You probably are.

The "small subset" argument is profoundly unconvincing, and inconsistent with both neurobiology of the human brain and the actual performance of LLMs.

The transformer architecture is incredibly universal and highly expressive. Transformers power LLMs, video generator models, audio generator models, SLAM models, entire VLAs and more. It not a 1:1 copy of human brain, but that doesn't mean that it's incapable of reaching functional equivalence. Human brain isn't the only way to implement general intelligence - just the one that was the easiest for evolution to put together out of what it had.

LeCun's arguments about "LLMs can't do X" keep being proven wrong empirically. Even on ARC-AGI-3, which is a benchmark specifically designed to be adversarial to LLMs and target the weakest capabilities of off the shelf LLMs, there is no AI class that beats LLMs.

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bigyabai 2 hours ago | parent [-]

> Human brain isn't the only way to implement general intelligence - just the one that was the easiest for evolution to put together out of what it had.

The human brain is not a pretrained system. It's objectively more flexible than than transformers and capable of self-modulation in ways that no ML architecture can replicate (that I'm aware of).

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ACCount37 2 hours ago | parent [-]

Human brain's "pre-training" is evolution cramming way too much structure into it. It "learns from scratch" the way it does because it doesn't actually learn from scratch.

I've seen plenty of wacky test-time training things used in ML nowadays, which is probably the closest to how the human brain learns. None are stable enough to go into the frontier LLMs, where in-context learning still reigns supreme. In-context learning is a "good enough" continuous learning approximatation, it seems.

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bigyabai 2 hours ago | parent [-]

> In-context learning is a "good enough" continuous learning approximatation, it seems.

"it seems" is doing a herculean effort holding your argument up, in this statement. Say, how many "R"s are in Strawberry?

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ACCount37 2 hours ago | parent [-]

If you think that "strawberry" is some kind of own, I don't know what to tell you. It takes deep and profound ignorance of both the technical basics of modern AIs and the current SOTA to do this kind of thing.

LLMs get better release to release. Unfortunately, the quality of humans in LLM capability discussions is consistently abysmal. I wouldn't be seeing the same "LLMs are FUNDAMENTALLY FLAWED because I SAY SO" repeated ad nauseam otherwise.

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bigyabai 2 hours ago | parent [-]

I can ask a nine-year-old human brain to solve that problem with a box of Crayola and a sheet of A4 printer paper.

In-context learning is professedly not "good enough" to approximate continuous learning of even a child.

	▲	ACCount37 an hour ago \| parent [-]
		You're absolutely wrong! You can also ask an LLM to solve that problem by spelling the word out first. And then it'll count the letters successfully. At a similar success rate to actual nine-year-olds. There's a technical explanation for why that works, but to you, it might as well be black magic. And if you could get a modern agentic LLM that somehow still fails that test? Chances are, it would solve it with no instructions - just one "you're wrong". 1. The LLM makes a mistake 2. User says "you're wrong" 3. The LLM re-checks by spelling the word out and gives a correct answer 4. The LLM then keeps re-checking itself using the same method for any similar inquiry within that context In-context learning isn't replaced by anything better because it's so powerful that finding "anything better" is incredibly hard. It's the bread and butter of how modern LLM workflows function.