[bigyabai]

> 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).

[ACCount37]

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.

[bigyabai]

> 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?

[ACCount37]

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.

[bigyabai]

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]

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.

[squeaky-clean]

This is false. You can ask it to spell out strawberry and count the letters and it will still say 2 (it's unable to actually count the letters by the way). The only way to get a model that believes strawberry has 2 R's to consistently give the correct answer is to ask it to code the problem and return the output.

In fact, asking a model not to repeat the same mistake makes it more likely to commit that mistake again, because it's in it's context.

I think anyone who uses LLMs a lot will tell your that your steps 3 and 4 are fictional.

[bigyabai]

> it's so powerful that finding "anything better" is incredibly hard.

We're back around to the start again. "Incredibly hard" is doing all of the heavy lifting in this statement, it's not all-powerful and there are enormous failure cases. Neither the human brain nor LLMs are a panacea for thought, but nobody in academia or otherwise is seriously comparing GPT to the human brain. They're distinct.

> There's a technical explanation for why that works, but to you, it might as well be black magic.

Expound however much you need. If there's one thing I've learned over the past 12 months it's that everyone is now an expert on the transformer architecture and everyone else is wrong. I'm all ears if you've got a technical argument to make, the qualitative comparison isn't convincing me.

[8note]

why is the breakdown from words to letters your highest priority thing to add to the training data?

what problem does this allow you to solve that you couldnt otherwise?

[squeaky-clean]

This comment is tangential to their point that a transformer architecture can or cannot be functionally equivalent to a human brain. Practicality of those limitations is a different discussion