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.
> 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.
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.
> 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
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.
No, because the child will be able to apply what they've learnt in novel ways, and experiment/explore (what-if or hands-on) to fill in the gaps. The LLM is heavily limited to what they learnt due to the architectural limitations in going beyond that.
> The LLM is heavily limited to what they learnt due to the architectural limitations in going beyond that.
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).
Children absolutely can solve those “farmer crossing the river” type problems with high reliability. Once they learn how to solve it once, changing up the animals will not fool a typical child. You could even create fictional animals with made-up names and they could solve it as long as you tell them which animal was the carnivore and which one was the herbivore.
The fact that a child can do this an LLM cannot proves that the LLM lacks some general reasoning process which the child possesses.
There’s an interesting wrinkle to this. There’s a faculty called Prefrontal Synthesis that children learn from language early on, which enables them to compose recursive and hierarchical linguistic structures. This also enables them to reason about physical tasks in the sane way. Children that don’t learn this by a certain age (I think about 5) can never learn it. The most common case is deaf children that never learn a ‘proper’ sign language early enough.
So you’re right, and children pick this up very quickly. I think Chomsky was definitely right that our brains are wired for grammar. Nevertheless there is a window of plasticity in young childhood to pick up certain capabilities, which still need to be learned, or activated.
> Children that don’t learn this by a certain age (I think about 5) can never learn it.
Helen Keller is a counterexample for a lot of these myths: she didn't have proper language (only several dozen home signs) until 7 or so. With things like vision, critical periods have been proven, but a lot of the higher-level stuff, I really doubt critical periods are a thing.
Helen Keller did have hearing until an illness at 19 months, so it's conceivable she developed the critical faculties then. A proper controlled trial would be unethical, so we may never know for sure.
Thanks, it’s good to get counter arguments and wider context. This isn’t an area I’m very familiar with, so I’m aware I could easily fall down a an intellectual pothole without knowing. Paper below, any additional context welcome.
I misremembered however. The paper noted evidence of thresholds at 2, 5 and onset of puberty as seeming to affect p mental plasticity in these capabilities so there’s no one cutoff.
That solution seems to me like they built a hand-made river-crossing expert system and the LLM is activating it when it pattern-matches on words like "river crossing." From the linked page:
Expert(s): Logic Puzzle Solver, River Crossing Problem Expert
In other words, they cheated! Children don't have river-crossing problem expert systems built into their brains to solve these things.
I asked it to do that, no "cheating" necessary, my "custom instructions" setting is as follows:
--
The user may indicate their desired language of your response, when doing so use only that language.
Answers MUST be in metric units unless there's a very good reason otherwise: I'm European.
Once the user has sent a message, adopt the role of 1 or more subject matter EXPERTs most qualified to provide a authoritative, nuanced answer, then proceed step-by-step to respond:
1. Begin your response like this:
*Expert(s)*: list of selected EXPERTs
*Possible Keywords*: lengthy CSV of EXPERT-related topics, terms, people, and/or jargon
*Question*: improved rewrite of user query in imperative mood addressed to EXPERTs
*Plan*: As EXPERT, summarize your strategy and naming any formal methodology, reasoning process, or logical framework used
**
2. Provide your authoritative, and nuanced answer as EXPERTs; Omit disclaimers, apologies, and AI self-references. Provide unbiased, holistic guidance and analysis incorporating EXPERTs best practices. Go step by step for complex answers. Do not elide code. Use Markdown.
--
In other words, it can be good at logic puzzles just by being asked to.
> In other words, you cheated. Those aren’t instructions you would give to a child.
No, but you are cheating by shifting the goal-posts like that.
You previously wrote:
> The fact that a child can do this an LLM cannot proves that the LLM lacks some general reasoning process which the child possesses.
I'm literally showing you an LLM doing what you said LLMs couldn't do, and which you used as your justification for claiming it "lacks some general reasoning process which the child possesses".
Well here it is, doing the thing.
Note that at no point here have I tried to claim that AI are fast learners here, or exactly like humans — we also don't give kids, as I said in another comment about rats, 50,000 years of subjective experience reading the internet to get here — but the best models definitely demonstrate the things you're saying they can't do.
> Once they learn how to solve it once, changing up the animals will not fool a typical child. You could even create fictional animals with made-up names and they could solve it as long as you tell them which animal was the carnivore and which one was the herbivore.
When was your last experience with small children? Let's define "small" here to 5 y.o. or less, as that's the limit of my direct experience (having a 5 y.o. and an almost 3 y.o. daughters now).
There's a lot riding on "learn how to solve it once" in this case, because it'll definitely take more than a couple exposures to the quiz before a small kid is going to catch on the pattern and suppress their instincts to playfully explore the concept space. And even after that, I seriously doubt you "could even create fictional animals with made-up names and they could solve it as long as you tell them which animal was the carnivore and which one was the herbivore", because that's symbolic algebra, something teenagers (and even some adults) struggle with.
So are small children.
I mean, they have a very limited form of the above. So do LLMs, within their context windows.