Idk. There’s something going on in how humans learn which is probably fundamentally different from current ML models.

Sure, humans learn from observing their environments, but they generally don’t need millions of examples to figure something out. They’ve got some kind of heuristics or other ways of learning things that lets them understand many things after seeing them just a few times or even once.

Most of the progress in ML models in recent years has been the discovery that you can get massive improvements with current models by just feeding them more and data. Essentially brute force. But there’s a limit to that, either because there might be a theoretical point where the gains stop, or the more practical issue of only having so much data and compute resources.

There’s almost certainly going to need to be some kind of breakthrough before we’re able to get meaningful further than we are now, let alone matching up to human cognition.

At least, that’s how I understand it from the classes I took in grad school. I’m not an expert by any means.

The big difference between people and LLMs is that an LLM is static. It goes through a learning (training) phase as a singular event. Then going forward it's locked into that state with no additional learning.

A person is constantly learning. Every moment of every second we have a ton of input feeding into our brains as well as a feedback loop within the mind itself. This creates an incredibly unique system that has never yet been replicated by computers. It makes our brains a dynamic engine as opposed to the static and locked state of an LLM.

It's not specifically related, but biological neurons and artificial neurons are quite different in how they function. Neural nets are a crude approximation of the biological version. Doesn't mean they can't solve similar problems or achieve similar levels of cognition , just that about the only similarity they have is "network of input/output things".

I’d love to hear about any studies explaining the mechanism of human cognition.

You're just asking for any intro to cognitive psychology textbook.

Ehhh.... It depends on what you mean by human cognition. Usually when tech people are talking about cognition, they're just talking about a specific cognitive process in neurology.

Tech enthusiasts tend to present human cognition in a reductive manor that for the most part only focuses on the central nervous system. When in reality human cognition includes anyway we interact with the physical world or metaphysical concepts.

There's something called the mind body problem that's been mostly a philosophical concept for a long time, but is currently influencing work in medicine and in tech to a lesser degree.

Basically, it questions if it's appropriate to delineate the mind from the body when it comes to consciousness. There's a lot of evidence to suggest that that mental phenomenon are a subset of physical phenomenon. Meaning that cognition is reliant on actual physical interactions with our surroundings to develop.

There's a lot we understand about the brain, but there is so much more we dont understand about the brain and "awareness" in general. It may not be magic, but it certainly isnt 100% understood.

(working with the assumption we mean stuff like ChatGPT) mKay... Tho math and logic is A LOT more than just statistics. At no point did we prove that statistics alone is enough to reach the point of cognition. I'd argue no statistical model can ever reach cognition, simply because it averages too much. The input we train it on is also fundamentally flawed. Feeding it only text skips the entire thinking and processing step of creating an answer. It literally just take texts and predicts on previous answers what's the most likely text. It's literally incapable of generating or reasoning in any other way then was already spelled out somewhere in the dataset. At BEST, it's a chat simulator (or dare I say...language model?), it's nowhere near an inteligence emulator in any capacity.