I don't understand the reasoning behind drawing a conclusion that if something fails a task that requires reasoning implies that thing cannot reason.
To use chess as an example. Humans sometimes play illegal moves. That does not mean Humans cannot reason. It is an instance of failing to show proof of reasoning. Not a proof of the inability to reason.
I don't think that's a fair representation of the argument.
The argument is not "here's one failure case, therefore they don't reason". The argument is that systematically if you given an LLM problem instances outside training sets in domains with clear structural rules, they will fail to solve them. The argument then goes that they must not have an actual model or understanding of the rules, as they seem to only be capable of solving problems in the training set. That is, they have failed to figure out how to solve novel problem instances of general problem structures using logical reasoning.
Their strict dependence on having seen the exact or extremely similar concrete instances suggests that they don't actually generalize—they just compute a probability based on known instances—which everyone knew already. The problem is we just have a lot of people claiming they are capable of more than this because they want to make a quick buck in an insane market.
That still seems unfalsifiable. If it fails one instance the claim is that the failure is representative of things outside the training set. If it succeeds the claim is that it is in the training set. Without a definitive way to say something is not in the training set (a likely impossible task) the measure of success or failure is the only indicator of the purported reason reason for the success or failure.
Given models can get things wrong even when the training data contains the answer, failure cannot show absence.
I do think there are cases which, in controlled environments, there is some degree of knowledge as to what is in the training set. I also don't thin it's as impossible as you assume.
If you really wanted to ensure this with certainty just use the natural numbers to parameterize an aspect of a general problem. Assume there are N foo problems in the training set, then there is always a case N+1 parameter not in the training set, and you can use this as an indicative case. Go ahead and generate an insane number of these and eventually the probability that the Mth instance is not in the set is effectively 1.
Edit: Of course, it would not be perfect certainty, but it is probabilistically effectively certain. The number of problem instances in the set is necessarily finite, so if you go large enough you get what you need. Sure, you wouldn't be able to say there is a specific problem instance not in the set, but the aggregate results would evidence whether or no the LLm deals with all cases or (on assumption) just known ones.
Well there are models that can sum two many-digit numbers. They certainly have not been trained on every pair of integers up to that level. That either makes the claim they can't do things that they haven't seen trivially false, or the criteria for counting something as being in the training data includes a degree of inference.
What happens when someone makes a claim that they have gotten a model to do something not in the training data and another person claims it must be encoded in the training data in some form. It seems like an impasse.
It is the side that is arguing that it is reasoning that is lacking rigor and evidence. The side that arguing it isn't is saying you need more rigor and evidence when you claim it is reasoning by pointing out simple cases where it fails.
Humans who know how to play chess do not play illegal chess moves. Humans can learn chess in an afternoon and never make an illegal move again. The rules are pretty simple, and they are rules that every LLM has seen dozens of not hundreds of times in their training data. They still play illegal moves because they are not learning anything except how to simulate conversation.
Another algorithmic learning breakthrough, on the order of perceptrons, deep learning, transformers, etc is necessary to get anywhere near AGI.
Most humans wouldn't even be able to play like this. Reasonably experienced chess players would play a lot of illegal moves.
The reason is that the encoding above requires cumulatively applying a series of actions to a two-dimensional model to which you apply rules that are described in a two-dimensional fashion.
It'd be interesting to see what the results would be if each prompt contained a two dimensional representation of the up to date board state.
I'm not sure how you consider this to be an anthropomorphic fallacy, the comparison to the situation with a human exists only because people are prepared to stipulate that humans can reason. That does not assume something about AI behaviour to be like a human's. It is showing the same test applied to a human.
Your statement that AI knows the rules would be considered anthropomorphising by many, I take it more to mean it 'knows' in the same sense that an election 'wants' to be at a lower energy level.
That said, humans who have written entire books on chess have been known to play illegal moves. That should count as proof by counterexample that your reasoning as to why humans fail at tasks is false.
Did you read those? These are the "illegal" moves listed:
5. Mouse slip
4. Forgot to call check
3. Accidentally touched 2 pieces, tried to fix it
2. Forgot to hit the clock button
1. Castle through attacked square
So, the only one of these that was an acual "illegal move" of the sort LLMs make was the castle through attacked square.
LLMs sometimes just move pieces wherever. And that does not happen when humans who know the rules play. Yes, they may mess up en passant or promotion too. But a basic "how a single piece moves" rule is what LLMs f up.
But really, so what? We already have specialised chess engines (stockfish, leela, alphazero etc) that are far far stronger than humans will ever be, so insofar as that’s an interesting goal, we achieved it with deep blue and have gone way way beyond it since. The fact that a large Language model isn’t able to discern legal chess moves seems to me to be neither here nor there. Most humans can’t do that either. I don’t see it as evidence of lack of a world model either (because most people with a real chess board in front of them and a mental model of the world can’t play legal chess moves).
I find it astonishing that people pay any attention to Gary Marcus and doubly so here. Whether or not you are an “AI optimist”, he clearly is just a bloviator.
To use chess as an example. Humans sometimes play illegal moves. That does not mean Humans cannot reason. It is an instance of failing to show proof of reasoning. Not a proof of the inability to reason.