I'm sure the community will find something to hate about this as well, since this isn't an article about an LLM failing at something.

.

Usually when I see this it's using other machine learning approaches than LLM, and the researchers behind it are usually very careful not to use the term AI, as they are fully aware that this is not what they are doing.

There's huge potential in machine learning, but LLMs are very little more than bullshit generators, and generative AI is theft producing soulless garbage. LLMs are widely employed because they look impressive, but for anything that requires substance machine learning methods that have been around for years tend to perform better.

If you can identify cancer in x-rays using machine learning that's awesome, but that's very seperate from the AI hype machine that is currently running wild.

That's not research. That's simply surfacing tidbits it found on the net the happen to be true

.I've asked many questions of many llms in my chosen areas of interest and modest expertise , seeking more than basic knowledge( which it often surprisingly lacks ) it always has at least one error. Often so subtle it goes on noticed until it's too late.

.

So what you’re saying is that it’s good for research, because you can’t research what you don’t know about.

It’s good for giving starting points which is exactly what I meant.

Next time I’ll write a dissertation with hyper specifics because it seems it’s necessary every time LLMs are involved as there’s always someone looking to nitpick the statements.

No you rude fuck.

If i ask a simple question about a subject, let's say foraging as a I do that a lot. And it's wrong, it's friggin wrong.

I'll ask about a specific plant. Full disclosure this is one of my things. 40 years at it. Ok? No big stretch to think I know a thing or two.

So I ask about let's say, Japanese barberry. An invasive plant that is hated by many and rightly so at times. The question is , is it edible?

The answer given was no. The truth is the opposite. It is edible. Hell there's recipes online for barberry jam. Now don't go just eating them though. Smart to test one or two leaves to see if an individual is allergic. That's not part of the answer, that's foraging 101. But I digress.
The a.i was wrong and then argued about it until I pulled up all of the evidence. The a.i then admitted it was wrong, but who cares? It's not alive. Winning an argument with a.i is like beating oneself at poker.

Another example
I'll ask about intervals in music ( guitar teacher as my main profession now, as my passion for 48 years). It got the major scale intervals wrong.

I asked ask if yogurt can replace eggs as a binding agent to one of them (can't remember which, apologies ) and it said no. That's a friggin home ec tip that's been around for at least a century

People who give dissertations don't brag about it. Especially to make a point in a thread. It only makes one seem like a person who isn't confident in what they're saying so they drop a line that they feel will impress others. It doesn't

Others experience is as important, vital and real as yours, regarding the answers given by a.i , but you'll brush it off because you feel that some how you have more insight than others. You dont. You just have more time to pour through a.i's mistakes to massage it to getting something close to what you want. That shows an abundance of time available. Which means you aren't doing the things I'm talking about.

Or it means this is something you do for your job and it works for those specific needs . Which is fine, but your needs are not the world's. My need have been poorly met by that tool you spouse. Much like a rake won't help a guy digging a hole, a.i is the wrong tool for most jobs.

Which means your opinion of my evaluation of a. I results is skewed because you don't value others experience, no matter how intelligent you are. And that is a sign of ignorance.

I wish you a good day

Usually when I see this it's using other machine learning approaches than LLM, and the researchers behind it are usually very careful not to use the term AI, as they are fully aware that this is not what they are doing.

There's huge potential in machine learning, but LLMs are very little more than bullshit generators, and generative AI is theft producing soulless garbage. LLMs are widely employed because they look impressive, but for anything that requires substance machine learning methods that have been around for years tend to perform better.

If you can identify cancer in x-rays using machine learning that's awesome, but that's very seperate from the AI hype machine that is currently running wild.

Machine learning is a subset of the AI branch of computer science. I agree that the pop culture definition of AI is different than the computer science one, but the computer science one is still valid.

ETH Zurich and EPFL will release a large language model (LLM) developed on public infrastructure. Trained on the “Alps” supercomputer at the Swiss National Supercomputing Centre (CSCS), the new LLM marks a milestone in open-source AI and multilingual excellence.

• In late summer 2025, a publicly developed large language model (LLM) will be released — co-created by researchers at EPFL, ETH Zurich, and the Swiss National Supercomputing Centre (CSCS).
• This LLM will be fully open: This openness is designed to support broad adoption and foster innovation across science, society, and industry.
• A defining feature of the model is its multilingual fluency in over 1,000 languages.

A language model built for the public good

This summer, EPFL and ETH Zurich will release a large language model (LLM) developed on public infrastructure. Trained on the “Alps” supercomputer at the Swiss National Supercomputing Centre (CSCS), the new LLM marks a milestone in open-source AI and multilingual excellence.

(actu.epfl.ch)