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
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
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
That’s fine, I don’t think you’re being an ass at all. Brette is saying that just because there is a correspondence between the measured spike signals and the presented stimuli, that does not qualify the measured signals to be a representation. In order for it to be a representation, it also needs a feature of abstraction. The relation between an image and neural firing depends on auditory context, visual context, behavioural context, it changes over time, and imperceptible pixel changes to the image also substantially alters neural firing. According to Brette, there is little left of the concept of neural representation once you take into account all of this and you’re better off calling it a neural correlate.
hmm thank you for articulating that!
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.
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.
*because confidently relying on the model (in this case informational) prediction like ooh, we could do brain no problem in computer space, you are not exactly making a good scientific prediction. Good scientific prediction is that model is likely garbage, until proven otherwise, and thus shouldn’t be end all be all.
But then if you take information processing model, what it gives you, exactly, in understanding of the brain? The author contention that it is hot garbage framework, it’s doesn’t fit with how the brain works, your brain is not tiny hdd with ram and cpu, and until you think that it is, you will be searching for mirages.
Yes neural networks are much closer (because they are fucking designed to be), and yet even they has to be force fed random noise to introduce fuzziness in responses, or they’ll do the same thing every time. You reboot and reload neural net, it will do the same thing every time. But brain is not just connections of axons, it’s also extremely complicated state of the neuron itself with point mutations, dna repairs, expression levels, random rna garbage flowing about, lipid rafts at synapses, vesicles missing cause microtubules decided to chill for a day, the hormonal state of the blood, the input from the sympathetic neural system etc
We haven’t even fully simulated one single cell yet.
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.
And computers can count, that’s all they can do as turing machines, we can’t or not that well, feels there is a mismatch here in mediums🤔. If I took 10 people knowing same poem, what are the odds I’ll get same word from all of them?
Is that linked list in the brain can be accessed in all contexts then? Can you sing hip hop song while death metal backing track is playing?
Moreover linked list implies middle parts are not accessible before going through preceding elements, do you honestly think that’s a good analogue for human memory?
Humans have fingers so they can count, so the odds 10 people get the same word should be 100%.
I can plug my ears.
I could implement a linked list connected to a hash map that can be accessed from the middle.
Lol @100 percent.
So which one brain does? Linked list with hash maps then? Final simple computer analogy? Maybe indexed binary tree? Or maybe it’s not that?
When I want to recall a song, I have to remember one part, and then I can play it in my head. However, I can’t just skip to the end.
Linked list
So if second verse plays you can’t sing along until your brain parses through previous verses? I find it rather hard to believe
Do you think humans are so dumb they can’t count to 300?
you can try to find 200th word on physical book page, I suspect on first tries you’ll get different answers. It’s not dumbness, with poem it’s rather complicated counting and reciting (and gesturing, if you use hands), and direct count while you are bored (as in with book), might make mind either skip words, or cycle numbers. We aren’t built for counting, fiddling with complicated math is simpler than doing direct and boring count
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
.bmpfile wouldn’t be able to (without tools other than just basic stuff that allows it to show you.bmpimages). In different contexts we remember different things about stuff.Generally you can’t though. of course there are people who remember in different ways, or who can remember pi number to untold digits. Doesn’t mean there are tiny bytes-like engravings in their brain, or that they could remember it perfectly some time from now. Computer can tell what is 300 pixel of that image, while you don’t even have pixels, or fixed visual memory shape. Maybe it’s wide shot of nature, or maybe it’s a reflection of the light in the eyes of your loved one
People don’t think that brains are silicon chips running code through logic gates. At least, the vast majority of people don’t.
The point we’re making here is that both computers and human minds follower a process to arrive at a give conclusion, or count to 300, or determine where the 300th pixel is in a computer. A computer doesn’t do that magically. There’s a program that runs that counts to 300. A human would have to dig out a magnifying glass and count to three hundred. The details are different, but both are counting to 300.
because that’s a task for computer, my second example: giving you two words, it would be slower for computer than arriving at 300 th word, while for you it would be significantly faster than counting.
fundamentally a question is brain a turing machine? I rather think not, but it could be simulated as such with some untold complexity.
because that’s a task for computer, my second example: giving you two words, it would be slower for computer than arriving at 300 th word, while for you it would be significantly faster than counting
If your thesis is that human brains do not work perfectly the same way, and not that the analogy with computers in general is wrong, then sure, but nobody disagrees with that thesis, then. I don’t think that any adult alive has proposed that a human brain is just a conventional binary computer.
However, this argument fails when it comes to the thesis of analogy with computers in general. Not sure how it is even supposed to be addressing it.
fundamentally a question is brain a turing machine? I rather think not
Well, firstly, a Turing machine is an idea, and not an actual device or a type of device.
Secondly, if your thesis is that Turing machine does not model how brains work, then what’s your argument here?of course I can’t prove that brain is not a turing machine, I would be world famous if I could. Computers are turing machines yes? They cannot do non-Turing machines operations (decisions or whatever that’s called)
What comparing computer with brain gives to science, I’m asking again for third time in this thread. What insight it provides, aside from mechanizing us to the world? That short term memory exists? a stone age child could tell you that. That information goes from the eyes as bits like a camera? That’s already significantly wrong. That you recall like a photograph read out from your computer? Also very likely wrong
Firstly, I want to say it’s cool you’re positively engaging and stimulating a lot of conversation around this.
As far turing machines go - It’s only a concept that’s meant to show a fundamental “level” of computing (“turing completeness”), what a computing device can or cannot achieve. As you agree a turing machine could ‘simulate’ a brain (and we know brains can simulate a turing machine - we invented them!), then conceptually, yes, the brain is computationally equivalent, it is ‘turing complete’, albeit with some randomness thrown in.
some randomness thrown in.
I remain extremely mad at the Quantum jerks for demonstrating that the universe is almost certainly not deterministic. I refuse to be cool about it.
We can simulate a water molecule, does it make a turing machine then? Is single protein? A whole cell? 1000 cells in some invertebrate?
Simulation doesn’t work backwards, it’s not an implied equivalency of turing completeness for both directions. If brain is a turing machine we can map one to one it’s whole function to any existing turing machine, not simulate it with some degree of accuracy.
Again, though, this simply works to reinforce the computer analogy, considering stuff like file formats. You also have to concede that a conventional computer that stores the poem as a
.bmpfile isn’t going to tell you what the 300th word in it is (again, without tools like text recognition), just like a human is generally not going to be able to (in the sort of timespan that you have in mind that is - it’s perfectly possible and probable for a person who has memorised the poem to tell what the 300th word is, it would just take a bit of time).Again, we can also remember different things about different objects, just like conventional computers can store files of different formats.
A software engineer might see something like ‘O(x)’ and immediately think ‘oh, this is likely a fast algorithm’, remembering the connection between time complexity of algorithms with big-O notation. Meanwhile, what immediately comes to mind for me is ‘what filter base are we talking about?’, as I am going to remember that classes of finally relatively bounded functions differ between filter bases. Or, a better example, we can have two people that have played, say, Starcraft. One of them might tell you that some building costs this amount of resources, while the other one won’t be able to tell you that, but will be able to tell you that they usually get to afford it by such-and-such point in time.Also, if you are going to point out that a computer can’t tell if a particular image is of a ‘wide shot of nature’ or of a ‘reflection of the light in the eyes of one’s loved one’, you will have to contend with the fact that image recognition software exists and it can, in fact, be trained to tell such things in a lot of cases, while many people are going to have issues with telling you relevant information. In particular, a person with severe face blindness might not be able to tell you what person a particular image is supposed to depict.
I’m talking about visual memory what you see when you recall it, not about image recognition. Computers could recognize faces 30 years ago.
I’m suggesting that it’s not linked lists, or images or sounds or bytes in some way, but rather closer to persistent hallucinations of self referential neural networks upon specified input (whether cognitive or otherwise), which also mutate in place by themselves and by recall but yet not completely wildly, and it’s rather far away picture from memory as in engraving on stone tablet/leather/magnetic tape/optical storage/logical gates in ram. Memory is like a growing tree or an old house is not exactly most helpful metaphor, but probably closer to what it does than a linked list
I’m talking about visual memory what you see when you recall it, not about image recognition
What is ‘visual memory’, then?
Also, on what grounds are you going to claim that a computer can’t have ‘visual memory’?
And why is image recognition suddenly irrelevant here?So far, this seems rather arbitrary.
Also, people usually do not keep a memory of an image of a poem if they are memorising it, as far as I can tell, so this pivot to ‘visual memory’ seems irrelevant to what you were saying previously.I’m suggesting that it’s not linked lists, or images or sounds or bytes in some way, but rather closer to persistent hallucinations of self referential neural networks upon specified input
So, what’s the difference?
which also mutate in place by themselves and by recall but yet not completely wildly
And? I can just as well point out the fact that hard drives and SSDs do suffer from memory corruption with time, and there is also the fact that a computer can be designed in a way that its memory gets changed every time it is accessed. Now what?
Memory is like a growing tree or an old house is not exactly most helpful metaphor, but probably closer to what it does than a linked list
Things that are literally called ‘biological computers’ are a thing. While not all of them feature ability to ‘grow’ memory, it should be pretty clear that computers have this capability.
What is visual memory indeed in informational analogy, do tell me? Does it have consistent or persistent size, shape or anything resembling bmp file?
The difference is neural networks are bolted on structures, not information.
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.
I can as well, hash numbers are much worse due to 16 number system.
I was mainly pointing out it’s not typical brain activity to remember info which we don’t perceive as memorable, despite its information contents. Its not a poke at nd folks why would people remembring 10000 digits of pi be nd? but i’ll change it, you are right
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.
The op its not arguing it has a metaphisical component. Its arguing the structure of the brain is diferent frome the structure of your pc. The metaphor bit is important because all thinking is metaphor with different levels of rigor and abstraction. A faulty metaphor forces you to think the wrong way.
I do disagree with some things, whats a metaphor if not a model? Whats reacting to stimuli if not processing information?
The op its not arguing it has a metaphisical component.
Yes they are. They might scream in your face that they’re not, but the argument they’re making is based not on science and observation but rather the chains of a christian culture they do not feel and cannot see.
A faulty metaphor forces you to think the wrong way.
The Sapir-Whorf hypothesis, if it’s accurate at all, does not have a strong effect.
whats a metaphor if not a model?
To quote the dictionary; “a figure of speech in which a word or phrase is applied to an object or action to which it is not literally applicable.” Which seems to be the real problem, here; Psychologists and philosophers hear someone using a metaphor and think they must literally believe what the psychologist or philosopher believes about the symbol being used.
I think you are rigth. Our dissagrement comes from thinking the metaphor refers to structure rather than just language. Lets say an atomic model were the electrons ar flying around a nucleus formimg shells, is also not literaly aplicable. But we think of it as a useful metaphore because its close enough.
The same should apply to the most sophisticated mathematical models. A useful metaphor should then be a more primitive form of thise process where it illustrates a mechanism. If the mechanism is different from the mechanism in the metaphor then it should be wrong.
If the metaphor is just there to provide names, then you are offcourse rigth that it should not change anything.
Whether the metaphor of computers and brains is correct or not should also have no effect on wether we can simulate a brain in a computer. Computers can after all simulate many things that do not work like computers.
I could describe it as gold hunter with those sluice thingies, throwing water out and keeping gold, there I described short term memory.
I don’t disagree it’s a material process, I just think we find most complex analogy we have at the time and take it (as author mentions), but then start taking metaphor too far
Yeah but we, if “we” is people who have a basic understanding of neuroscience, aren’t taking it to far. The author is yelling at a straw man, or at lay people which is equally pointless. Neuroscientists don’t think of the mind or the brain it runs on as being a literal digital computer. They have their own completely incomprehensible jargon for discussing the brain and the mind, and if this article is taken at face value the author either doesn’t know that or is talking to someone other than people who do actual cognitive research.
I’ma be honest, i think there might be some academic infighting here. Psychology is a field with little meanginful rigor and poor explanatory power, while neuroscience is on much firmer ground and has largely upended the theories arising from Epstein’s heyday. I think he might be feeling the icy hand of mortality in his chest and is upset the world has moved past him and his ideas.
Also, the gold miner isn’t a good metaphor. In that metaphor information only goes one way and is sifted out of chaos. There’s no place in the metaphor for a process of encoding, retrieving, or modifying information. It does not resemble the action of the mind and cannot be used as a rough and ready metaphor for discussing the mind.
I work in neuroscience and I don’t agree that it is on much firmer ground that psychology. In fact, as some people in the community have noted, the neuroscience mainstream is probably still in the pre-paradigmitic stage (using Kuhn). And believe it or not, a lot of neuroscientists naively do believe that the brain is like a computer (maybe not exactly one, but very close).
Sure there is: encoding is taking sand from the river (taking noise from the world into comprehensible inputs) storage is taking the gold, modifying is throwing some bits out or taking them to the smith.
From the bottom up (and in the middle, if we take partial electro, ultrasound or magnetic stimulation) neuroscience andvances are significant but rather vague. We likely know how on molecular level memory works, but that has jack shit to do with information pipelines, but rather rigorous experiments, or in case of machine human interface more like skilled interpretation of what you see and knowing where to look for it (you can ascribe it to top down approach).
Neuroscientists likely dont, but I think you have rather nicer opinion of tech bros than I do or their ideas among people
My opinion of tech bros is that anyone deserving the label “tech bro” is a dangerous twit who should be under the full time supervision of someone with humanities training, a gun, and orders to use it if the tech bro starts showing signs of independent thought. It’s a thoroughly pathological world view, a band of lethally competent illiterates who think they hold all human knowledge and wisdom. If this is all directed at tech bros I likely didn’t realize it because I consider trying to teach nuance to tech bros about as useful as trying to teach it to a dog and didn’t consider someone in an academic field would want to address them.
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
I’ve read long ago that replicating all the functions of a human brain is probably possible with computers around one order of magnitude less powerful than the brain because it’s kind of inefficient
There’s no way we can know that, currently. The brain does work in all sorts of ways we really don’t understand. Much like the history of understanding DNA, what gets written off as “random inefficiency” is almost certainly a fundamental part of how it works.
because it’s kind of inefficient
relative to what and in what sense do you mean this?
I mean for the most extreme example, it takes approximately 1 bazillion operations to solve 1+1
It doesn’t actually. Bees can be trained to do simple arithmetic and they have relatively few neurons.
I hypothesize that it takes fewer watts for a bee brain to do arithmetic than it does for my gpu to simulate an incredibly simple and highly reduced model of a biological neural network to do the same thing.
No I mean the human brain does that, and adding 1 and 1 can be done with like a few wires, or technically two rocks if you wanna be silly about it
This thing adds 1 to any number from 0 to 15 and it’s tremendously less complex than a neuron, it’s like 50 pieces of metal or whateverSorry for being thick. You mean the human brain does which?
The human brain does that many operations to add 1 and 1
You can probably do it with a pentium processor if you know how. The brain is very slow, and pentium processors are amazingly fast. Its jut that we have no idea.
Resident dumb guy chipping in, but are these two facts mutually exclusive? Assuming both are true, it just means you’d need a computer that’s 1e12x as powerful as our supercomputers to simulate the brain, which is itself 1e13x as powerful as a supercomputer. So we’re still not getting there anytime soon.
*With a very loose meaning of what “powerful” means seeing as the way the brain works is completely different to a computer that calculates in discrete steps.
We really don’t know enough about the brain to make any sweeping statements about it at all beyond “it’s made of cells” or whatever.
Also, Dr. Epstein? Unfortunate.We really do, though. Like we really, really do. Not enough to build one from scratch, but my understanding is we’re starting to be able to read images people are forming in their minds, to locate individual memories within the brain, we’re starting to get a grasp on how at least some of the sub systems of the mind function and handle sensory information. Like we are making real progress at a rapid pace.
We can’t yet really read images people are thinking of, but we have got a very vague technology that can associate very specific brainwave patterns with specific images after extensive training with that specific image on the individual. Which is still an impressive 1% of the way there.
I would love to see some studies that you believe show this. I have seen several over the last decade and come to the conclusion that most of these are bunk or just able to recognize one brain signal pattern, and that that pattern actually is indistinguishable from some others (like lamp and basket look nothing the same, but then the brain map for lamp also returns for bus for some reason).
It’s not a useful endeavor in my opinion, and using computer experience and languages as a model is a pretty shit model, is my conclusion. More predictive possibilities than psychology, but wildly inaccurate and unable to predict it’s innaccuracy. It’s good to push back because it’s accuracy is wildly inflated by stembros
I have seen several over the last decade and come to the conclusion that most of these are bunk or just able to recognize one brain signal pattern
The fmri ones are probably bunk. That said, if you could manage the heinous act of cw: body gore
spoiler
implanting several thousands of very small wires throughout someone’s visual cortex, and record the responses evoked by specific stimuli or instructions to visualize a given stimulus, you could probably produce low fidelity reconstructions of their visual perception
are you familiar with the crimes of Hubel and Weisel?
I am not, and I will look it up in a minute.
But my point is that such a low-fidelity reconstruction, when interpreted through the model of modern computing methods, lacks the accuracy for any application AND, crucially, has absolutely no way to account for and understand its limitations in relation to the intended applications. That last part is a more philosophy of science argument than about some percentage accuracy. It’s that the model has no way to understand its limitations because we don’t have any idea what those are, and discussion of this is limited to my knowledge, leaving no ceiling for the interpretations and implications.
I think a big difference in positions in this thread though is between those talking about how the best neuroscientists in the world think about this, and about those who are more technologists who never reached that level and want to Frankenstein their way to tech-bro godhood. I’m sure the top neuros get this, and are constantly trying to find new and better models. But their publications don’t appear in science journals on the covers
Did this motherfucker really write more than 4000 words because nobody told them “all models are wrong but some are useful”?
A spectre is haunting Hexbear — the spectre of UlyssesT.
This was a really cool and insightful essay, thank you for sharing. I’ve always conceptualized the mind as a complex physical, chemical, and electrical pattern (edit: and a social context) - if I were to write a sci fi story about people trying to upload their brain to a computer I would really emphasize how they can copy the electrical part perfectly, but then the physical and chemical differences would basically kill “you” instantly creating a digital entity that is something else. That “something else” would be so alien to us that communication with it would be impossible, and we might not even recognize it as a form of life (although maybe it is?).
Would you put your brain in a robot body?
Nails are like candy to robots. And we’ll eat tires instead of licorice.
yes, a robot body with a dumptruck ass (it is an actual dumptruck)
On a more serious note, techbros’ understanding of the brain as a computer is just their wish to bridge subjectivity and objectivity. They want to be privy to your own subjectivity, perhaps even more privy to your own subjectivity than you yourself. This desire stems from their general contempt for humanity and life in general, which pushes them to excise the human out of subjectivity. In other words, if you say that the room is too hot and you want to turn on the AC, the techbro wants to be able to pull out a gizmo and say, “uh aktually, this gizmo read your brain and it says that your actual qualia of feeling hot is different from what you’re feeling right now, so aktually you’re not hot.”
Too bad for the techbro you can never bridge subjectivity and objectivity. The closest is intersubjectivity, not sticking probes into people’s brains.
imagine placing intentional limits on your own desire to understand the universe like this, as though subjective experience isn’t the weirdest fucking thing imaginable and so understanding it is of obvious interest to anybody with any curiosity whatsoever
You understand another person’s subjectivity by talking to them like a normal person, not studying them like a lab rat.
I’m glad he mentioned that we aren’t just our brains, but also our bodies and our historical and material contexts.
A “mind upload” would basically require a copy of my entire brain, my body, and a detailed historical record of my life. Then some kind of witchcraft would be done to those things to combine them into the single phenomenal experience of me. Basically:
So, ironically I think the author is falling in to the trap they’re complaining about. They’re talking about an “upload” as somehow copying a file from one computer to another.
Instead, consider transferring your brain to a digital system a little at a time. Old cells die, new cells are created. Do you ever lose subjective continuity during that process? Let one meat cell die and a digital cell grow. Do you stop being yourself once all your brain cells are digital? Was there ever a loss of phenomenalalaogy?
You’ve completely misunderstood their criticism of mind uploading.
The author asserts that you are not really your brain. If you copied your brain into a computer, that hapless brain would immediately dissociate and lose all sense of self because it has become unanchored from your body and your sociocultural and historical-materialist context.
You are not just a record of memories. You are also your home, your friends and family, what you ate for breakfast, how much sleep you got, how much exercise you’re getting on a regular basis, your general pain and comfort levels, all sorts of things that exist outside of your brain. Your brain is not you. Your brain is part of you, probably the most important part, but a computer upload of your brain would not be you.
You are not just a record of memories. You are also your home, your friends and family, what you ate for breakfast, how much sleep you got, how much exercise you’re getting on a regular basis, your general pain and comfort levels, all sorts of things that exist outside of your brain. Your brain is not you.
Embodied cognition. I don’t see this as implying that what we’re doing isn’t computation (or information processing) in some sense. It’s just that the way we’re doing it is deeply, deeply different from how even neural networks instantiated on digital computers do it (among other things, our information processing is smeared out across the environment). That doesn’t make it not computation in the same way that not having a cover and a mass in grams makes a PDF copy of Moby Dick not a book. There are functional, abstract similarities between PDFs and physical books that make them the same “kinds of things” in certain senses, but very different kinds of things in other senses.
Whether they’re going to count as relevantly similar depends on which bundles of features you think are important or worth tracking, which in turn depends on what kinds of predictions you want to make or what you want to do. The fight about whether brains are “really” computers or not obscures the deeply value-laden and perspectival nature of a judgement like that. The danger doesn’t lie in adopting the metaphor, but rather in failing to recognize it as a metaphor–or, to put it another way, in uncritically accepting the tech-bro framing of only those features that our brains have in common with digital computers as being things worth tracking, with the rest being “incidental.”
I think I agree.
One metaphor I quite like is the brain as a ball of clay. Whenever you do anything the clay is gaining deformities and imprints and picking up impurities from the environment. Embodied cognition, right? Obviously the brain isn’t actually a ball of clay but I think the metaphor is useful, and I like it more than I like being compared to a computer. After all, when a calculator computes the answer to a math problem the physical structure of the calculator doesn’t change. The brain, though, actually changes! The computation metaphor misses this.
This is really useful for understanding memory, because every time you remember something you pick up that ball of clay and it changes.
After all, when a calculator computes the answer to a math problem the physical structure of the calculator doesn’t change
What counts as “physical structure?” I can make an adding machine out of wood and steel balls that computes the answer to math problems by shuffling levers and balls around. A digital computer calculates the answer by changing voltages in a complicated set of circuits (and maybe flipping some little magnetic bits of stuff if it has a hard drive). Brains do it by (among other things) changing connections between neurons and the allocation of chemicals. Those are all physical changes. Are they relevantly similar physical changes? Again, that depends deeply on what you think is important enough to be worth tracking and what can be abstracted away, which is a value judgement. One of the Big Lies of tech bro narrative is that science is somehow value free. It isn’t. The choice of model, the choice of what to model, and the choice of what predictive projects we think are worth pursuing are all deeply evaluative choices.
In dwarf fortress you can make a computer out of dwarfs, gates, and levers, and it won’t change unless the dwarfs go insane from sobriety and start smashing stuff.
Great example! Failure modes are really important. Brains and dwarf fortresses might both be computers, but their different physical instations give them different ways to break down. Sometimes that’s not important, but sometimes it’s very important indeed. Those are the sorts of things that get obscures by these dogmatic all-or-nothing arguments.
Isn’t that what this article is about? That “brain as computer” is a value judgement, just like “brain as hydrolic system” and “brain as telegraph” were? These metaphors are all useful, I think the article was just critiquing the inability for people to think of brains outside of the orthodox computational framework.
I’m just cautioning against taking things too far in the other direction: I genuinely don’t think it’s right to say “your brain isn’t a computer,” and I definitely think it’s wrong to say that it doesn’t process information. It’s easy to slide from a critique of the computational theory of mind (either as it’s presented academically by people like Pinker or popularly by Silicon Valley) into the opposite–but equally wrong–kind of position that brains are doing something wholly different. They’re different in some respects, but there are also very significant similarities. We shouldn’t lose sight of either, and it’s important to be very careful when talking about this stuff.
Just 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.
It strikes me as totally wrong to say that this process is free of computation. The computation that’s going on here has interesting differences from what goes on in a ball-catching robot powered by a digital computer, but it is computation.
Your analogy reminds me a bit of the Freud essay on the mystical writing pad
That should be a red flag to treat it with caution. Freud was a crank and his only contribution to psychology was being so wrong it inspired generations of scientists to debunk him.
This really sounds like mind-body dualism. Setting aside the body, which you could just stick the new brain back in your body and hook the nervous system back in, why would you be cut off from your sociocultural and historical material context? Go home. pet your cat with your robot arms. Hang out with your family, eat breakfast. Wherein lies the problem? All of those things exist inside your brain. Your brain is taking the very shoddy, very poor information being supplied by your sensory organs and assembling it in to approximations of what’s happening around you. We don’t interact with the world directly. Our interaction is moderated by our sensory organs and lots of non-conscious components of the mind, and they’re often wrong. If you hook up some cameras and a nervous system and some meat to this theoretical digital mind it has all the things a meat mind has. The same sensory information.
Like, really, where is the problem? A simulated brain is a brain is a brain. Put the same information in you’re going to get the same results out. Doesn’t matter if the signals are coming from a meat suit or a mathematical model of a meat suit, as long as they’re the right signals.
Okay, imagine your family does not recognize your mind upload as a person. That’s not hard to imagine, I bet most people today would struggle with that kind of barrier. Maybe they treat it like a memorial of a dead person and do not treat it like a member of the family, maybe they shun it because it’s creepy and don’t want anything to do with it, maybe they try to destroy it in the memory of who you were.
That’s your sociocultural and historical material context. The mind upload would become a new person entirely because of the insurmountable differences between being made of meat and being made of data. That upload could never be the person you are now, because the upload becomes someone different as soon as they stop being made of meat and stop eating hot chip and stop paying taxes etc.
Okay, imagine your family does not recognize your mind upload as a person.
That’s the day to day lived experience of like half the queer people in the world. I rejected part of my family and kicked them out of my life. i don’t have to imagine it, I’ve lived it.
You seem to have an assumption that some kind of digital transfer of consciousness would not physically interact with the world. You could just plug them in to a body. If you can run an entire brain on a computer hooking it in to a meat suit sounds fairly trivial. You can go right on eating hot chip and paying taxes. I think we’re operating from different perspectives here and I’m not sure what yours is. A digital mind could have a digital body and eat digital hot chip and they’d definitely be forced to pay taxes.
That’s the day to day lived experience of like half the queer people in the world. I rejected part of my family and kicked them out of my life. i don’t have to imagine it, I’ve lived it.
Okay, and are you the same person you were before that? I’ve experienced that too, and I don’t think I am the person I was before. I think I became someone else in order to survive. If I could somehow go back in time and meet myself we would hardly recognize each other!
they’d definitely be forced to pay taxes.
No, they’d be property and someone else would have to pay taxes for owning them.
When I talk about the sociocultural context I’m talking about the fact that society would not treat the mind upload as a person. We have not developed far enough along our historical material context to recognize uploads as people. The trauma of that experience would necessitate becoming a different person entirely, and thus, they’re no longer an upload of you. That’s the problem with talking about mind uploads.
You are not just your brain (and its supporting structures). You are your context.
What is the ratio of meat parts to machine parts at which point “that chair you once sat on” or “the dust bunnies you haven’t swept up yet even though you keep meaning to” are no longer materially a part of you and you subsequently lose your self as a result?
If someone loses their leg and gets a prosthetic does this alienate them from the birds that woke them up last week?
If someone loses their leg and gets a prosthetic does this alienate them from the birds that woke them up last week?
This trope in cyberpunk pisses me off to no end. Writers just out there saying “using a wheelchair makes you less human and the more wheelchair you use the less human you become.”
Like people are out there, living, surviving, retaining their sanity, in comas where they have no access to sensory input. Those people wake up and they’re still human after living in the dark for years. People who have no sensation or control below the neck go right on living without turning in to psycho-murderers because they’re so alienated from humanity because they can’t feel their limbs. How is it that people somehow lose their humanity and turn in to monsters just because they’ve got some metal limbs? You can cut out half of someone’s brain and there will still be a person on the other side. They might be pretty different, but there’s still a person there. people survive all kinds of bizarre brain traumas.
How is it that people somehow lose their humanity and turn in to monsters just because they’ve got some metal limbs?
Corporate bloatware/adware in cyber-limbs. That’s the explanation in my cyberpunk setting.
Yeah. At least there’s been a movement in the genre towards “ok, it’s not cybernetic implants in general, it’s chronic pain from malfunctioning or poorly calibrated implants, it’s the trauma of a violent and alienated society intersecting with people who are both suffering and who have a massively increased material capacity to commit violence, etc” there. Like Mike Pondsmith himself has still got a bit of a galaxy brain take on it, but even he’s moved around to something like “cyberpsychosis is a confluence of trauma and machines that are actively causing pain, nervous system damage, etc and which need a cocktail of drugs to manage which also have their own health and psychiatric consequences.”
I don’t see that as an improvement or a recognition of what is wrong with “cyberpsychosis” and related concepts. People live with severe trauma, severe chronic pain, and severe psychiatric problems and manage to keep it together. Pondsmith is making up excuses to keep “wheelchairs make you evil” in his game instead of recognizing the notion for what it is and discarding it.
I think it’s a good example of ingrained, reflexive ableism. It’s a holdover from archaic 20th century beliefs about disabled people being less human, less intelligent, less capable. Cybernetics are not a good metaphor for capitalist alienation or any other kind of alienation. They are, no matter how you cut it, aids and accomodations for disability. You just cannot say that cyberware makes you evil without also saying that disabled people using aids in your setting are alienating themselves from humanity and becoming monsters. If you wanted to argue that getting wired reflexes, enhanced musculature, getting your brain altered so you can shut off empathy or fear, things that you do voluntarily to make yourself a better tool for capitalism, gradually resulted in alienation, go for it.
Ghost in the Shell does a good job with that. Kusanagi isn’t alienated from humanity because she’s a cyborg, but her alienation from humanity grows from questioning what it means for her to be a cyborg, a brain in a jar. She’s got super-human capabilities - she’s massively stronger and more resilient, she’s a wizard hacker augmented with cyberware that let’s her directly interface with the net in a manner most people simply don’t have the skills for. Her digestive and endocrine systems are under her conscious fine control. These things don’t make her an alien or a monster, they create questions in her mind about her identity, her personhood, and how she can even relate to normal humans as her perspective and understanding of the world moves further and further away from them.
And this isn’t a bad thing. It doesn’t lead her to self-destruction or a berserk rage. Instead it leads her to growth, change, and evolution. She ambiguously dies, but in dying brings forth new life. Her new form is not an enemy of humanity or a threat, but instead a new kind of being that is a child or inheritor of humanity, humanity growing past it’s limitations to seek new horizons of potential.
The key difference is Kusanagi has agency. The cybernetics don’t force her towards alienation. They don’t damage her mind and turn her in to a monster with no agency. Kusanagi’s alienation grows from her own lived experience, her own thoughts and learning. They grow from her interactions with the people in her life and her day to day experiences. Her cybernetics are an important part of that experience, but she is in control of her cyberbody. It is not controlling her and turning her in to a hapless victim.
Basically; the Cyberpunk paradigm says that using a wheelchair makes you violent and evil. The GitS paradigm says using a wheelchair makes you consider the world from a different perspective. In the former disability, both physical and mental (false dichotomy I know) is villainized and demonized. In the latter disability is a state that creates separation from “normal” people in a way that reflects the experiences of real disabled people, but is otherwise neutral.
Pondsmith is making up excuses to keep “wheelchairs make you evil” in his game instead of recognizing the notion for what it is and discarding it.
At this point as I understand it his take is “alienation/isolation, trauma from a violent society, and denial of access to necessary medical care can eventually break someone, and someone who can bench press a car and has a bunch of reflex enhancers jacked directly into their spine is more likely to lash out in a dangerous way when their back’s to the wall, they think they’re going to die, and they panic,” with a whole lot of emphasizing social support networks as being important for surviving and enduring trauma like that.
It’s still not as good a take as “cyberpsychosis isn’t real, it’s just a bullshit diagnosis applied to people pushed past the brink by their material circumstances, acting in the way that a society that revolves around violence has ultimately taught them to act, who then just double down on it because they know they’re going to be summarily executed by the police who have no interest in deescalation or trying to take them alive, compounded with the fact that they can bench press a car and react to bullets fast enough to simply get out of the way, at least for a while” would be, but it’s earnest progress from someone who’s weirdly endearing despite being an absolute galaxy brained lib.
Weirdly, Cyberpunk 2077 seems to have had a better take on it than Pondsmith himself, with “cyberpsychos” mostly being just people with an increased capacity for violence dealing with intolerable material conditions until they fight back a little too hard against a real or perceived threat, with one who’s not even on a rampage and instead is just a heavily augmented vigilante hunting down members of a criminal syndicate that had murdered someone close to him. The police chatter also has the player branded a cyberpsycho when you get stars, reflecting the idea that it’s more a blanket term applied to anyone with augments who’s doing a violent crime than a real thing.
I have no idea, but neither do you, which is kind of the point. How much of you is your brain, your body, your context?
Even if we can’t put a number on it, I think it’s trivial to assert that you are not just your brain. So, if you copied only your brain into some kind of computer, there would be parts of you that are missing because you left them behind with your meat.
neither do you
Sure I do: that ratio does not exist, and no you don’t get alienated from your material context if you have a prosthetic limb. We’re made up of parts that perform functions, and we can remain ourselves despite the loss of large chunks of those - so long as someone remains alive and the brain keeps working they’re still themself, they’re still in there.
If someone could keep the functions of the brain ongoing and operating on machine bits they’d still be in there. It may be a transformative and lossy process, it may be unpleasant and imperfect in execution, but the same criticism applies to existing in general: at any point you may be forced out of your normal material context by circumstance and this is traumatic, you may lose the healthy function of large swathes of your body and this is traumatic, you may suffer brain damage and this is traumatic, you’re constantly losing and overwriting memories and this can be normal or it can be traumatic, etc, but through it all you are you and you’re still in there because ontologically speaking you’re the ongoing continuation of yourself, distinct from all the component parts that you’re constantly losing and replacing.
Sure I do: that ratio does not exist, and no you don’t get alienated from your material context if you have a prosthetic limb.
Does body dysmorphic disorder not exist? Or phantom limb? A full body prosthetic would undoubtedly be a difficult adjustment!
And would an upload be a person, legally speaking? Would your family consider the upload to be a person? That’s pretty alienating.
I didn’t say “you are perfectly happy and have no material problems whatsoever dealing with a traumatic injury and imperfect replacement,” but rather that this doesn’t represent some sort of fundamental loss of self or unmooring from material contexts. People can lose so much, can be whittled away to almost nothing all the way up to the point where continued existence becomes materially impossible due to a loss of vital functions, but through that they still exist, they remain the same ongoing being even if they are transformed by the trauma and become unrecognizable to others in the process.
And would an upload be a person, legally speaking? Would your family consider the upload to be a person? That’s pretty alienating.
If you suffer a traumatic brain injury and lose a large chunk of your brain, that’s going to seriously affect you and how people perceive you, but you’re still legally the same person. If instead that lost chunk was instead replaced with a synthetic copy there may still be problems but less so than just losing it outright. So if that continues until you’re entirely running on the new synthetic replacement substrate, then you have continued to exist through the entire process just as you continue to exist through the natural death and replacement of neurons - for all we know copying and replacing may not even be necessary compared to just adding synthetic bits and letting them be integrated and subsumed into the brain by the same processes by which it grows and maintains itself.
A simple copy taken like a photograph and then spun up elsewhere would be something entirely distinct, no more oneself than a picture or a memoir.
And people survive all of that stuff, and are still people. I really don’t understand what you’re getting at here.
This is just god of the gaps. “we don’t know so it’s not possible”. Saying “just copy the brain” is a reductive understanding of what’s being discussed. If we can model the brain then modelling the endocrine system is probably pretty trivial.
I didn’t read it as being impossible? I think you could upload a human mind into a computer, but it can’t just be their brain. Your mind, your phenomenal self, is more than just your brain because your brain isn’t just a hard drive. That’s what I took away from the article, anyway.
You are some mix of your brain, your body, and your context. Whatever upload magic exists would need all of that to work.
Aight I think we might be stuck in a semantics disagreement here. I’m using brain to mean the actual brain organ plus whatever other stuff is needed to support brain function - the endocrine system, nervous system, whatever. The physical systems of cognition in the body. i do not mean literally only the brain organ with no other systems.
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Yeah, pretty much. I think people are stuck in this idea that we’d somehow take a picture of the mind all at once and there’d be a loss of continuity and then you’re not the same person, but like, we’ve already got the intelligence making machine, why not just swap the parts out with maths one at a time until it’s all maths? Why would that be a problem? I sincerely think that a lot of people who object to this either aren’t really thinking of the concept beyond what’s presented in pop sci-fi or are still stuck on the idea of an incorporeal mind separate from material reality.
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Yeah, it’s the illusion of self and continuity of consciousness thing. People assume that any upload would be necessarily destructive and that there’d be a loss of continuity. No one ever seems to think “well, wait, why couldn’t I be awake for the whole operation in a way that preserves subjective continuity?”
In the ship of theseus, brain being replaced cell by cell concept you don’t get your head chopped off and imaged, you’re very gradually shifting what hardware your mind is running on. The lights never go out, there’s never a drastic, shocking moment of change, you never wake up in a jar.
That still relies on being able to digitally emulate wet cognition, which may not be possible.
Sure, maybe it’s not, but afaik there’s nothing that rules out the possibility. We can’t do it now, but I’m not aware of any physical laws or limits that make it impossible for us to do it at some point when we’ve got a better understanding of what’s happening and a lot of silicon to run the maths on.
Yeah maybe, but I don’t think we’re figuring it out anytime soon
I really like the description of evolution outcomes as “totally bonkers bs”
I genuinely don’t know how to explain what evolution is as a process most of the time “imagine a drop of water seeking the sea, but the drop of water really wants to fuck, and sometimes it gets hit by an asteroid?”
“Complex self-replicating systems reversing local entropy while undegoing variation caused by entropy until they lose equilibrium and can no longer self replicate” ?
It’s an incredibly simple concept. Water seeks the sea. And it’s also an incredibly complicated, obtuse concept. I think a huge part of the difficulty is cultural - we anthropomorphize and ascribe agency to everything, and evolution is the absolute and total absence of agency, the pure action of entropy
If our brains were computers we wouldn’t have computers.
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.
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.
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.
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.
“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.
you literally ignore the actual part of the text that adresses your problems.
you can use the word ‘tantrum’ while you ignore the literal words used and their meanings if you want but it only makes you seem illiterate and immature.
‘intuition worldviews thoughts beliefs our conscience’ are specific words with specific meanings. no computer (information processing machine) has ‘consciousness’, no computer has ‘intuition’, no computer has internal subjective experience - not even an idealized one with ‘infinite processing power’ like a turing machine. humans do. therefore humans are not computers. we cannot replicate ‘intuition’ with information processing, we cannot replicate ‘internal subjective experience’ with information processing. we cannot bridge the gap between subjective internal experience and objective external physical processes, not even hypothetically, there is not even a theoretical experiment you could design for it, there is not even theoretical language to describe it without metaphor. We could learn and simulate literally every single specific feature of the brain and it would not tell us about internal subjective experiences, because it is simply not the kind of phenomena that is understood by the field of information processing. If you have a specific take on the ‘hard problem of consciousness’ thats fine, but to say that ‘anyone who disagrees with me about this is just stupid’ is immature and ignorant, especially in light of your complete failure to understand things like Turing machines.
I usually like your posts and comments but this thread has revelaed a complete ignorance of the philosophical and theoretical concepts under discussion here and an overzealous hysteria regarding anything that is remotely critical of a mechanistic physicalist reductionist worldview. you literally ignore or glazed over any relevant parts of the text i quoted, misunderstood the basic nature of what a turing machine is, misunderstood the nature of the discourse around the brain-as-computer discourse, all with the smuggest redditor energy humanly possible. I will not be further engaging after this post and will block you on my profile, have a nice life.
Well, Traumadumpling isn’t going to read this, so I’m just amusing myself.
we cannot bridge the gap between subjective internal experience and objective external physical processes, not even hypothetically, there is not even a theoretical experiment you could design for it, there is not even theoretical language to describe it without metaphor. We could learn and simulate literally every single specific feature of the brain and it would not tell us about internal subjective experiences, because it is simply not the kind of phenomena that is understood by the field of information processing.
This is all because subjectivity isn’t falsifiable and is not currently something that the scientific method can interact with. As far as the scientific method is concerned it doesn’t exist. idk why people are even interested in it, I don’t see why it’s important. The answer to “P-zombies” is that it doesn’t matter and isn’t interesting. If something performs all the observable functions of an intelligent mind with a subjective experience… well… it performs all the observable functions of an intelligent mind. Why are you interested in subjectivity if you can’t evaluate whether it’s even happening? You can’t test it, you can’t confirm or deny it. So just put it back in the drawer and move on with your life. It’s not even a question of whether it does or doesn’t exist. It’s that the question isn’t important or interesting. It has no practical consequences at all unless people, for cultural reasons, decide that something that performs the functions of an intelligent mind doesn’t deserve recognition as a person because of their ingrained cultural belief3 in the existence and importance of a soul.
I do see this as directly tied to atheism. Part of making the leap to atheism and giving up on magic is admitting that you can’t know, but based on what you can observe the gods aren’t there. No one can find them, no one can talk to them, they never do anything. If there are transcendental magic people it’s not relevant to your life.
Phenomenology is the same way. It just doesn’t matter, and continuing to carry it around with you is an indication of immaturity, a refusal to let go and accept that some things are unknowable and probably always will be. Hammering on and on that we can’t explain how subjectivity arises from physical processes doesn’t change the facts on the ground; We’ve never observed anything but physical processes, and as such it is reasonable to assume that there is a process by which subjectivity emerges from the physical. Because there’s nothing else. There’s nothing else that could be giving rise to subjectivity. And, again, we don’t know. Maybe there is a magic extradimensional puppeteer. But we don’t know in the same sense that we don’t know that the sun will rise tomorrow. It’s one of the not particularly interesting problems with the theory of science - We assume that things that happened in the past predict things that will happen in the future. We do not, and cannot know if the sun will rise tomorrow. But as a practical matter it isn’t important. With nothing else to explain the phenomena we observe, we can assume within the limits in which anything at all is predictable that the subjective experience is an emergent property of the crude, physical, boring, terrifyingly mortal meat.
More and more philosophy’s dogged adherence to these ideas strikes me as an refusal to let go, to grow up, to embrace the unpredictable violence of a cold, hostile, meaningless universe. Instead of saying we don’t and cannot know, and therefor it’s not worth worrying about, philosophers cling to this security blanket of belief that we are, somehow, special. That we’re unique and our existence has meaning and purpose. That we’re different from the unthinking matter of stars or cosmic dust.
mechanistic physicalist reductionist worldview
https://en.wikipedia.org/wiki/Physicalism
Like this is just materialism. Physicalism isn’t a belief, it’s a scientific observation. We haven’t found anything except the physical and as much as philosophers obsess about subjectivity and qualia and what have you those concepts, while mildly interesting intellectual topics, aren’t relevant to science. You can’t measure them, you cannot prove if they exist or do not exist. Maybe someday we’ll have a qualia detector and we’ll actually be able to do something with them, but right now they’re not relevant. I’m a reductionist physicalist mechanist because I’m tired of hearing about ghosts and souls and magic. No question is being raised. There’s no investigation that can proceed from these concepts. You can’t do anything with them except yell at people who think, based on evidence, that physics is the only system that we can observe and investigate. And it’s not “these things don’t exist”, it’s whether they exist or not, we can’t observe or interact with them so we can’t do anything with them. You can’t test qualia, you can’t measure it. If we can some day, cool. But until then it’s just… not useful.
AI is everywhere.
I didn’t read the article, just commented on the excerpts. And when I do read the article this is the first line? Conflating LLMs and neural nets with AI? Accepting the tech bro marketing buzzword at face value?
Terms like “neural networks” certainly have not helped and, from Musk to Hawking, some of the greatest minds have propagated this myth.
Neural networks are called that because they’re modeled on the behavior of neurons, not the other way around. Hawking could be a dork about some things but why put him in the same sentence as an ignorant buffoon like Musk?
Is what we’re arguing here actually that psychologists and philosophers are yelling at tech bros because they think that neuroscientists using computer metaphors actually believe a seventy year old theory of cognition originating from psychology when psychologists were still mostly criminals and butchers?
Like saying the brain is a biological organ? That’s not a gotcha when biological computers exist and research teams are encoding gigabytes of data, like computer readable data, 1s and 0s, as DNA. Whatever the brain is, we can build computers out of meat, we’ve done it, it works. There is no distinction between biological and machine, artifact and organ, meat and metal. It’s an illusion of scale combined with, frankly, superstition. A living cell operates according to physical law just like everything else. It has a lot of componenents, some of them are very small, some of them we don’t understand and I’m sure there are processes and systems we haven’t identified, but all those pieces and processes and systems follow physical laws the same as everything else in creation. There’s no spooky ghosts or quintessence down there.
Like, if the message here is to tell completely ignorant laypeople and tech bros who haven’t read a book that wasn’t about webdev that the brain does not literally have circuitry in it, fine, but say that. But right now we’re very literally bridging the perceived gap between mechanical human artifacts and biology. We’re building biological machines, biological computers. These are not completely different categories of things that can never be unified under a single theory to explain their function.
Let’s take a step back, look at “Capitalism as a real god”, what Marx called it, or “Holy shit capitalism is literally Cthulu” which is the formulation many people are independently arriving at these days. Capitalism is a gigantic system that emerges from the interactions of billions of humans. It’s not located in any single human, or any subset of humans. It emerges from all of us, as we interact with each other and the world. There’s no quintessence, no “subjectivity” that we could ever evaluate or interogate or find. We can’t say whether capitalism has a subjective experience or cosciousness, whether there is an “I think therefore I am” drifting across the maddening complexity of financial transactions, commodity fetishism, resource extraction, and cooking dinner.
The brain has ~80 million neurons (plus glial matter I know I know bear with me). There are about 8 billion humans, and each of us is vastly more complex than a brain cell. So if humans actually are components in an emergent system that is intelligent and maybe self-aware, there’s only one order of magnitude fewer humans than there are cells in a human brain that, given lack of any other explanations, we must assume give rise to a thinking mind.
Is it impossible for such a system to have a subjective experience? Is it a serious problem? As it stands we can’t assess whether such subjectivity exists in the system, whether the system has something meaningfully resembling a human mind. The difference in experience is likely so vast as to be utterly unbridgeable. A super-organism existing on a global level would, likely, not be able to communicate with us due to lack of any shared referents or experiences at all. A totally alien being unlike us except that it emerges from the interaction of less complex systems, seeks homeostasis, and reacts to its environment.
But, like, who cares? Whether capitalism is a dumb system or an emergent intelligence there’s nothing we can do about it. We can’t investigate the question and an answer wouldn’t be useful. So move along. Have your moment of existential horror and then get on with your life.
I think that’s what really bothers me about this whole subjectivity, qualia, consciousness thing. It’s boring. It’s just… boring. Being stuck on it doesn’t increase my knowledge or understanding. It doesn’t open up new avenues of investigation.
The conclusion I’m coming to is this whole argument isn’t about computers or brains or minds, but rather phenomenology having reached a dead end. It’s a reaction to the discipline’s descent in to irrelevance. The “Hard Problem of Consciousness” simply is not a problem.
Well said Frank, you’re carrying this thread.
o7
(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.
literally read anything about a Turing Machine because you are comically misunderstanding these articles.
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.
please read the entire article, you are literally not understanding the text.
Unless the author redefines the words used in the bit that you quoted from them, I addressed their argument just fine.
In the case the author does redefine those words, then the bit that you quoted is literally meaningless unless you also quote the parts where the author defines the relevant words.“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.”
The author is just arbitrarily placing on algorithms the requirement that they ‘can be followed mechanically, with no insight required’. This is silly for a few reasons.
Firstly, that’s not how algorithms are defined in mathematics, nor is that how they are understood in the context of relevant analogies. Going to just ignore the ‘mechanically’ part, as the author seems to not be explaining what they meant, and my interpretations are all broad enough to conclude that the author is obviously incorrect.
Secondly, brains perform various actions without any sort of insight required. This part should be obvious.
Thirdly, the author’s problem is that computers usually work without some sort of introspection into how they perform their tasks, and that nobody builds computers that inefficiently access some random parts of memory vaguely related to their tasks. The introspection part is just incorrect, and the point about the fact that we don’t make hardware and software that does inefficient ‘insight’ has no bearing on the fact that computers that do those things can be built and that they are still computers.The author is deeply unserious.
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 their problem is that the analogy is not insightful, then fine. However, their thesis seems to be that the analogy is not applicable well enough, which is different from that.
If we want to narrow the usage of the word computers to systems like our laptops, desktops, etc.
Okay, so their thesis is not that the computer analogy is inapplicable, but that we do not work exactly the way PCs work? Sure.
I don’t know why they had to make bad arguments regarding algorithms, though.you can make Boolean logic…
There is no such thing as ‘Boolean logic’. There is ‘Boolean algebra’, which is an algebraisation of logic.
The author also seems to assume that computers can only work with classical logic, and not any other sort of logic, for which we can implement suitable algebraisations.with the most important part being that those physical states (and their relationship to the computational variables) are specified by us!!!
This is silly. The author is basically saying ‘but all computers are intelligently made by us’. Needless to say, they are deliberately misunderstanding what computers are and are placing arbitrary requirements for something to be considered a computer.
All the systems that we refer to as modern day computers and want to restrict our usage of the word computers to
Who is this ‘we’?
Again, the author is deeply unserious.
Unless the author redefines the words used in the bit that you quoted from them, I addressed their argument just fine.
so you aren’t going to read the article then.
No Investigation, No Right to Speak.
Here follows some selections from the article that deal with exactly the issues you focus on.
I strongly advise reading the entire article, and the two it is in response to, and furthermore reading about what a Turing Machine actually is and what it can be used to analyze.
The debate on whether the brain is a computer or not seems to have died down given the recent success of computer science ideas in both neuroscience and machine learning. I have seen a few recent articles on this subject from scientists, who have made strong claims that the brain is in fact literally a computer, and not just a useful metaphor backed up with their reasons to believe so. One such article is this one by Dr. Blake Richards (and here is another one by Dr. Mark Humphries). I will mainly deal with the first one — a really good and extensive article. I would encourage readers to go through it slowly, and in detail for it provides a good look at how to think about what a computer is, and deals well with a lot of the weaker arguments brought against the ‘brain is a computer’ claim (like the ones here.) Dr. Richards addressed a good variety of objections that people might rise to the claim that “the brain is a computer” towards the end of his article. I will raise an argument here that I feel lies at the heart of this discussion, not addressed in the post and is often overlooked or dismissed as non-existent. The reason I think it is important to discuss this question (and/or objection) in detail is that I strongly believe it affects how we study the brain. Describing the brain like a computer allows for a useful computational picture that has been very successful in the fields of neuroscience and artificial intelligence (specifically the sub-area of machine learning over the recent past). However as an engineer interested in building intelligent systems, I think this view of the brain as a computer is beginning to hurt us in our ability to engineer systems that can efficiently emulate their capabilities over a wide range of tasks.
the bolded part above is ‘why the author has a problem with the computer metaphor’ since you seem so confused by that.
There are a few minor/major problems (depends on how you look at it) in the definitions used to get to the conclusion that the brain is in fact a computer. Using the definitions put forward in the blog post —
(1) an algorithm is anything a Turing machine can do, (2) computable functions are defined as those functions that we have algorithms for, (3) a computer is anything which physically implements algorithms in order to solve computable functions.
these are the definitions the author is using, not ones he made up but ones he got from one of the articles he is arguing against. note the similarities with the definitions on https://en.wikipedia.org/wiki/Algorithm :
One informal definition is “a set of rules that precisely defines a sequence of operations”,[11][need quotation to verify] which would include all computer programs (including programs that do not perform numeric calculations), and (for example) any prescribed bureaucratic procedure[12] or cook-book recipe.[13] In general, a program is an algorithm only if it stops eventually[14]—even though infinite loops may sometimes prove desirable. Boolos, Jeffrey & 1974, 1999 define an algorithm to be a set of instructions for determining an output, given explicitly, in a form that can be followed by either a computing machine, or a human who could only carry out specific elementary operations on symbols.[15]
note the triviality criticism of the informal definition that this author previously addressed, and the ‘human who could only carry out specific elementary operation on symbols’ is a reference to Turing Machines and the Chinese Room thought experiment, both of which i recommend reading about.
The concept of algorithm is also used to define the notion of decidability—a notion that is central for explaining how formal systems come into being starting from a small set of axioms and rules. In logic, the time that an algorithm requires to complete cannot be measured, as it is not apparently related to the customary physical dimension. From such uncertainties, that characterize ongoing work, stems the unavailability of a definition of algorithm that suits both concrete (in some sense) and abstract usage of the term.
this is still a matter under academic discussion, there are not widely agreed on definitions of these terms that suit all uses in all fields.
Most algorithms are intended to be implemented as computer programs. However, algorithms are also implemented by other means, such as in a biological neural network (for example, the human brain implementing arithmetic or an insect looking for food), in an electrical circuit, or in a mechanical device.
algorithms can be implemented by humans, intentionally or not, the hardware is irrelevant to the discussion of Turing machines since they are an idealized abstraction of computing.
enough wikipedia now back to the article
Number (3) is the one we will focus on for it is vitally important. To complete those definitions, I will go ahead an introduce from the same blog post, an intuitive definition of algorithm — “ an algorithm is a finite set of instructions that can be followed mechanically, with no insight required, in order to give some specific output (e.g. an answer to yes/no integer roots) for a specific input (e.g. a specific polynomial like 6x³yz⁴ + 4y²z + z — 9).” And the more technical definition of algorithm in (1) as “ An algorithm is anything that a Turing machine can do.” This equivalence of course arises since attempts to achieve the intuitive definition about following instructions mechanically can always be reduced to a Turing machine. The author of the post recognizes that under this definition, any physical system can be said to be ‘computing’ it’s time evolution function and the meaning of the word loses it’s importance /significance. In order to avoid that, he subscribes to Wittgenstein and suggests that since when we think about modern day computers, we are thinking about machines like our laptops, desktops, phones which achieve extremely powerful and useful computation, we should hence restrict the word computers to these type of systems (hint: the problem is right here!!). Since our brains also achieve the same, we find that our brains are (uber) computers as well (I might be simplifying/shortening the argument, but I believe I have captured it’s essence and will once again recommend reading the complete article here.) Furthermore, he points out that our modern day computers and brains, have the capability of being Turing complete, but are not of course due to physical constraints on memory, time and energy expenditure. And if we do not have a problem with calling our non-Turing complete, von Neumann architecture machines as computers, then we should not let the physical constraints that prevent the brain from being Turing complete stop us from calling it a computer as well. I agree that we should not restrict ourselves to only referring to Turing complete systems as computers, for that is far too restrictive. The term ‘computer’ does have a popular usage and meaning in everyday life that is independent on whether or not the system is Turing complete. It makes a lot more sense to instead refer to those computers that are in fact Turing complete as ‘Turing complete computers’.
this explains the author’s reasoning for their definitions further, he is not making these up, these are the common definitions in use in the discourse.
so you aren’t going to read the article then.
No Investigation, No Right to Speak.I have investigated the parts that you have quoted, and that is what I am weighing-in on… They are self-contained enough for me to weigh-in, unless the author just redefines the words elsewhere, in which case not quoting those parts as well just means that you are deliberately posting misleading quotes.
I strongly advise reading the entire article
From the parts already quoted, it seems that the author is clueless and is willing to make blatantly faulty arguments. The fact that you opted to quote those parts of the article and not the others indicates to me that the rest of the article is not better in this regard.
and furthermore reading about what a Turing Machine actually is and what it can be used to analyze
Firstly, the term ‘Turing machine’ did not come up in this particular chain of comments up to this point. The author literally never referred to it. Why is it suddenly relevant?
Secondly, what exactly do you think I, as a person with a background in mathematics, am missing in this regard that a person who says ‘Boolean logic’ is not?(1) an algorithm is anything a Turing machine can do
This contradicts the previous two definitions the author gave.
(2) computable functions are defined as those functions that we have algorithms for
Whether we know of such an algorithm is actually irrelevant, actually. For a function to be computable, such an algorithm merely has to exist, even if it is undiscovered by anybody. A computable function also has to be N->N.
(3) a computer is anything which physically implements algorithms in order to solve computable functions
That’s a deliberately narrow definition of what a computer is, meaning that the author is not actually addressing the topic of the computer analogy in general, but just a subtopic with these assumptions in mind.
To complete those definitions, I will go ahead an introduce from the same blog post, an intuitive definition of algorithm — “ an algorithm is a finite set of instructions that can be followed mechanically, with no insight required, in order to give some specific output (e.g. an answer to yes/no integer roots) for a specific input (e.g. a specific polynomial like 6x³yz⁴ + 4y²z + z — 9).” And the more technical definition of algorithm in (1) as “
This directly contradicts the author’s point (1), where they give a different, non-equivalent definition of what an algorithm is.
So, which is it?This equivalence of course arises since attempts to achieve the intuitive definition about following instructions mechanically can always be reduced to a Turing machine
This is obvious nonsense. Not only are those definitions not equivalent, the author is also not actually defining what it means for instructions to be followed ‘mechanically’.
The author of the post recognizes that under this definition, any physical system can be said to be ‘computing’ it’s time evolution function and the meaning of the word loses it’s importance /significance
Does the author also consider the word ‘time’ to have a meaning without ‘importance’/‘significance’?
In order to avoid that, he subscribes to Wittgenstein and suggests that since when we think about modern day computers, we are thinking about machines like our laptops, desktops, phones which achieve extremely powerful and useful computation, we should hence restrict the word computers to these type of systems (hint: the problem is right here!!)
I have already addressed this.
At this point, I am not willing to waste my time on the parts that you have not highlighted. The author is a boy who cried ‘wolf!’ at this point.
EDIT: you seem to have added a bunch to your previous comment, without clearly pointing out your edits.
I will address one thing.note the triviality criticism of the informal definition that this author previously addressed, and the ‘human who could only carry out specific elementary operation on symbols’ is a reference to Turing Machines and the Chinese Room thought experiment, both of which i recommend reading about.
The author seems to be clueless about what a Turing machine is, and the Chinese Room argument is also silly, and can be summarised as either ‘but I can’t imagine somebody making a computer that, in some inefficient manner, does introspection, even though introspection is a very common thing in software’ or ‘but what I think we should call “computers” are things that I think do not have qualia, therefore we can’t call things with qualia “computers”’. Literally nothing is preventing something that does introspection in some capacity from being a computer.
I have investigated the parts that you have quoted, and that is what I am weighing-in on… They are self-contained enough for me to weigh-in, unless the author just redefines the words elsewhere, in which case not quoting those parts as well just means that you are deliberately posting misleading quotes.
and yet you ignore the definitions the author provided
Firstly, the term ‘Turing machine’ did not come up in this particular chain of comments up to this point. The author literally never referred to it. Why is it suddenly relevant? Secondly, what exactly do you think I, as a person with a background in mathematics, am missing in this regard that a person who says ‘Boolean logic’ is not?
Turing machines are integral to discussions about computing, algorithms and human consciousness. The author uses the phrase ‘turing complete’ several times in the article (even in parts i have quoted) and makes numerous subtle references to the ideas, as i would expect from someone familiar with academic discourse on the subject. focusing on a semantic/jargon faux pas does not hide your apparent ignorance of the subject.
This contradicts the previous two definitions the author gave.
there were no previous definition, this is the first definition given in the article. i am not quote-mining in sequence, i am finding the relevant parts so that you may understand what i am saying better. Furthermore, since you seem to miss this fact many times, the author is using the definitions put forward in another article by someone claiming that the brain is a computer and that it is not a metaphor. By refusing to read the entire article you only demonstrate your lack of understanding. Was your response written by an LLM?
Whether we know of such an algorithm is actually irrelevant, actually. For a function to be computable, such an algorithm merely has to exist, even if it is undiscovered by anybody. A computable function also has to be N->N.
‘we have’ in this case is equivalent to ‘exists’, you are over-focusing on semantics without addressing the point.
That’s a deliberately narrow definition of what a computer is, meaning that the author is not actually addressing the topic of the computer analogy in general, but just a subtopic with these assumptions in mind.
i have no idea what you mean by this, according to wikipedia: “A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation).” which is identical in content to the author’s definition.
This directly contradicts the author’s point (1), where they give a different, non-equivalent definition of what an algorithm is. So, which is it?
The point that the author is making here is that the definitions are functionally equivalent, one is the result of the implications of the other.
This is obvious nonsense. Not only are those definitions not equivalent, the author is also not actually defining what it means for instructions to be followed ‘mechanically’.
‘mechanically’ just means ‘following a set of pre-determined rules’, as in a turing machine or chinese room. you would know this if you were familiar with either. There is absolutely no way you have a background in mathematics without knowing this.
Does the author also consider the word ‘time’ to have a meaning without ‘importance’/‘significance’?
the author referred to here is not the author of the article i am quoting, but the author of the article it is in response to.
I have already addressed this.
you have not. this is the author of the pro-brain-as-computer article restricting his definitions, that the article i am quoting is arguing against using the same definitions. I am not sure you understood anything in the article, you seem like you do not understand that the author of the article i quote was writing against another article, and using his opponent’s own definitions (which i have shown to be valid anyway)
in short you are an illiterate pompous ass, who lies about their credentials and expertise, who is incapable of interpreting any nuance or meaning from text, chasing surface level ghost interpretations and presenting it as a Gotcha. I am done with this conversation.
and yet you ignore the definitions the author provided
Which definitions am I ignoring? I have quite literally addressed the parts where the author gives definitions.
The author is really bad at actually providing definitions. They give three different ones for what an ‘algorithm’ is, but can’t give a single one to what the expression ‘mechanically following instructions’ means.Turing machines are integral to discussions about computing, algorithms and human consciousness
They are irrelevant to the parts that you quoted prior to bringing up Turing machines.
The author uses the phrase ‘turing complete’ several times in the article
Not in any part that you quoted up to that point.
even in parts i have quoted
I looked for those with ctrl+f. There are no mention of Turing machines and of Turing completeness up to the relevant point.
and makes numerous subtle references to the ideas
Expecting the reader of the article to be a mind reader is kind of wild.
In any case, the author is not making any references to Turing machines and Turing completeness in the parts you quoted up to the relevant point.
Also, the author seems to not actually use the term ‘Turing machine’ to prove any sort of point in the parts that you quoted and highlighted.focusing on a semantic/jargon faux pas does not hide your apparent ignorance of the subject
I bring up a bunch of issues with what the author says. Pretending that my only issue is the author fumbling their use of terminology once just indicates that, contrary to your claims, my criticism is not addressed.
there were no previous definition
This is a lie. Here’s a definition that is given in the parts that you quoted previously:
(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
I’m going to note that this is not the first time I’m catching you being dishonest here.
Furthermore, since you seem to miss this fact many times, the author is using the definitions put forward in another article by someone claiming that the brain is a computer
Okay, I went and found the articles that they are talking about (hyperlinked text is not easily distinguishable by me on that site). Turns out, the author of the article that you are defending is deliberately misunderstanding that other article. Specifically, this part is bad:
In order to avoid that, he subscribes to Wittgenstein and suggests that since when we think about modern day computers, we are thinking about machines like our laptops, desktops, phones which achieve extremely powerful and useful computation, we should hence restrict the word computers to these type of systems (hint: the problem is right here!!)
Here’s a relevant quote from the original article:
As such, these machines that are now ubiquitous in our lives are a much more powerful form of computer than a stone or a snowflake, which are limited to computing only the functions of physics that apply to their movement
Also, I’d argue that the relevant definitions in the original article might be/are bad.
Onto the rest of your reply.
and that it is not a metaphor
So far, I don’t see any good arguments against that put forth by the author you are defending.
By refusing to read the entire article you only demonstrate your lack of understanding
I came here initially to address a particular argument. Unless the author redefines the relevant words elsewhere, the rest of the article is irrelevant to my criticism of that argument.
Was your response written by an LLM?
Cute.
‘we have’ in this case is equivalent to ‘exists’
I do not trust the author to not blunder that part, especially considering that they are forgetting that computable functions have to be N->N.
i have no idea what you mean by this, according to wikipedia: “A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation).” which is identical in content to the author’s definition
‘The English Wikipedia gives this “definition”, so it must be the only definition and/or understanding in this relevant context’ is not a good argument’.
I’m going to admit that I did make a blunder regarding my criticism of their point (3), at least internally. We can consider myself wrong on that point. In any case, sure, let’s go with the definition that the author uses. Have they provided any sort of argument against it? Because so far, I haven’t seen any sort of good basis for their position.The point that the author is making here is that the definitions are functionally equivalent, one is the result of the implications of the other
They are not equivalent. If something is an algorithm by one of those ‘definitions’ (both of them are not good), then it might not be an algorithm by the other definition.
The author is just plain wrong there.‘mechanically’ just means ‘following a set of pre-determined rules’
Care to cite where the author says that? Or is this your own conjecture?
In any case, please, tell me how your brain can operate in contradiction to the laws of physics. I’ll wait to see how a brain can work without following ‘a set of pre-determined rules’.as in a turing machine or chinese room
Or in any kind of other system, judging by the ‘definition’.
you would know this if you were familiar with either
Cute.
you have not. this is the author of the pro-brain-as-computer article restricting his definitions
You mean this part?
As I argued above, I think it’s reasonable to restrict their usage to machines, like the brain, that not only solve the functions of physics, but a much larger array of computable functions, potentially even all of them (assuming the space of possible brains is Turing complete)
Or the part where, again, the same author literally calls stones and snowflakes ‘computers’ (which I am going to back as a reasonable use of the word)?
I am not sure you understood anything in the article
I was addressing particular arguments. Again, unless the author redefines the words elsewhere in the article, the rest of the article has no bearing on my criticism.
in short you are an illiterate pompous ass incapable of interpreting any nuance or meaning from text
Cool. Now, please, tell me how my initial claim, ‘this is a rather silly argument’ is bad, and how the rest of the article is relevant. Enlighten me, in what way is me saying that the particular argument that you quoted, and for which you have failed to provide any sort of context that is significant to my criticism making me ‘illiterate’?
In case you still don’t understand, ‘read the entire rest of the article’ is not a good refutation of the claim ‘this particular argument is bad’ when the rest of the article does not actually redefine any of the relevant words (in a way that is not self-contradictory).
In return, I can conclude that you are very defensive of the notion that brains somehow don’t operate by the laws of physics, and it’s all just magic, and can’t actually deal with criticism of the arguments for your position.
I’ve heard people saying that the Chinese Room is nonsense because it’s not actually possible, at least for thought experiment purposes, to create a complete set of rules for verbal communication. There’s always a lot of ambiguity that needs to be weighed and addressed. The guy in the room would have to be making decisions about interpretation and intent. He’d have to have theory of mind.
The Chinese Room argument for any sort of thing that people would commonly call a ‘computer’ to not be able to have an understanding is either rooted on them just engaging in endless goalpost movement for what it means to ‘understand’ something (in which case this is obviously silly), or in the fact that they assume that only things with nervous systems can have qualia, and that understanding belongs to qualia (in which case this is something that can be concluded without the Chinese Room argument in the first place).
In any case, Chinese Room is not really relevant to the topic of if considering brains to be computers is somehow erroneous.
My brain is a pentium overdrive without a fan and I am overheating
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.
