I love thinking about life as computation. Cells are computers, enzymes are functions, ribosomes are compilers, nucleic acids are source code...
Enzymes in particular are a lot like unix pipelines. An enzyme catalyzes its substrate's conversion into its product which is the substrate of another enzyme. When cells ingest glucose, it flows through the glycolysis metabolic pathway until it becomes pyruvate, and may be reduced even further depending on available resources. It's a huge pipeline of enzymes. They just kinda float around within the cell and randomly perform their tasks when their substrates chemically interact with them. No explicit program exists, it emerges from the system within the cell.
Cell - Computer
Enzyme - Function / Process / Filter
Substrate - Data
Product - Data
Metabolic pathway - Program / Script
I've been playing in my mind with an idea for an esoteric programming language modeled around enzymes. The program defines a set of enzymes which are functions that match on the structure of data, automatically apply themselves to them and produce a modified version of the input which may in turn match against other enzymes. The resulting program metabolizes input by looping over the set of enzymes and continuously matching and applying them until the data is reduced to its final form. If no enzymes match, the output is the unmodified input.
I think the issue with this way of thinking is that humans think in abstractions.
Abstractions don't really exist, they're a product of the human mind, but then we apply them to nature. Calling DNA code, comparing NNs and the brain, etc. But those abstractions fall apart when you look a little too deeply at what actually happens in nature.
Is DNA code? Or is it more like a machine? Is it neither, or is it something embedded in such a complex space that our simple abstractions can't capture the full nature of its being?
When you look at the nature of DNA, it does more than simply act as code. It can edit and self-modify, self-assemble, self-replicate, it can turn genes on and off, it can perform what can be argued as computations itself. If you limit yourself to thinking of it as code, you might miss crucial ways it exists/performs in real life.
> When you look at the nature of DNA, it does more than simply act as code.
> It can edit and self-modify, self-assemble, self-replicate, it can turn genes on and off
Unless my knowledge of biology is very outdated or incomplete, all of those things you cited are done to DNA. They don't happen spontaneously.
DNA doesn't self-replicate, a whole bunch of enzymes come and actively copy it. Genes don't spontaneously turn on and off, some enzyme comes and attaches or removes a methyl group. DNA doesn't self-assemble, it is actively coiled around histones to form nucleosomes. Bacteria have a huge variety of enzymes for manipulating native and foreign DNA, they have their own CRISPR mechanisms.
Well the funny thing about abstractions is they are physically real in our imaginations even if only ephemerally.
Human imagination allows us to explore as a simulation anything we want with a form of physicalized internal coherence.
Does internal coherence align with repeatable external coherence? That's what we call empirical.
Humans are the known meaning generators of the universe, we are interesting and special and our unique/random walks are important in an uncomputable and unbound sense. Who knows what casual chains will lead us, where they'll take us or how they might save us (from asteroids let's say) or might reshape the topology of spacetime.
> I think the issue with this way of thinking is that humans think in abstractions.
Isn't that the entire point of making abstractions? Understanding things "as they are" is impossible, so we need simplifications. Of course it should be appreciated that the abstractions are always "wrong".
"A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness."
I don't think ontology is quite that simple. They maybe don't exist in the same way as molecules and atoms do, but abstract concepts have some kind of reality to them.
You could also think about life as a "Factory" (instead of a computer) too:
HUMAN FACTORY COMPUTER
----- ------- --------
Cell Factory Computer
Enzyme Worker Functions
Ribosome Assembler Compiler
Acids Blue Print Source Code
The difficulty with this type of analogy is so many things need these various capabilities that it's not unique to a computer, or a factory or even a human.
You're one of those cats that provides a subtle reminder that Dr. Alan Kay (invented the tablet/Xerox ALTO interface) was first a biologist. Thank you for the enlightened smalltalk! (;3)
You might be interested in Tinkercell, though at this point it may be somewhat outdated and old. There are lots of other more granular systems biology/chemical reaction network software tools, the most ambitious is probably OpenWorm which is still active.
Just keep in mind though that you have to think of cells as very slow, but massively parallel computers.
This feels like the kind of popsci that's written for people who already agree with the author - there's nothing resembling an argument, or even a definition of "computation." There are nods to Church-Turing, but the leap from "every effectively calculable function is computable" to "life is a computation" is larger than anything you could fit in a book.
Reminds me of Wolfram's "Principle of Computational Equivalence"[1].
1. Things in nature have a maximum complexity which is like computation
2. Most things get this complicated
3. Therefore most things are "computationally equivalent"
4. "For example, the workings of the human brain or the evolution of weather systems can, in principle, compute the same things as a computer. "
The leap between things being in an equivalence class according to some relation and being "in principle, the same" might present difficulty if you've done any basic set theory, but that's just because you lack vision.
This principle is just applying Turing equivalence to the hypothesis that there is nothing in nature that is effectively computable but exceeds the Turing computable (which would be the "maximal level of computational power")
Given we have no evidence of the existence of anything effectively computable that is not Turing computable, it's a reasonable hypothesis, with no evidence pointing towards falsifying it, nor any viable theories for what a "level of computational power" that exceeds this hypothetical maximum would look like.
And, yes, if that hypothesis holds, then life is equivalent, to the point of at least being indistinguishable from when observed from the outside, computation.
A lot of people get upset at this, because they want life to be special, and especially human thought. If they want to disprove this, a single example of humans computing a function that is outside the Turing computable would be a very significant blow to this hypothesis, and the notion of life as a computation (it wouldn't conclusively falsify it, as to do that you'd need to also disprove that there might we ways to extend computers to compute the set of newly discovered functions that can't be computed by a Turing machine, but it would be a very significant blow)
Yes, the article appears to be a short excerpt from a book and probably loses a lot of context because of that. I am interested in the questions raised by the author but will wait for the book to come out. The good news is that it appears the book will be open access - MIT Press seems to be encouraging this lately (at least by allowing this as an option for authors).
Is the author advancing a new argument? Has anyone read the book? A quick review suggests that the author posits that symbiogenesis is central to evolution, and artificial intelligence. This is interesting because I recall no mention of this mechanism in the current AI literature. The promise of a symbiotic relationship with artificial life sounds like a balm to people anxious about the future. It is a possibility, not a certainty. https://en.wikipedia.org/wiki/Symbiogenesis
I'm not too impressed with this article since it doesn't really give a definition for computing, just picks a few similarities between what we see as computing (in the practical sense) and what cells do.
It's a shame because there *has* been a lot of deep work done on what kind of computer life is.
People often use the Chomsky Hierarchy (https://en.wikipedia.org/wiki/Chomsky_hierarchy) to define the different types of computer vs automata. Importantly, a classical Turing machine is Type-0 on the Chomsky Hierarchy. Depending on what parts you include from a biological system, you could argue it's anywhere from Type-0 to Type-4.
Interestingly, the PhD thesis of well-known geneticist Aviv Regev was to show that certain combinations of enzymes with chemical concentration states are enough to emulate pi-calculus, and therefore are Turing machines!
https://psb.stanford.edu/psb-online/proceedings/psb01/regev....
This is the kind of evolved computer science that was going on when I was a teenager. Have an upvote eig!
My addition: it's funny for how much speculation we get in the, "hard cognitive science" (RIP) that in lieu of the big insights we get from Godel, Turing, Russell that many/most undergraduates and even post-graduates still haven't internalized Wittgenstein's work especially the Tractatus. I feel like it gets us to, "the questions you're asking about how life works and the questions about what is at the core of logic and mathematics (language) are definitely related but not in any of the fundamental ways you hope they are..."
For the uninitiated-- try reading the thing in one sitting. It takes about an hour:
The Aviv Regev paper you link was recently recommended to me as a useful reference for something. It was a nice surprise to see that Regev's thesis advisor was Ehud Shapiro, known to the Prolog community from his co-authorship of one of the good Prolog books (The Art of Prolog, with Leon Sterling - https://mitpress.mit.edu/9780262691635/the-art-of-prolog/). Indeed, Regev's thesis (and the paper above) propose a system based on a Flat Concurrent Prolog.
Shapiro was also the author of one of the two PhD theses that were a major influence to Inductive Logic Programming, a field at the intersection of logic programming and machine learning.
A lot of the kind of "deep work" you mention used to be done in the logic programming and ILP community in times past, before everyone seemingly switched to neural nets and statistical machine learning.
I don't see the point of asking this question. Like, sure, all physical systems follow certain rules, so any such process will develop in a way that it look like a computation of an algorithm. Also, evolution itself is constantly optimizing organisms to best adapt to their environment, just like a computation.
So asking if life is a computation seems mostly like a semantic musing. Define "life" and define "computation", then see if they're the same.
The title should definitely be "Is it possible to simulate living organism?" given the last sentence is "Simulations like these show how computation can produce lifelike behavior across scales".
Nothing about life is discussed here, it's not even defined once.
> Also, evolution itself is constantly optimizing organisms to best adapt to their environment, just like a computation.
There is no optimization, if organisms can reproduce, they'll continue to exist. That does not mean they are the "best adapted" or on a trajectory toward better adaptation.
It's entirely possible for a germ line to become less fit over time, even to the point of extinction, and that's still evolution. Time has shown that is the case for most germ lines.
> Time has shown that is the case for most germ lines.
This is true, but that sure seems unfair. ;) You have multiple competing systems, in the case of a germ. The system that human related germs are competing with is around 30 trillion times the size, with the advantage of some fairly incredible emergent properties that come from that. The germ is evolving, but in a system that completely overwhelms it, with evolved tricks to specifically force the germ along the "unhappy path" of evolution.
Evolution is not optimizing anything. What's happening in the biosphere is a process of mutation & selection, it's not optimization towards any particular goal or objective. Furthermore & slightly more abstractly, b/c of conservation of mass & energy, what's actually happening is re-organization of existing biomass into different life forms enabled by solar radiation.
That's a rather strong statement, but incorrect in both result and formulation.
How is mutation and selection entail it's not optimization? Your motivating the lack of a goal for a process by describing it's composition. It seems like a logical (Non sequitur fallacy) and categorical erorr.
For reference
> optimization = the selection of a best element, with regard to some criteria, from some set of available alternatives
What's the selection selecting from, what's evolution evolving towards?
Moreover, you motivate with conservation. Conservation is an optimization criterion.
I suppose I fail to see why evolution through natural selection is not optimizing. That was Darwin's big idea, right? That given heredity, selection, and variation you end up with life forms we'd consider optimized for their environments?
Or do you mean that optimization by definition must include intent, and evolution as a mindless process has no intentionality?
"However, we should be careful with the metaphors and paradigms commonly introduced when dealing with the nervous system. It seems to be a
constant in the history of science that the brain has always been compared
to the most complicated contemporary artifact produced by human industry
[297]. In ancient times the brain was compared to a pneumatic machine, in
the Renaissance to a clockwork, and at the end of the last century to the telephone network. There are some today who consider computers the paradigm
par excellence of a nervous system. It is rather paradoxical that when John
von Neumann wrote his classical description of future universal computers, he
tried to choose terms that would describe computers in terms of brains, not
brains in terms of computers."
I have no idea what the submitted MIT article is trying to say. Does the MIT article try to make the point that neural networks can be used for computation given ridiculous amounts of memory? They can, but that still does not explain real intelligence. Otherwise, the article makes the same mistakes as pointed out in the above quote.
To me, the article just ask "Is it possible to simulate living organism features?" and say a small yes by saying "Simulations like these show how computation can produce lifelike behavior across scales".
I'm not expert to judge the result of "drawing a missing hand by using neural network on each pixels"(if it's what it's done? Again not an expert).
Articles like this indicate we should lock down the definition of "computation" that meaningfully distinguishes computing machines from other physical phenomena - a computation is a process that maps symbols (or strings of symbols) to other symbols, obeying certain simple rules[1]. A computer is a machine that does computations.
In that sense life is obviously not a computation: it makes some sense to view DNA as symbolic but it is misleading to do the same for the proteins they encode. These proteins are solving physical problems, not expressing symbolic solutions to symbolic problems - a wrench is not a symbolic solution to the problem of a symbolic lug nut. From this POV the analogy of DNA to computer program is just wrong: they are both analogous to blueprints, but not particularly analogous to each other. We should insist that DNA is no more "computational" than the rules that dictate how elements are formed from subatomic particles.
I don't think it's necessary to completely discard the idea. However, I do think it's important, at the end of it all, to ask: Okay, so what's the utility of this framework? What am I getting out of setting up my point of view this way?
I'm reminded of an old YouTube video [0] that I rewatched recently. That video is "Every Zelda is the Darkest Zelda." Topically, it's completely different. But in it Jacob Geller talks about how there are many videos with fan theories about Zelda games where they're talking about how messed up the game is. Except, that's their only point. If you frame the game in some way, it's really messed up. It doesn't extract any additional meaning, and textually it's not what's present. So you're going through all this decoding and framing, and at the end your conclusion is... nothing. The Mario characters represent the seven deadly sins? Well, that's messed up. That's maybe fun, but it's an empty analysis. It has no insight. No bite.
So, what's the result here other than: Well, that's neat. It's an interesting frame. But other than the thought to construct it, does it inform us of anything? Honestly, I'm not even sure it's really saying life is a form of programming. It seems equally likely it's saying programming is a form of biochemistry (which, honestly, makes more sense given the origins of programming). But even if that were so, what does that give us that we didn't already know? I'm going to bake a pie, so I guess I should learn Go? No, the idea feels descriptive rather than a synthesis. Like an analogy without the conclusion. The pie has no bite.
> I don't think it's necessary to completely discard the idea. However, I do think it's important, at the end of it all, to ask: Okay, so what's the utility of this framework? What am I getting out of setting up my point of view this way?
That's the important question indeed. In particular, classing life as a computation means that it's amenable to general theories of computation. Can we make a given computation--an individual--non-halting? Can we configure a desirable attractor, i.e. remaining "healthy" or "young"? Those are monumentally complex problems, and nobody is going to even try to tackle them while we still believe that life is a mixture of molecules dunked in unknowable divine aether.
Beyond that, the current crop of AI gets closer to anything we have had before to general intelligence, and when you look below the hood, it's literally a symbols-in symbols-out machine. To me, that's evidence that symbol-in symbol-out machines are a pretty general conceptual framework for computation, even if concrete computation is actually implemented in CPUs, GPUs, or membrane-delimited blobs of metabolites.
The very immediate utility is that if life is computation, would be to tell us that life is possible to simulate, and that AGI is possible (because if there is no "magic spark" of life, then the human brain would be existence proof that a power and space- efficient computer capable of general intelligence can be constructed; however hard it might be).
If life is not a computation, then neither of those are a given.
But it has other impacts too, such as moral impacts. If life is a computation, then that rules out any version of free will that involves effective agency (a compatibilist conception of free will is still possible, but that does not involve effective agency, merely the illusion of agency), and so blaming people for their actions would be immoral as they could not at any point have chosen differently, and moral frameworks for punishment would need to center on minimising harm to everyone including perpetrators. That is hard pill to swallow for most.
It has philosophical implications as well, in that proof that life is computation would mean the simulation argument becomes more likely to hold.
> a computation is a process that maps symbols (or strings of symbols) to other symbols, obeying certain simple rules[1]
There are quite a number of people who believe this is the universe. Namely, that the universe is the manifestation of all rule sets on all inputs at all points in time. How you extract quantum mechanics out of that... not so sure
> In that sense life is obviously not a computation: it makes some sense to view DNA as symbolic but it is misleading to do the same for the proteins they encode.
Proteins can also be seen as sequence of symbols: one symbol for each aminoacid. But that's beyond the point. Computational theory uses Turing Machines as a conceptual model. The theories employ some human-imposed conceptual translation to encode what happens in a digital processor or a Lego computer, even if those are not made with a tape and a head. Anybody who actually understands these theories could try to make a rigorous argument of why biological systems are Turning Machines, and I give them very high chances of succeeding.
> These proteins are solving physical problems, not expressing symbolic solutions to symbolic problems
This sentence is self-contradictory. If a protein solves a physical problem and it can only do so because of its particular structure, then its particular structure is an encoding of the solution to the physical problem. How can that encoding be "symbolic" is more of a problem for the beholder (us, humans), but as stated before, using the aminoacid sequence gives one such symbolic encoding. Another symbolic encoding could be the local coordinates of each atom of the protein, up to the precision limits allowed by quantum physics.
The article correctly states that biological computation is full of randomness, but it also explains that computational theories are well furnished with revolving doors between randomness and determinism (Pseudo-random numbers and Hopfield networks are good examples of conduits in either direction).
> ... whatever.
Please don't use this word to finish an argument where there are actual scientists who care about the subject.
our relationship to computation got weird when we moved to digital computers. Like, I don’t think anyone was saying “life is like millions of slide-rules solving logarithms
in parallel”. but now that computers are de-materialized, they can be a metaphor for pretty much anything
Good point - maybe the analogy to computation arises simply because digital computation and the synthesis of DNA, RNA and proteins are all performed by discrete-state machines?
By your defdinition, life is obviously a computation.
The symbolic nature of digital computers is our interpretation on top of physical "problems". If we attribute symbols to the proteins encoded by DNA, symbolic computation takes place. If we don't attribute symbols to the voltages in a digtal computer, we could equally dismiss them as not being computers.
And we have a history of analogue computers as well, e.g. water-based computation[1][2], to drive home that computers are solving physical problems in the process of producing what we then interpret as symbols.
There is no meaningful distinction.
The question of whether life is a computation hinges largely on whether life can produce outputs that can not be simulated by a Turing complete computer, and that can not be replicated by an artificial computer without some "magic spark" unique to life.
Even in that case, there'd be the question of those outputs were simply the result of some form of computation, just outside the computable set inside our universe, but at least in that case there'd be a reasonable case for saying life isn't a computation.
As it is, we have zero evidence to suggest life exceeds the Turing computable.
I don't think they are. The things analog computers work on are still symbolic - we don't care about the length of the rod or what have you, we care about the thing the length of the rod represents.
In what sense? I agree the tech industry fucking sucks right now, but I don't see how this has anything to do with that.
A physical computer is still a computer, no matter what it's computing. The only use a computer has to us is to compute things relative to physical reality, so a physical computer seems even closer to a "real computer" or "real computation" to me than our sad little hot rocks, which can barely simulate anything real to any degree of accuracy, when compared to reality.
No, and this is a very philosophically confused post because it weirdly does not really give any definition of computation.
Computation really is a fancy word for calculation. What matters about computation is that its teleological. Computers are physical systems designed towards a particular end. A computer is, physically, no different than any other system. What differentiates it is that it's designed and we're interpreting its behaviour in a particular way.
Unless you're trying to make a grand theological argument in which "life" is taken to be some Hitchhikers Guide-like machination towards some end, it's not a computation. Life doesn't compute anything, the same way a falling pen doesn't compute gravity unless in a metaphorical sense.
The article is a pretty good example honestly of the problems of taking metaphors literally, common in the AI space where the author hails from. A similar case "artificial neurons" which are really metaphorical neurons. You have to be particularly careful when making comparisons between intentionally designed technological artifacts and biological and physical processes.
this question reminded me of the poetry of terrence mckenna. "Technology is the real skin of our species. Humanity, correctly seen in the context of the last five hundred years, is an extruder of technological material. We take in matter that has a low degree of organization; we put it through mental filters, and we extrude jewelry, gospels, space shuttles. This is what we do. We are like coral animals embedded in a technological reef of extruded psychic objects. All our tool making implies our belief in an ultimate tool. That tool is the flying saucer, or the soul, exteriorized in three-dimensional space."
Readit News
Enzymes in particular are a lot like unix pipelines. An enzyme catalyzes its substrate's conversion into its product which is the substrate of another enzyme. When cells ingest glucose, it flows through the glycolysis metabolic pathway until it becomes pyruvate, and may be reduced even further depending on available resources. It's a huge pipeline of enzymes. They just kinda float around within the cell and randomly perform their tasks when their substrates chemically interact with them. No explicit program exists, it emerges from the system within the cell.
I've been playing in my mind with an idea for an esoteric programming language modeled around enzymes. The program defines a set of enzymes which are functions that match on the structure of data, automatically apply themselves to them and produce a modified version of the input which may in turn match against other enzymes. The resulting program metabolizes input by looping over the set of enzymes and continuously matching and applying them until the data is reduced to its final form. If no enzymes match, the output is the unmodified input.Abstractions don't really exist, they're a product of the human mind, but then we apply them to nature. Calling DNA code, comparing NNs and the brain, etc. But those abstractions fall apart when you look a little too deeply at what actually happens in nature.
Is DNA code? Or is it more like a machine? Is it neither, or is it something embedded in such a complex space that our simple abstractions can't capture the full nature of its being?
When you look at the nature of DNA, it does more than simply act as code. It can edit and self-modify, self-assemble, self-replicate, it can turn genes on and off, it can perform what can be argued as computations itself. If you limit yourself to thinking of it as code, you might miss crucial ways it exists/performs in real life.
> It can edit and self-modify, self-assemble, self-replicate, it can turn genes on and off
Unless my knowledge of biology is very outdated or incomplete, all of those things you cited are done to DNA. They don't happen spontaneously.
DNA doesn't self-replicate, a whole bunch of enzymes come and actively copy it. Genes don't spontaneously turn on and off, some enzyme comes and attaches or removes a methyl group. DNA doesn't self-assemble, it is actively coiled around histones to form nucleosomes. Bacteria have a huge variety of enzymes for manipulating native and foreign DNA, they have their own CRISPR mechanisms.
Human imagination allows us to explore as a simulation anything we want with a form of physicalized internal coherence.
Does internal coherence align with repeatable external coherence? That's what we call empirical.
Humans are the known meaning generators of the universe, we are interesting and special and our unique/random walks are important in an uncomputable and unbound sense. Who knows what casual chains will lead us, where they'll take us or how they might save us (from asteroids let's say) or might reshape the topology of spacetime.
It's early days yet.
Isn't that the entire point of making abstractions? Understanding things "as they are" is impossible, so we need simplifications. Of course it should be appreciated that the abstractions are always "wrong".
"A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness."
https://en.wikipedia.org/wiki/Map%E2%80%93territory_relation
Deleted Comment
I don't think ontology is quite that simple. They maybe don't exist in the same way as molecules and atoms do, but abstract concepts have some kind of reality to them.
Malbolge
[1] https://en.wikipedia.org/wiki/Luca_Cardelli
[2] https://en.wikipedia.org/wiki/Systems_biology
Just keep in mind though that you have to think of cells as very slow, but massively parallel computers.
1. Things in nature have a maximum complexity which is like computation 2. Most things get this complicated 3. Therefore most things are "computationally equivalent" 4. "For example, the workings of the human brain or the evolution of weather systems can, in principle, compute the same things as a computer. "
The leap between things being in an equivalence class according to some relation and being "in principle, the same" might present difficulty if you've done any basic set theory, but that's just because you lack vision.
[1] https://mathworld.wolfram.com/PrincipleofComputationalEquiva...
Given we have no evidence of the existence of anything effectively computable that is not Turing computable, it's a reasonable hypothesis, with no evidence pointing towards falsifying it, nor any viable theories for what a "level of computational power" that exceeds this hypothetical maximum would look like.
And, yes, if that hypothesis holds, then life is equivalent, to the point of at least being indistinguishable from when observed from the outside, computation.
A lot of people get upset at this, because they want life to be special, and especially human thought. If they want to disprove this, a single example of humans computing a function that is outside the Turing computable would be a very significant blow to this hypothesis, and the notion of life as a computation (it wouldn't conclusively falsify it, as to do that you'd need to also disprove that there might we ways to extend computers to compute the set of newly discovered functions that can't be computed by a Turing machine, but it would be a very significant blow)
"It's not even wrong" - Pauli
https://publicservicesalliance.org/2025/05/24/what-is-intell...
https://en.wikipedia.org/wiki/Edward_Fredkin
Edit: On further reflection, I suppose he didn't, if we consider the effort to span Gödel Escher Bach and I Am a Strange Loop.
It's a shame because there *has* been a lot of deep work done on what kind of computer life is. People often use the Chomsky Hierarchy (https://en.wikipedia.org/wiki/Chomsky_hierarchy) to define the different types of computer vs automata. Importantly, a classical Turing machine is Type-0 on the Chomsky Hierarchy. Depending on what parts you include from a biological system, you could argue it's anywhere from Type-0 to Type-4.
Interestingly, the PhD thesis of well-known geneticist Aviv Regev was to show that certain combinations of enzymes with chemical concentration states are enough to emulate pi-calculus, and therefore are Turing machines! https://psb.stanford.edu/psb-online/proceedings/psb01/regev....
My addition: it's funny for how much speculation we get in the, "hard cognitive science" (RIP) that in lieu of the big insights we get from Godel, Turing, Russell that many/most undergraduates and even post-graduates still haven't internalized Wittgenstein's work especially the Tractatus. I feel like it gets us to, "the questions you're asking about how life works and the questions about what is at the core of logic and mathematics (language) are definitely related but not in any of the fundamental ways you hope they are..."
For the uninitiated-- try reading the thing in one sitting. It takes about an hour:
https://wittgensteinproject.org/w/index.php/Tractatus_Logico...
Shapiro was also the author of one of the two PhD theses that were a major influence to Inductive Logic Programming, a field at the intersection of logic programming and machine learning.
A lot of the kind of "deep work" you mention used to be done in the logic programming and ILP community in times past, before everyone seemingly switched to neural nets and statistical machine learning.
So asking if life is a computation seems mostly like a semantic musing. Define "life" and define "computation", then see if they're the same.
Nothing about life is discussed here, it's not even defined once.
There is no optimization, if organisms can reproduce, they'll continue to exist. That does not mean they are the "best adapted" or on a trajectory toward better adaptation.
It's entirely possible for a germ line to become less fit over time, even to the point of extinction, and that's still evolution. Time has shown that is the case for most germ lines.
This is true, but that sure seems unfair. ;) You have multiple competing systems, in the case of a germ. The system that human related germs are competing with is around 30 trillion times the size, with the advantage of some fairly incredible emergent properties that come from that. The germ is evolving, but in a system that completely overwhelms it, with evolved tricks to specifically force the germ along the "unhappy path" of evolution.
How is mutation and selection entail it's not optimization? Your motivating the lack of a goal for a process by describing it's composition. It seems like a logical (Non sequitur fallacy) and categorical erorr.
For reference
> optimization = the selection of a best element, with regard to some criteria, from some set of available alternatives
What's the selection selecting from, what's evolution evolving towards?
Moreover, you motivate with conservation. Conservation is an optimization criterion.
Or do you mean that optimization by definition must include intent, and evolution as a mindless process has no intentionality?
I'm just not sure what you're driving at.
And the flux of geothermal and chemical energy
A shark is pretty damn optimized bunch of molecules to survive in water, would you not agree?
I suppose this boils down to your definition of "optimize".
https://www.inf.fu-berlin.de/inst/ag-ki/rojas_home/documents...
"However, we should be careful with the metaphors and paradigms commonly introduced when dealing with the nervous system. It seems to be a constant in the history of science that the brain has always been compared to the most complicated contemporary artifact produced by human industry [297]. In ancient times the brain was compared to a pneumatic machine, in the Renaissance to a clockwork, and at the end of the last century to the telephone network. There are some today who consider computers the paradigm par excellence of a nervous system. It is rather paradoxical that when John von Neumann wrote his classical description of future universal computers, he tried to choose terms that would describe computers in terms of brains, not brains in terms of computers."
I have no idea what the submitted MIT article is trying to say. Does the MIT article try to make the point that neural networks can be used for computation given ridiculous amounts of memory? They can, but that still does not explain real intelligence. Otherwise, the article makes the same mistakes as pointed out in the above quote.
I'm not expert to judge the result of "drawing a missing hand by using neural network on each pixels"(if it's what it's done? Again not an expert).
In that sense life is obviously not a computation: it makes some sense to view DNA as symbolic but it is misleading to do the same for the proteins they encode. These proteins are solving physical problems, not expressing symbolic solutions to symbolic problems - a wrench is not a symbolic solution to the problem of a symbolic lug nut. From this POV the analogy of DNA to computer program is just wrong: they are both analogous to blueprints, but not particularly analogous to each other. We should insist that DNA is no more "computational" than the rules that dictate how elements are formed from subatomic particles.
[1] Turing computability, lambda definability, primitive recursion, whatever.
I'm reminded of an old YouTube video [0] that I rewatched recently. That video is "Every Zelda is the Darkest Zelda." Topically, it's completely different. But in it Jacob Geller talks about how there are many videos with fan theories about Zelda games where they're talking about how messed up the game is. Except, that's their only point. If you frame the game in some way, it's really messed up. It doesn't extract any additional meaning, and textually it's not what's present. So you're going through all this decoding and framing, and at the end your conclusion is... nothing. The Mario characters represent the seven deadly sins? Well, that's messed up. That's maybe fun, but it's an empty analysis. It has no insight. No bite.
So, what's the result here other than: Well, that's neat. It's an interesting frame. But other than the thought to construct it, does it inform us of anything? Honestly, I'm not even sure it's really saying life is a form of programming. It seems equally likely it's saying programming is a form of biochemistry (which, honestly, makes more sense given the origins of programming). But even if that were so, what does that give us that we didn't already know? I'm going to bake a pie, so I guess I should learn Go? No, the idea feels descriptive rather than a synthesis. Like an analogy without the conclusion. The pie has no bite.
[0]: https://youtu.be/O2tXLsEUpaQ
That's the important question indeed. In particular, classing life as a computation means that it's amenable to general theories of computation. Can we make a given computation--an individual--non-halting? Can we configure a desirable attractor, i.e. remaining "healthy" or "young"? Those are monumentally complex problems, and nobody is going to even try to tackle them while we still believe that life is a mixture of molecules dunked in unknowable divine aether.
Beyond that, the current crop of AI gets closer to anything we have had before to general intelligence, and when you look below the hood, it's literally a symbols-in symbols-out machine. To me, that's evidence that symbol-in symbol-out machines are a pretty general conceptual framework for computation, even if concrete computation is actually implemented in CPUs, GPUs, or membrane-delimited blobs of metabolites.
If life is not a computation, then neither of those are a given.
But it has other impacts too, such as moral impacts. If life is a computation, then that rules out any version of free will that involves effective agency (a compatibilist conception of free will is still possible, but that does not involve effective agency, merely the illusion of agency), and so blaming people for their actions would be immoral as they could not at any point have chosen differently, and moral frameworks for punishment would need to center on minimising harm to everyone including perpetrators. That is hard pill to swallow for most.
It has philosophical implications as well, in that proof that life is computation would mean the simulation argument becomes more likely to hold.
There are quite a number of people who believe this is the universe. Namely, that the universe is the manifestation of all rule sets on all inputs at all points in time. How you extract quantum mechanics out of that... not so sure
Proteins can also be seen as sequence of symbols: one symbol for each aminoacid. But that's beyond the point. Computational theory uses Turing Machines as a conceptual model. The theories employ some human-imposed conceptual translation to encode what happens in a digital processor or a Lego computer, even if those are not made with a tape and a head. Anybody who actually understands these theories could try to make a rigorous argument of why biological systems are Turning Machines, and I give them very high chances of succeeding.
> These proteins are solving physical problems, not expressing symbolic solutions to symbolic problems
This sentence is self-contradictory. If a protein solves a physical problem and it can only do so because of its particular structure, then its particular structure is an encoding of the solution to the physical problem. How can that encoding be "symbolic" is more of a problem for the beholder (us, humans), but as stated before, using the aminoacid sequence gives one such symbolic encoding. Another symbolic encoding could be the local coordinates of each atom of the protein, up to the precision limits allowed by quantum physics.
The article correctly states that biological computation is full of randomness, but it also explains that computational theories are well furnished with revolving doors between randomness and determinism (Pseudo-random numbers and Hopfield networks are good examples of conduits in either direction).
> ... whatever.
Please don't use this word to finish an argument where there are actual scientists who care about the subject.
The symbolic nature of digital computers is our interpretation on top of physical "problems". If we attribute symbols to the proteins encoded by DNA, symbolic computation takes place. If we don't attribute symbols to the voltages in a digtal computer, we could equally dismiss them as not being computers.
And we have a history of analogue computers as well, e.g. water-based computation[1][2], to drive home that computers are solving physical problems in the process of producing what we then interpret as symbols.
There is no meaningful distinction.
The question of whether life is a computation hinges largely on whether life can produce outputs that can not be simulated by a Turing complete computer, and that can not be replicated by an artificial computer without some "magic spark" unique to life.
Even in that case, there'd be the question of those outputs were simply the result of some form of computation, just outside the computable set inside our universe, but at least in that case there'd be a reasonable case for saying life isn't a computation.
As it is, we have zero evidence to suggest life exceeds the Turing computable.
[1] https://en.wikipedia.org/wiki/Water_integrator
[2] https://news.stanford.edu/stories/2015/06/computer-water-dro...
A physical computer is still a computer, no matter what it's computing. The only use a computer has to us is to compute things relative to physical reality, so a physical computer seems even closer to a "real computer" or "real computation" to me than our sad little hot rocks, which can barely simulate anything real to any degree of accuracy, when compared to reality.
Computation really is a fancy word for calculation. What matters about computation is that its teleological. Computers are physical systems designed towards a particular end. A computer is, physically, no different than any other system. What differentiates it is that it's designed and we're interpreting its behaviour in a particular way.
Unless you're trying to make a grand theological argument in which "life" is taken to be some Hitchhikers Guide-like machination towards some end, it's not a computation. Life doesn't compute anything, the same way a falling pen doesn't compute gravity unless in a metaphorical sense.
The article is a pretty good example honestly of the problems of taking metaphors literally, common in the AI space where the author hails from. A similar case "artificial neurons" which are really metaphorical neurons. You have to be particularly careful when making comparisons between intentionally designed technological artifacts and biological and physical processes.