The problem is not them being random.

They are not random, that’s the point. They’re entirely deterministic and very precise, and they aren’t hiding anything; they will give you the most likely (not blacklisted) sequence of characters to follow your input according to their model. What they won’t give you is information, except by accident.

If they were random (hidden or not) they’d be harmless, no one would trust them any more than one of those eight ball toys, or your average horoscope.

The issue is that they’re very not random, so much that there’s no way to know if what they are saying bears any accidental semblance to the truth without fact checking… and that very soon they’ll have replaced any feasible way to fact check them, since all the supposed “facts” we’ll have access to will have been generated by LLMs train on LLM generated garbage.

If the models are random then we shouldn’t be trusting them to do anything, let alone serious applications.

That’s not the reason we shouldn’t be using them for anything other than generating lorem ipsum style text or dialogue for non quest critical NPCs in games.

The reason is that, paraphrasing Neil Gaiman, LLMs don’t generate information, they generate information shaped sentences.

Specifically, an LLM takes a sequence of characters (not a word or text; LLMs have no concept of words, or text, or anything else for that matter; they’re just an application of statistics on large volumes of sequences of characters; no meaning or intelligence involved, artificial or not)… as I was saying, an LLM takes a sequence of characters, pushes it through its model, and outputs the sequence of characters most likely to follow it in the texts its model has been trained on (or rather, the most likely after discarding the ones its creators have labelled as politically incorrect).

That’s all they do, and they’ll excellent at it (or would be if it weren’t for the aforementioned filters), but that’ll never give you a cure for cancer unless there already was one in their training data.

They take texts written by humans, shred them, and give you their badly put back together dessicated corpses, drained of any and all meaning or information, but looking very convincingly (until you fact check them) like actually meaningful or informative texts.

That is what makes them dangerous. That and the fact that the bastards selling them are marketing them for the jobs they’re least capable of doing, that is, providing reliable information.

(And that’s while they can still be trained on meaningful and informative texts written by humans — inasmuch as anything found on reddit, facebook, or xitter can be considered to be meaningful or informative —, but given that a higher and higher percentage of the text on the internet is being generated by LLMs soon enough it’ll be impossible to train new models on anything but 99% LLM generated garbage, at which point the whole bubble will implode, as anyone who’s wasted time, paper, and toner playing with a photocopier or anyone familiar with the phrase “garbage in, garbage out” will already have realised… which is probably why the LLM peddlers are ignoring robots.txt and copyright laws in a desperate effort to scrape whatever’s left of the bottom of the barrel.)

Hades didn’t really seem like my kind of game, so I torrented it to try it out. Then I bought it, and later Hades 2, too.

I’ve also bought some comics I’d previously read on the computer, too, if they were good enough and I’ve come across a nice edition.

https://www.youtube.com/watch?v=a6y8cqayjQI

curiosity (…) patience

Eh, I used to use those back in the day, but for the last decade or so I’ve been using mostly concentrated spite and it seems to work just fine (though I can’t wait for the AI bros to invent a computer that can feel pain… now that’ll make computer wrangling fun again!).

downright messianic

Yeah, tell that to the rest of the intelligent life in the galaxy…

Oh, wait, you can’t, because by the time humans got there these downright messianic robots had already murdered everything and hidden the evidence…

Asimov didn’t design the three laws to make robots safe.

He designed them to make robots break in ways that’d make Powell and Donovan’s lives miserable in particularly hilarious (for the reader, not the victims) ways.

(They weren’t even designed for actual safety in-world; they were designed for the appearance of safety, to get people to buy robots despite the Frankenstein complex.)

it was not the person calling the police.

At this point anyone calling the police in the US is a necessary accomplice, and guilty of conspiracy to commit murder, aggravated battery, and probably several other crimes.

Read the room, bot. 🤦‍♂️

To be fair, it also rusts when not under the rain.

I trust NASA

They’ve killed 21 astronauts so far (not counting X-15 pilots), most of them through criminal negligence…

They’ve launched dogs, monkeys, and whatnot before.

Sure, those were still way more skilled than Boeing executives, but there’s also uncrewed launches, so that’s no excuse.

Call them payload or cargo or ballast instead of astronauts if you want, what counts is launching the fuckers into space and (hopefully) not bringing them back.

Why rename the files when you could just categorise and index them…?

This seems unnecessarily destructive.

LLM’s are not AI, though. They’re just fancy auto-complete. Just bigger Elizas, no closer to anything remotely resembling actual intelligence.

Some of them are inventing completely new ways of doing things

No, they’re not. All the money is now on the LLM autocomplete chatbots.

Real progress on AI won’t resume until after the LLM bubble has burst. (And even then investors will probably be wary of putting money in AI for probably a few decades, because LLMs are being marked as AI despite having little to do with it.)

It’s quite depressing, really.

All the money’s going into the LLM bubble, so there won’t be any left for actual AI research until it bursts.

I’m not talking about “machines” or any other generic term.

I’m talking specifically about LLMs. And their limitations are evident. For instance, maths is one of the many things they can’t do (and will never be able to do in any efficient way).

We have indeed, developed programs that play chess better than people (though sadly, until the LLM bubble pops we probably won’t get any further). But they’re not LLMs, or anything resembling an LLM. Because one of the other many things an LLM can’t do is play games of skill. Or reason. Or solve puzzles. Or even have a concept of strategy.

LLMs, again, can only do one single thing. And that’s to pick up the one card from their deck that’s been picked up most often after the sequence of cards on the table according to their training model.

That’s all they do. That’s all they’ll ever be able to do. Because that’s how they work. And, sure, with that you can make it look like they’re holding a conversation (until you ask them something that isn’t in their model), but that’s it.

They’ll put words after another according to statistics (not, keep that in mind, according to meaning, or strategy, or anything like that; they don’t, and can’t know or care what the words mean, or whether the sentence they’ve put together makes any sense, or whether what it’s stating is true or false), and that’s that.

They won’t play chess, they won’t write good innovative code, they won’t write original stories, and they won’t drive your car.

We don’t need to know how what we call consciousness works to know that. We just need to know how LLMs work. And that we most definitely do.

Because there are many aspects of what we understand as “actual thinking” (understanding concepts, learning, or solving puzzles, for instance) that LLMs are fundamentally incapable of achieving no matter how larger or more complex we make them or how much we optimise them.

They do one single thing (which, granted, they do relatively well): they take an input, they apply it to every token in their training data, generating a score for each of them, and they output the one with the highest score. And that’s all they do.

And that’s why, for instance, you’ll never be able to make a LLM that’s any good at playing chess, because there simply wouldn’t be enough atoms in the universe for it to store all possible states of the game, which it would need to have in its training model in order to auto complete its next move (and that’s not even accounting for the actual score computation, both in space and time).

They’re a cool fancy gimmick, possibly useful in certain cases as long as you can account for their hallucinations, but they’re not any closer to actual intelligence than Eliza ever was.

LLMs are incapable of “recognising” any patterns they haven’t been trained on.

And they don’t really even recognise those, they’re just fancy auto complete engines, simply outputting the highest scored token from their training base based on their input.

They’re pattern matching machines; there’s no recognition, inner modelling of new knowledge, self referencing, or understanding of any kind, merely blind statistics.

They’re just bigger and fancier Eliza’s, and just as distant as Eliza was from any practical form of intelligence, artificial or natural.

While I personally do believe that achieving AGI¹, on a Turing machine is possible, LLMs and how they work are an excellent example in support of John Searle’s arguments against it with his Chinese room though experiment.

1— Or at least something equivalent to human intelligence, or better, in the measures by which we consider ourselves to be intelligent, though it’s arguable whether we can really be considered intelligent at all, or we’re just better, more complex, Chinese rooms.

Exactly, but LLMs are preventing further advances in AGI.