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> I see no reason whatsoever to believe that what your wet meat brain is doing now is any different from what an LLM does.
I don't think this follows though. Birds and planes can both fly, but a bird and a plane are clearly not doing the same thing to achieve flight. Interestingly, both birds and planes excel at different aspects of flight. It seems at least plausible (imo likely) that there are meaningful differences in how intelligence is implemented in LLMs and humans, and that that might manifest as some aspects of intelligence being accessible to LLMs but not humans and vice versa.
Intelligence isn’t "implemented" in an LLM at all. The model doesn’t carry a reasoning engine or a mental model of the world. It generates tokens by mathematically matching patterns: each new token is chosen to best fit the statistical patterns it learned from its training data and the immediate context you give it. In effect, it’s producing a compressed, context-aware summary of the most relevant pieces of its training data, one token at a time.
The training data is where the intelligence happened, and that's because it was generated by human brains.
That said, to whatever extent they're intelligent or not, by almost any definition of intelligence, I don't think they're achieving it through the same mechanism that humans do. That is my main argument. I thing confident arguments that "LLMs think just like humans" are very bad, given that we clearly don't understand how humans achieve intelligence and the vastly different substrates and constraints that humans and LLMs are working with.
If they don't have any training data that covers a particular concept, they can't map it onto a world model and make predictions about that concept based on an understanding of the world and how it works. [This video](https://www.youtube.com/watch?v=160F8F8mXlo) illustrates it pretty well. These things may or may not end up being fixed in the models, but that's only because they've been further trained with the specific examples. Brains have world models. Cats see a cup of water, and they know exactly what will happen when you tip it over (and you can bet they're gonna do it).
Analyzing an image generation failure modes from the dall-e family of models isn't really helpful in understanding if the invoking LLM has a robust world model or not.
SD models have a much higher propensity to fixate on proximal in distribution solutions because of the way they de-noise.
For example.. you can ask nano banana for a "Completely full wine glass in zero g" which I'm pretty sure is way more out of distribution, the model does a reasonable job at approximating what they might look like.
But what it won't be able to do, which does require a world model, is put a frog on the moon, and be able to imagine what that frog's body would look like on the moon in the vacuum of space as it dies a horrible death.
"Create an image of what a watermelon would look like after being teleported to the surface of the moon for 30 seconds."
You'll see a burst frozen melon usually.
Completely false equivalency. We did in fact back then completely understand "how a bird works", how the physics of flight work. The problem getting man-made flying vehicles off the ground was mostly about not having good enough materials to build one (plus some economics-related issues).
Whereas in case of AI, we are very far from even slightly understanding how our brains work, how the actual thinking happens.
"LLMs think like people do" is the equivalent of flat earth theory or UFO bros.
Flerfers run on ignorance, misunderstanding and oppositional defiant disorder. You can easily prove the earth is round in quite a lot of ways (the Greeks did it) but the flerfers either don't know them or refuse to apply them.
There are quite a lot of reasons to believe brains work differently than LLMs (and ways to prove it) you just don't know them or refuse to believe them.
It's neat tech, and I use them. They're just wayyyyyyyy overhyped and we don't need to anthropomorphize them lol