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Your brain does not process information and it is not a computer | Aeon Essays

guy recently linked this essay, its old, but i don't think its significantly wrong (despite gpt evangelists) also read weizenbaum, libs, for the other side of the coin

170 comments

  • As a REDACTED who has published in a few neuroscience journals over the years, this was one of the most annoying articles I've ever read. It abuses language and deliberately misrepresents (or misunderstands?) certain terms of art.

    As an example,

    That is all well and good if we functioned as computers do, but McBeath and his colleagues gave a simpler account: to catch the ball, the player simply needs to keep moving in a way that keeps the ball in a constant visual relationship with respect to home plate and the surrounding scenery (technically, in a ‘linear optical trajectory’). This might sound complicated, but it is actually incredibly simple, and completely free of computations, representations and algorithms.

    The neuronal circuitry that accomplishes the solution to this task (i.e., controlling the muscles to catch the ball), if it's actually doing some physical work to coordinate movement in in a way that satisfies the condition given, is definitionally doing computation and information processing. Sure, there aren't algorithms in the usual way people think about them, but the brain in question almost surely has a noisy/fuzzy representation of its vision and its own position in space if not also that of the ball they're trying to catch.

    For another example,

    no image of the dollar bill has in any sense been ‘stored’ in Jinny’s brain

    in any sense?? really? what about the physical sense in which aspects of a visual memory can be decoded from visual cortical activity after the stimulus has been removed?

    Maybe there's some neat philosophy behind the seemingly strategic ignorance of precisely what certain terms of art mean, but I can't see past the obvious failure to articulate the what the scientific theories in question purport nominally to be able to access it.

    help?

    • The deeper we get in to it the more it just reads as old man yells at cloud and people who want consciousness to be special and interesting being mad that everyone is ignoring them.

    • Yeah, this is just as insane as the people who think GPT is conscious. I've been trying to give a nuanced take down thread (also an academic, with a background in philosophy of science rather than the science itself). I think this resonates with people here because they're so sick of the California Ideology narrative that we are nothing but digital computers, and that if we throw enough money and processing power at something like GPT, we'll have built a person.

    • As someone who also works in the neuroscience field and is somewhat sympathetic to the Gibsonian perspective that Chemero (mentioned in the essay) subscribes to, being able to decode cortical activity doesn't necessarily mean that the activity serves as a representation in the brain. Firstly, the decoder must be trained and secondly, there is a thing called representational drift. If you haven't, I highly recommend reading Romain Brette's paper "Is coding a relevant metaphor for the brain?"

      He asks a crucial question, who/what is this representation for? It certainly is a representation for the neuroscientist, since they are the one who presented the stimuli and are then recording the spiking activity immediately after, but that doesn't imply that it is a representation for the brain. Does it make sense for the brain to encode the outside world, into its own activity (spikes), then to decode it into its own activity again? Are we to assume that another part of this brain then reads this activity to translate into the outside world? This is a form a dualism.

      • being able to decode cortical activity doesn't necessarily mean that the activity serves as a representation in the brain

        I'm sorry: I don't mean to be an ass, but this seems nonsensical to me. Definitionally, being able to decode some neuronal signals means that those signals carry information about the variable they encode. Thus, if those vectors of simultaneous spike trains are received by any other part of the body in question, then the representation has been communicated.

        Firstly, the decoder must be trained and secondly, there is a thing called representational drift.

        Why does a decoder needing to be trained for experimental work that reverse engineers neural codes imply that neural correlates of some real world stimulus are not representing that stimulus?

        I have a similar issue seeing how representational drift invalidates that idea as well, especially since the circuits receiving the signals in question are plastic and dynamically adapt their responses to changes in their inputs as well.

        I started reading Brette's paper that you recommended, and I'm finding the same problems with Romain's idea salad. He says things like, "Climate scientists, for example, rarely ask how rain encodes atmospheric pressure. "

        and while I think that's not exactly the terminology they use, in the sense that they might model rain = couplingFunction(atmospheric pressure) + noise, they're in fact mathematically asking that very question!

        Am I nit-picking or is this not an example of Brette doing the same deliberate misunderstanding of the communications metaphor as the article in the original post?

        Does it make sense for the brain to encode the outside world, into its own activity (spikes), then to decode it into its own activity again?

        It might?, but the question seems computationally silly. I would expect efferent circuitry receiving signals encoded as vectors of simultaneous spikes would not do extra work to try to re-map the lossy signal they're receiving into the original stimulus space. Perhaps they'd do some other transformations on it to integrate it with other information, but why would circuitry that was grown by STDP undo the effort of the earlier populations of neurons involved in the initial compression?

        sorry again if my stem education is preventing me from seeing meaning through a forest of mixed and imprecisely applied metaphors

        I'm going to go read Brette's responses to commentary on the paper you linked and see if I'm just being a thickheaded stemlord

  • Just over a year ago, on a visit to one of the world’s most prestigious research institutes, I challenged researchers there to account for intelligent human behaviour without reference to any aspect of the IP metaphor. They couldn’t do it, and when I politely raised the issue in subsequent email communications, they still had nothing to offer months later. They saw the problem. They didn’t dismiss the challenge as trivial. But they couldn’t offer an alternative. In other words, the IP metaphor is ‘sticky’. It encumbers our thinking with language and ideas that are so powerful we have trouble thinking around them.

    I mean, protip, if you ask people to discard all of their language for discussing a subject they're not going to be able to discuss the subject. This isn't a gotcha. We interact with the world through symbol and metaphors. Computers are the symbolic language with which we discuss the mostly incomprehensible function of about a hundred billion weird little cells squirting chemicals and electricity around.

    Yeah I'm not going to finish this but it just sounds like god of the gaps contrarianness. We have a symbolic language for discussing a complex phenomena that doesn't really reflect the symbols we use to discuss it. We don't know how memory encoding and retrieval works. The author doesn't either, and it really just sounds like they're peeved that other people don't treat memory as an irreducibly complex mystery never to be solved.

    Something they could have talked about - Our memories change over time because, afaik, the process of recalling a memory uses the same mechanics as the process of creating a memory. What I'm told is we're experiencing the event we're remembering again, and because we're basically doing a live performance in our head the act of remembering can also change the memory. It's not a hard drive, there's no ones and zeroes in there. It's a complex, messy biological process that arose under the influence of evolution, aka totally bonkers bs. But there is information in there. People remember strings of numbers, names, locations, literal computer code. We don't know for sure how it's encoded, retrieved, manipulated, "loaded in to ram", but we know it's there. As mentioned, people with some training and recall enormous amounts of information verbatim. There are, contrary to the dollar experiment, people who can reproduce images with high detail and accuracy after one brief viewing. There's all kinds of weird eiditic memory and outliers.

    From what I understand most people are moving towards a system model - Memories aren't encoded in a cell, or as a pattern of chemicals, it's a complex process that involves a whole lot of shit and can't be discrete observed by looking at an isolated piece of the brain. YOu need to know what the system is doing. To deliberately poke fun at the author - It's like trying to read the binary of a fragmented hard drive, it's not going to make any sense. You've got to load it in to memory so the index that knows where all the pieces of the files are stored on the disk so it can assemble them in to something useful. Your file isn't "stored" anywhere on the disk. Binary is stored on the disk. A program is needed to take that binary and turn it in to readable information. 'We're never going to be able to upload a brain" is just whiney contrarian nonesense, it's god of the gaps. We don't know how it works now so we'll never know how it works. So we need to produce a 1:1 scan of the whole body and all it's processes? So what, maybe we'll have tech to do that some day. maybe we'll, you know, skip the whole "upload" thing and figure out how to hook a brain in to a computer interface directly, or integrate the meat with the metal. It's so unimaginative to just throw your hands up and say "it's too complicated! digital intelligence is impossible!" Like come on, we know you can run an intelligence on a few pounds of electrified grease. That's a known, unquestionable thing. The machine exists, it's sitting in each of our skulls, and every year we're getting better and better at understanding and controlling it. There's no reason to categorically reject the idea that we'll some day be able to copy it, or alter it such a way that it can be copied. It doesn't violate any laws of physics, it doesn't require goofy exists only on paper exotic particles. it's just electrified meat.

    Also, if bozo could please explain how trained oral historians and poets can recall thousands of stanzas of poetry verbatim with few or no errors I'd love to hear that, because it raises some questions about the dollar bill "experiment".

    • Moreover, we absolutely do have memory. The concept existed before computers and we named the computer's process after that. We have memories, and computers do something that we easily liken to having memories. Computer memory is the metaphor here

      • Yeah, it's a really odd thing to harp about. Guy's a psychologist, though, and was doing most of his notable work in the 70s and 80s which was closer to the neolithic than it is to modernity. I think this is mostly just "old man yells at clouds" because he's mad that neuroscience lapped psychology a long time ago and can actually produce results.

    • Ok, that was great and all, but could you give this short essay again without mentioning any of the brain's processes or using vowels? If you can't, it proves your whole premise is flawed somehow.

      • Right? This is what happens when you let stem people loose without a humanities person to ride herd on them. Any anthropologist would tell you how silly this is.

    • You don’t remember the text though, and stanzas recounting can sometimes have word substitutions which fit rhythmically.

      If I asked you what is 300th word of the poem, you cannot do it. Computer can. If I start with two words of the verse, you could immediately continue. It’s sequence of words with meaning, outside of couple thousands of competitive pi-memorizers, people cannot remember gibberish, try to remember hash number of something for a day. It’s significantly less memory, either as word vector or symbol vector than a haiku.

      Re: language, and how far along did the mechanical analogy took us? Until equations or language corresponding to reality are used, you are fumbling about fitting round spheres in spiral holes. Sure you can use ptoleimaic system and add new round components, or you can realize orbits are ellipses

      History of science should actually horrify science bros, 300 years scientists firmly believed phlogiston was the source of burning, 100 years ago aether was all around us, and our brains were ticking boxes of gears, 60 years ago neutrinos didn’t have mass, while dna was happily deterministically making humans. Whatever we believe now is scientific truth by historic precedent likely isn’t (correspondence between model and reality), they are getting better all the time (increasing correspondence), but I don’t know perfect scientific theory (maybe chemistry is sorta solved with fiddling around the edges).

      • Why would that horrify us? That's how science works. We observe the world, create hypothesis based on those observations, developed experiments to test those hypothesis, and build theories based on whether experimentation confirmed our hypothesis. Phlogiston wasn't real, but the theory conformed to the observations made with the tools available at the time. We could have this theory of phlogiston, and we could experiment to determine the validity of that theory. When new tools allowed us to observe novel phenomena the phlogiston theory was discarded. Science is a philosophy of knowledge; The world operates on consistent rules and these rules can be determined by observation and experiment. Science will never be complete. Science makes no definitive statements. We build theoretical models of the world, and we use those models until we find that they don't agree with our observations.

      • Computers know the 300th word because they store their stuff in arrays, which do not exist in brains. They could also store it in linked lists, like a brain does, but that's inefficient for the silicon memory layout.

        Also, brains can know the 300th word. Just count. Guess what a computer does when it has to get the 300th element of a linked list: it counts to 300.

      • If I asked you what is 300th word of the poem, you cannot do it. Computer can

        I'm sorry, but this is a silly argument. Somebody might very well be able to tell you what the 300th word of a poem is, while a computer that stored that poem as a .bmp file wouldn't be able to (without tools other than just basic stuff that allows it to show you .bmp images). In different contexts we remember different things about stuff.

      • outside of couple of weirdos, people cannot remember gibberish, try to remember hash number of something for a day

        Don't appreciate the ableist language here just because nerudodivergence is inconvenient to your argument. I can fairly easily memorize my credit card number.

    • the point is that humans have subjective experiences in addition to, or in place of, whatever processes we could describe as information processing. since we aren't sure what is responsible for subjective experiences in humans, (we understand increasingly more of the physical correlates of conscious experience, but have no causal theories that can explain how the physical brain-states produce subjectivity) it would be presumptuous of us to assume we can simulate it in a digital computer. It may be possible with some future technology, field of science, or paradigm of thinking in mathematics or philosophy or somwthing, but to assume we can just do it now with only trivial modifications or additions to our theories is like humans of the past trying to tackle disease using miasma theory - we simply don't understand the subject of study enough to create accurate models of it. How exactly do you bridge the gap from objective physical phenomena to subjective experiential phenomena, even in theory? How much, or what kind, of information processing results in something like subjective experiential awareness? If 'consciousness is illusory', then what is the exact nature of the illusion, what is the illusion for the benefit of (i.e. what is the illusion concealing, and what is being kept ignorant by this illusion?) and how can we explain it in terms of physics and information processing?

      it is just as presumptuous to assume that digital computers CAN simulate human consciousness without losing anything important, as it is to assume that they cannot.

    • Also, if bozo could please explain how trained oral historians and poets can recall thousands of stanzas of poetry verbatim with few or no errors I'd love to hear that, because it raises some questions about the dollar bill "experiment".

      Through learned, embodied habit. They know it in their bones and muscles. It isn't the mechanical reproduction of a computer or machine.

      Imo I don't think we could ever "upload a brain" and even if we did, it would be a horrific subjective experience. So much of our sense of self and of consciousness is learned and developed over time through being in the world as a body. Losing a limb has a significant impact on someones consciousness, phantom limbs which can hurt, imagine losing your entire body. This thought experiment is still under the assumption that the brain alone is the entire seat of conscious experience, which is doubtful as this just falls into a mind/body dualism under the idea that the brain is a CPU which could be simply plugged into something else.

      Could there be an emergent conscious AI at some point? Perhaps, but as far as we can tell it may very well require a kind of childhood and slow development of embodied experience in a similar capacity to how any known lifeform becomes conscious. Not a human brain shoved into a vat.

  • This essay is ridiculous, it's arguing against a concept that nobody with the minutest understanding or interest in the brain has. He's arguing that because you cannot go find the picture of a dollar bill in any single neuron, that means the brain is not storing the "representation" of a dollar bill.

    I am the first to argue the brain is more than just a plain neural network, it's highly diversified and works in ways beyond our understanding yet, but this is just silly. The brain obviously stores the understanding of a dollar bill in the pattern and sets of neurons (like a neural network). The brain quite obviously has to store the representation of a dollar bill, and we probably will find a way to isolate this in a brain in the next 100 years. It's just that, like a neural network, information is stored in complex multi-layered systems rather than traditional computing where a specific bit of memory is stored in a specific address.

    Author is half arguing a point absolutely nobody makes, and half arguing that "human brains are super duper special and can never be represented by machinery because magic". Which is a very tired philosophical argument. Human brains are amazing and continue to exceed our understanding, but they are just shifting information around in patterns, and that's a simple physical process.

    • This whole thing is incredibly frustrating. Like his guy did draw a representation of a dollar bill. It was a shitty representation, but so is a 640x400 image of a Monet. What's the argument being made, even? It's just an empty gotcha. The way that image is stored and retrieved is radically different from how most actual physical computers work, but there is observably an analogous process happening. You point a camera at an object, take a picture, store it to disk, retrieve it, you get an approximation of the object as perceived by the camera. You show someone the same object, they somehow store a representation of that object somewhere in their meat, and when you ask them to draw it they're retrieving that approximation and feeding that approximation to their hands to draw the imagine. I don't get why the guy thinks these things are obviously, axiomatically uncomparable.

  • Meh, this is basically just someone being Big Mad about the popular choice of metaphor for neurology. Like, yes, the human brain doesn't have RAM or store bits in an array to represent numbers, but one could describe short term memory with that metaphor and be largely correct.

    Biological cognition is poorly understood primarily because the medium it is expressed on is incomprehensibly complex. Mapping out the neurons in a single cubic millimeter of a human brain takes literal petabytes of storage, and that's just a static snapshot. But ultimately it is something that occurs in the material world under the same rules as everything else, and does not have some metaphysical component that somehow makes it impossible to simulate using software in much the same way we'd model a star's life cycle or galaxy formations, just unimaginable using current technology.

  • Did this motherfucker really write more than 4000 words because nobody told them "all models are wrong but some are useful"?

  • here are some more relevant articles for consideration from a similar perspective, just so we know its not literally just one guy from the 80s saying this. some cite this article as well but include other sources. the authors are probably not 'based' in a political sense, i do not condone the people but rather the arguments in some parts of the quoted segments.

    https://medium.com/@nateshganesh/no-the-brain-is-not-a-computer-1c566d99318c

    Let me explain in detail. Go back to the intuitive definition of an algorithm (remember this is equivalent to the more technical definition)— “an algorithm is a finite set of instructions that can be followed mechanically, with no insight required, in order to give some specific output for a specific input.” Now if we assume that the input and output states are arbitrary and not specified, then time evolution of any system becomes computing it’s time-evolution function, with the state at every time t becoming the input for the output state at time (t+1), and hence too broad a definition to be useful. If we want to narrow the usage of the word computers to systems like our laptops, desktops, etc., then we are talking about those systems in which the input and output states are arbitrary (you can make Boolean logic work with either physical voltage high or low as Boolean logic zero, as long you find suitable physical implementations) but are clearly specified (voltage low=Boolean logic zero generally in modern day electronics), as in the intuitive definition of an algorithm….with the most important part being that those physical states (and their relationship to the computational variables) are specified by us!!! All the systems that we refer to as modern day computers and want to restrict our usage of the word computers to are in fact our created by us(or our intelligence to be more specific), in which we decide what are the input and output states. Take your calculator for example. If you wanted to calculate the sum of 3 and 5 on it, it is your interpretation of the pressing of the 3,5,+ and = buttons as inputs, and the number that pops up on the LED screen as output is what allows you interpret the time evolution of the system as a computation, and imbues the computational property to the calculator. Physically, nothing about the electron flow through the calculator circuit makes the system evolution computational. This extends to any modern day artificial system we think of as a computer, irrespective of how sophisticated the I/O behavior is. The inputs and output states of an algorithm in computing are specified by us (and we often have agreed upon standards on what these states are eg: voltage lows/highs for Boolean logic lows/highs). If we miss this aspect of computing and then think of our brains as executing algorithms (that produce our intelligence) like computers do, we run into the following -

    (1) a computer is anything which physically implements algorithms in order to solve computable functions.

    (2) an algorithm is a finite set of instructions that can be followed mechanically, with no insight required, in order to give some specific output for a specific input.

    (3) the specific input and output states in the definition of an algorithm and the arbitrary relationship b/w the physical observables of the system and computational states are specified by us because of our intelligence,which is the result of…wait for it…the execution of an algorithm (in the brain).

    Notice the circularity? The process of specifying the inputs and outputs needed in the definition of an algorithm, are themselves defined by an algorithm!! This process is of course a product of our intelligence/ability to learn — you can’t specify the evolution of a physical CMOS gate as a logical NAND if you have not learned what NAND is already, nor capable of learning it in the first place. And any attempt to describe it as an algorithm will always suffer from the circularity.

    https://www.theguardian.com/science/2020/feb/27/why-your-brain-is-not-a-computer-neuroscience-neural-networks-consciousness

    And yet there is a growing conviction among some neuroscientists that our future path is not clear. It is hard to see where we should be going, apart from simply collecting more data or counting on the latest exciting experimental approach. As the German neuroscientist Olaf Sporns has put it: “Neuroscience still largely lacks organising principles or a theoretical framework for converting brain data into fundamental knowledge and understanding.” Despite the vast number of facts being accumulated, our understanding of the brain appears to be approaching an impasse.

    In 2017, the French neuroscientist Yves Frégnac focused on the current fashion of collecting massive amounts of data in expensive, large-scale projects and argued that the tsunami of data they are producing is leading to major bottlenecks in progress, partly because, as he put it pithily, “big data is not knowledge”.

    The neuroscientists Anne Churchland and Larry Abbott have also emphasised our difficulties in interpreting the massive amount of data that is being produced by laboratories all over the world: “Obtaining deep understanding from this onslaught will require, in addition to the skilful and creative application

    https://www.forbes.com/sites/alexknapp/2012/05/04/why-your-brain-isnt-a-computer/?sh=3739800f13e1

    Adherents of the computational theory of mind often claim that the only alternative theories of mind would necessarily involve a supernatural or dualistic component. This is ironic, because fundamentally, this theory is dualistic. It implies that your mind is something fundamentally different from your brain - it's just software that can, in theory, run on any substrate.

    By contrast, a truly non-dualistic theory of mind has to state what is clearly obvious: your mind and your brain are identical. Now, this doesn't necessarily mean that an artificial human brain is impossible - it's just that programming such a thing would be much more akin to embedded systems programming rather than computer programming. Moreover, it means that the hardware matters a lot - because the hardware would have to essentially mirror the hardware of the brain. This enormously complicates the task of trying to build an artificial brain, given that we don't even know how the 300 neuron roundworm brain works, much less the 300 billion neuron human brain.

    But looking at the workings of the brain in more detail reveal some more fundamental flaws with computational theory. For one thing, the brain itself isn't structured like a Turing machine. It's a parallel processing network of neural nodes - but not just any network. It's a plastic neural network that can in some ways be actively changed through influences by will or environment. For example, so long as some crucial portions of the brain aren't injured, it's possible for the brain to compensate for injury by actively rewriting its own network. Or, as you might notice in your own life, its possible to improve your own cognition just by getting enough sleep and exercise.

    You don't have to delve into the technical details too much to see this in your life. Just consider the prevalence of cognitive dissonance and confirmation bias. Cognitive dissonance is the ability of the mind to believe what it wants even in the face of opposing evidence. Confirmation bias is the ability of the mind to seek out evidence that conforms to its own theories and simply gloss over or completely ignore contradictory evidence. Neither of these aspects of the brain are easily explained through computation - it might not even be possible to express these states mathematically.

    What's more, the brain simply can't be divided into functional pieces. Neuronal "circuitry" is fuzzy and from a hardware perspective, its "leaky." Unlike the logic gates of a computer, the different working parts of the brain impact each other in ways that we're only just beginning to understand. And those circuits can also be adapted to new needs. As Mark Changizi points out in his excellent book Harnessed, humans don't have a portions of the brain devoted to speech, writing, or music. Rather, they're emergent - they're formed from parts of the brain that were adapted to simpler visual and hearing tasks.

    If the parts of the brain we think of as being fundamentally human - not just intelligence, but self-awareness - are emergent properties of the brain, rather than functional ones, as seems likely, the computational theory of mind gets even weaker. Think of consciousness and will as something that emerges from the activity of billions of neural connections, similar to how a national economy emerges from billions of different business transactions. It's not a perfect analogy, but that should give you an idea of the complexity. In many ways, the structure of a national economy is much simpler than that of the brain, and despite that fact that it's a much more strictly mathematical proposition, it's incredibly difficult to model with any kind of precision.

    The mind is best understood, not as software, but rather as an emergent property of the physical brain. So building an artificial intelligence with the same level of complexity as that of a human intelligence isn't a matter of just finding the right algorithms and putting it together. The brain is much more complicated than that, and is very likely simply not amenable to that kind of mathematical reductionism, any more than economic systems are.

    • https://www.infoq.com/articles/brain-not-computer/

      Given these facts, Jasanoff argues, you could build a chemistry-centric model of the brain with electrical signals of neurons facilitating the movement of chemical signals, instead of the other way around. The electrical signals could be viewed as part of a chemical process because of the ions they depend on. Glia cells affect the uptake of neurotransmitters which in turn affects neuron firing. From an evolutionary perspective, the chemical brain is no different than the chemical liver or kidneys.

      An epigenetic understanding of dopamine, drug addiction, and depression focuses on the chemistry in the brain, not the electrical circuitry.

      Our brains function just like the rest of our biological body, not as an abstraction of hardware and software components. To Jasanoff, there is no distinction between a mental event and a physical event in the body.

      https://intellerts.com/sorry-your-brain-is-not-like-a-computer/

      Humans rely on intuition, worldviews, thoughts, beliefs, our conscience. Machines rely on algorithms, which are inherently dumb. Here’s David Berlinski’s definition of an algorithm:

      “An algorithm is a finite procedure, written in a fixed symbolic vocabulary, governed by precise instructions, moving in discrete steps, 1, 2, 3, . . ., whose execution requires no insight, cleverness, intuition, intelligence, or perspicuity, and that sooner or later comes to an end.”

      But not every machine relies on dumb algorithms alone. Some machines are capable of learning. So, we must dive a little deeper to understand the inner workings of AI. I like this definition from John C. Lennox PhD, DPhil, Dsc – Professor of Mathematics (Emeritus) at the University of Oxford:

      “An AI system uses mathematical algorithms that sort, filter and select from a large database.

      The system can ‘learn’ to identify and interpret digital patterns, images, sound, speech, text data, etc.

      It uses computer applications to statistically analyse the available information and estimate the probability of a particular hypothesis.

      Narrow tasks formerly (normally) done by a human can now be done by an AI system. It’s simulated intelligence is uncoupled from conscience.”

      Sort, filter and select. If you put it as simply as this, which to my opinion is the case, then you realize that AI is completely different from the human brain, let alone who we are as human beings.

      • You can build a computer out of anything that can flip a logic gate, up to and including red crabs. It doesn't matter if you're using electricity or chemistry or crabs. That's why it's a metaphor. This really all reads as someone arguing with a straw man who literally believes that neurons are logic gates or something. "Actually brains have chemistry" sounds like it's supposed to be a gotcha when people are out there working on building chemical computers, chemical data storage, chemical automata right now. There's no dichotomy there, nor does it argue against using computer terminology to discuss brain function. It just suggests a lack of creativity, flexibility, and awareness of the current state of the art in chemistry.

        It's also apparently arguing with people who think chat-gpt and neural nets and llms are intelligent and sentient? In which case you should loudly specify that in the first line so people know you're arguing with ignorant fools and they can skip your article.

        Humans rely on intuition, worldviews, thoughts, beliefs, our conscience. Machines rely on algorithms, which are inherently dumb. Here’s David Berlinski’s definition of an algorithm: “An algorithm is a finite procedure, written in a fixed symbolic vocabulary, governed by precise instructions, moving in discrete steps, 1, 2, 3, . . ., whose execution requires no insight, cleverness, intuition, intelligence, or perspicuity, and that sooner or later comes to an end.”

        And what the hell is this? Jumping up and down and screaming "i have a soul! Consciousness is privileged and special! I'm not a meat automata i'm a real boy!" Is not mature or productive. This isn't an argument, it's a tantrum.

        The deeper we get in to this it sounds like dumb guys arguing with dumb guys about reductive models of the mind that dumb guys think other dumb guys rigidly adhere to. Ranting about ai research without specifying whether you're talking about long standing research trends or the religious fanatics in California proseletyzing about their fictive machine gods isn't helpful.

    • Almost all of this is people assuming other people are taking the metaphor to far.

      The mind is best understood, not as software, but rather as an emergent property of the physical brain.

      No one who is worth talking to about this disagrees with this. Everyone is running on systems theory now, including the computer programmers trying to build artificial intelligence. All the plagiarism machines run on systems theory and emergence. The people they're yelling at about reductive computer metaphors are doing the thing the author is saying they don't do, and the plagiarism machines were only possible because people were using systems theory and emergent behaviors arising from software to build the worthless things!

      . The brain is much more complicated than that, and is very likely simply not amenable to that kind of mathematical reductionism, any more than economic systems are.

      This author just said that economics isn't maths, that it's spooky and mysterious and can't be undersyood.

      This is so frustrating. "You see, the brain isn't like this extremely reductive model of computation, it's actually" and then the author just lists every advance, invention, and field of inquiry in computation for the last several decades.

      But looking at the workings of the brain in more detail reveal some more fundamental flaws with computational theory. For one thing, the brain itself isn't structured like a Turing machine. It's a parallel processing network of neural nodes - but not just any network. It's a plastic neural network that can in some ways be actively changed through influences by will or environment. For example, so long as some crucial portions of the brain aren't injured, it's possible for the brain to compensate for injury by actively rewriting its own network. Or, as you might notice in your own life, its possible to improve your own cognition just by getting enough sleep and exercise.

      "The brain isn't a computer, it's actually a different kind of computer! The brain compensates for injury the same way the internet that was in some ways designed after the brain compensates for injury! If you provide the discrete nodes of a distributed network with the inputs they need to function efficiently the performance of the entire network improves!"

      This is just boggling, what argument do they think they're making? Software does all these things specifically because scientists are investigating the functions of the brain and applying what they find to the construction of new computer systems. Our increasing understanding of the brain feeds back to novel computational models which generate new tools, data, and insight for understanding the brain!

      • "The brain isn't a computer, it's actually a different kind of computer! The brain compensates for injury the same way the internet that was in some ways designed after the brain compensates for injury! If you provide the discrete nodes of a distributed network with the inputs they need to function efficiently the performance of the entire network improves!"

        Not even that. They literally did not provide any argument that brains are not structured like Turing machines. Hell, the author seems to not be aware of backup tools in hardware and software, including RAID.

      • https://medium.com/the-spike/yes-the-brain-is-a-computer-11f630cad736

        people are absolutely arguing that the human brain is a turing machine. please actually read the articles before commenting, you clearly didn't read any of them in any detail or understand what they are talking about. a turing machine isn't a specific type of computer, it is a model of how all computing in all digital computers work, regardless of the specific software or hardware.

        https://en.wikipedia.org/wiki/Turing_machine

        A Turing machine is a mathematical model of computation describing an abstract machine[1] that manipulates symbols on a strip of tape according to a table of rules.[2] Despite the model's simplicity, it is capable of implementing any computer algorithm.[3]

        A Turing machine is an idealised model of a central processing unit (CPU) that controls all data manipulation done by a computer, with the canonical machine using sequential memory to store data. Typically, the sequential memory is represented as a tape of infinite length on which the machine can perform read and write operations.

        In the context of formal language theory, a Turing machine (automaton) is capable of enumerating some arbitrary subset of valid strings of an alphabet. A set of strings which can be enumerated in this manner is called a recursively enumerable language. The Turing machine can equivalently be defined as a model that recognises valid input strings, rather than enumerating output strings.

        Given a Turing machine M and an arbitrary string s, it is generally not possible to decide whether M will eventually produce s. This is due to the fact that the halting problem is unsolvable, which has major implications for the theoretical limits of computing.

        The Turing machine is capable of processing an unrestricted grammar, which further implies that it is capable of robustly evaluating first-order logic in an infinite number of ways. This is famously demonstrated through lambda calculus.

        A Turing machine that is able to simulate any other Turing machine is called a universal Turing machine (UTM, or simply a universal machine). Another mathematical formalism, lambda calculus, with a similar "universal" nature was introduced by Alonzo Church. Church's work intertwined with Turing's to form the basis for the Church–Turing thesis. This thesis states that Turing machines, lambda calculus, and other similar formalisms of computation do indeed capture the informal notion of effective methods in logic and mathematics and thus provide a model through which one can reason about an algorithm or "mechanical procedure" in a mathematically precise way without being tied to any particular formalism. Studying the abstract properties of Turing machines has yielded many insights into computer science, computability theory, and complexity theory.

    • (1) a computer is anything which physically implements algorithms in order to solve computable functions.
      (2) an algorithm is a finite set of instructions that can be followed mechanically, with no insight required, in order to give some specific output for a specific input.
      (3) the specific input and output states in the definition of an algorithm and the arbitrary relationship b/w the physical observables of the system and computational states are specified by us because of our intelligence,which is the result of…wait for it…the execution of an algorithm (in the brain).
      Notice the circularity? The process of specifying the inputs and outputs needed in the definition of an algorithm, are themselves defined by an algorithm!! This process is of course a product of our intelligence/ability to learn — you can’t specify the evolution of a physical CMOS gate as a logical NAND if you have not learned what NAND is already, nor capable of learning it in the first place. And any attempt to describe it as an algorithm will always suffer from the circularity.

      This is a rather silly argument. People hear about certain logical fallacies and build cargo cults around them. They are basically arguing 'but how can conscious beings process their perception of material stuff if their consciousness is tied to material things???', or 'how can we learn about our bodies if we need our bodies to learn about them in the first place? Notice the circularity!!!'.
      The last sentence there is a blatant non sequitur. They provide literally no reasoning for why a thing wouldn't be able to learn stuff about itself using algorithms.

      • This whole discussion is becoming more and more frustrating bc it's clear that most of the people arguing against the brain as computer don't grasp what metaphor is, have a rigid understanding of what computers are and cannot flex that understanding it to use it as a helpful basis of comparsion, and apparently have just never heard of or encountered systems theory?

        Like a lot of these articles are going "nyah nyah nyah the mind can't be software running on brain hardware that's duaism you're actually doing magic just like us!" And it's like my god how are you writing about science and you've never encountered the idea of complex systems arising from the execution of simple rules? Like put your pen down and go play Conway's Game of Life for a minute and shut up about algorithms and logic gates bc you clearly can't even see the gaping holes in your own understanding of what is being discussed.

      • please read the entire article, you are literally not understanding the text. the following directly addresses your argument.

        “an algorithm is a finite set of instructions that can be followed mechanically, with no insight required, in order to give some specific output for a specific input.” Now if we assume that the input and output states are arbitrary and not specified, then time evolution of any system becomes computing it’s time-evolution function, with the state at every time t becoming the input for the output state at time (t+1), and hence too broad a definition to be useful. If we want to narrow the usage of the word computers to systems like our laptops, desktops, etc., then we are talking about those systems in which the input and output states are arbitrary (you can make Boolean logic work with either physical voltage high or low as Boolean logic zero, as long you find suitable physical implementations) but are clearly specified (voltage low=Boolean logic zero generally in modern day electronics), as in the intuitive definition of an algorithm….with the most important part being that those physical states (and their relationship to the computational variables) are specified by us!!! All the systems that we refer to as modern day computers and want to restrict our usage of the word computers to are in fact our created by us(or our intelligence to be more specific), in which we decide what are the input and output states. Take your calculator for example. If you wanted to calculate the sum of 3 and 5 on it, it is your interpretation of the pressing of the 3,5,+ and = buttons as inputs, and the number that pops up on the LED screen as output is what allows you interpret the time evolution of the system as a computation, and imbues the computational property to the calculator. Physically, nothing about the electron flow through the calculator circuit makes the system evolution computational.

  • A sidenote but you may like a book called Action in Perception. It's more of a survey of contemporary cognitive science in relation to perception, but still relevant to perceptual consciousness.

  • I just think of brains like they're anything else, a lot of arrangements of molecules, but animated by other molecules which are arranged in ways that self-replicate (RNA essentially). As a physical organ its structure changes as a result of feedback from continued survival and the environment. Just really complicated patterns of matter and energy that have emerged from the conditions on Earth.

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