Comment by akoboldfrying
3 days ago
LLMs and human brains are both just mechanisms. Why would one mechanism a priori be capable of "learning abstract thought", but no others?
If it turns out that LLMs don't model human brains well enough to qualify as "learning abstract thought" the way humans do, some future technology will do so. Human brains aren't magic, special or different.
Human brains aren’t magic in the literal sense but do have a lot of mechanisms we don’t understand.
They’re certainly special both within the individual but also as a species on this planet. There are many similar to human brains but none we know of with similar capabilities.
They’re also most obviously certainly different to LLMs both in how they work foundationally and in capability.
I definitely agree with the materialist view that we will ultimately be able to emulate the brain using computation but we’re nowhere near that yet nor should we undersell the complexity involved.
When someone says "AIs aren't really thinking" because AIs don't think like people do, what I hear is "Airplanes aren't really flying" because airplanes don't fly like birds do.
This really shows how imprecise a term 'thinking' is here. In this sense any predictive probabilistic blackbox model could be termed 'thinking'. Particularly when juxtaposed against something as concrete as flight that we have modelled extremely accurately.
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If I shake some dice in a cup are they thinking about what number they’ll reveal when I throw them?
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Whenever someone paraphrases a folksy aphorism about airplanes and birds or fish and submarines I suppose I'm meant to rebut with folksy aphorisms like:
"A.I. and humans are as different as chalk and cheese."
As aphorisms are a good way to think about this topic?
That's a fallacy of denial of the antecedent. You are inferring from the fact that airplanes really fly that AIs really think, but it's not a logically valid inference.
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I agree we shouldn't undersell or underestimate the complexity involved, but when LLM's start contributing significant ideas to scientists and mathematicians, its time to recognize that whatever tricks are used in biology (humans, octopuses, ...) may still be of interest and of value, but they no longer seem like the unique magical missing ingredients which were so long sought after.
From this point on its all about efficiencies:
modeling efficiency: how do we best fit the elephant, with bezier curves, rational polynomials, ...?
memory bandwidth training efficiency: when building coincidence statistics, say bigrams, is it really necessary to update the weights for all concepts? a co-occurence of 2 concepts should just increase the predicted probability for the just observed bigram and then decrease a global coefficient used to scale the predicted probabilities. I.e. observing a baobab tree + an elephant in the same image/sentence/... should not change the relative probabilities of observing french fries + milkshake versus bicycle + windmill. This indicates different architectures should be possible with much lower training costs, by only updating weights of the concepts observed in the last bigram.
and so on with all other kinds of efficiencies.
ofc, and probably will never understand because of sheer complexity. It doesn't mean we can't replicate the output distribution through data. Probably when we do in efficient manners, the mechanisms (if they are efficient) will be learned too.
DNA inside neurons uses superconductive quantum computations [1].
[1] https://www.nature.com/articles/s41598-024-62539-5
As the result, all living cells with DNA emit coherent (as in lasers) light [2]. There is a theory that this light also facilitates intercellular communication.
[2] https://www.sciencealert.com/we-emit-a-visible-light-that-va...
Chemical structures in dendrites, not even neurons, are capable to compute XOR [3] which require multilevel artificial neural network with at least 9 parameters. Some neurons in brain have hundredths of thousands of dendrites, we are now talking of millions of parameters only in single neuron's dendrites functionality.
[3] https://www.science.org/doi/10.1126/science.aax6239
So, while human brains aren't magic, special or different, they are just extremely complex.
Imagine building a computer with 85 billions of superconducting quantum computers, optically and electrically connected, each capable of performing computations of a non-negligibly complex artificial neural network.
All three appear to be technically correct, but are (normally) only incidental to the operation of neurons as neurons. We know this because we can test what aspects of neurons actually lead to practical real world effects. Neurophysiology is not a particularly obscure or occult field, so there are many many papers and textbooks on the topic.(And there's a large subset you can test on yourself, besides, though I wouldn't recommend patch-clamping!)
Electric current is also quantum phenomena, but it is also very averaged in most circumstances that lead to practical real world effects.
What is wonderful here is that contemporary electronics wizardry that allowed us to have machines that mimic some of thinking, also is very concerned of the quantum-level electromagnetic effects at the transistor level.
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They are extremely complex, but is that complexity required for building a thinking machine? We don't understand bird physiology enough to build a bird from scratch, but an airplane flies just the same.
The complexities of contemporary computers and complexities of computing-related infrastructure (consider ASML and electricity) are orders of magnitudes higher than what was needed for first computers. The difference? We have something that mimics some aspects of (human) thinking.
How complex our everything computing-related should be to mimic thinking (of humans) little more closely?
Are we not just getting lost in semantics when we say "fly"? An airplane does not at all perform the same behavior as a bird. Do we say that boats or submarines "swim"?
Planes and boats disrupt the environments they move through and air and sea freight are massive contributors to pollution.
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(Motors and human brains are both just mechanisms, the reason one is a priori capable of learning abstract thought and not the other ?)
While I agree to some extent with the materialistic conception, the brain is not an isolated mechanism, but rather the element of a system which itself isn't isolated from the experience of being a body in a world interacting with different systems to form super systems.
The brain must be a very efficient mechanism, because it doesn't need to ingest the whole textual production of the human world in order to know how to write masterpieces (music, litterature, films, software, theorems etc...). Instead the brain learns to be this very efficient mechanism with (as a starting process) feeling its own body sh*t on itself during a long part of its childhood.
I can teach someone to become really good at producing fine and efficient software, but on the contrary I can only observe everyday that my LLM of choice keeps being stupid even when I explain it how it fails. ("You're perfectly right !").
It is true that there's nothing magical about the brain, but I am pretty sure it must be stronger tech than a probabilistic/statistical next word guesser (otherwise there would be much more consensus about the usability of LLMs I think).
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I'm not arguing that human brains are magic. the current AI models will probably teach us more about what we didn't know about intelligence than anything else.
Right, I'm just going to teach my dog to do my job then and get free money as my brain is no more magic, special or different to theirs!
There isn't anything else around quite like a human brain that we know of, so yes, I'd say they're special and different.
Animals and computers come close in some ways but aren't quite there.
For some unexplainable reason your subjective experience happens to be localized in your brain. Sounds pretty special to me.
There is nothing special about that either. LLM's also have self awareness/introspection, or at least a some version of it.
https://www.anthropic.com/research/introspection
Its hard to tell sometimes because we specifically train them to believe they don't.
Thanks for the link, I haven't seen this before and it's interesting.
I don't think the version of self awareness they demonstrated is synonymous with subjective experience. But same thing can be said about any human other then me.
Damn, just let me believe all brains are magical or I'll fall into solipsism.
> LLMs and human brains are both just mechanisms. Why would one mechanism a priori be capable of "learning abstract thought", but no others?
“Internal combustion engines and human brains are both just mechanisms. Why would one mechanism a priori be capable of "learning abstract thought", but no others?”
The question isn't about what an hypothetical mechanism can do or not, it's about whether the concrete mechanism we built does or not. And this one doesn't.
The general argument you make is correct, but you conclusion "And this one doesn't." is as yet uncertain.
I will absolutely say that all ML methods known are literally too stupid to live, as in no living thing can get away with making so many mistakes before it's learned anything, but that's the rate of change of performance with respect to examples rather than what it learns by the time training is finished.
What is "abstract thought"? Is that even the same between any two humans who use that word to describe their own inner processes? Because "imagination"/"visualise" certainly isn't.
> no living thing can get away with making so many mistakes before it's learned anything
If you consider that LLMs have already "learned" more than any one human in this world is able to learn, and still make those mistakes, that suggests there may be something wrong with this approach...
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> but that's the rate of change of performance with respect to examples rather than what it learns by the time training is finished.
It's not just that. The problem of “deep learning” is that we use the word “learning” for something that really has no similarity with actual learning: it's not just that it converges way too slowly, it's also that it just seeks to minimize the predicted loss for every samples during training, but that's no how humans learn. If you feed it enough flat-earther content, as well a physics books, an LLM will happily tells you that the earth is flat, and explain you with lots of physics why it cannot be flat. It simply learned both “facts” during training and then spit it out during inference.
A human will learn one or the other first, and once the initial learning is made, it will disregards all the evidence of the contrary, until maybe at some point it doesn't and switches side entirely.
LLMs don't have an inner representation of the world and as such they don't have an opinion about the world.
The humans can't see the reality for itself, but they at least know it exists and they are constantly struggling to understand it. The LLM, by nature, is indifferent to the world.
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Thermometers and human brains are both mechanisms. Why would one be capable of measuring temperature and other capable of learning abstract thought?
> If it turns out that LLMs don't model human brains well enough to qualify as "learning abstract thought" the way humans do, some future technology will do so. Human brains aren't magic, special or different.
Google "strawman".