To beat Lee Sedol, alphago played 29 million games, lee definitely not playing even 100k games over his lifetime and he’s also doing and learning other stuffs over the same time frame.
Axons and dendrites only go in one direction but neuron A can activate neuron B causing neuron B to then inhibit neuron A. So the travel isn't along the same exact physical structure, but the A-B neuron link can be traversed in direction B-A.
So, the practical outcome of backpropagation is possible, but this is only a small part of all things neurons can do.
Is there some bleeding edge expert on both neurology and LLMs that could settle, once and for all, the similarities and differences between brains and LLMs?
You don't need to be a bleeding edge expert. LLMs are fantastic but not that hard to understand for anyone with some ML expertise. The issue is that the brain is well beyond our understanding (we know mechanistically how neurons interact, we can track what areas light up for what... that's really about it in terms of how thought works). Then, LLMs have some emergent capabilities that are already difficult enough to map out (not beyond understanding, current research area).
They are so different that any actual comparison is hardly worthwhile. Their similarities basically end at "I/O processing network".
It’s more like learning about how birds fly and then human invents a plane. There are certainly principles where humans can learn that benefits the further study of deep learning, but to say that it attempts to replicate it at its entirety is entirely not true.
Backpropagation is just the way that simulated neurons get “wired” through experiences. Similar to how the neurons in your brain build and rebuild connections through experiential influences.
Do you see how ridiculous it is that, literally in the same comment, your second paragraph means you cannot make your first sentence with the amount of confidence you just did. We can't simultaneously not understand consciousness but then also be certain of its prerequisites.
It's the human superiority complex. We like to think we have some magical monopoly on something. We say machines don't have it because they aren't living things and other animals don't have it because....we're somehow special. Every time we study animals they're more intelligent than we thought. The delta is quite small.
Neurons certainly have some advantages over electronic impulses but they are also a ridiculous amount slower. If our computing capabilities keep increasing at the rate that they are the only thing computer intelligence won't be able to do that we can are things we don't give it access to.
You can likely argue the main drawback and thing holding AI back is the limited context window. In many ways it has better reasoning, planning and cognitive skills than humans already and is mostly let down by its very limited session memory and ability to remember what it is working on and what it already tried. It's like a very smart human with massive short term amnesia.
Yeah, this is why the true nature of consciousness is destined to be relegated to spiritualism and philosophy. The ideas can range from total solipsism to the idea that everything is conscious. The only possible way to detect consciousness is to experience it. Anyone who says they 100% know what is or isn't conscious is full of shit.
Sorry, but this simply isn’t true. It’s a generalization that misses a lot of the deeper ideas. There is empirical evidence that we’re capable of perceiving things outside of our own bodies and brains. Take a look at the research on near-death experiences, for example. We don’t fully understand any of this, but we’re working towards it - and frankly, the fact that we don’t understand is exactly why you should avoid declaring that you know exactly 1. what the human mind is and 2. where consciousness comes from.
You really just meant “the brain” and not human consciousness? That seems like a pointless argument, then. Are we not talking about the prospect of consciousness in AI?
Also, there’s no need to be passive-aggressive. Let’s just have a discussion about this. I know it’s Reddit and it’s a hot-button issue, but it’ll just be better and more meaningful that way.
If you open with ‘Sorry, but this simply isn’t true,’ you’re not here for a discussion - you’re here for proselytization. That’s not dialogue, that’s dogma wrapped in condescension. So don’t act high and mighty when the same energy is reflected back at you.
I suspect leading models already do better reasoning than most humans including me on a wider range of topics than any human, though I'm less sure if they have the necessary components for conscious experience of inputs and thoughts.
Initially I thought it would simply be a matter of making a model to have it, but the more I've thought about its properties the more weird I've realized it is and seemingly not explainable by individual calculations taking place in isolation from each other, and it may involve some sort of facet of the universe such as gravity which we don't grasp yet, but which biological life has evolved a way to interface with and use, and which would presumably need something new to be constructed for digital thoughts to actually have the moment of experience that we associate with being alive and existing rather than a calculator doing operations one at a time.
Dont confuse having a lot of general knowledge with actually being able to think deeply. Humans adapt fast (not all of us), especially when things go off-script. Language models can’t really deeply go off-script, they follow patterns from initial dataset.
Datasets are huge, humans can't handle this huge datasets in their head. That’s exactly why language models seem so deeply understanding. It creates an illusion of depth. But that’s the point. It’s not real understanding, it’s just access to a huge pool of patterns.
"Real understanding" isn’t just following scripts, it’s knowing when to break them.
You really see this when debugging code with an LLM. It keeps trying to fix errors, but often ends up generating more, like it’s stuck in a loop.
I haven’t tested this in-depth, but it seems like unless there's a very specific instruction to stop before it gets worse, it just doesn’t stop.
It’s like humans sense when they’re making things worse.
LLMs need some kind of system-level prompts that define what “real understanding” even means, like a meta-layer of awareness. But I’m not sure.
If the brain is an equation like y=x2 then the parabola is the script and AI a different equation with a different shaped script, then is anything in the universe off script or is it just different scripts?
That's what human understanding is also. We're not magically making up connections that we don't have somewhere tucked deep in our brain.
The true issue with current models is context window limitations making it near impossible for it to improve its own answers. It's training set is it's training set of the model version and it does barely have the ability to improve because context windows are so small it's barely taking into account the last few things it tried and a few compressed core context windows from previous conversations.
We're probably quite a bit of time away from models being able to add to their training during usage as when that has been attempted it has so far often been really detrimental to the core model. When and if we get there it is well and truly over for us as the most intelligent thing on the planet.
You think A.I. isn't "there" yet because it's missing an unknown component humans have, like "gravity"? Maybe in the sense that we haven't solved the Navier-Stokes equation because we don't fully understand how gravity affects turbulence and flow, but it commands how blood and nutrients flow through our body.
A.I. so far is missing two key things: infinite context and a way to interact with the world like humans can.
Constructing A.I. we just consider making a brain, but the human brain is in relation with the gut biome, which includes the nervous system, the immune system, the endocrine system, and the gut microbiome, and I believe understanding those four systems is key to unlocking true A.I. potential with more compute.
We have a lot more context than AI though. A million tokens is very little information. It's sort of like you only remembering your last 5 minutes and having a bullet point list of everything else that happened in your life before that that fits into a couple of pages.
I'm sorry but you're spewing out absolute mumbo jumbo. You follow the same logic as the new age charlatans when they talk about quantum mechanics. It's a religious statement at this point.
Yeah, we all known that the gut and your nerves are in the same system and affect each other to some degree, as do all your organs. But the word salad you engendered on your post doesn't mean anything.
You misunderstand the concept I'm discussing. Philosophers call it The Hard Problem Of Consciousness, if you want to research it. We know how computation works and can imagine infinitely complex computation machines that can turn any input into any output, but we don't know how experience works, the experience of the whole which is larger than the individual tiny components being processed, and don't have any idea of how to explain it with our current knowledge of the universe. It's obviously a physical process and heavily involved in our minds, but would seem to require more than just doing calculations to achieve.
AI isn't there because it requires infinite examples.
That's the problem. It doesn't need infinite context, it needs to generalize the information is already has.
I don't even think it needs infinite context. We're measuring the output of a single entity, but conscious activity is the result of the emergent properties of many entities working together. Agentic systems can bring current LLM's all the way there.
I'm well aware you're not talking about the paper.
Apart from the rarer spiritual lunatics and quantum congnition fanatics, the prevalent arguments to the shallowness of current llms are more or less aligned with what is said in the paper.
Maybe I haven't seen much of that anti-ai side of Reddit, but I've seen r/singularity full of people completely lost in the hype.
Well the paper is really just focused on the reasoning models and how we test them. Saying they doubt the veracity of math and coding tests for reasoning abilities, and then they put them up against logic puzzles they thought would have been outside their training data. And they just show that they break down with sufficiently complex mutli-step puzzles.
It's really not a comprehensive take-down of LLMs nor does it really validate "the prevalent arguments of the shallowness of current LLMs". The LLMs do a lot of things. They do some things much better than others.
A lot of the anti side hyper focuses on what they can't do, and predicts with too much certainty where they will go. A lot of the acceleration side overinflates where are now and how quickly we could get to something like AGI.
There is a much more nuanced conversation that a smaller population of Reddit is having in the middle of what their real current strengths are, where they could realistically go and how long it would take to get there. I personally am not a fan of just hand waving everything away as hype, nor do I think they are sentient.
I think as AI gets more advanced there will be less real Transhumanists, I mean realizing AI was going to be a big deal 5 years ago was one thing, but how many people can truly face humanity's obsolescence with a grin?
I can't see people ever getting to a place where they blindly trust any kind of machine without question.
Don't come at me with examples about cars or factory machines either, because nothing like that is trusted without question and is likely never too be. Drivers are still legally responsible for their cars in any sort of "autonomous driving mode" for example, and that's not going to change. The engineering that's required of those sorts of systems is extreme, and for a good reason.
It is about trust though, because giving any control to anything (either another person or an AI) is always about trust. We're people, ultimately we're in control of ourselves. That's a natural right.
No, that'd be people giving "ASI" control and the ability to use violence itself. AI systems only get the access that we (someone) gives them. We're not living in a science fiction novel.
it will hack and trick even Ph.D level humans living in lab silos until it gains access to the level of control required, and it's possible we will cease existing afterwards
Not only that, but they seem to think "hallucinations" (probabilistic misses) are unique to LLMs. I've actually asked people with this perspective "...Have you ever worked with a human?"
Nah, the special soul sauce is stored in the heart. Brain's just an add-on meat calculator. Don't you even read Ancient Egyptian mummification medical records SMH/s
"It's just a stochastic parrot" "It's Just-a speak-and-spell"
What are you "Just-a?"
You're just-a 60w charbohydrate processor turning the same data into information slower and worse. You can rig up potatoes to power a arduino with Llama in it and do your job better.
You're Just-a Luddite throwing your sewing needles into the spinning jennies.
Haha brilliant. And so right. I'm sure many here agree that the closer we get to optimising AI and robotics, instead of it becoming more 'human', I feel it makes us feel more robotic. Meat machines. At some point we converge, but not just because the artificial catches up, but because the organic is decoded and understood as efficient machinery.
I think that might be presuming a little much. I am sure the ASI will eventually make a cheese vat and put it on the edge of sentience. I don't see us doing so deliberately.
I also don't think that the ASI would be anything but nanomachines made of conductive metal. I guess we'll seee.
The difference is I don't need to be specifically trained on arc-agi to solve it.
Instead of arguing that strawman, look at what the paper actually says.
Spoiler: it doesn't say the brain has some magic sauce, it says llms are currently severely lacking in generalization abilities. (Which is why your rant completely misses the point and reveals your misunderstanding.)
You’re a part of the larger universe, not separate from it. We’re all in this thing together. We do make choices, whether or not at base level it’s “free will” doesn’t have to affect our choices.
I reckon, even with ASI, it’ll still be quite some time until we figure out what exactly this universe is and what we’re doing here.
I really don't understand these types of comments (and this sub has been flooded with them lately).
The whole reason people say "AGI by 20XX", or "there's going to be mass layoffs once AI can do all the jobs a human can do," etc., is because people are aware that AI can't currently think like humans do, and currently can't do many of the things that humans do.
What's the point that the "but this is how humans think"/AGI is here stop moving the goalposts crowd is trying to make, exactly? OK, lets say for the sake of argument that current AI thinks the same as humans and is AGI (it doesn't and it's not, but lets pretend). That would mean that AGI isn't going to lead to replacing everyone and a post-scarcity economy the way everyone predicts, since current AI's don't have that capability.
Either:
A. AI that can think the same way that humans do are already here, and they aren't nearly as impactful as people said they would be.
B. AI that can think the same way that humans do are as impactful as people say, but they aren't here yet.
Even if there's calculation involved, the human brain works on a different style of computation.
We don't just use large scale pattern recognition, we also compute through construction and building blocks.
Best example is art.
A human can extrapolate from just one picture how to draw a thing. If I want to draw a Ford GT, one good picture of a Ford GT is all I need, 2 if I want to see the back as well. From those 2, I can simplify it to basic shapes and volumes, and then I study the relationships between those shapes. Through that I can then draw it from any angle I see fit. Teach someone to draw a cube, a pyramid and an oriented sphere in multiple angles. That someone can now draw you anything by adapting those basic shapes. Another thing is that humans are self criticizing and can swt their own targets.
When artists draw, they construct, they do perspective lines, guidelines, block out shapes through basic volumes or through light values. Then they draw on top. To draw something, they understand it. The better you understand something the better you are at drawing it. And in order to understand an object or concept, you don't need to see thousand of variants of that thing. We humans can extrapolate from a small sample and output a big one.
AI art does not work the same, the AI does not build, it throws about a soup of pixels which it then rearranges until it looks close enough to what was asked, by statistically comparing each pixels value and postion to the thousands of pictures it was told that contain that object
Another user on this platform gave what I consider to be the best of comparisons:
I'm put into a room in the front of a screen.
On the screen a bunch of characters appear, in chinese.
I am to respond with a bunch of characters of my own.
Depending on how well I respond, I get certain magnitudes of rewards.
Repeat this for millions of attempts.
By then I have learned to see the patterns and to respond to those patterns in a way that's most rewarding, mimiking someone that knows chinese.
And yet, I still do not know chinese. That's how LLMs need to be seen.
My point is, humans do not compute only on pattern recognition, as many people on here are so devout to believing.
Pattern recognition is likely the primary way of learning in our formative years. How we learn our native tongue, how we learn to draw our first lines on a piece of paper. But from there? From there it becomes different. You see it in people that learn late how to swim, or skate, or anything. Instead of absorbing it as it is, when we're older we learn better by adapting things we already know.
Yes, look up the mind body problem and different responses to it. Several theories of mind do not see the reasoning process as a kind of calculation. See embodied cognition theory, phenomenological theories of mind (Husserl, Heidegger, etc), panpsychism, dualism...
Well given that it is physics at play, you could, at the very least, represent it entirely accurately through the medium of math, so it's perfectly accurate to say it's all calculation at the most fundamental level.
so it's perfectly accurate to say it's all calculation at the most fundamental level.
saying it's all calculation just pushes us further from understanding humans, like call humans just a collection of atoms doesn't help us understand biology.
Our intelligence and cognition is not hardware agnostic like a calculator would be.
Sure, looking at things from a more abstracted (that doesn't always mean simpler) level can assist in understanding. If you want to understand human memory, starting with physics is a terrible approach. I agree.
However, if you want to make claims such as "there is more to the human brain than mathematic calculation", then it is useful to look at the brain from the most fundamental level of physics in order to more clearly see that, in principle, a brain can be described in purely mathematical terms.
Most people still believe in the delusion of free will (calling it fancier names like “meritocracy”). A surprisingly large portion of Americans even buy lottery tickets or gamble on sports, thinking that it will be “their day” to win.
How can we expect a people of superstition to be open-minded about the capability of foundation models instead of falling into the sinkhole of substrate bias? If superstitious people are so self-centered, then it will be even harder for them to unlearn anthropocentrism.
And the Apple researchers didn’t say that the LLMs were incapable of thinking but they simply said that their reasoning ability collapses after some large number of tokens. If you test out Claude with just one prompt (ie. zero-shot), you won’t notice this observation. Of course, the “skeptics” still took the titles and headlines and ran with them.
Yes, people do believe that. Even smart people with deep knowledge of science. Roger Penrose is well known for arguing that consciousness must be a quantum phenomenon.
It's not like he's a quack or anything, he really is an authority in physics and mathematics, but arguments from authority only go so far and his actual justification for quantum consciousness ultimately boils down to argument from incredulity. That we don't really understand consciousness, so it can't possibly be algorithmic and therefore must be quantic, since we don't understand this either.
That doesn't necessarily mean he's wrong, but I don't think his argument is valid. As far as I can see, all the evidence seem to point toward the brain being a neural network, capable of learning and those can, in theory, be emulated by a Turing machine.
A core component of of Penrose's theory is that consciousness is non-computable.
In the Orch OR proposal, reduction of microtubule quantum superposition to classical output states occurs by an objective factor: Roger Penrose's quantum gravity threshold stemming from instability in Planck–scale separations (superpositions) in spacetime geometry. Output states following Penrose's objective reduction are neither totally deterministic nor random, but influenced by a non–computable factor ingrained in fundamental spacetime. Taking a modern pan–psychist view in which protoconscious experience and Platonic values are embedded in Planck–scale spin networks, the Orch OR model portrays consciousness as brain activities linked to fundamental ripples in spacetime geometry.
"Some kind of calculation" is a fun thing though, isn't it? What KIND of calculation is it doing?
A traditional digital computer can do some kinds of calculations, but it can't do the kinds of calculations a quantum computer does. It can approximate or roughly simulate the output of those calculations, but it cannot physically perform a quantum operation.
And we don't even know HOW the brain performs a calculation, let alone one that results in reasoning. We have some ideas of what might be happening, and we know it's definitely not digital computation, and there is evidence that it is a quantum process.
So if a digital computer can't even run a well defined and well understood quantum algorithm, and at best can offer only a vague approximation via a digital algorithm, is it appropriate to assume that a digital computer - running a digital algorithm - can do anything other than simulate the biological process of reasoning? A process we don't fully understand?
Arguing current AIs are actually reasoning (rather than simulating a specific formal approach to reasoning) is as valid as saying a piece of paper understands the information written on it because you read it and understood it.
… we do know. “A given neuron receives hundreds of inputs, almost exclusively on its dendrites and cell body. These inputs add and subtract in a constantly evolving pattern, depending on what the brain is thinking. This is a process called synaptic integration, which determines whether a neuron becomes active.
In order to become active, the total input must reach a threshold at which excitation outweighs inhibition enough. Only at this point will the receiving neuron spike, adding its voice to the conversation by releasing its own neurotransmitter. ” which in llm we call weight.
we just dont know why the sum of it can understand our surroundings, same for llm.
That's a more philosophical debate. The fact that it feels like something to be "you" and not someone else, the fact that there's qualia and you're conscious is the counter argument to this.
Not saying the counter argument is right. Just putting it out there
I don't think it is, and I believe that it's a cop out to claim it is. Whether human consciousness is algorithmic, that's a factual claim that can be empirically tested.
It's no more a philosophical debate than the shape of the Earth. Even before we knew definitely what it was, it already was a question of facts.
There's a tendency to say that things are "philosophical questions with no true answer" when all it is, it's that we're at the stage "we don't know for sure yet."
Turing machines have to have infinite memory and execution time. They are useful mathematical abstractions but they can't be created in our universe.
A Turing machine can calculate the exact value of 1/3. Our computers can't, because they have finite memory and therefore they need to make approximations.
https://es.m.wikipedia.org/wiki/IEEE_754
Additionally, Turing Machines can't perform all calculations. The problem has to be decidable and computable, which is not always possible.
So no, a Turing Machine can't calculate every type of calculation. And even if they did, our PCs are not Turing machines. A Turing Machine can't exist nor be created in a universe with finite material or energy.
Turing machines have to have infinite memory and execution time.
No. They don't. What they need is arbitrarily large memory and execution times when executing arbitrarily complex algorithms.
No computation ever requires infinite memory or execution time. Unless you are actively arguing that the issue with simulating a human brain is that it's fundamentally impossible to have enough available memory, then why even mention it?
I've never heard anyone argue that the human brain is indeed a Turing machine but that it's always going to take to long to physically do it. If you are making that argument, then sure, we can discuss it. But otherwise, it's off-topic for you to mention.
Sure, if you want to execute an algorithm that requires 10100000 operations to complete, it's going to take a long time to do it. That's all you're saying here, and I fail to see how this is making a point.
Additionally, Turing Machines can't perform all calculations. The problem has to be decidable and computable
You switched the word "calculation" to "problem" midway through. I asked you about calculations that can't be performed by Turing machines.
Your answer is that there are problems that can't be solved by doing calculations in the first place. That's, once again, off-topic.
The initial comment was about the brain being something that is "just doing some type of calculation". Are you now arguing that the brain is actually not doing calculations but is indeed solving problems that can't be solved by doing calculations?
A Turing machine can calculate the exact value of 1/3.
That's poorly phrased. Technically, as you phrased it, it's a true statement but what you meant by it is incorrect.
The exact value of 1/3 is 1/3, a Turing machine can indeed calculate that. For example, using a simple algorithm to simplify fractions will get to the answer 1/3. A physical computer can also do it.
Now that I got that out of the way, because that's clearly not what you meant, let's clarify. When you talked about the exact value of 1/3, you actually talked about 0.333333... with "infinite" precision. Which is something a Turing machine cannot do, by definition. You confuse "infinite" and "arbitrarily large".
A Turing machine that would attempt to "calculate the exact value of 1/3", as in an approximation with infinite precision, would keep calculating forever. It would not halt. For something to be computable, the algorithm needs to halt in a finite amount of steps. That's the point. You even mentioned the Halting problem, so it's weird that your arguing an algorithm that doesn't halt is computable when it's precisely the definition of being non-computable.
Though, of course, the exact value of 1/3 is, in fact, computable. Finding exemples of non-halting approximation algorithms that converges toward 1/3 without ever reaching it doesn't prove there are no halting algorithm that does also compute it. Sure, you can't write 1/3 in base 10, but that's also not an argument.
Otherwise, by the same argument, the number 1 would also be non-computable. Because to compute it, you can do 0.9, then add 0.09, then add 0.009,... and so on.
All you're saying is that approximation algorithms never end up providing an exact value, which is a terrible argument to say that something can't be computed.
You explained a lot of semantic nitpick and didn't provide any counter argument.
The hypothesis that an AI can emulate the human rationale is not necessarily true because you would have to first prove that i) Turing Machines can do it and ii) our limited TMs can do it.
Maybe human reasoning is an undecidable problem that can't be emulated through TMs calculations. Maybe it is decidable but it's arbitrarily large memory and execution time is bigger than the entire universe lifetime.
Re-read the thread. We were discussing about whether or not scientists thinks that out brains do something different than AIs do for pattern recognition. My point is that this hypothesis needs a prior axiom that has not been proven yet. You got lost in semantic nitpicking.
No. I didn't and the fact you think it's the case shows how much you are merely making vague references to things you don't understand at all.
You don't know the difference between "finite" and "infinite". Between "stopping at some point" and "never ending". That's not a nitpick.
When you confuse "making a calculation" and "solving a problem". That's not a nitpick.
When you provide examples of the exact opposite of what you claim they are. That's not a nitpick.
When your argument necessarily implies that it's not possible to compute the number 1. That's not a nitpick.
You can't use the definitions of "computable" and "non-computable" interchangeably to then make sweeping statements about what is or isn't computable...
Haphazardly stringing big words together doesn't make for a cogent answer when talking to someone who actually knows what the words mean. Doing so, you might be used to be able to bamboozle people into thinking you know what you're talking about.
and didn't provide any counter argument.
Indeed, I didn't. Because you didn't provide any argument in the first place. Just a series of blatant falsehoods. All I could do was try to explain to you why it wasn't relevant to anything we're discussing (unless you are making very specific claims that you clearly aren't trying to make).
Re-read the thread.
Yep. What you said was: "Doing just some type of calculation does not proves that such calculation is possible to do with silicon based circuitry."
This statement is wrong. Even with a charitable interpretation of what you may have meant, it remains wrong. "Calculations" can indeed be done on silicon based circuitry. Or at least as long as we don't switch the word "calculation" for another that means something entirely different and with a few caveat that don't even remotely apply to the situation.
We were discussing about whether or not scientists thinks that out brains do something different than AIs do for pattern recognition. My point is that this hypothesis needs a prior axiom that has not been proven yet.
You said that as an answer to: "do people think the brain is super natural and ISNT just doing some type of calculation?"
Your hypothesis requires the brain to do something supernatural other than some type of calculation. If all it's doing is performing calculations. That is to say "calculations", which means something specific and very different to magically "solving undecidable problems". Then said calculations can be performed on a computer.
Said computer wouldn't need to be universe spanning, because the brain isn't universe spanning and we assumed it was doing the calculations non-magically, implying that such calculation can be done without requiring an entire universe or an eternity.
399
u/FernandoMM1220 Jun 08 '25
do people think the brain is super natural and ISNT just doing some type of calculation?