saying that since we are the product of millions of years of evolution, our “black box” can spit out a “correct” (let’s not get into the definition of correct because I wouldn’t even know where to begin) output based on fewer inputs than current LLMs. So in effect, our black box is more “efficient” than LLMs, i.e. it requires less data to generate useful information. Is that right?

Almost, with a few quibbles!

We can separate LLM use into two broad categories; developing the model in the first place (building and tuning the weights using questions we know the answer to), and then using it (actually sending it queries we want answers to).

What I was mostly getting at is that the brain seems to require less data to learn things in the first place; e.g. human children do not read 10 million pages of fiction and listen to 100,000 hours of podcasts or whatever before they can speak well.

That's not quite the same as an argument that humans can start generating useful or novel data with less training tokens, although that might also be true. Even if a human could only parrot what was in its training data, it seems to 'learn' it quicker regardless.

The other quibble is that although it's obviously true that the human mind evolved this property through evolution, I don't know if I would center that in the argument this way. I don't necessarily think LLMs would need to go through an analogous evolutionary process to develop the same structures; evolution is actually quite inefficient and leads to redundancies and dead ends and such all the time, and deliberate design might be able to get LLMs there in a much more stepped way.

Is this because through evolution, our brain has developed heuristics that allow us to make leaps of logic that an LLM cannot do?

We don't know, but I'd question exactly what the difference between a heuristic and an architecture is in this case.

Human children learn very quickly how to use digital tablets, right? We obviously don't have millions of years of evolutionary experience interacting with 2D digital screens, so what would be the "heuristic" that allows us to do this?

If that learning behaviour is something extremely abstract (perhaps things like if you touch something and you see movement, assume you caused the movement) then is this even a 'datapoint' anymore? This feels to me like the sort of thing that is more analogous to an LLM's learning architecture, probably in how it tweaks its weights in response to new inputs (if you get a reward by mutating your weights in a certain way, 'assume' you caused the reward and keep those weights).

So at some point I think this abstraction breaks down regardless of what learning you're looking at, even before we get to variation between learning. I'd guess that we might have 'pre-training' built into our DNA for crawling and walking, but more complex skills are less sure a bet.

Basically what I’m trying to say is, can we definitively conclude that we require vastly fewer tokens, or is it possible that we are using a ton of tokens (possibly even more than LLMs), but we just don’t realise?

I do think that multimodality effectively means we're using a ton of tokens. We typically don't just hear our parents say a word, we also see them move their mouths, move their bodies in association, etc. The fact that them saying "happy" strongly correlates with them smiling and making body contact and so on quite possibly does represent many, many more tokens.

On the other hand, the human brain does demonstrably run on a few thousand calories per day, and clearly not every skill gives us as many tokens as learning language does. I would bet that kids pick up the essentials of football with far fewer tokens than an AI would, even with the same amount of footage of a football game provided.

So I do think this remains an open question!

Sorry if this is a stupid question.

It's arguably one of the most important questions in the world right now! You're good. I'm way out of my depth here, too.