, oh, you know, the electron is going through Scranton, Pennsylvania and Milwaukee and the International Space Station all at the same time. So that is totally different from our previous dynamical equations. And that's why the second piece of the quantum mechanical formulism is just as important. So there's Schrodinger's equation and then there's this thing called the Born rule. So what the Born rule does is the Born rule takes the output of the Schrodinger equation, what we call the wave function, and it turns all of its different possibilities into probabilities for what you'd see when you make a measurement. So there's two crazy things about this, right? The first part is suddenly there's probabilities in our dynamical equations. That didn't happen with Newton's equations. Newton's equations were precise. Now suddenly quantum mechanics through the Born rule says, well, there's a 50% probability that the electron will show up in Scranton, Pennsylvania, and there's a 5% probability that it'll show up on the space station. So those probability, the introduction of fundamental probabilities, not because you don't have enough knowledge, but that these probabilities are built into the structure of reality, that was weird enough. But even weirder was the term when you make a measurement. It's the Born rule. That's where the measurement problem comes from. So why is that so weird? In Newton's dynamical equation, in the Maxwell's dynamical equation, you never had to worry about the measurer. You didn't have to worry about when measurements were being made. If a measurement was made, you know, it didn't affect anything. In quantum mechanics, through the Born rule, the measurements and measurers who's ever making the measurement now suddenly step into the theory. And it's as if the measurement process is just as important for reality to take on the properties that it has, as it is to have all the other things that we usually associate with physics, you know, some kind of inputs and conditions, et cetera. So this introduction of measurement, which then became the measurement problem in quantum mechanics was really pushed back on by physicists, right? We love our dynamical equations. We don't want human beings, if they're the measurers with their, you know, their messy particularities, um, you know, and their subjective problems, we don't want them to be part of any physics problem. So what has happened in trying to interpret quantum mechanics as the years went on is people tried to get away from the Born rule in some way. They wanted in some sense to make dynamical equations great again, because what people wanted to do was somehow circumvent the Born rule. As one, uh, physicist said, the Born rule is in some sense, a, um, an ugly scar on an otherwise beautiful theory. And that's why you have this whole class of interpretations that in some sense, try to preserve the reality of the dynamical equation of Schrodinger's equation and sort of downgrade, uh, you know, the role of measurement, the importance of measurement. So that's how you end up with things say like the many worlds interpretation of quantum mechanics, where all of the parts of the Schrodinger equation, all of the parts of the dynamical equation are real and stay real even after a measurement is made. So in the many worlds interpretation, every time a measurement is made, the universe splits off into, you know, separate, uh, uh, parallel paths where you look and find the electron in Scranton, Pennsylvania, but there's another version of you that's finding the electron in Milwaukee and another version that's finding it in the space station. So the many worlds interpretation and other interpretations like this, which are called ontological interpretations because they take the wave function and Schrodinger's equation as being absolutely real. They exist independent of us out there, so to speak in the universe. And what I find interesting with something like the many worlds interpretation is that in order to have that, uh, uh, ontology to be able to preserve the reality of the Schrodinger's equation and the wave function, you're willing to accept essentially an infinite number of unobservable universes. You're willing to pay that price to not take the, the message that it seems to me that quantum mechanics is telling you, which is that we matter, that those who make measurements, um, are part of the process of sort of establishing the, uh, the properties that, that the world has the kind of properties that it has. So what if you took the presence of those probabilities in quantum mechanics at face value? What if you took the Bourne rule and really took to heart what it was trying to tell you about measurement and measurers? If you do that, then you would end up with something that's called quantum Bayesianism or its most fully developed version, cubism. And cubism was formulated by Chris Fuchs and collaborators. And it's a really brilliant idea that tries to understand what those probabilities are trying to tell us what those, what that reliance on measurements is trying to tell us about the structure of us and the world. Thomas Bayes was a mathematician in the 1700s. And he came up with a very different way of looking at probabilities. What he said was probabilities were personalist. It was a personalist view of probabilities. If you have some probability distribution, some chance, you know, description of the odds of something happening, that's not the world's probability distribution. It's your probability distribution. It's yours, your, it's about your information that you have about the world. So what quantum Bayesianism and cubism do is they take the Bayesian perspective on those quantum, uh, on, on quantum probabilities. And what it recognizes is, is, is that the wave function, this thing that comes out of the Schrodinger equation, it's not the electron's wave function. It's your wave function. It's your description of the electron and what the electron is doing. So in cubism, what you have are agents, us poking the world by doing experiments and then having the world poke back by giving us results of those experiments. In many ways, many of the, the weirdnesses of quantum mechanics fall away, right? You don't need these sci-fi science fiction plot devices, like, you know, infinite numbers of infinite, uh, parallel realities. But you do have to understand that you and the world are intricately connected and that what gives the world the properties that you see are your interactions with it. And what the quantum, uh, formalism, the mathematics of quantum mechanics gives us, although those pieces, the Schrodinger equation and the born rule is the encoding of how that information passes back and forth. It's a user's guide. Quantum mechanics is a user guide for making sense of the world. It's not some pure description of the world by itself. It is a description of us interacting in the world. And in that way, it is stays very true to the born rule where it says that measurements and measurers are central to the whole process of interacting with the world. And in that way, cubism is not just a, a theory of quantum mechanics. It's kind of a deeper theory of science. It says that you can't take us out of the picture when you're trying to describe the world. We're always there. And that's why sometimes cubism has been described as a vision of, of a participatory universe. Participatory universe is a term that the great physicist John Wheeler came up with. John Wheeler was the one who came up with the term black holes. And Wheeler was all of his life was bothered by deeply bothered by the quantum. And in some sense, I think Wheeler's, uh, perspective, cubism and quantum Bayesianism embrace that by understanding that it's always us and the world together. So where does cubism and quantum Bayesianism stand right now? Well, as a good friend of mine, the, uh, quantum physicist, uh, Andrew Jordan says a theory or an interpretation is going to be as good as its ability to suggest new experiments. And I think one of the things that's interesting about quantum Bayesianism and cubism is that it comes from, it's a, it comes from a very modern reading of quantum mechanics, which, which is what is called quantum information theory. Quantum information theory is all about measurement. Um, it is the, the science, which is driving quantum computing. And from the, the, uh, quantum information perspective, I think there is a way in which these kind of Bayesian perspectives, um, uh, the, the focus on measurement is really central to quantum information theory. And I think, you know, we're just starting down the road in many ways with, uh, cubism and quantum Bayesianism. But I think we, I think it is the right path because it understands the importance of agency, which is something I'm interested in across the board and, you know, any theories of life really have to take agency into account. And it just so happens that agency is central, you know, measurements are made by measurers who are agents who are making decisions about what, what kind of measurement to make. So, um, in that way, I believe that, uh, really cubism and quantum Bayesianism is going to be quite a fruitful path in the, in the future for our understanding of quantum mechanics because it gets, it, it responds to the central challenge of quantum mechanics. It doesn't sweep it under the rug. It recognizes that agents, i.e. measurers are central to the story. [00:40:33] Speaker 2: Chapter three, the blind spot, the illusion hindering scientific progress. [00:40:39] Adam Frank: So here's the thing. Most people think or are told that science gives us this kind of God's eye view, this, this view from nowhere. It's, it's a view that doesn't have a view, right? That's independent of, you know, anybody's individual perspective, that's independent of human beings and their messy lives. You know, if I were to ask you to imagine the universe before, you know, anybody was around, what would you do? You'd sort of like imagine yourself floating in space, looking out an array of galaxies. But really, what are you doing? You're just putting yourself there, looking out through your eyes, having a perspective on those galaxies, but you're pretending like you're not really there. And what that shows is there's no such thing as a perspective-less perspective. It is unexperienced and unexperient-able, right? And so essentially what we're saying, when you really start looking at the God's eye view and the view from nowhere, is that it's a story. It's a narrative, a very powerful story that science began to develop early on as a way of looking at the world. But when it comes right down to it, it doesn't exist and it can't exist. Now, of course, there is a world without us. It's just not the one we actually live and do science in. It's not the world we experience. It's not the world with us. So my colleagues, Evan Thompson, who's a philosopher, and Marcelo Gleiser, who's a theoretical physicist, we started to unpack the God's eye view and the view from nowhere and where that narrative of science went wrong. The first is reductionism, which is the idea that you are nothing but your neurons, and your neurons are nothing but the electrons and atoms, and they are nothing but whatever theory of everything people will propose. The second one is physicalism, or sometimes it's called materialism, that the only thing that exists in the world is the stuff of physics. And that's it. Once you've listed all of those things, you've exhausted all the possibilities. Another idea is what we call the ontological frame, that science gives us this perfect, flawless view that is independent of us. Then there's Platonism, or what we call the reification of mathematics, that somehow these mathematical laws of physics exist in some kind of independent, timeless realm. Finally, which kind of brings it all together, is epiphenomenalism, which says your experience, it's an illusion in some sense, there's really nothing to it. It's just the result of neural computations happening in your neurons, which of course are nothing but molecules, which are nothing but the atoms, etc. So that experience, it's not very important to the universe. The point of bringing up these questions about the view from nowhere and the narratives, the stories we get about science and their problems is not to criticize science itself. The point is to recognize that a lot of these narratives are really about a philosophy. Reduction, for example, is fine. Science uses reduction all the time. It's a very powerful method. The problem comes when the metaphysics steps in, when reduction as a method in science becomes reductionism. We call this the blind spot. And that became the title of a book that we did. So why is there a problem with this blind spot philosophy? Well, on one level, it leads us off into paradoxes and problems that we then have to end up doing this all kinds of mental yoga to get through. But before we even get to those actual problems within cognitive science, within cosmology, within artificial intelligence, the first thing is what kind of universe does it describe? And essentially, the blind spot universe is dead. It's fundamentally dead. It is literally just a bunch of atoms careening around meaninglessly. There's no color. There's no sound really, right? I mean, those things are all things that have to be arranged by the brain in the head. It is a dead world that really, if you were the god of this kind of universe that only, you know, that could recognize where every particle was at every moment, you would never be able to understand which systems were alive and which systems weren't. You know, the formal methods of mathematics and mathematical physics and theoretical biology are incredibly powerful, and they need an account. But turning them into some kind of alternate, timeless reality leads to all of these problems, all these paradoxes. One of them, the big one, is the mind-body problem, right? In quantum mechanics, there's a problem of measurement, that measurement seems to matter so much for the theory. In AI, there's the problem of, you know, the, uh, what they call relevance realization, the ability to actually choose what's important for yourself. So, you know, all of these problems, uh, the science has struggled to understand and ends up creating sort of more paradoxes and problems for itself than would be necessary if you adopted a different view of looking at what the problem is about. So how did we get to the blind spot? Like, you know, where did it come from? As I like to say, it made sense at the time, right? If you look at the history of science, going back to the 1400s, 1500s, et cetera, what you see is this attempt to push experience out of the way or subjectivity out of the way to develop what we were calling the structural invariance, to find a way to tell the stories of, uh, of nature without us. And so developing that framework, which we call the blind spot was actually really important. It made sense at the time. And in particular, it was Newton and Newton's laws that really gave us a view like, oh, we have these mathematical formulations, these laws that operate independent of us. And as Newton's laws became more formalized, became more abstracted, say what's called the Lagrangian formulation, which was even more abstract. And then the Hamiltonian formulation was even more abstract after that. There was this ascending spiral of abstraction, very powerful. And it allowed us to sort of think that, like, oh, these equations, this world of the equations was more real than the world of experience. That somehow in the end, you could explain away experience by using, you know, these kinds of equations. So you get to, you know, 1900, and this is the pinnacle of what we call classical physics. Along comes quantum mechanics, right? Along comes quantum mechanics and relativity in its way, both of which do their damage to this kind of view, but particularly quantum mechanics. And it's interesting, that's around the time when Hercel is developing phenomenology. And he was really interested in experience. He was trying to understand how can we get at experience without bringing all the baggage of sort of preconceived ideas about it? How do we get to like direct or raw experience? When I say experience, a lot of people are going to be like, oh, you mean like being an observer? And it's like, no, actually being an observer is very far downstream, theoretically, from just raw experience. Like right now, as you're sitting watching this, your butts in the chair, right? There's maybe a small, small breeze blowing across your face, right? Um, you, you know, your hands are on the table to have experience. It's, it's so close as a Zen saying says, it's so close. That's why it's so hard to see. It's the idea that before you ever think about doing science, before you ever think about doing anything, you are already embedded in a world. Every morning, you open up your eyes and you find yourself in a world. That's what we mean by experience. And, and the important thing about to understand about it, it's not reducible to anything. You can't, you don't have atoms of experience. It is a, it is a entire, it is a whole. William James called it direct experience. Sometimes it's called presence. Sometimes it's called raw experience. And Herschel called that the life world. That's his term. It's the idea that you are already embedded in a world that has others. You're not like out here alone as a hermit. You are part of a community of other language users of other living entities. And then the important thing is we do science in the life world. That's the precondition for coming up with any kind of scientific ideas, having a workshop where we're going to do experiments and talk to each other about how to arrange those experiments. And that the problem arises is when we substitute the things that we do in science, things, theoretical ideas, abstract theoretical ideas, and we make them more real. We think of them as being more real than our actual experience. So Herschel called that the surreptitious substitution. And he said, it's like you're taking something that is really a method and you're making that more real than your direct experience. So if we want a good example of this, let's look at where the idea of, you know, thermometers and degrees Celsius or Fahrenheit came from. It started with direct experience, right? Everybody has a feeling of hot and cold. You know, should I put on a coat? Should I not put on a coat? And early on in science, right, in the 1600s or even earlier, they were looking for a way to formalize, to find what has been called what the philosopher Michelle Bitball calls the structural invariance of my and your subjective feeling of hot and cold. So then it was like, oh, we'll build a tube with some mercury in it. And then we'll stick that in water and we'll see how far it rises, right? That was the beginning of thermometry. So that was the beginning of sort of finding away a structural invariant, the level in the tube of going beyond subjective experience of hot and cold. But then people realized like, well, for certain things, like if I'm trying to find where on the thermometer does water boil, at what point they realized like, well, if you're at the bottom of a valley, you're going to get a different answer than if you're at the top of a mountain. So that led to people had to start thinking about thermodynamics. And then people realized, well, you know, what is stuff made of? It's made of atoms. There was a whole new idea and abstraction called atoms at the time. And so then thermodynamics turned in statistical mechanics and this beautiful theory of statistical mechanics. And so what you had was this ascending spiral of abstraction, very powerful. But by the end of it, what happened was degrees Celsius started to seem, and everything that was attached to it, more real than your feeling of hot and cold. When you go outside and the thermometer is saying like, hey, it's, you know, 50 degrees, but actually you're freezing because of the wind chill. So science then adds this other level of description and abstraction that says, oh, well, the wind is blowing. So now we have to have a correction to our other abstraction, which is degrees. And that is what Herschel called the amnesia of experience. We started to think like, oh yeah, you know, the abstract descriptions we have of atoms, something that's called phase space and a 10 to the 36 dimensional space in which we do statistical mechanics. That's what's really real. And so the important thing to remember here is not that these aren't, these abstractions aren't powerful. They are, and they need an account, but that experience is their root and experience comes first. Those abstractions are maps, right? And you don't want to mistake the map for the terrain. The terrain is the experience, the experience of being human in the life world. And it is, it always overflows every description, right? There are no words, words will always fail. And this is the frontier for our science is to understand how to approach that. Another of our philosophical heroes in the book, The Blind Spot was Alfred North Whitehead, who's arguably the greatest mathematician or one of the greatest mathematicians of the early part of the 20th century. And Whitehead, he had a bunch of different critiques of this blind spot philosophy. And one of the ways he would describe that was what he called the fallacy of misplaced concreteness. And what he was saying is that experience, it's just given to you, right? It doesn't reduce to anything. It's just given to you. And so it is what's concrete. That is what we mean by the concrete. It is the given. Whereas these abstractions, these are the Einstein's equations, the equations of statistical mechanics, those are not concrete. And so what he, you know, those are abstractions. And he said, um, the, the, the job of philosophy is to not to critique the given because you can't, it's just what, you know, it's what you wake up to every day. It's to, it's to critique the ways, the abstractions we use to describe that. And that's also where Whitehead comes up with another important idea, which is the bifurcation of nature. So what Whitehead was arguing there is that what happens with science is we split the world into two systems. Think about the sunset, right? The red of a sunset. What science tells you is what's really real are wavelengths and photoreceptors and molecules, et cetera. And that your experience of the sunset, the actual red of it and the beauty and your response to it. Well, they are less real than all the scientific descriptions. And Whitehead was like, no, that's, that's wrong. You should, you know, you can't separate the world into those two systems where one is higher than the other. And what he wanted was to develop a philosophy, which for him was process philosophy, which understood that both of them had to be taken together. Neither was more real. And when we think about going beyond the blind spot and this new vision of nature, I think Whitehead was at least pointing in the right direction. We don't have to take on Whitehead's process philosophy. But what is required now is to understand that we need to enlarge our toolkit, enlarge our vision of what nature is so that it includes us. So that includes the experience of being alive, that that is something that is taken as being fundamental and primary, and then beginning to work out from that point, a different kind of vision of nature and a different kind of vision of science. It would still be science. It would still have abstraction, but it wouldn't make that surreptitious substitution that becomes so sort of deadly in some sense for further progress. So going beyond the blind spot, we don't know exactly what it would look like, right? That's the good work of science and philosophy, right? It took a couple of hundred years for the, you know, sort of metaphysical stance of the enlightenment to be worked out. And that's just beginning now. But what one thing we can do is understand what it would give us as we went down that road. And we've been told with the old view that we live in a meaningless universe, right? Where we're not important. But with this view, actually, we're central to the story. What it really tells us is there is no way to tell the universe's story without including us. And that meaning is not something that is alien to the universe, meaning is central to the universe because it is central to all the descriptions of the organisms, you know, of the ones that have perspective that, well, not being perspective, literally have perspective, to be able to tell those stories at all. So what happens is you go from a dead universe to a life centric universe. And for multiple reasons, that is essential for human culture moving forward. [00:56:20] Speaker 2: Chapter four, the search for extraterrestrial life. [00:56:25] Adam Frank: So often when people find out that I study life in the universe, the first thing they're going to ask me is about UFOs, about finding aliens in our atmosphere, flying around. And, you know, I have to tell them that from my experience, from what I've seen when I look at it, is that UFOs, and now we call them UAPs, Unidentified Aerial Phenomena, are just not really good evidence of anything associated with extraterrestrial life. So let me break that down a little bit. So in general, when you look at evidence surrounding UFOs, there's two things you get. You get fuzzy blob photos or fuzzy blob videos, and then you get personal testimony. So let's do the personal testimony. So you have pilots who are, you know, I think are being very honest about what they say, they, you know, what they they're reporting. Or you get other people saying they've seen something. But here's the problem. Lots of evidence shows that personal testimony is the worst form of evidence. Any cop or, you know, a district attorney can tell you that that personal testimony is not very accurate. People may want to tell you the truth, you know, may want to accurately report what they saw. But because they were excited, because other things were happening, because of the way attention focuses, they're not going to be doing a very good job of reporting what actually happened. There's clear evidence that this is true. And then there's the videos. We have 70 years of fuzzy blob videos. And what I'm stunned by actually, is that even all of these new videos that are being released, they're still fuzzy blob videos. We don't know anything about the cameras. We don't know anything about the, you know, the circumstances. And the resolution is terrible. So there's not a whole lot you can do with those either. And in fact, since the modern era of UFOs, which was 2017, when the New York Times published a story about the Pentagon having a program to look at UFOs and UAPs, they find that 95% of all of those fuzzy blob videos or all of those reports can be explained. And then of those 5%, a few percent of that, you just don't have enough information to even begin an explanation. You ask the person, well, what time of day was it? They're like, I don't remember. Which direction were you facing? I don't remember. So there's not even enough. So you just have a few percent that resist explanation. But those 3%, just because they're not explained, doesn't allow you to go to, oh, they're extraterrestrials, right? There could be a lot of other explanations for them. For example, there were those three videos that came out with that 2017 New York Times story. And, you know, people said, oh my God, these things show that things are moving in the sky that can't be explained. And there was one of them called Go Fast, which, you know, people will probably remember if they've seen them. It's something appears to be zooming right over the surface of the water. You can hear the pilot as they're trying to get the camera to focus on it. And people were making all kinds of claims that it's actually dipping under the water. It's moving at impossible speeds. And then it turns out that on the screen, on the camera screen, there's actually some numbers that you can do simple trigonometry to figure out where is this thing and how fast is it moving? Turns out it was moving at 40 miles an hour at about 15,000 feet or so. And so there's a word for that. It's called a balloon. It was basically moving with the winds. So it just goes to show you that the problem with what we have with UFOs now and UAPs is there's a lot of hype and there's very rarely any solid evidence that a scientist can do anything with. And this is particularly true after these explosive hearings that we've had in Congress where former members of intelligence organizations have stood up and said, "Oh, the government has a routinely captured fallen spaceships. They have the spaceships, they have the alien bodies, and now the government's starting to release the documents that they have. And there's nothing in there about spaceships, you know, about recovered spaceships. There's not even what I would want is just how about some of the tests that were done on these supposed alien bodies or the supposed, the metal that they emit the spaceships. Clearly somebody must have done, you know, scientific tests. Like when I get my blood work done the next day, I get a bunch of scientific tests back that tell me, you know, the conditions and the chemical conditions in my blood. Where are those tests? So looking for alien life in the atmosphere is kind of like saying you want to find Nebraskans. You want to go find some Nebraskans. So you're going to go look in a small mountain village in the Himalayas for Nebraskans. Like that's crazy. Why don't you just go to Nebraska where they live?" And that is really leads us to the good place, the right place to look for alien life, which is alien planets. And we can now do that because of advances in telescope technology. We have found alien planets orbiting alien stars, right? Which is something that in, you know, when I was a graduate student, we didn't know whether there were any other planets orbiting stars other than the ones in our solar system. It could have been that exoplanets, as we call them, could have been very rare. Turns out they're, they're dull as dirt. Every star you see in the sky has a family of worlds orbiting it. And every five, if you count up five of them, there's, there's one of those stars that has a world orbiting it, a planet in orbiting in the right place for life to form. And now we figured out how to look at these planets for evidence of life. So this is something, a field called biosignatures. And what we can do is, is we can look at the atmosphere from a distance, from a hundred light years away. We can look at light passing through the atmosphere of one of these alien planets, and we can detect the chemicals that are in the atmosphere. And so what that means is we can tell whether or not there's a biosphere there. Because if there's a, a, a, a robust biosphere on a planet, it will change the chemical makeup of the planet's atmosphere. And how do we know that? Because that's what happened on earth. When earth first formed, there was no oxygen in the atmosphere. For the first two and a half billion years, there was no oxygen in the atmosphere. It was life in the form of a new form of, uh, photosynthesis, which, um, uh, evolution produced in microbes that gave us the oxygen atmosphere. So if we, for example, were to be able to find oxygen in the atmosphere of a distant planet, that would be pretty good evidence that there was a biosphere there. And we're building the telescopes, uh, that in the next 10, 20, 30 years, we'll be able to make, we'll at least be able to search for those biosignatures in detail. And in fact, the James Webb Space Telescope was already kind of on the hairy edge of being able to do this. And just a year or so ago, there was a planet called K2 18b, which some researchers proposed that they had actually found a biosignature on it. Now, the interesting thing about K2 18b is it's a new class of planet. What is called a high scene world? It's a, uh, a planet that's quite large, maybe eight times the mass of the earth. And it's got a thick hydrogen atmosphere that keeps the inner parts of the planet, which are mostly water, warm and liquid. So a high scene world could have a 700 kilometer deep ocean. And what was proposed was that the James Webb Space Telescope had actually found evidence of dimethyl sulfide, which is a chemical that algae on earth pumps into the atmosphere. Now, this was astonishing. And really, in the end, nobody believed it. And that's fine. That's how the science is going to work. There's going to be a very high burden of proof on any attempt to find a biosignature. But what was so exciting about K2 18b was that here it was for the first time, we actually had a sophisticated enough instrument, the James Webb Space Telescope to even get us to the point where we had a spectra of a planet's atmosphere where we could think we were finding the right wiggle, the right signature of something like dimethyl sulfide. So that's biosignatures. But what about techno signatures, right? What about the possibility of finding life that wasn't just passively changing its environment? What about a life that had produced technology and that technology was actively changing the environment? For a long time, the search for extraterrestrial intelligence or SETI was considered to be very marginal. It was something that, you know, people sort of like, oh, sure, if you want to do it, but you know, it was a career killer in some sense. But then in 2018, NASA hosted a meeting where they actually asked a group of us to propose ideas for looking for techno signatures, signatures of technological life on another planet. And that was really the birth of the modern field. Techno signatures went from being something that was kind of snickered about to suddenly, even the term techno signature was new. We used to call it SETI. Now, you know, in general, we call it techno signatures. And I was, with a group of other researchers, I was the principal investigator on NASA's first grant to study planetary techno signatures or atmospheric techno signatures. And this is incredibly exciting, because in some ways techno signatures would be easier to find potentially than bio signatures or easier to recognize. So, for example, one of the things we looked at was what about atmospheric pollutants? Like, let's say there's a civilization that is purposely putting chlorofluorocarbons, CFCs, into its atmosphere. Now, on Earth, we did that by mistake. We did it. It was stuff that was released from air conditioners and spray cans and it was destroying the ozone layer. But it turns out that chlorofluorocarbons are great greenhouse gases. And if you wanted to purposely warm up your planet, you might pump chlorofluorocarbons into the atmosphere. We showed that it would be pretty easy for the James Webb Space Telescope to find chlorofluorocarbons in an atmosphere. They are like they would be a bright light in the spectra of a distant atmosphere of a different planet, because they're so nature can't make chlorofluorocarbons. So CFCs are one example of a technosignature. Another example that we investigated was imagine that you covered a portion of your planet in solar panels, right? The glint, the reflected light off those solar panels is something that you might be able to detect across 100 light years. What about city lights? What if you had a planet like Trantor in Isaac, Asiminoff's Foundation series, a city planet. If there was a city planet, a city planet. If there was a planet like Trantor, a city planet. If there was a planet like Trantor, a city planet. If there was a planet like Trantor, a city planet. If there was a planet like Trantor, a city planet. If there was a planet like Trantor, a city planet. If there was a planet like Trantor, a planet like Trantor, a planet like Trantor. That's something that you might be able to detect across 100 light years. What about city lights? What if you had a planet like Trantor in Isaac Asiminoff's Foundation series, a city planet. If there was a lot of city lights on your planet, those could be detected across great distances. So one of the coolest things about thinking about technosignatures in particular is you have to think about the trajectory of civilizations. Like how in general do civilizations progress? Are there sort of, you know, rules or their commonalities in how any civilization would progress on a planet? And the exciting thing about thinking along these lines is it ends up turning itself around and focusing right back on us. The history of the Earth and its life shows us that when life gets really successful, it begins to transform its planet. This comes from something called the Gaia hypothesis. And it's the idea that as life becomes more robust on the planet's surface, it begins to use more of the planet's resources and harvest energy from wherever it can. Most of all sunlight. By using all of that energy, it has feedbacks. It has to have feedbacks. There's what's called the second law of thermodynamics, which tells you if you use a lot of energy, there's always waste, there's always feedbacks, right? So what's interesting about looking at the history of the biosphere is we can see three and a half billion years of the biosphere affecting the evolution of the planet and the planet affecting the evolution of the biosphere. So we talked about the oxygen, right? So that is one of the best examples. Two and a half billion years ago that the evolution of this new form of photosynthesis triggered what is called the Great Oxidation Event. And it pumped oxygen into not only the atmosphere, but also into the oceans and then into the geosphere, into the rocks. It changed the entire functioning of the planet. And we called this in a paper that my colleagues and I did. We called this planetary intelligence because when we think about intelligence, we think it usually happens in, you know, inside our heads, you know, inside individuals. But then you look at something like an ant colony where the intelligence, the ability to react and respond is happening. It's distributed, right? It's distributed across the entire colony. In some sense, the colony is the thing that has the intelligence. And so maybe what happens, this is what we were proposing is that the biosphere at some point becomes robust enough that you get this kind of intelligence, not somebody thinking, not making decisions, but just the ability to respond to the environment and make changes that are good for you. Right. Uh, that that is what happened with the biosphere that somewhere around, you know, two and a half billion, 3 billion years ago, the biosphere went from being, um, we like to call it also immature to becoming mature. It was a mature, intelligent biosphere that in some sense could exert, not conscious, but could exert controls on the rest of the planet. So once you have that idea of this, you know, uh, mature biosphere that went from the immature state to the mature state and the mature state was planetary intelligence, right? Where it was, it was changing the planet for its own ends. Um, you can now look at what we're doing and you can think about civilization as the technosphere, right? If the biosphere is the sum total of life covering the planet, the technosphere is the sum total of technology. And the technosphere that we have now is immature in the sense that it's like the early version of the biosphere where it's not helping its own case, right? It's not, in fact, the, the bio, the, the technosphere is not creating the conditions that will allow it to persist. In fact, it's actually degrading the biosphere condition. So the technosphere lives on top of the biosphere in some sense, right? And so what the technosphere is doing now with climate change, it's degrading the conditions in the biosphere that it needs to survive. So what you see is like, oh, what we need to do is to become a mature technosphere. It tells you that we need to make this transition to a kind of technosphere that understands that it needs to work with the biosphere, work with the planet so that it, you know, persists over geological timescales, a thousand years, 10,000 years, 100,000 years. So what's beautiful for me about this idea of planetary intelligence embodied in a mature technosphere is it tells you exactly where you got to go, right? Where we yell and scream about with, you know, climate change. What do we got to do? Where are we headed? Some people think it's a hoax. But by looking at the earth as just one example of a planet that produced life where the life eventually created a mature biosphere and now has produced an immature technosphere, it tells you what a mature technosphere with planetary intelligence should look like. So what does that look like? So for example, right in the biosphere, the biosphere, all of the networks always work to keep the biosphere healthy in the technosphere. What would that look like? So think about banking, right? Right now, I cannot go to a bank and say, hey, I'd like a loan to build a nuclear weapon, right? The bank would be like, you can't do that. Like, we don't give loans for nuclear weapons. In a mature technosphere, you'd go and say, hey, I'd like a loan to be able to make a, to build a coal fired energy, you know, power plant. And they'd be like, no, you can't do that. You know, we don't give loans for coal fired power plants, right? The, the whole, the systems of the technosphere, the financial, political, technological systems would always be working to maintain the health of the technosphere and the biosphere that lived underneath it. So what's exciting to me about this idea of planetary intelligence is it kind of tells you exactly what we're going to be trying to build over the next century or so. As we go through the very difficult decades, probably that are in front of us as the planet does change, but it points us in exactly the right direction that we need. What kind of changes that we need to eventually become a mature technosphere and have planetary intelligence and then be the kind of civilization that lasts long enough that somebody else can find us, right? Because when we go looking for civilizations, the ones who are fine are going to be the long lived ones. And those will be the ones that produced mature technospheres that had this, this, this property of planetary intelligence. So it's really game on now with both biosignatures and technosignatures. The next big space telescope, the one that will replace the James Webb Space Telescope is something called the Habitable World Observatory. And that one is specifically designed to find biosignatures and technosignatures. So really that's why I say in the next 10, 20 or 30 years, we're really going to have the data to tell us whether life is common or whether it's rare. And it is, it's just hard to sort of tell people how exciting this moment is after literally 2,500 years of people arguing with their opinions about whether life exists in the universe that we're finally, we're going to get to know. So one thing people ask is like, let's say we do finally find life, you know, uh, what would happen? Would it be super important? Would people be rioting in the streets? Would it be the end of religion? Religions are pretty adaptable and pretty malleable over time. I think they'd figure out a way of doing it. But sometimes people say like, well, you know, from a religious point of view, what makes us special is that we're, there's only one there, you know, we're the only example of life in the universe. We're the only time God created a life. But that, I just don't see that as being all that useful, or I don't see that being carrying a whole lot of weight. What makes us special is where life, we have this capacity to create. We have this capacity to do new things. We don't need to be just one of one to be special. And then if there are others, then we have a community. We're part of a community of life. And that would mean that we're, give us membership in this extraordinary community. I will say that I think it will be one of the most important events in human history, that it will, it would change the course of history because of its impact on our understanding of ourselves. But before I go any further, let me say, I don't think anybody's going to riot, right? You know, I'm always a little bit, you know, why would people riot? I mean, unless they told us that the aliens are here and they're here to eat us. Sure. I'm going to riot. But if I just find out that there's aliens, I think people have other things to do that, you know, then riot over that. But it still would be on the long-term when the most important events in human history, because it would show us whether it was intelligent or not intelligent. A lot of times people say like, oh, if we found microbes, what's the big deal? The important thing to understand is that as of right now, we may be an accident, right? We may be the only place in the entire universe where life formed. But the thing about life is it is unlike any physical system in the universe. Because the thing is, if you were to give me a star at the beginning of its lifetime, if you gave me a few pieces of information, I could tell you pretty much how that star is going to, what it's going to be like, how it's going to evolve. Oh, what's going to happen in a few billion years. If you gave me a cell on a planet and said, what's going to happen? There is no way. I have no mechanism to be able to predict like, oh yeah, you're going to get a giant rabbit that could punch you in the face. Right. A kangaroo. Right. Life is the only physical system that innovates. Life is the only physical system that creates. And to find out that just one example, if we find just one other example of this crazy system that creates and evolves and innovates, then it means there's lots of others, right? You know, there's going to be others. And that means it's part of the universe. This innovative, this creative capacity is part of what the universe does. We would be part of a community of life. And because life innovates, what it means is it's open ended. You don't know where it's gone. You don't know what it's possible of. You know, uh, it opens up mind boggling possibilities. Uh, maybe life, some form of life has been able to crawl into the laws of, you know, of physics itself. Like, you know, choose your favorite science fiction version, uh, or story. So I think in that way, um, finding any kind of life would really force us to revise our understanding of who we are, what we are and where we're going. If we found intelligent life that would just amplify it even more, it would be helpful perhaps because we would be able to, it would just be an example perhaps of a long lived civilization. If we found an active civilization, you know, we wouldn't have to have communication. I think communication gets over played. You know, the distance between the stars is so vast that, you know, communication. It's like, you know, if they're 10 light years away, we get a message. They're like, Hey, we're here. And then we send something back and you know, 10 years later they get like, Whoa, what's up? And then another 10 years we get like, what are you doing? What are you doing? You know, it's hard to imagine communication really being that fruitful, but just knowing they exist, you know, and being able to observe them from a distance would change everything. [01:18:45] Speaker 2: Four times a year. We print a magazine worth putting your phone down for big things. Premium print magazine is built with custom artwork and expert insight from the world's biggest thinkers. No way I slap, no rage bait, just big ideas for curious people support the media. You want to see in the world. Go to big think.com slash join to become a member today. Become a member today.