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Giuseppe Longo and Adam Nocek, "The Organism Is a Theory: Giuseppe Longo on Biology, Mathematics, and AI" (U Minnesota Press, 2026)
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- [00:02]
Adam Nosek
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- [00:59]
Marshall Po
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- [02:06]
Gregory McNiff
Welcome to the New Books Network. I'm your host Gregory McNiff and I'm excited to be joined by the co authors of the book the Organism Is a Theory Giuseppe Longo on Biology, Mathematics and AI Written by Giuseppe Longo and Adam Nosek. The book is published by University of Minnesota Press in January of 2026. Giuseppe is research Director Emeritus at The national center for Scientific Research in France and the author or co author of five books and more than 100 peer reviewed scientific articles. Adam is Associate professor of the Philosophy of Technology and Science and Technology Studies at Arizona State University. He is co editor of the Lore of Whitehead and author of Molecular the Animation of Biology, both published by University of Minnesota Press. I selected the organism as a theory because it questions the idea that life and intelligence can be explained just by computation or algorithms. The book explains in a clear but deep way why living systems change and evolve in ways that fix models can't fully capture. Giuseppe and Adam, thank you so much for joining us today to discuss your book.
- [03:17]
Adam Nosek
Thanks for having us.
- [03:18]
Giuseppe Longo
Yeah, thank you.
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Gregory McNiff
Being as your co authors, just start alphabetically by first name with the first question here. Adam, why did you co write the organism as a theory and who is the target audience?
- [03:32]
Adam Nosek
Yeah, thanks for this. That's a good question. I think, you know, the time is right right now, especially in, I guess you could say Anglophone academia that in the, I guess you could say the theoretical humanities, broadly construed. But for Giuseppe's work to really get out there. And Giuseppe's work is, I mean he has been working on fundamental problems in theoretical biology, of course, in the computational sciences and mathematics that really sort of get at questions that people in philosophy and theoretical humanities in the Anglophobe world have been sort of stabbing at and trying to get at. But I think Giuseppe has this really, really incisive way of, of sort of kind of cutting to the chase when it comes to what we get right and what we get wrong about thinking about the relationship between biological systems and computational systems as well as the relationship between physical sciences and biological sciences more generally. And I think the time was right for the Anglophone world to sort of be exposed to this. And I think it sort of speaks to, as I said, these sort of larger questions in the theoretical humanities and philosophy that people are trying to get at but don't necessarily have the sort of scientific expertise or mathematical expertise to do so. And Giuseppe does and I think, yeah.
- [05:06]
Giuseppe Longo
Well for me it's been really wonderful experience because Adam came up with this idea of an interview after reading many of my papers and bringing in his own philosophical experience, in particular in Whitehead, but also in general in what is called continental philosoph. So these questions have been very pertinent and the interaction with the philosophical perspective has been enriching for me personally. I've been working as a mathematician in problems related to computability and then programming languages. They're semantics. So their understanding and design of programming languages via also geometric tools. So their description via spaces of meaning, so to say, you see, when one writes a program, it has been a natural question when programming was beginning in the 60s and 70s. I define a function in which geometrical spaces is this function defined by giving meaning to programming structures. Many of us contributed to the construction of new programming languages because you add features to existing ones that turned out in some cases to be very useful and fruitful. I've been also collaborating with Digital, a software company many years ago, because my work purely in mathematics in mathematical logic turned out to be useful for some languages they were designing. So let's say that's been my fundamental experience equals 31 in the digital world. Based though on this need to give meaning in geometrical structures, essentially, and also on my experience about negative results. Something we should discuss about, I think.
- [07:02]
Gregory McNiff
Absolutely. Giuseppe, you hit on, not surprisingly, the key themes of the book there, I should say. The book is divided into two parts. The first is an introduction which includes a nice overview essay by Adam and then a conversation between Adam and Giuseppe, I think, over the course of four days at Giuseppe's house outside Paris. And both really situate the key themes of the book. The second part is a collection of essays, seven essays on specific topics. Adam, I'll turn back to you. In this first essay labeled the Power of the Negative, you state that Giuseppe's view is that planet is not a computer and living systems are not computational. Could you, and you talk about how he draws a line in the sand there. Could you talk about.
- [07:47]
Adam Nosek
Yeah, no, that's, that's fine, that's fine. I would say that this is one of the difficulties that people are facing when they're talking about artificial intelligence. Artificial intelligence is imagined to be some sort of superintelligence that can basically, that can replicate or compute all planetary functions. And that's just not basically, that's based on a kind of myth of what computing can do. Living systems from single celled organisms all the way up to multicellular, very complex systems do not have computable functions. They are specific systems, they're not generic systems. And they require the evolution of very, very specific relationships that develop over time. And this is just something that computing systems can't do, can't compute. This is not to say, for example, and I think that people would sort of jump in and I know a number of my colleagues would say yes, but of course we have neural networks that are non linear and so forth. And part of the problem Is that these things work based on approximations. These are not actually computing. And when you don't have. And it also presumes a target function. And there is a massive metaphysical assumption baked into thinking about neural networks computing entire planetary systems, that there are actually target functions out there. That's a massive metaphysical assumption. And I think Giuseppe's work does an amazing job of actually articulating how we can't think about living systems as having a computable generic target function.
- [09:37]
Giuseppe Longo
Yes, indeed, the limits of computations are inherent to their very invention. Very ancient time. I mean, you know mathematics, Western mathematics begins with Pythagoras school. They taught that anything could be measured and then computed by integer numbers or ratio between integer numbers. It doesn't work. And that's really the beginning of worse than mathematics. One could prove that the diagonal of a square, which is such a simple construction, the top of rationality, was irrational. So it was analogous. It was a disaster for Repytagorean school. There is a claim that they killed the person who did that. Because you see, everything was number and computations of number and ratio of numbers. No, is false. The diagonal of the square is not a rational number. It's not a ratio of two integer numbers. Indeed, it never ends. The square root of two never ends. Like the PI, like the ratio between the circle and the radius in a circle. Which was an amazing fact. And geometry started geometry just based on rotation and translation of borders. Greek geometry followed this negative result, the irrationality of the diagonal of the square, followed by inventing the first mathematical structure, namely the notion of border definition Beta, the line has no thickness. That's amazing invention. You see, it has nothing to do with computation. It's an invention of a concept. Poor nonsense. There are no line with no thickness. However, if you want to do a coherent theory of measurement of surfaces, you need your border to be with no thickness. If the border accepts thickness, which is the surface of your figure. So they really invented mathematics, Surely by reasoning and by deduction and by logic. But also, and mostly I would say by inventing the first very absurd concept of borderline with no thickness. That's nothing to do with computation, it's just a mathematical proposal. And that's how mathematics goes. Then the negative result, the irrationality opens this way. And this happened over and over in history. This goes together discussion that you proposed also to do about democracy. Critical thinking is at the core of science. I mean, someone who says, no, it's not that way, it should go in another direction. That's happened in Particular with these limitative results that open to new ways. So I've been working technically and discussing about results of incompatibility. But also I refer to results in physics like Poincare's three body problem. These results invent a new area by saying limitations of things that were taught to be covering the universe. So computability was born as a negative result. Namely what happened in the 30s, Godel, church and Turing and others. But these were the trained main authors 1930s. This proved a major conjecture of the time, that once given an axiomatic system, by deducing in a constructive in a computable way, formally theorems from the axiomatic system, one could prove all results of mathematics, in particular arithmetic. But you know, to do mathematics you read arithmetic. And they showed that this is not the case, namely that there are theorems that are not provable in that way and that there are functions that are not computable. In order to give non computable functions, they had to specify what computability means. Because people could compute functions before could define algorithms. The Greek, the Arabs algorithmis comes from there. But they had to specify a general notion of algorithm to say there are functions that are not algorithmic. So you see, the negative limitative result gave structured an entire area giving the notion of computable function and also provided examples of very interesting non computable functions. And later on very interesting non provable theorems in a constructive way. So again, computability was born by limitation, thinking that one cover everything is false, even within arithmetic. Because the incomputable function and provable theorems were given in number theory, in the theory of numbers. So within that it's not need to go elsewhere, think of string stuff. No, no, I mean within there weren't producing computable results. And this is crucially related to many things in biology, like predictability and so on. We may talk about.
- [14:46]
Gregory McNiff
Absolutely. I just have a quick follow up on this notion of the line with no thickness. In the conversation you have with Adam, you state like the line with no thickness of the Greeks. This is at the core of our mathematical invention of infinity. Also, the line with no sickness is an infinitary notion. What specifically do you mean by that? A line with no infinity. And why is that so important, a.
- [15:11]
Giuseppe Longo
Line with no thickness? Well, as I said, because they invented the notion of border, which is fundamental in mathematics in the 20th century, an entire area of mathematics. Coborgis generalizes this due to Rene Tom and this is essential to do a consistent measure theory of surfaces. If Your board has a thickness which is the surface. So this was a fundamental invention and it started the role of conceptual invention in mathematics. As I say, there are many more. You know, inventing Hilbert spaces for quantum mechanics which are totally absurd, or inventing the notion of topos in geometry. I mean, that's the core of mathematics. Mathematics is not the cumulative addition of axioms. And formal deduction is the invention of new concepts and structures that may be essential also to prove theorems in number theory. You need to go to geometry, to very abstract spaces in order to prove results about integer numbers.
- [16:16]
Gregory McNiff
Fascinating. I want to return to another comment, Adam, you make in this introduction, this introductory essay censor. Still, there is no direct route to understanding Longo and his colleagues theory of the organism. I say this because humanity scholars tend to find what they like in Giuseppe's auois. They generally focus on his critique of molecular biology, his theories of ontogenesis, his unapologetic embrace of theory, and his sympathies for continental philosophy. Could you briefly talk about Giuseppe's theory of ontogenesis? And then I'll ask Jeffrey Giuseppe to comment on it.
- [16:55]
Adam Nosek
Yeah, thanks. Look at, I mean, Giuseppe's theory of ontogenesis. And I think I talk a bit about this in the introductory essay. It goes beyond what your average biologist means by ontogenesis, which there's a very sort of fixed theory of ontogenesis in some ways. I mean, ontogenesis for Giuseppe has to do with. With the, I guess you could say the environmental and historical specificity under which an organism individuates itself in relationship to its environment. And this way of thinking about the, if you like, ontogenetic architecture of the organism is very germane to the way in which, I guess you could say, continental philosophers have been thinking about onto ontogenesis. People from more process oriented philosophy, people like Whitehead and Alfred North Whitehead, and sort of the way in which I approach the question of ontogenesis, but also through Gilbert Simondon and Gilles Deleuze and others, where the individual is not necessarily what explains, rather the individual is what needs to be explained. And I think this is the sort of a kind of refrain, if you like, in continental philosophy, or it has become a collective refrain in continental philosophy, people who are interested in philosophy of biology, but the way in which Giuseppe is articulating this through theoretical biology and also thinking about it through the limits of computation, but also the physical sciences. That's also. We've been talking about computing, but we need to think about the physical sciences as well. And the way in which the physical sciences frame things like Randomness and Giuseppe's thinking about the importance of randomness as being inherent to the individuation of biological systems where biological systems just are random systems, where randomness is not external to the theory of our organism, but it's inherent to it. This is very much how I think that philosophy has been for, I don't know, half century more, I mean perhaps, I mean since Whitehead at least, but before, well before that has been sort of gesturing to something like this, but I guess doesn't really have the empirical but also mathematical and computational knowledge to sort of get. Get at what they mean by this sort of question of randomness in the physical and biological sciences. And I think what you have with Giuseppe is a kind of marriage of a philosophical program with a scientific program as well.
- [19:50]
Giuseppe Longo
Yes, yeah, if I can comment on. Yes. I got to work in theoretical biology after some 10 years work with theoretical physicists, three of them in particular, one with whom I wrote a book and lots of papers. So I had the experience of the depth and the breadth of theoretical thinking in biology. Sorry in physics since five centuries, four centuries in biology. The main reference at beginning for me has been evolutionary biology, beginning with Darwin and many other wanting Gould and so on. When I got to collaborate on the theory of organisms I had this kind of background and I bumped into colleagues who are concerned in particular with endocrine disruptors and their consequence on cancer, who needed to propose a new theoretical frame to understand this phenomena concerning the organism. So following their early proposals, we in a group of eight people still collaborating between Boston and Paris, in eight people we are constructing. We already have at least two books, three with their own book. A proposal which brings in a sense brings Darwinian principles also in the analysis of the organs. The first one is the first principle by Darwin that is not enough quoted 4 chapters out of 6. Reproduction with variation and motility. So that's added. But it's very explicit in Darwin. The first principle is that anytime there's a reproduction, there's a variation. What is it grounded on? Darwin has a beautiful page on the so called, what he calls the sensitivity of the organism to external conditions, ecosystemic conditions, but also to internal change. Doesn't know anything about cell and the proteome and so on. But he proposes this variation to any reproduction that we now understand by the fact that even at cedar level anytime there is a. A cell reproducing splits in two that are different for the major role of randomness in the distribution of proteol there will be different kind of molecules going in one or the other, the DNA is not copied exactly because there are huge oscillations of the molecules of DNA itself and it splits. There will be different parts. The membranes also are not identically split. And so on. Immediate risk, variation. And of course, as Dalgarvin says, this is more so even in multicellular organ. He doesn't have the ignorant. He thinks to animals and plants, and he observes, even the breeders who have a cow producing a lot of milk cannot stabilize that population, that species, because at each reproduction it will change. So it's a very clear sensitivity to himself to this dependence on the high sensitivity to some role of randomness that he cannot specify where. But he understands the key issue. We have been bringing this within the organism. Also, cellular reproduction is always with variation and motility under constraints. In a tissue, a cell is massively constrained. Of course, the red. There is the hormone system. There are interest. But sometimes both the tissue structure and the hormone system, the other form of regulation or reproduction may be disrupted. And that's their analysis. In so long you have cancer. Jointly too, of course, to changes in the DNA and so on. That's the approach we are proposing now. Since 15 years, I would say we are working on these and elaborating a lot. And of course, this derives from their laboratory experience. A lot of work with mice and endocrine disruptors and carcinogens. And the theoretical port of a few of us, like me, my students, my former students, more theoretically oriented, coming from mathematics or physics or philosophy, and contributing to this approach to the organism, which connects the principles making understandable ontogenesis to the principles that we inherited mostly from Darwin and make phylogenesis, evolution, understandable.
- [24:28]
Adam Nosek
So can I just jump in really quickly? I think one thing that's really, really important here to sort of emphasize or underscore is the fact that this is not. I mean, this is not a theory of the organism or ontogenesis which is against in any way preservation or stability. It's rather that preservation and stability are. Are based on change and transformation. So any sort of coherent structure that you end up with. Right, of course, it's generating its own norms, it's generating its own values. And those constraints that it generates for itself are absolutely critical for its ongoing maintenance. But it's to say that change and transformation are at the heart of that.
- [25:16]
Giuseppe Longo
Yeah. And there are constraints stabilizing temporarily with the characteristic time, not forever. All the phenomena, all the perfect phenomena.
- [25:26]
Gregory McNiff
Excellent.
- [25:28]
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- [25:56]
Gregory McNiff
Want to Giuseppe, I want to get back to your discussion around cancer and crispr, because you do talk about the central dogma in biology. But before we go there, I want to read a quote and I think you both just sort of addressed it, you anticipated it, but I'll say again, this is Adam, your introductory essay. To paint this picture, I begin with Longo's characterization of theory in mainstream biological sciences. As we'll see, the road of lack of attention to what counts as an adequate theory of the organism has to do with a general failure to appreciate the history of negative results in the sciences and what can and cannot be done with them. Of particular interest is the status of random events in biology versus noise. It turns out that the field of noise biology is based on a misunderstanding of how random events function in the physical and biological sciences. To me, those sentences were a big part of the overall thesis of this book. And like I said, Adam and Giuseppe, you sort of, you definitely hit on the key themes there. But Adam, maybe could you just expand on that, this notion of randomness, negative results, and how the biological and physical sciences might treat those concepts differently.
- [27:13]
Adam Nosek
Yeah, I think that's super important. I think, to think about, look at one of the, one of the ways I was trying to frame, I guess you could say, as you said, a sort of overarching thesis of this is in terms of the productivity of negative results. And I and Giuseppe was earlier talking about how computability theory or computing was generated out of a series of negative results and how negative results can actually be very empowering and can and can spearhead and generate new frameworks for scientific research. And I think this is pretty much what I'm arguing in, in the opening chapter is that what's actually not appreciated today in the biological sciences is the sort of history of those negative results and in fact how that history itself is very important for what it takes to be the theory of the organism that they're working with but don't actually interrogate. I mean, if you look at sort of the theory, the theory, it's not to say that there's not a theory of the organism with People working in biotechnology or working with crispr. It's just implicit. It's not, it's not actually engaged as such. And as I, as I sort of argue it's the theory of the organism seems to be working quite well for them. And that's the sort of opera operative assumption. The problem of course, is that we're not actually looking at the sort of long term, the long timescales of whether or not these results are actually with CRISPR and so on and other sort of biotechnologies, what they're actually getting at, and I guess the point is in terms of negative results, is that what Giuseppe is arguing, I think, is that ultimately negative results can generate new biological theory. If we understand the limitations of physics, I mean physical systems, but also computing systems. This also opens up a space for thinking about randomness being internal to the organization of the living. And that's really the space. This is a new sort of space for inquiry. It's also a new space for mathematical biology. I think this is one of the things that we often confuse, especially in philosophy, is that the limitation of computing does not mean the limitation of formal description and that this is actually a marvelously inventive space for mathematical biology. And that is actually the space that negative results or a history of negative results could actually open up for the biological sciences.
- [30:25]
Giuseppe Longo
Yes, if I can add on randomness, same thing, the notion of randomness, modern contemporary notion of random in classical physics begins with the negative results by Poincare at the end of 19th century. By the way, I got the name negative result from his own work. He calls negative results his very famous three body problems. The answer to the what's the problem? When you have one sun and one planet, you can derive from Newton equations the Keplerian properties, which was just marvelous, a few equations and you derive all these complex properties and you can solve the equations and you give the trajectory in a deterministic and predictable way. But if you have two bodies, two gravitational bodies going around the sun, so you have three bodies, the interference, the interactions, nonlinear interaction between the three gave a problem to the set of equations. Even Newton was aware of that. He couldn't solve the three body problems. And he thought maybe there is no solution and this doesn't improve the stability of the system. This means that God time to time, gets there and stabilizes the trajectories. Many others like Laplace could not accept this idea of God going back and stabilizing system. We have to solve the equations, give the solution, improve the stability of the solar system. They tried a lot with linear approximations. In particular with Fourier, who invented Fourier series just for the purpose of giving linear approximations to this set of equations. In particular NGL2 systems. A fantastic work, fantastic invention. But couldn't get an approximation of the system equation. So Poincare stepped in in 1892, showing, which is very difficult, showing that there is no general integral. There is no general solution for the system. And even that almost everywhere there is no linear approximation. And this means that there may be interactions that give unpredictable. You cannot predict because you cannot give solution nor approximate it. This is classical randomness, a determination. Because the printhead system is deterministic, you can write equation. But in predictable determination. This means that you cannot solve the equations. Or more exactly, it showed that the lack of solution present same situation like bifurcations and others more difficult bifurcations. So suddenly the bodies may go left or right in a bifurcation because of a minor fluctuation. That is below measurement. Minor fluctuation and get an unpredictable phenomenon. That's the first analysis of classical randomness. Because there was a lot of probability calculus since Pascal and Laplace. They treat probabilities. Even Galileo had some nice work and Cardano on probabilities. They treat in probabilities cases like playing cards or distributing cards or playing dice. But they don't give any meaning to randomness, which is measured by probability. Probability is a measure of randomness. It doesn't explain randomness. This was done by negative results, as Poikare calls it, showing a lack of solution. Then a new science begins, which is called geometry of dynamical system. Will lead to the modern theories of deterministic chaos. So very important branch of physics, nonlinear deterministic chaotic systems and so on, widely studied. And we understood what is random now in biology. This is an essential component. Because huge molecules have oscillation. Random oscillation due to Brownian motion, due to temperature. They're inherent, they're essential. They participate, for example, to the diffusion of molecules during metabolism. It's not an accident, it's essential. The fact that molecules, big molecules oscillate, they open size, they interact, then they interact with others. Then there are. There is oxygen. There are other components of nurture in cells that are diffused by an entropic phenomena, which is largely random. This is essential and very constructive. We understand it in these terms of random phenomena. In general, macromolecular interactions are stochastic. Must be given in probabilities. And these probabilities depend on the context that has been established by a Huge amount of papers, a fundamental one since 2002 in Nature and many others. However, these are put aside by this myth that the DNA is a program. The determination is exactly predictable, like in a computer program. You couldn't want it to work. Randomness of that's against evidence. But, you know, there is this myth that you can control evolution by reprogramming DNA. I mean, a cascade of failures since 71. I mean, I'm referring to cancer, when genetic therapies for cancer were produced, brutalized by 76 and still now. I mean, the lack of attention to endocrine disruptor ectosystemic causes of cancer and the expectation of the miracle of general genetic therapies that exist. Few cases of leukemia, very few individual cases, count for nothing with respect to the disaster of the doubling of incidence of cancer in 40 years.
- [36:30]
Adam Nosek
So I think it's really important also to emphasize that this is kind of what I meant by we're sort of homing in on something like a negative result here for the extension of computability or the organisms being computable systems. This is not to say, and I think Giuseppe did a great job of outlining the history of negative results coming from Poincare through Godel, of course, and Turing and Church and so on. And this is one of the things I emphasize in the opening essay, and is that what Giuseppe is getting at, Giuseppe and his colleagues are getting at is it's in the spirit of these negative results. It's not to say that this has been sort of mathematically proven a negative result, but it's in the spirit. And I think. I mean, it's not to say that there one day won't be a sort of mathematical proof, but it is to say that we're sort of homing in on something that feels like a resistance. Well, it is a real resistance. And this was already. And this is one of the reasons that I guess you could say that Turing is kind of a refrain in the book, is that Turing shows up in different ways. Turing, of course, is touted as the sort of father of artificial intelligence and so on, but at the same time, he is incredibly important for thinking about the limitations of computing and the emergence of an organism, biological systems that actually cannot be captured by computing systems. And I think this is really a part of this story that any genealogy of negative results has to refer back to Turing's absolutely undervalued reflections on morphogenesis and the 1950s.
- [38:29]
Gregory McNiff
Not surprisingly, you both hit on the key themes. And, Adam, I want to get to those, what I refer to as the Turing Papers, the 1950 Imitation Game Paper and then the 52 Morphogenesis Paper and how they touch on this notion of randomness, predictability and the distinction between the biological sciences. And that was an excellent discussion. I got to say Giuseppe, I really was fascinated by your conversation and really insight into Punakare's work and this negative result around the three body problem, you know, basically leading to this new field of research that opened up particularly I think you say, in physics, maybe less so in mathematics. So really enjoyed that. Adam, I guess I want to ask you a question. You at one point in this introductory essay say science isn't interested in negative results. I realize this is probably a PhD thesis, but why not? Given that the success we've seen as Giuseppe is clearly articulated with Poincare leading to a whole new field of research and insights into predictability.
- [39:34]
Adam Nosek
Because I would just say, I mean it's slow and it doesn't. And as I said, I think I say in the introduction that it doesn't smell like progress. It's an actual limitation for the methods that we're currently employing. And I mean the business of science has fundamentally of course changed in the 20th century and negative results have to do with the sort of. Doesn't mean you also have to have an understanding of the temporality of science and how the temporality of science is quite different from the temporality of technoscience. I think Giuseppe and I have talked a lot about sort of the relationship between technoscience and science and perhaps there's no science left in technoscience when it's driven by the sort of, the speed and the scale at which results are demanded and results for existing theories. I mean this is exactly what we see in, you know, the heyday of molecular biology where you know, we have the organism is a, needs to be understood as an information processing system. And I think Giuseppe has gone over why that is a fundamental myth and how the determinant reproducibility and predictability of these systems, it just doesn't work out. When you think about the overall ecosystems that are required for anything like I mean like protein synthesis requires. I mean there is no protein synthesis without a cell, without an organism, without an ecosystem. Right. So this coming up at this sort of limitations, I mean this is the problem, this is the problem with negative results that it smells like limitations, but it also defines new fields of research. And I think this is one of the things. And also I sort of talk about this and I think this is One of the things that's really important about Giuseppe's work is that. It's a fundamental investment in the sort of theoretical sciences and that we've lost touch with. And this is one of the arguments that we've lost touch with the importance of theory and how important theory is for the sciences. And that a lot of times, you know, especially in the biological sciences, that experimentation, you know, experimentation is the sort of what generates results and so on, but experimentation itself comes with its own theories. And a lot of these theories are not actually biology's theories. They're theories inherited from mathematical physics. And yeah, I would say that this is one of the reasons, I think that that negative results aren't appreciated is because the temporality, it's a question of time.
- [42:39]
Gregory McNiff
Yeah. Giuseppe, would you like to comment on that?
- [42:44]
Giuseppe Longo
Yes, I think sciences uses them all the time. I mean, also in quantum physics, indetermination, in a sense, it may came out from empirical work, but it's been theoretically proposed, is negative, but it's been very productive because jointly to other phenomena that contradict classical physics are even incompatible as field theory with respect to. In quantum physics, with respect to relativity theory. But yet they opened an entirely new area. This opens all the time. So I wouldn't say that science is against. I mean, it's a history of science, there is some resistance. For example, the field medal has never given to negative result, which is amazing. I mean, God didn't get one, for example. Why he would have deserved it. And it's a century saying his result changed the century in a sense, but nonetheless, I mean, science may live on it and the piling up of technologies doesn't admit that the idea that you use more and more techniques one on top the other. We have an amazing example of that. These, the fantastic technologies we invented in one century and a half to extract everything from Earth. You know, we can dig up to 8,000 meters below the surface of Earth or under the oceans and extract everything we want, we need. And this was done without the theoretical reflection. What are we doing to the atmosphere, to the oceans? I mean, let's require the direct reflections. Only 30 years ago, some departments of physics started areas like the physics of the ocean, the physics of the atmosphere. In my own institution, only 20, 30 years ago, after a century and a half of doing lots of fantastic, very powerful technology, constructing them, piling them up and making major problems to the atmosphere and the ocean and our own lives. That's a typical example. They did work in other cases, like in biotechnologies. I Doubt that they work so much well, sometimes yes, of course you have insights, but most of the time the promises have been amazing. As for biotechnologies and very few successes. If you think that in laboratories they do GMOs by hundreds, they can do thousands of GMOs. Only five or six actually work in the C ecosystem. They mostly work because of the resilience of the ecosystem itself. By lowering diversity, at least by the increased use of pesticides, they are making more damages than positive results. And that's again because of the denial of constructing a theoretical frame that would allow to compare these technologies, see what they really do in the ecosystem. Getting to the understanding that there is no magic palette. That's the key point. I mean, the belief that you get molecule, even antibiotics, you kill exactly that bacteria is false. We all know that you immediately have side effects. You need antibiotics. They are fundamental, but may be used in a scientific way. Understanding that the but its interactions with the receptors are largely stochastic. They are very highly, probably acting on with high, very high probabilities on the intended receptor, no doubt. But they act on almost all other cells, which sometimes with very, very low probabilities. But they affect a context in a very broad sense where again randomness is a fundamental tool for understanding, not for denying knowledge, is for increasing knowledge.
- [46:42]
Gregory McNiff
Yeah, excellent follow up question for both of you. To develop a theory in biology, do we need to disentangle mathematics from algorithmic processing?
- [46:52]
Giuseppe Longo
Well, mathematics itself is not only construction of algorithms. I mean as I said, geometry, the construction of the continuum, understanding movement is made in continua. And so per se, these tools are outside the scope of algorithmic approximations. Because my first job as an assistant professor for three years was teaching numerical and algorithmic solution to partial differential equations. And we know that approximation works very little. We don't go very far most of the time. As soon as you have non linearity, then mathematical analysis gives an index intelligibility which is not computing. You understand how it goes, you understand the geometry, you understand the general existence of an attractor, the fact that you obtain certain directions to the others, but you cannot compute. Exactly. That's the internal to mathematics. And it is well known facts. Now, by algorithms we do a lot. I mean we do compute a lot. And we particularly have been constructing these fantastic digital networks that give memory and knowledge of humanity at disposal of humanity. This is just great. But we have to understand the limitations of this. Because after all we use statistical methods, we use lots of tools implemented in the end in a discrete State of machine of which Turing was the first to say the limitations.
- [48:25]
Adam Nosek
Yeah, I mean, look, I think you also have this sort of understanding of the way computer scientists are trained right now, not actually understanding the mathematics behind the algorithms or the techniques that they keep on piling on top of each other. But if you do understand the actual mathematical tools that are behind the algorithms that we're deploying at scale, then you would have to understand that there is obviously a limitation, but also that there is a mathematical richness that can't be captured by algorithmic description and that this would pave the way for a very different understanding. I mean, I think that just in terms of, and I'm coming from the humanities, but understanding that there is a mathematical richness and very elegant formalizations that happen or that are possible with the mathematical description of organisms and what mathematics can capture when it comes to formal analysis that is not reducible to organisms having computable functions is tremendous. And I think that this could actually open up very different spaces for philosophical formalization.
- [49:53]
Gregory McNiff
Okay, I want to move to this conversation that Giuseppe and Adam you have and I certainly want to focus on Turing and I should say the first essay on the second part is a letter from Giuseppe to Turing. That's really fascinating on a number of levels, but I want to specifically ask you about the difference between Turing's imitation game paper and his morphogenesis paper. Could you talk about that? The context of them and why they're so valuable and particularly why the second one isn't receives as much attention as the first one.
- [50:26]
Giuseppe Longo
Yeah, well, the first one, the very first paper of Turing is the founding one, which proves the existence of non computable functions by giving a notion of computability which is needed in order to show a non computable function. And setting the grounds for the invention of two major ideas in computing, namely the distinction software hardware which is there and a style of programming languages. Programming languages are imperative if they follow Turing style. There are others that are functional for declarative falling church style of computability. Also church result was given as a negative result. And all these systems are equivalent from the point of view of the functions computed for computability. However, the programming styles are very different. And these two papers in mathematical logic, they were not inventing machines, they were doing mathematical logic. Started later on two major programming styles. Then they were others object oriented languages and many more slightly people use now in AI. But these were really the founding frame. Then he wrote in 1950 another paper where he makes a game, describes A game. The Imitation Game.
- [51:44]
Gregory McNiff
What is it?
- [51:46]
Giuseppe Longo
Is the attempt to show that by asking questions on a teletype to a woman or to a computer, what could not make a difference. I think is also a dramatic paper. Turing as homosexual. This was against the law in Britain in 1950. He knew any moment the police could ask him, are you a man? Are you a woman? And he says, I will life my machine. Answer to this question is ironic. This British humor is dramatic. And the conclusion is that, well, in 50 years, 30% of the times, the machine will win. But that's not a test, it is a game. A dramatic game. And in that paper he gives lots of hints to what he's already doing working at the physics of morphogenesis. So he says the brain is surely not a Turing machine, as placidly said, because in a brain there are also continuous phenomena, oscillation, deformation. That I better understand with continuum, because there are chemical phenomena. That's very important. And so it's not. But I can try to imitate. Okay, that's very different. Not modeling. The morphogenesis paper is about a model equals it model. What does it model? It's tried to give a mathematical representation that spell out the causal structure. The determination is a tentative to understand by a model what causes what. In an imitation, you just try to cheat. You are imitating. And the morphogenesis paper has no distinction hardware software explicitly there is only deformation of the hardware with no need, says Turing, of a pre given design, no need of a program. Morphogenesis takes pains because in continua there is a fluctuation of inaction causing a reaction, causing a diffusion. That's what this fantastic set of equations in Codima spells out. A major invention that's been rediscovered. It was written in 52, published in 52, rediscovered only in 72 by Ebony Fox Keller. Probably nobody else ever looked at that paper before because there's nothing to do with computing. Totally different idea. He says it explicitly. If for guess the decision hardware software it is in continua. It deals with fluctuations in continua action reaction system. So it was totally overlooked by computer scientists, of course, but also biologists didn't go to a tilt work of Evelyn Fox Keller. And now it became a major field of research morphogenesis, largely developing and going well beyond of course, the Turing spring. Of course treated by equations in continual nonlinear equations with all the difficulty of implementing it in computers, because you can, you know, approximate to a certain moment, a certain point. So Turing has been working these Two different areas of his full consciousness of what he was doing. Imitation versus modeling. Understanding the causal structure in a continuous dynamics.
- [55:07]
Adam Nosek
I should add though, that second paper by Turing, you know, it came out at a very unfortunate time, right? I mean you have Watson and Crick and their sort of results. And this is very unfortunate timing for that paper. I mean there were people who were interested. And of course Waddington was interested in that paper. But Waddington also was very skeptical of the kind of molecular program. For molecular program and DNA. DNA reductionism that was. That was sort of prevalent at the time. And of. Anyway, I just wanted to sort of add that that you know, in other words, it kind of had to wait. And Evelyn Fox Keller's work was really important for sort of unearthing its importance and in many ways why it was ahead of its time.
- [56:03]
Gregory McNiff
Yeah, no, you make that point in the book. And again, fascinating, and Turing is such a fascinating and tragic figure. But Giuseppe, just a quick follow up there. What's so important about the distinction Turing makes between hardware and software?
- [56:18]
Giuseppe Longo
Well, it's been very important, very fruitful in computer science because it allow to develop a mathematical theory of programming without having a commitment to the specific hardware. There's been major progress and, and changes in the hardware, but the theory program has been developed independently. Then of course you need engineers constructing hardware that do give you a discrete state machine. And there are more and more immense size of base database and speed and so on. And programming languages been also enriched because of that. But still they stay in the church Turing paradigm with lots of mixture like object oriented programming. As I said, lots of more have been invented. Anyway, it's a solar writing writing that allows to describe computations independently of the hardware. That's been very productive. However, there is a problem when you go to quantum computing. That's exactly the distinction. You cannot do anymore because you are using in programming a feature of the hardware, quantum entanglement. That's one of the challenges of this beautiful and very stimulating area, which is quantum computing, where there are major theoretical problem, physical problems. The problems are very slow, unfortunately. There are deep thermodynamics problems. They need very, very low temperatures. Lots of problem physics. Exactly. Because you cannot do. And these problems affect programming because you cannot split the two.
- [57:51]
Gregory McNiff
Excellent. In the time I have remaining, I want to touch on two essays in the second part, Giuseppe. The first is one you have on the debate between Bergson and Einstein on time. What do you think biologists could learn from Bergson's reflection on the consciousness of time?
- [58:10]
Giuseppe Longo
Well, the timing, Bergson is a psychological time discussed by many philosophers. And to it, Einstein opposes the view of physicists since Aristotle. To him, time is what measured by clock. I'm very satisfied of both approaches. From the point of view of biology, you see, because time is much more complex in biology, is due to certainly physical frequencies. So physical clocks, they are very important. They are directed because it's thermodynamical time. But organisms have their autonomous rhythms. Think of the circadian rhythm, day, night, of course, that's a frequency the sun turning around the world, according to Aristotle and according to the animals, to life. But we get to have our circadian rhythms, which is surely due to the circadian frequencies. But it gets its own autonomy. So when you fly, go very fast, you have jet lag, because your body keeps on its own autonomous rhythms. And then it gets adjusted. Of course, you need a week if you fly from America to Europe or so, and then you are forced to adjust. But there is an autonomy of the rhythms heart beating and respiration that is induced, but then becomes independent of the physical frequencies. So we developed an extra dimension to develop mathematically, to analyze mathematically rhythms. And then there is another form of time, evolutionary time, where the space of possibilities, the space of observable changes and the parameters you need to describe change. Because in evolution you have new phenotypes, new organisms. There are new observables that pop out, produced by the biological production of diversity, which is analyzed properly at the Darwinian level. This pre production of novelty, which is probatobiology, produces its own time, which is a network of interaction of rhythms. This network undergoes deformations because it is also due to changes in the physical frequencies, the seasons, the plants and then the animals that go there and pollinators. This coordination of rhythms and this deformation of biological rhythms is at the core of the time of an ecosystem, which is very different from physical clocks, because it goes with these rhythms that evolutionary adjust to each other. And sometimes a change of rhythms may be a reason for evolutionary change. And there is a lot of scientific work on the role of these in West Edinburgh, for example. So we need the science of time, which is much richer, I may say, that was present in the debate Bergson, Einstein. That follows on one side, the analysis of psychological time, which is very interesting. It goes on since St. Augustine. Husser also did some remarkable work. On the other, the analysis of time of Einstein, which is of course very interesting, because relativistic time is very regional with respect to classical time, but still measured by clocks that's what time is, is what is measured by clocks in biology. Seizure is much more complex, as I said, because of this coordination of rhythms.
- [61:53]
Gregory McNiff
Yeah. The last essay, Adam, is by you and it's comparing Giuseppe and Whitehead's theory of the organism. Could you talk a little bit about what you think they have in common?
- [62:06]
Adam Nosek
Yeah, thanks. Look at. There's lots of unexplored territory and I guess that's what I'm trying to open up in that essay. I think you could see two main ways of approaching Longo's work and Whitehead's work. I think one is just at the level of content in terms of their understanding of the organism where Whitehead has. And if we're thinking about the biological organism, Whitehead has a process based understanding of the organism. And that can't be captured by any kind of mechanistic theory. They were both working against different, obviously Whitehead and Giuseppe working in different eras, but still nevertheless fighting a kind of mechanistic theory of the organism. Whitehead was dealing with the materialism of his day. And of course Giuseppe is working with, excuse me, the overwhelming demand for theories of organisms that could be computed with various. The latest forms of biotechnology. Whether that's, you know, from synthetic biology, crispr and now that we're using AI, we have a whole new era. Era of thinking about designer proteins and alphafold and whatnot. So at the level of way of thinking, their approach to thinking about the biological organism is very similar. And there's also no pre. Given understanding of. You can't state in advance the trajectory of development and development and evolution. Excuse me. So I think there's that, that they have, they have in common. And I think that's. That's a. I mean that's a really important point. I think that there's another level at which I'm interested in engaging both of their thought and that has to do with method. I think that both Whitehead and Longo are sort of zeroing in. I mean, Whitehead talks about it in terms of what he calls the fallacy of misplaced concreteness. And I gesture towards that a few times and I think in that essay. And what the fallacy of misplaced concreteness has to do with basically not overestimating the value of your abstractions. In other words, the idea is that philosophers, scientists, artists, poets, whatever are generating different kinds of abstractions. And I think one of the things that Whitehead is invested in is understanding that on the one hand that these abstractions are incredibly useful. I mean, we're talking about the abstractions of a physicist, for example, Very, very important for understanding various material and physical phenomenon, but they can't explain everything. And I think that understanding the limitations of that is absolutely central to Whitehead's project and philosophy. Speculative philosophy in particular, is a sort of articulation of the possibility, but also the limitations of those abstractions. And I think that one of the things that Giuseppe's work does so well in charting the history of negative results is thinking precisely about the limitations of abstractions and not overestimating their value. This is what we get with computability and computing, and this is what we get with the physical sciences. Also what we need to understand about the biological sciences as well. Biological sciences, as important and as unique as they are, even though they haven't been, I guess you, given their sort of their due to develop their own theories, also can't go and explain social and political phenomenon. Right. That's also another terrain. Right. It's not to say that they don't. Don't interact, but we can't use biological theories to go and understand social architectures and social theories. So there are limitations, of course, in either way. So I think that you have, with Whitehead, you have obviously an understanding of the organism, the biological organism, that's deeply. There's a sort of. There's a connection between Longo and Whitehead that needs to be explored. But I think there are also really important methodological connections as well and sort of articulating the limits and possibilities of scientific abstraction.
- [66:39]
Gregory McNiff
Yeah, I really found that interesting that parallels he drew between the two. My last question is on this article in the New Atlanta Saving Science, where the author concludes that scientists should not be left alone to do their work. Adam, I'll give you the first response there. And Giuseppe, I'll close with you. Adam, why should scientists not be left alone to do their work, or should they be left alone?
- [67:04]
Adam Nosek
I don't think it's an either or. I think that's part of the problem that I have with that piece is that this is about the mobilization of sciences. There's this sort of gesture towards sciences need to be mobilized. Scientists need to be mobilized to be working on grand projects to save the planet, save the species, and so on. And it's not to say that they shouldn't, but it's also not to say that that's the only thing that scientists should be doing. And I think that one of the things that I'm. And this is one of the reasons that, you know, theoretical physics and pure mathematics are underfunded. You don't have a lot of PhD students going into those areas anymore because they seem to be, you know, they seem to have a uselessness about them. In other words, they're not applied in the same ways. And that's what I'm arguing against that. And I think this is what Giuseppe and his colleagues are arguing against, that it shouldn't be either or it shouldn't be either. You're this or you're that. I think that it needs to be both in some ways. And, you know, the theoretical work involved in the theoretical. I mean, the theoretical work involved in theoretical biology is time consuming. It also involves things that don't look like positive, productive results right away. And I'll just. I'll just leave it at that.
- [68:36]
Giuseppe Longo
Yes, well, I think that's. There is a major connection within these and democracy in the sense that, you know, democracy surely based on majority vote, that's unfortunately. But at least as important, if not more, is the possibility of having critical thinking people proposing other ways. And that's where sits science. Science goes on because within a school there are no isolated scientists. Someone says, oh, it's not that way. We have to go this other direction. Of course, the top is when you have a concrete, massive negative result that allows to open new ways. Has happened several times in history. As I said before. I would say every time there is new novelty is a new way, is a way of criticizing something existing, a critical thinking. And that's related to democracy. I mean, you need, of course, the majority thinking, but you need that there is a strong minority, an autonomous minority. Proposing new ways and expressing critiques about what's happening according to the majority is absolutely essential. Plus some autonomy. Independence of the judges, typically that's very important. Which goes with the need to have a tenure, to have certain autonomy for science so that you can explore something which may seem crazy to others. And that's how science goes all the time, really. Various degrees. Of course, at times it may be integrated in large projects. That's very important to have an analysis of, I don't know, the atmosphere undergoing certain transformation. So you make a huge progress, but also you must have small groups which do not agree and go in another direction. And that's really the richness and plurality. This is essential both to democracy and science. And science sits in exactly this space, in this area where you develop, first of all, a critical thinking, propose new ways of going. Science is not the piling up of techniques one after the other, not at all. It's suddenly having a new insight, a new perspective to propose. Some of them work, some other not. You must allow a space for that and possibly give people time to do that in groups, very isolated and whatever happens in their diversity with some strong project, important project, of course, which must be there. So that's the connection. I insist, and we insist about in that paper, the specific paper and goes together with the views I said on the productivity constructivity of negative results, limitative results that have been major moments. And we strongly need to keep thinking in these possible ways.
- [71:24]
Adam Nosek
And I think this is sort of. But this also, I mean to the extent that these minority positions are getting cut out, I mean this is a really important point, right? That not allowing to be able to have a minority position like this in the university setting. This is a huge problem. And this is one of the reasons. And just to go back to the earlier part of the conversation that it's questionable whether or not there is science left in technoscience.
- [71:54]
Gregory McNiff
Giuseppe and Adam, this concludes our interview. Thank you so much for a very rich and thought provoking conversation. And like I said, the book really challenges our, our perceptions of science, knowledge and life. I really enjoyed it. Thank you both for your time.
- [72:09]
Giuseppe Longo
Thank you.
- [72:10]
Adam Nosek
Thank you very.