Comment by WarmWash
14 days ago
>Biological brains exist, we study them, and no they are not like computers at all.
You are confusing the way computation is done (neuroscience) with whether or not computation is being done (transforming inputs into outputs).
The brain is either a magical antenna channeling supernatural signals from higher planes, or it's doing computation.
I'm not aware of any neuroscientists in the former camp.
Neuroscience isn't a subset of computer science. It's a study of biological nervous systems, which can involve computational models, but it's not limited to that. You're mistaking a kind of map (computation) for the territory, probably based on a philosophical assumption about reality.
At any rate, biological organisms are not like LLMs. The nervous systems of human may perform some LLM-like actions, but they are different kinds of things.
Who says it is a subset of computer science?
But computational models are possibly the most universal thing there is, they are beneath even mathematics, and physical matter is no exception. There is simply no stronger computational model than a Turing machine, period. Just because you make it out of neurons or silicon is irrelevant from this aspect.
Turing machines aren't quantum mechanical, and computation is based on logic. This discussion is philosophical, so I guess it's philosophy all the way down.
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> The brain is either a magical antenna channeling supernatural signals
There’s the classic thought-terminating cliche of the computational interpretation of consciousness.
If it isn’t computation, you must believe in magic!
Brains are way more fascinating and interesting than transistors, memory caches, and storage media.
You would probably be surprised to learn that computational theory has little to no talk of "transistors, memory caches, and storage media".
You could run Crysis on an abacus and render it on board of colored pegs if you had the patience for it.
It cannot be stressed enough that discovering computation (solving equations and making algorithms) is a different field than executing computation (building faster components and discovering new architectures).
Not surprised at all.
My point is that it takes more hand-waving and magic belief to anthropomorphize LLM systems than it does to treat them as what they are.
You gain nothing from understanding them as if they were no different than people and philosophizing about whether a Turing machine can simulate a human brain. Fine for a science fiction novel that is asking us what it means to be a person or question the morals about how we treat people we see as different from ourselves. Not useful for understanding how an LLM works or what it does.
In fact, I say it’s harmful. Given the emerging studies on the cognitive decline of relying on LLMs to replace skill use and on the emerging psychosis being observed in people who really do believe that chat bots are a superior form of intelligence.
As for brains, it might be that what we observe as “reasoning” and “intelligence” and “consciousness” is tied to the hardware, so to speak. Certainly what we’ve observed in the behaviour of bees and corvids have had a more dramatic effect on our understanding of these things than arguing about whether a Turing machine locked in a room could pass as human.
We certainly don’t simulate climate models in computers can call it, “Earth,” and try to convince anyone that we’re about to create parallel dimensions.
I don’t read Church’s paper on Lambda Calculus and get the belief that we could simulate all life from it. Nor Turing’s machine.
I guess I’m just not easily awed by LLMs and neural networks. We know that they can approximate any function given an unbounded network within some epsilon. But if you restate the theorem formally it loses much of its power to convince anyone that this means we could simulate any function. Some useful ones, sure, and we know that we can optimize computation to perform particular tasks but we also know what those limits are and for most functions, I imagine, we simply do not have enough atoms in the universe to approximate them.
LLMs and NNs and all of these things are neat tools. But there’s no explanatory power gained by fooling ourselves into treating them like they are people, could be people, or behave like people. It’s a system comprised of data and algorithms to perform a particular task. Understanding it this way makes it easier, in my experience, to understand the outputs they generate.
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