before anyone immediately smashes "downvote" and comes here arms raised, "AI" includes ML, as in models specifically trained for this, and models have been around for 20+ years. AI != LLMs
this post was submitted on 12 Jul 2026
73 points (86.1% liked)
Technology
86355 readers
3931 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 3 years ago
MODERATORS
One of the insanely powerful uses of AI is just parsing large amounts of data and looking for patterns. However it still requires human oversight and may overlook patterns also. I think this is the problem with AI in general: The vast majority of people can't be trusted to verify the results. Scientists are extremely thorough in general so that's probably fine.
I had a family member arguing with me about a particular piece of law that I happened to be very familiar with. They are actually an attorney. And they flaunted that as if it made them right. The problem is they didn't even actually review the law, they just typed it into chat GPT. So they were breaking the law based on advice from a chat bot.
the greatest power of machine learning algorithms is the source of its greatest drawback. they are essentially heuristic models of something, constructed in a way that they are much cheaper to execute than a traditional algorithmic approach. this cheapness results in error, which for a lot of applications is fine because you can refine/check the result with more accurate tools, but it also means you can never just trust it like you can with more traditional tools. this problem is baked into the technology so theres no amount of scale that will make it go away, as we've seen time and again with LLMs. failing to understand this is the mistake most people make when it comes to "AI", and results in all kinds of bad decision making. but in the hands of people who understand the limitations, machine learning can truly be a game changing technology. not chatbots though those are fucking stupid and i hope they go away when the bubble bursts.
I disagree with your assessment. Modern machine learning methods will result in a far more complex algorithm than any human could device on their own. It will be "cheap" to execute, but only because of the vast amount of data it's been trained on. That's the strength. The raw number of calculations it can do per second means it can sift through more data in a day than a human could in its lifetime.
The biggest danger is hidden local minimums that can potentially distort new results and findings. But that's not unique to machine learning. And you can let the AI grade its result with how certain it is. Automatically flag any result that falls below a certain threshold. And again, this is where the danger of local minimum come in. Because it can raise the certainty of results when maybe it shouldn't be so certain. Though you will find out quickly when results are false with high certainty. Then you go back and you keep working at it.
As of this particular topic of earthquakes. There are no traditional tools of accurately predicting them. We've never been able to do it before. Now they think this might be able to. So we will just have to wait and see if its predictions are accurate or not.
i can see that youve had some coolaid. i hope you can stop drinking and start recovering. a lot of what you said is gibberish, but throwing more data at machine learning models has been shown to result in diminishing returns, and doesnt change that the technology is inherently fallible. it might get very close to 100% reliable, but the hallucination problem will never fully go away. thats not the end of the world, but it requires machine learning algorithms to be paired with more deterministic tools to be sure of results. it happens with our own brains too, which is part of why we invented math and computers in the first place.
What is it you think we're talking about? This isn't some LLM. It's a very specific purposebuilt piece of software to analyse data from earthquakes and recordable shifts in the tectonic plates.
Also, LLMs doesn’t equal generalist trained chatbots.
How is that relevant here?
Not relevant to the article which isn’t about LLMs… but it IS relevant to “this isn’t about LLMs, don’t downvote!” Which implicitly implies LLM discussion is intrinsically worth downvoting.
As usual, the term AI is used for Machine Learning. Nothing to do with the Techbros large scale language model dead end. Sad that a success like this could be misconstrued to be a Techbro success.
They‘re based on the same principle and are therefore both AI or based on machine learning. The actual misconception comes from LLMs being treated as AI when they‘re just a product of machine learning. Both things are based on the same principles of pattern recognition and training data, though.
The difference here is that scientist use machine learning for something useful while tech bros use it to make word salad generators.
I wish they picked a better acronym than LLM, it's really awkward to say. Maybe then people wouldn't call everything "A.I.". It's the equivalent of calling everything from a phone to a desktop to a traffic light "a computer"
LLM is fine to say, not more awkward than element. And it is AI.
And I wish we'd call phones computers. It's been very profitable for corps for people to not realize that they are.
My head already has:
- LVM - Logical Volume Management
- LLVM - Low Level Virtual Machine
LLM keeps keep corrupted in my mouth by those two.
It's more awkward to say because it's "ell-ell-emm" not "el-em-ent". It's like that middle part of the alphabet that kids always fuck up "ell emm enn" and it's practically a tongue twister. It's a very unusual mouth movement for English speakers. Which is why many people started calling them "llamas" because the longer word is actually easier to say.
I hate that everyone just calls everything "AI" now. At this point, this term has basically lost its whole meaning. But I guess writing "Scientists Used Machine Learning to Find ..." is not as attention grabbing. Though "Machine Learning" is also a very broad term
"Fuck AI... Already did that... the sensors must of improved. There will always be some word salad to explain away the skepticism but AI has yet to turn a profit. If it ain’t making dollars then it ain’t makin cents. We just got a lot of suckers but eventualy they will dry up." - Put that in the history books
How about you read the article before you start being outraged over your own false assumptions