The load-bearing word in that claim is "undue", and it's not justified here. I'm not doing arcane rules-lawyering, I'm just saying people should avoid doing things the site guidelines quite specifically ask them not to do.

> I’m not advocating for this rule to change (I’d appreciate if you didn’t straw man and mischaracterise what I said),

I wasn't suggesting you did, I was suggesting the person I originally replied to might.

https://en.wikipedia.org/wiki/Generic_you

Does that mean I now repeat your parenthetical back to you? ;)

Now please sit a moment and reflect on what you've done. :P

(Aside: it's better not to be pedantic, but if you must be pedantic you should remember to be correct as well.)

> Fen's law: copy-paste is free; abstractions are expensive.

edit: I should add, this is aimed at situations like when you need a new function that's very similar to one you already have, and juniors often assume it's bad to copy-paste so they add a parameter to the existing function so it abstracts both cases. And my point is: wait, consider the cost of the abstraction, are the two use cases likely to diverge later, do they have the same business owner, etc.

FWIW, personally I think it muddies things to frame the question as if "..using statistical token generation" was a limitation. NNs are Turing-complete, so what LLMs do can just be considered "computation" - the fact that they compute via statistical token generation is an implementation detail.

And if you're like most people, "can cognition happen via computation?" is a less controversial question, which then puts LLMs/cognition topics easily into the "in principle, obviously, but we can debate whether it's achievable or how to measure it" category.

Then the "Literature result" columns have a citations for where similar published results were found. The ones with no "Literature" column, like in the first section, are cases where no similar published results have been found (implying that the solution would not have been trained on). Note that in some cases a published solution was found but it wasn't similar to the AI's.

(this is all explained with more detail and caveats at the top of the page)

The term of art for that is steelmanning, and HN tries to foster a culture of it. Please check the guidelines link in the footer and ctrl+f "strongest".

> We went from 2 + 7 = 11 to "solved a frontier math problem" in 3 years, yet people don't think this will improve?

All that says is that the speaker thinks models will improve past where they are today. Not that it's a logical certainty (the first thing you jumped on them for), and certainly not anything about "limitless potential for growth" (which nobody even mentioned). With replies like this, invoking fallacies and attacking claims nobody made, you're adding a lot of heat and very little light here (and a few other threads on the page).

Basically it's an engineering tradeoff. There is more demand for LLMs that can solve open math problems, but can't count the Rs in strawberry, than there is for models that can count letters but are bad at everything else.

If anybody really wanted a model that could multiply and count letters in words, they could just train one with a tokenizer and training data suited to those tasks. And the model would then be able to count letters, but it would be bad at things like translation and programming - the stuff people actually use LLMs for. So, people train with a tokenizer and training data suited to those tasks, hence LLMs are good at language and bad at arithmetic,

Emoji originally came from Docomo phones in Japan around 1999. (Or I think those were the first ones actually called "emoji"; some other earlier devices had similar character sets.)

https://news.ycombinator.com/item?id=47256093