1) The abstract "dictionary" version: It'd be technically correct to say that the body is a machine under the definition of "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action.".
2) But there's also the less abstract/technical: "The body is alike the complex machines we have built", and this is much less true. Especially for the brain. The "neuron" analogy in machine learning is effective, but entirely wrong; We do not fully know how even a single neuron works, nevermind any complex system made out of multiple of them.
With regard to AI, there's a lot of people extrapolating "There is no magical animating spirit, the brain is just a pile of stochastic molecules following the laws of physics" into "The brain is an inert pile of matter, computers are an inert pile of matter, ergo AI/LLMs are like the brain!"
Especially so by people who have a financial/legal interest in doing so. "AI is just like a brain, fire your employees and buy our LLM now!", "AI is just like a brain, so it's totally not copyright infringement!"
Why do you need a specific organization of molecules for a phenomenon similar to consciousness to arise? Does anyone seriously consider a brain to be something other than “a pile of molecules following the laws of physics”? If so that’s not science or philosophy, that’s religion. You have a virtually complete phenomenological model of the universe for all intents and purposes and yet somehow the onus is on the person being like “hey no laws of physics are being broken ==> the brain is simply following the laws of physics”
How is it possible that people think of subjective experience and get rabbit holed into some mystical world where subjective experience is this special exception to everything else that is simply an emergent property of complex physical systems? “AI/LLMs are just like the brain” is a strawman, why does this claim need to be true for LLMs or any artificial system to be considered to have something akin to the thing you think of as consciousness? It’s more: consciousness is not some mystical or religious thing outside of the realm of physics, it’s an emergent property of a complex system. AI is a relatively complex system. We don’t really know or understand the relationship between the raw physics and again what we consider consciousness, so it’s simply a statement of “we can’t refute that these systems exhibit something similar” because we don’t know enough to refute that
I don't actually think the commentator you responded to is arguing for either of these narratives and I thought it was a pretty useful way to look at some of these arguments.
It doesn't need to be true but a lot of people make it/assume it.
There's a lot of, perhaps casual and uninformed, conversations that strongly imply a deeper understanding of the "physics" of brain chemistry than we actually have, mostly by comparing it to machines we've constructed.
(I believe) We don't need to replicate human neurons and dendrites and whatever else is in there in order to create a sapient "machine", but whether or not we've actually done that isn't being helped by arguing that what we currently have is all that similar to a human brain.
https://www.quantamagazine.org/neural-dendrites-reveal-their...
In which case: modern LLMs are still running in a capacity-starved regime!
Even Mythos 5, the 10-trillion monster LLM, the scaling law boogeyman, the harbinger of Vera Rubin NVL72, doesn't quite rise to 100T-to-1000T of synapses. Anything the light of today's AI touches still lives in the shadow of what evolution has managed to cram into a single human skull.
We're arguing about the limitations of AI while our best AIs are still very subhuman in the scale dimension. The one dimension we know how to push. And it's already this tight.
Even those comparisons need to be cautioned. The complexity of biology is enormous, and more importantly yet, it's simply not comparable. And doing so invited a bunch of bad assumptions.
An ANN could quite probably model a single in vitro neuron with reasonable accuracy. Whether that requires a hundred or a hundred million nodes isn't terribly relevant.
But the way neurons combine in vivo is completely unlike the way machine learning systems are built. Both "locally" in how neurons interface which is vastly more complex than a weighted sum of inputs, and the macro scale interactions of hormones and other chemicals.
It's not even a given that large numbers of neurons will create the emergent behaviour of human intelligence; Elephants have significantly more neurons, but they're not the triple galaxy brains writing all our science papers. Other animal intelligence similarly is only loosely correlated with brain complexity. (Heck, not to be forgotten is the other end of the scale. Plenty of microscopic life that manages shockingly complex behaviour without any dedicated neurons)
This also applies to ANNs. There's no reason to expect that stuffing enough matrix multiplications into a program will make it intelligent or turn out conscious.
Really, the history of machine learning suggests the opposite; That the big gains are primarily had in architectural changes.
In this regard, I find the talk of the "limits of AI" quite credible. LLMs have already hit the diminishing returns on their growth, and even reasoning/agentic models display failure modes that confirm they're not "thinking" in the ways that humans do.
This is not to say that we've hit the final limits of what AI in the broad sense can do, it's just that the next advancement won't be "LLM but even bigger"
Don't make assumptions. Make a setup where the gradient descent can make them for you.
Empirically? LLMs are nowhere near "the wall". We've been hearing "the wall is nigh" since 2020. Six years in, we're still scaling LLMs, and the graveyards are full of "LLM killers". The system that kills the LLM is always a bigger, badder LLM, and never a new revolutionary architecture. The scaling doesn't just keep working - it works so well that it's seen as the only viable path forward at the frontier of reasoning and agentic work. Or even outside it. ChatGPT Images 2.0 is an image model with an agentic LLM at its core - generational gains in compositional capability.
For just about every "failure mode that confirms they're not thinking", you see one of two things. The first is that a new LLM releases a few months after and the "fundamental" issue abruptly goes away. The second is that we take a good, long look at a human, and find that the human also fails like this - and thus, "not thinking". Often both! Always funny when it's both.
One thing that's very biologically distinct is: local connectivity. In a GPU, global connectivity is cheap. In a brain, it's prohibitively expensive. The brain has no true backpropagation because it has no true global connectivity, and has to make do with local rules. A GPU is a strictly more expressive substrate connectivity-wise. So any point in the design of a computational substrate where you could remove complexity or increase performance by adding more connectivity? Silicon advantage. The brain isn't a "strictly better computational substrate" - it makes different tradeoffs. Which tradeoffs are better for attaining intelligence is an open question.
And, sure. Having a substrate with a capacity for intelligence doesn't mean having intelligence. No elephant has ever learned to code. The problem is: LLMs already did! LLMs already compete with humans on just about every task that was once thought to "require human intelligence". They don't always win - but they perform significantly above chance, and often above an average non-expert human.
So, my bet is on "LLM but even bigger". If there's a point where LLMs begin to lag behind and novel architectures get a sharp advantage, we are yet to hit it.
> So, my bet is on "LLM but even bigger". If there's a point where LLMs begin to lag behind and novel architectures get a sharp advantage, we are yet to hit it. We are already hearing this 'we are about to hit it' since the late 60s. The difference now is that the market is willingly investing insane amounts of money to make it possible. But again, there is no philosophical, theoretical, epistemological or biological clue that we are getting any closer to human intelligence level. What we did observe in the last decade though, is that we can build enormous machines that can statistically mimic statistical human outputs. Language and images being some of them. But that is not thinking.
Second, what is the difference? Is it that one thing has an immortal soul, and thus Actual Intelligence and Actual Reasoning and Actual Learning, and the other has no soul, and a Pale Imitation of Intelligence, At Best?
Because I've seen versions of this "it's not actually thinking" for actual fucking years, and the difference between "actually thinking" and "not actually thinking" always seems to boil down to "I don't want LLMs to be actually thinking, so I will bend the definitions and twist the qualifiers and move the goalposts until they aren't". No one ever made an ActualThinkingBenchmark on which humans score 100% and LLMs score 0%.
Nothing but human insecurity, in my eyes. There was never a principled difference. Just a way to operationalize some "I'm unique and special and better than a matrix math machine" vibes.
1) This definition could actually be expanded (for example, with definitions from Mumford or Reuleaux). But still this definition cannot be applied directly to living organisms. 2) This is in my opinion one of the sources of misunderstanding. We mainly operate on analogies and metaphors, so we have build this 'analogy space' around the idea that living organisms are machines. But it is only when we say 'alike' that we can truly gather some meaning out of it all, going beyond the 'behaves like' or 'is conceptualized as' when it gets messy.
> With regard to AI, there's a lot of people extrapolating "There is no magical animating spirit, the brain is just a pile of stochastic molecules following the laws of physics" into "The brain is an inert pile of matter, computers are an inert pile of matter, ergo AI/LLMs are like the brain!"
This is exactly my point. There is a fallacy operating from "A is not B" to "A is C". And this fallacy is pervasive in the AI research field, the book from Dreyfus (What Computers can't still do) explains that in much detail.
I'm not sure I understand this. Why not?
And I am as well baffled why people make such a big deal out of "subjective experience" and "consciousness".
I was joking that maybe I miss this properties, but now starting to really wonder if it might be the case. What if these phenomenons are present in humans to various extent? Check aphantasia. Only in XIX we discovered, that ability to visualize mental images is not universal, available to different people to various degree and some people completely miss it. My ability to visualize is weak. What if "consciousness" and "subjective experience" are similar?
And I am slightly worried when I am writing this that it might turn to be truth and in ~20 years I will be treated as "inferior human" without complete set of human rights.
More meta, all of the threads on this page are just people playing games with definitions. Eg, “qualia is something I have as a human but machines don’t have it. Therefore, LLMs do not have qualia.”
true
> and does not try to emulate one
citation please.
something like the universal approximation theory comes to mind, transformer architecture clearly has the shape of a universal algorithm approximator
Like at some level, yes, transformers are trying to emulate a human brain but the second you ask folks if they do a good job of it, I think most rationale people would say no.
That says nothing about emulating a human brain.
Neurons are themselves things we experience (indirectly). Once seen through a microscope or known about in some fashion the only way they "exist" is by you having the experience of knowing them. It's not the other way around. One thing is more fundamental here. What is this experience? What are the atoms of this? "Atomic particles"? How would you even approach an answer if your building blocks are themselves part of what needs to be explained?
The hard problem cannot even be touched if you start out like this.
Descartes made clear that subjective experience is the ONLY thing we know. Everything else is theories to explain the phenomena we subjectively experience.
We theorize that there is a physical world and other beings like us having similar subjective experiences, because that seems the best explanation for our subjective experiences. But we might be living in the Matrix, with all the people we think we are interacting with and just sophisticated simulations.
Nobody talked about anything out of neurons. The question is still open.
passive voice doing a hell of a lot of work in this phrase