Chebucto Regional Softball Club

@miki @KatS oh right, and what was the sample size of the test?

An N of 1 is worth fuck all

@miki @KatS > If you still believe that there's no possible use for a tool that can get gold on a never-before-used set of math olympiad question given a few hours of access to a reasonably powerful computer, and that the existence of that tool will have no interesting impact on the world...

How reliable is that source? And if that's true, is it really reasonable to bet everything on this, and let this do all your work when a) you end up completely dependent on the tech and b) utterly destroy the environment in that process?

Real world problems may be less complex but might require much more context.

Oh, and don't get me started on accountability. There's a reason why curl is closing their bug bounty program.

@miki @KatS if i memorize every possible answer to a specific test, i can pass too. doesn't mean i know shit about fuck.

There's no actual thinking or reasoning involved (and no, reasoning models don't actually "reason"), so yeah, an LLM isn't actually intelligent, it just shows how flawed our tests for intelligence are.

To get some actual intelligence, thinking or reasoning involved, I'd reckon we'd have to fundamentally change something in the architecture of LLMs, and use a fuckton more computing resources for a single model, and considering how much energy the current tech already wastes, and the whole shtick that made LLMs (and more broadly generative AI) work in the first place is "we discovered that there comes a point where the output gets better when we throw rediculous amounts of compute resources on the problem", and it's already getting super difficult to run and maintain.

Honestly, either you're unreasonably optimistic, or you've never taken a look at how things actually work under the hood, but I really recommend you to take a closer look at the technology you praise so much.

A couple things you could take a look at (without an AI summarizer, otherwise you'd learn jack shit):

Attention is all you need, which is the paper that sparked all that AI craze and the development of GPT models and The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
, which takes a closer look and tests reasoning models to infer strengths and weaknesses of reasoning models with all sorts of levels in problem complexity.

Honestly, before you make any claims about where the tech could be and what it could do, you should have a look at how things actually work under the hood and have a rough idea of how things work, otherwise, no offense, you're just talking out of your arse.

@KatS

lmao same actually xD

Wanna be friends?

@miki

@KatS look @miki don't get me wrong but any time i've tried using LLMs for my work, which isn't just some fun side project but actual production-running code, LLMs have been way too unreliable. It also resulted in me knowing jack shit about my own code, which is poison for long term maintainability.

Since these models are just statistically determining the next most likely token based on training data and fine tuning, without any actual understanding or thought behind it, I seriously can't see this tech being reliable enough one day. (reliable compared to humans, i don't seek 100% reliable in this case, natural language is too imprecise for that anyways. i would expect "good enough" as "as good as a professional in the given field")

The other part of the equation is the amount of compute and electrical energy necessary to train and operate such a level, and on that level, there's no way in hell that shit is ever gonna be worth it, financially and environmentally.

i'm not expecting the "make job for phone operators easier", i expect the "when i dial a number, it should be at least as reliable and efficient at routing it correctly as a phone operator would be".

you can call me whatever you want, even llm denier if you need to, but autocorrect on steroids isn't worth exploiting other people's work or boiling our oceans.

@miki @KatS so you're betting unfoundedly that the tech is gonna work right one day?

@miki @KatS that explains a lot

@miki @KatS don't get the wrong idea, this is pure curiosity.

I had the suspicion that the less someone knows about LLMs or ML, the more they think the tech is capable of, but that suspicion must be false, since you're around

@miki @KatS damn, you're studying data science or ML?

How come you place so much trust in this tech? You must have a reason i presume

@miki i'm curious to know: how much do you know exactly about how LLMs/generative AI works?

@KatS

@miki @KatS we're not talking about influences, but more akin to "retracing".

Besides, there are real implications regarding free software licenses and AI generated slop, so it's not exclusively a moral dilemma, but a legal one too.

legal != the right thing to do necessarily, but mangling a bunch of intellectual property that's not yours through a statistical computer program isn't exactly comparable with an aspiring artist learning to draw.

@miki and the most important destinction, which i honestly didn't think I had to make, is: humans aren't tech

@miki oh yeah, and humans, as opposed to generative AI, can actuually think and reason.

And no, "reasoning" models don't actually reason, they're just trained so the output looks like it under the hood. You can read up on advantages and limitations of that here

@miki professional software engineers are usually more reliable than LLMs...

@miki in the short term, it might look like you're right, but in the long term, letting unreliable tech do your job is a bad idea. I'm sure I don't have to give more reasons, right?

@miki yeah once you stop caring about wether or not your code runs optimal, or reliable, or does what it's actually supposed to, or can be maintained properly, you can surely be 20x productive, you'll just regret it afterwards at some point (or someone else who has to fix and maintain this mess will)

@miki that's a reasonable middle ground we can somewhat agree on.

I haven't seen AI-generated code being the "force multiplier" some folks swear by, especially with newer things like the config changes in pipewire last year, but i guess ymmv

@miki without trying to convince you of anything (your stance on ai is yours, i'm not trying to change it), I can assure you that the reasons why many developers see generating production code with AI as a bad idea are not made up.

I am all for exchanging ideas between folks with different opinions, but this had to be said.

alr the sentence "the most human thing there is is learning and creativity. the least human thing there is is trying to automate that away." goes so hard imma drop it in my bio now

the whole ai-bro shtick about "ai democritizes art/programming/writing/etc" seemed always so bs to me, but i couldn't put it into words, but i think i now know how.

ai didn't democritize any of these things. People did. The internet did. if all these things weren't democritized and freely available on the internet before, there wouldn't have been any training data available in the first place.

the one single amazing thing that today's day and age brought us is, that you can learn anything at any time for free at your own pace.

like, you can just sit down, and learn sketching, drawing, programming, writing, basics in electronics, pcb design, singing, instruments, whatever your heart desires and apply and practice these skills. fuck, most devs on fedi are self taught.

the most human thing there is is learning and creativity. the least human thing there is is trying to automate that away.

(not to mention said tech failing at it miserably)