fenomas

If you think the guidelines should be changed you can mail dang, but unless they change the civil thing would be to follow them.

Please read what the HN guidelines say about insinuations of astroturfing, because it very much applies here.

(vouched - just fyi this comment was dead, no idea why)

I'm fond of linguistic bugbears, and have actually sent that same article to people before :D But what you're missing is that the less/fewer debate is over their use as adjectives, and TFA's title uses "less" as an adverb. It's asking for AI agents to be less human, not for them to be fewer in number. Swapping it to "fewer" would make the title's meaning no longer match the article.

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

(The aside was for you - TFA's title is not a case where either word works.)

It's not a grammar issue; only "less" matches TFA's meaning.

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

Nice to have these all collected nicely and sharable. For the amusement of HN let me add one I've become known for at my current work, for saying to juniors who are overly worried about DRY:

> 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.

Nope, "less" is what TFA means.

Curmudgeonly comment from someone trying to sound like a wise elder about how actually all this was the norm even in the days of Usenet.

Not at all - didn't mean to sound snarky, I just wanted to add that I was omitting details and caveats.

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.

The high-order bit for for each case is the category it's in and the "Outcome" column - that summarizes if the solution was full/partial/wrong, if AI had assistance, etc. Then further discussion for each one is linked from the number.

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)

What you're asking for is exactly what's in the link you replied about. It collects analysis of each solution (or attempt), and info about whether the AI's solution could be found anywhere in the literature.

> generous interpretation

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".

The post you replied to was:

> 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).

It's not a side effect of tokenization per se, but of the tokenizers people use in actual practice. If somebody really wanted an LLM that can flawlessly count letters in words, they could train one with a naive tokenizer (like just ascii characters). But the resulting model would be very bad (for its size) at language or reasoning tasks.

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.

LLMs are bad at arithmetic and counting by design. It's an intentional tradeoff that makes them better at language and reasoning tasks.

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,

This is like saying chess engines don't actually "play" chess, even though they trounce grandmasters. It's a meaningless distinction, about words (think, reason, ..) that have no firm definitions.

Those are emoticons ;)

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.)

They would have if they could have - answered here:

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

I remember using that tool internally! Personally I think I only used it to get stats of which features/APIs were popular. But I think other teams used it for QA/conformance, like finding content that occurred in the wild but wasn't covered by test cases.

If it was possible they would have loved to - certainly by 2012 or so, and more likely by 2008-9. The reason I heard they couldn't is that by that time Flash Player was a massive 10+ year old codebase with lots of parts that were licensed or external, and nobody had ever tracked which parts would be to be relicensed or rewritten.

Source: I worked there at the time and knew the relevant PMs.

Adding my all-time favorite signature - Kurt Vonnegut, who often (not always) built a caricature of himself into his signature. What a flex!

E.g.: https://i0.wp.com/www.themarginalian.org/wp-content/uploads/...

(I think later in life his signatures lost the cigarette)

I think "Trucker's Hitch" refers generally to any setup where you tie a midline loop, then use it like a pulley and tie off the free end. There are several different ways of doing the loop knot and the ending knot, with their own names on their own, but Trucker's Hitch refers to the overall setup.

As kind of a knot guy, unlike other commenters this is my #1 favorite knot site.

Other sites with animated 3D models might be useful for visualizing the topology of knots, or something. But for actually tying the knots I find this site and its curated photos much more practically useful. The fact that it's not literally animated is a feature; it shows the key stages you go through, rather than every detail.

And the photos are just clearer and better than any other resource. (If you look closely you'll see a lot of editing work has been done on them like to minimize the diff between consecutive photos.)

Only downside is that I wish it had more minor knots!

The Da Vinci Code has a frontispiece page titled "FACTS" or similar, that lists various elements mentioned in the book and claims they're historically factual. That's the stuff lots of people believed (as did Dan Brown, one presumes).

Uh, kind of the opposite :D

The quote (from Dijkstra) is that asking whether machines think is as uninteresting as asking whether submarines swim. He's not saying machines don't think, he's saying it's a pointless thing to argue about - an opinion about whether AIs think is an opinion about word usage, not about AIs.

Nothing new is happening here - this is the government moving towards formally recommending the system that's already most widely used.

I'm confused, then why does your article throw shade at both the protection software and the VST?

It sounds like you didn't find any issues with either of them, except that the VST vendor chose not to protect the thing you were hoping to crack?

> No matter how much some try to gaslight, homosexuality is abnormal

This is an abjectly silly thing to say, and people who push back on it are not gaslighting. Homosexuality occurs naturally and it's not even rare - it's far more common than red hair, for example.

Calling something like that "abnormal" isn't in the domain of fact, it's purely a side-effect of what you label "normal".

Uh, among several other issues with this, what use are JSDoc comments for typing, without typescript to check them?