> No, doing so is considered a sign of not having grasped the material, and is the bane of secondary-level mathematics teachers everywhere. (Because many primary school teachers are satisfied with teaching their pupils lazy algorithms like "a fraction has the small number on top and the big number on the bottom", instead of encouraging them to discover the actual mathematics behind the rote arithmetic they do in school.)

You seem to be conflating "simple pattern" with the more general concept of "patterns".

What LLMs do is not limited to simple patterns. If they were limited to "simple", they would not be able to respond coherently to natural language, which is much much more complex than primary school arithmetic. (Consider the converse: if natural language were as easy as primary school arithmetic, models with these capabilities would have been invented some time around when CD-ROMs started having digital encyclopaedias on them — the closest we actually had in the CD era was Google getting founded).

By way of further example:

> By that logic, we'd have to say that Stockfish is unintelligent.

Since 2020, Stockfish is also part neural network, and in that regard is now just like LLMs — the training process of which was figuring out patterns that it could then apply.

Before that Stockfish was, from what I've read, hand-written heuristics. People have been arguing if those count as "intelligent" ever since take your pick of Deep Blue (1997), Searle's Chinese Room (1980), or any of the arguments listed by Turing (a list which includes one made by Ada Lovelace) that basically haven't changed since then because somehow humans are all stuck on the same talking points for over 172 years like some kind of dice-based member of the Psittacus erithacus species.

> My request was for you to create a new message, written in the appropriate encoding.

> Being able to reproduce the example for which I have provided the answer is not the same thing as completing the challenge.

Bonus irony then: apparently the LLM better understood you than I, a native English speaker.

Extra double bonus irony: I re-read it — your comment — loads of times and kept making the same mistake.

> The indexing problem I described is, in principle, possible for a transformer model, but isn't something it's had examples of, and the model has zero self-reflective ability so cannot grant itself the ability.

You think it's had no examples of counting?

(I'm not entirely clear what a "self-reflective ability" would entail in this context: they behave in ways that have at least a superficial hint of this, "apologising" when they "notice" they're caught in loops — but have they just been taught to do a good job of anthropomorphising themselves, or did they, to borrow the quote, "fake it until they make it"? And is this even a boolean pass/fail, or a continuum?)

Edit: And now I'm wondering — can feral children count, or only subitise? Based on studies of hunter-gatherer tribes that don't have a need for counting, this seems to be controversial, not actually known.

> (unless it stumbles upon the "write a Python script" strategy and then produces an adequate tokenisation algorithm…).

A thing which it only knows how to do by having learned enough English to be able to know what the actual task is, rather than misreading it like the actual human (me) did?

And also by having learned the patterns necessary to translate that into code?

> Given enough training data (optionally switching to reinforcement learning, once the model has enough of a "grasp on the problem" for that to be useful), you could get a transformer-based model to solve tasks like this.

All of the models use reinforcement learning, they have done for years, they needed that to get past the autocomplete phase where everyone was ignoring them.

Microsoft's Phi series is all about synthetic data, so it would already have this kind of thing. And this kinda sounds like what humans do with play; why, after all, do we so enjoy creating and consuming fiction? Why are soap operas a thing? Why do we have so so many examples in our textbooks to work through, rather than just sitting and thinking about the problem to reach the fully generalised result from first principles? We humans also need enough training data and reinforcement learning.

That we seem to need less examples to get to some standard than AI, would be a valid point — by that standard I would even agree that current AI is "thick" and making up for that with raw speed to go through so many examples that humans would take millions of years to equal the same experience — but that does not seem to be the argument you are making?