No - children have brains, same as adults. All they are missing is some life experience, but they are quite capable of applying the knowledge they do have to novel problems.
There is a lot more structure to the architecture of our brain than that of an LLM (which was never designed for this!) - our brain has all the moving parts necessary for reasoning, critically including "always on" learning so that it can learn from it's own mistakes as it/we figure something out.
Life experience directly prevents application of logic, as we shortcut to our knowledge associated with the word so that we can skip the expensive-and-hard "logic" thing. I've seen this first-hand with a modified version of the Queen of Hearts poem used as a logic puzzle at university, and most of us were trying to remember the poem rather than solve the puzzle — the teachers knew this and that was the point of the exercise, to get us to read the actual question instead of what we were expecting: https://en.wikipedia.org/wiki/The_Queen_of_Hearts_(poem)
If the missing gap was as you say, then approaches like Cyc's from 40 years ago would be highly effective and we wouldn't need or want neural nets for anything deeper than finding the inputs to send to that model: https://en.wikipedia.org/wiki/Cyc
> There is a lot more structure to the architecture of our brain than that of an LLM (which was never designed for this!)
Yes.
I don't know how much of that really matters given they are weirdly high performance given GPT-3 has a total complexity similar to the connectome of a mid-sized rodent, but they are indeed different.
On the other hand, converting the training run to biological terms would be like keeping said rodent alive for 50,000 years experiencing nothing but a stream of pre-tokenised text from the internet and giving it rewards or punishments according to how well it imagines a missing token. Perhaps a rat would do fine if it didn't typically die of old age 0.006% of the way through such a training process.
But that's just more agreement that it's all very alien.
> our brain has all the moving parts necessary for reasoning, critically including "always on" learning so that it can learn from it's own mistakes as it/we figure something out.
I'm not so sure about that. We have plenty of cognitive biases, and because of these even we have to take notes, check with others, or defer to computers, for more than the most trivial of logic problems.
Intelligence is prediction, and the simplest kind of prediction, and what literally comes to mind first is "this time will be same as next time", so in many familiar situations we are just reacting rather than reasoning/planning. It's when what comes to mind first doesn't work (we try it), or we can see the flaw without even trying, that we need to stop and think (reason), such as "hmm... how can I get this stuck lid off the jar - what do I have that can help?".
> If the missing gap was as you say, then approaches like Cyc's from 40 years ago would be highly effective
CYC vs LLM is an interesting comparison, and one that I've also made myself, but of course there are differences as well as similarities. The similarity is that both are rules based systems of sorts (maybe we could even regard an LLM as an expert system over the domain of natural language), and in both cases there is the wishful thinking that "scale it up and it'll become sentient and/or god-like"! The major difference is that CYC is essentially just using it's rules to perform a deductive closure over it's inputs (what can it deduce from inputs, using multiple applications of rules), whereas the LLM was trained explicitly with a predictive goal, and with it's domain of natural language it's able to predict (recall) human responses and therefore appear intelligent.
I think prediction (= intelligence) is the key difference here. An LLM is still limited in it's intelligence/predictive ability, most obviously when it comes to multi-step reasoning, but it's natural language ability and flexible (key-based self-attention) predictive architecture make it quite capable when operating on "in distribution" inputs.
I'm not sure how much of that is an architectural limit vs. an implementation limit.
Certainly they have difficulty significantly improving their quality due to the limitations of the architecture, but I have not heard anything to suggest the architecture has difficulty expanding in breadth of tasks — it just has to actually be trained on them, not merely be limited frozen weights used for inference.
(Unless you count in-context learning, which is cool, but I think you meant something with more persistence than that).