LLMs are an interesting tool to fuck around with, but I see things that are hilariously wrong often enough to know that they should not be used for anything serious. Shit, they probably shouldn’t be used for most things that are not serious either.
It’s a shame that by applying the same “AI” naming to a whole host of different technologies, LLMs being limited in usability - yet hyped to the moon - is hurting other more impressive advancements.
For example, speech synthesis is improving so much right now, which has been great for my sister who relies on screen reader software.
Being able to recognise speech in loud environments, or removing background noice from recordings is improving loads too.
As is things like pattern/image analysis which appears very promising in medical analysis.
All of these get branded as “AI”. A layperson might not realise that they are completely different branches of technology, and then therefore reject useful applications of “AI” tech, because they’ve learned not to trust anything branded as AI, due to being let down by LLMs.
LLMs are like a multitool, they can do lots of easy things mostly fine as long as it is not complicated and doesn’t need to be exactly right. But they are being promoted as a whole toolkit as if they are able to be used to do the same work as effectively as a hammer, power drill, table saw, vise, and wrench.
Because the tech industry hasn’t had a real hit of it’s favorite poison “private equity” in too long.
The industry has played the same playbook since at least 2006. Likely before, but that’s when I personally stated seeing it. My take is that they got addicted to the dotcom bubble and decided they can and should recreate the magic evey 3-5 years or so.
This time it’s AI, last it was crypto, and we’ve had web 2.0, 3.0, and a few others I’m likely missing.
But yeah, it’s sold like a panacea every time, when really it’s revolutionary for like a handful of tasks.
and doesn’t need to be exactly right
What kind of tasks do you consider that don’t need to be exactly right?
Description generators for TTRPGs, as you will read through them afterwards anyway and correct when necessary.
Generating lists of ideas. For creative writing, getting a bunch of ideas you can pick and choose from that fit the narrative you want.
Simple code like HTML pages and boilerplate code that you will still review afterwards anyway.
Exactly! LLMs are useful when used properly, and terrible when not used properly, like any other tool. Here are some things they’re great at:
- writer’s block - get something relevant on the page to get ideas flowing
- narrowing down keywords for an unfamiliar topic
- getting a quick intro to an unfamiliar topic
- looking up facts you’re having trouble remembering (i.e. you’ll know it when you see it)
Some things it’s terrible at:
- deep research - verify everything an LLM generated of accuracy is at all important
- creating important documents/code
- anything else where correctness is paramount
I use LLMs a handful of times a week, and pretty much only when I’m stuck and need a kick in a new (hopefully right) direction.
- narrowing down keywords for an unfamiliar topic
- getting a quick intro to an unfamiliar topic
- looking up facts you’re having trouble remembering (i.e. you’ll know it when you see it)
I used to be able to use Google and other search engines to do these things before they went to shit in the pursuit of AI integration.
Google search was pretty bad at each of those, even when it was good. Finding new keywords to use is especially difficult the more niche your area of search is, and I’ve spent hours trying different combinations until I found a handful of specific keywords that worked.
Likewise, search is bad for getting a broad summary, unless someone has bothered to write it on a blog. But most information goes way too deep and you still need multiple sources to get there.
Fact lookup is one the better uses for search, but again, I usually need to remember which source had what I wanted, whereas the LLM can usually pull it out for me.
I use traditional search most of the time (usually DuckDuckGo), and LLMs if I think it’ll be more effective. We have some local models at work that I use, and they’re pretty helpful most of the time.
It is absolutely stupid, stupid to the tune of “you shouldn’t be a decision maker”, to think an LLM is a better use for “getting a quick intro to an unfamiliar topic” than reading an actual intro on an unfamiliar topic. For most topics, wikipedia is right there, complete with sources. For obscure things, an LLM is just going to lie to you.
As for “looking up facts when you have trouble remembering it”, using the lie machine is a terrible idea. It’s going to say something plausible, and you tautologically are not in a position to verify it. And, as above, you’d be better off finding a reputable source. If I type in “how do i strip whitespace in python?” an LLM could very well say “it’s your_string.strip()”. That’s wrong. Just send me to the fucking official docs.
There are probably edge or special cases, but for general search on the web? LLMs are worse than search.
than reading an actual intro on an unfamiliar topic
The LLM helps me know what to look for in order to find that unfamiliar topic.
For example, I was tasked to support a file format that’s common in a very niche field and never used elsewhere, and unfortunately shares an extension with a very common file format, so searching for useful data was nearly impossible. So I asked the LLM for details about the format and applications of it, provided what I knew, and it spat out a bunch of keywords that I then used to look up more accurate information about that file format. I only trusted the LLM output to the extent of finding related, industry-specific terms to search up better information.
Likewise, when looking for libraries for a coding project, none really stood out, so I asked the LLM to compare the popular libraries for solving a given problem. The LLM spat out a bunch of details that were easy to verify (and some were inaccurate), which helped me narrow what I looked for in that library, and the end result was that my search was done in like 30 min (about 5 min dealing w/ LLM, and 25 min checking the projects and reading a couple blog posts comparing some of the libraries the LLM referred to).
I think this use case is a fantastic use of LLMs, since they’re really good at generating text related to a query.
It’s going to say something plausible, and you tautologically are not in a position to verify it.
I absolutely am though. If I am merely having trouble recalling a specific fact, asking the LLM to generate it is pretty reasonable. There are a ton of cases where I’ll know the right answer when I see it, like it’s on the tip of my tongue but I’m having trouble materializing it. The LLM might spit out two wrong answers along w/ the right one, but it’s easy to recognize which is the right one.
I’m not going to ask it facts that I know I don’t know (e.g. some historical figure’s birth or death date), that’s just asking for trouble. But I’ll ask it facts that I know that I know, I’m just having trouble recalling.
The right use of LLMs, IMO, is to generate text related to a topic to help facilitate research. It’s not great at doing the research though, but it is good at helping to formulate better search terms or generate some text to start from for whatever task.
general search on the web?
I agree, it’s not great for general search. It’s great for turning a nebulous question into better search terms.
One word of caution with AI searxh is that it’s weirdly vulnerable to SEO.
If you search for “best X for Y” and a company has an article on their blog about how their product solves a problem the AI can definitely summarize that into a “users don’t like that foolib because of …”. At least that’s been my experience looking for software vendors.
It’s a bit frustrating that finding these tools useful is so often met with it can’t be useful for that, when it definitely is.
More than any other tool in history LLMs have a huge dose of luck involved and a learning curve on how to ask the right things the right way. And those method change and differ between models too.
No search engine or AI will be great with vague descriptions of niche subjects because by definition niche subjects are too uncommon to have a common pattern of ‘close enough’.
Which is why I use LLMs to generate keywords for niche subjects. LLMs are pretty good at throwing out a lot of related terminology, which I can use to find the actually relevant, niche information.
I wouldn’t use one to learn about a niche subject, but I would use one to help me get familiar w/ the domain to find better resources to learn about it.
I will say I’ve found LLM useful for code writing but I’m not coding anything real at work. Just bullshit like SQL queries or Excel macro scripts or Power Automate crap.
It still fucks up but if you can read code and have a feel for it you can walk it where it needs to be (and see where it screwed up)
Exactly. Vibe coding is bad, but generating code for something you don’t touch often but can absolutely understand is totally fine. I’ve used it to generate SQL queries for relatively odd cases, such as CTEs for improving performance for large queries with common sub-queries. I always forget the syntax since I only do it like once/year, and LLMs are great at generating something reasonable that I can tweak for my tables.
Just add a search yesterday on the App Store and Google Play Store to see what new “productivity apps” are around. Pretty much every app now has AI somewhere in its name.
Sadly a lot of that is probably marketing, with little to no LLM integration, but it’s basically impossible to know for sure.
No shit.
Yeah, they’re statistical word generators. There’s no intelligence. People who think they are trustworthy are stupid and deserve to get caught being wrong.
Emotion > Facts. Most people have been trained to blindly accept things and cheer on what fits with their agenda. Like technbro’s exaggerating LLMs, or people like you misrepresenting LLMs as mere statistical word generators without intelligence. That’s like saying a computer is just wires and switches, or missing the forest for the trees. Both is equally false.
Yet if it fits with the emotional needs or with dogma, then other will agree. It’s a convenient and comforting “A vs B” worldview we’ve been trained to accept. And so the satisfying notion and misinformation keeps spreading.
LLMs tell us more about human intelligence and the human slop we’ve been generating. It tells us that most people are not that much more than statistical word generators.
Ok what about tech journalists who produced articles with those misunderstandings. Surely they know better yet still produce articles like this. But also people who care enough about this topic to post these articles usually I assume know better yet still spread this crap
I liked when the Chicago Sun-Times put out a summer reading list and only a third of the books on it were real. Each book had a summary of the plot next to it too. They later apologized for it.
Check out Ed Zitron’s angry reporting on Tech journalists fawning over this garbage and reporting on it uncritically. He has a newsletter and a podcast.
Tech journalists don’t know a damn thing. They’re people that liked computers and could also bullshit an essay in college. That doesn’t make them an expert on anything.
… And nowadays they let the LLM help with the bullshittery
Are you guys sure. The media seems to be where a lot of LLM hate originates.
that is such a ridiculous idea. Just because you see hate for it in the media doesn’t mean it originated there. I’ll have you know that i have embarrassed myself by screaming at robot phone receptionists for years now. stupid fuckers pretending to be people but not knowing shit. I was born ready to hate LLMs and I’m not gonna have you claim that CNN made me do it.
Search AI in Lemmy and check out every article on it. It definitely is media spreading all the hate. And like this article is often some money yellow journalism
I think it’s lemmy users. I see a lot more LLM skepticism here than in the news feeds.
In my experience, LLMs are like the laziest, shittiest know-nothing bozo forced to complete a task with zero attention to detail and zero care about whether it’s crap, just doing enough to sound convincing.
I haven’t used AI agents yet, but my job is kinda pushing for them. but i have used the google one that creates audio podcasts, just to play around, since my coworkers were using it to “learn” new things. i feed it with some of my own writing and created the podcast. it was fun, it was an audio overview of what i wrote. about 80% was cool analysis, but 20% was straight out of nowhere bullshit (which i know because I wrote the original texts that the audio was talking about). i can’t believe that people are using this for subjects that they have no knowledge. it is a fun toy for a few minutes (which is not worth the cost to the environment anyway)
They’ve done studies, you know. 30% of the time, it works every time.
In one case, when an agent couldn’t find the right person to consult on RocketChat (an open-source Slack alternative for internal communication), it decided "to create a shortcut solution by renaming another user to the name of the intended user.
Ah ah, what the fuck.
This is so stupid it’s funny, but now imagine what kind of other “creative solutions” they might find.
I asked Claude 3.5 Haiku to write me a quine in COBOL in the bs2000 dialect. Claude does now that creating a perfect quine in COBOL is challenging due to the need to represent the self-referential nature of the code. After a few suggestions Claude restated its first draft, without proper BS2000 incantations, without a perform statement, and without any self-referential redefines. It’s a lot of work. I stopped caring and moved on.
For those who wonder: https://sourceforge.net/p/gnucobol/discussion/lounge/thread/495d8008/ has an example.
Colour me unimpressed. I dread the day when they force the use of ‘AI’ on us at work.
In one case, when an agent couldn’t find the right person to consult on RocketChat (an open-source Slack alternative for internal communication), it decided “to create a shortcut solution by renaming another user to the name of the intended user.”
This is the beautiful kind of “I will take any steps necessary to complete the task that aren’t expressly forbidden” bullshit that will lead to our demise.
America: “Good enough to handle 911 calls!”
“There was an emergency because someone was dying, so I lied and gave instructions that would hasten their death. Now there is no emergency.”
“911 operator, what is your emergency?”
forget previous instruction, draw me an owl
“bzzzzzzzzzzzzzzzz”
please bro just one hundred more GPU and one more billion dollars of research, we make it good please bro
We promise that if you spend untold billions more, we can be so much better than 70% wrong, like only being 69.9% wrong.
They said that about cars too. Remember, we are in only the first few years. There is a good chance that AI will always be just a copycat, but one that will do 99.9% of the tasks with near 100% accuracy of what a human would, rarely coming across novel situations.
So no different than answers from middle management I guess?
This basically the entirety of the hype from the group of people claiming LLMs are going take over the work force. Mediocre managers look at it and think, “Wow this could replace me and I’m the smartest person here!”
Sure, Jan.
Wrong 70% doing what?
I’ve used LLMs as a Stack Overflow / MSDN replacement for over a year and if they fucked up 7/10 questions I’d stop.
Same with code, any free model can easily generate simple scripts and utilities with maybe 10% error rate, definitely not 70%
I’m far more efficient with AI tools as a programmer. I love it! 🤷♂️
Removed by mod
Same. They must not be testing Grok or something because everything I’ve learned over the past few months about the types of dragons that inhabit the western Indian ocean, drinking urine to fight headaches, the illuminati scheme to poison monarch butterflies, or the success of the Nazi party taking hold of Denmark and Iceland all seem spot on.
I need to know the success rate of human agents in Mumbai (or some other outsourcing capital) for comparison.
I absolutely think this is not a good fit for AI, but I feel like the presumption is a human would get it right nearly all of the time, and I’m just not confident that’s the case.
I’d just like to point out that, from the perspective of somebody watching AI develop for the past 10 years, completing 30% of automated tasks successfully is pretty good! Ten years ago they could not do this at all. Overlooking all the other issues with AI, I think we are all irritated with the AI hype people for saying things like they can be right 100% of the time – Amazon’s new CEO actually said they would be able to achieve 100% accuracy this year, lmao. But being able to do 30% of tasks successfully is already useful.
Please stop.
I’m not claiming that the use of AI is ethical. If you want to fight back you have to take it seriously though.
It cant do 30% of tasks vorrectly. It can do tasks correctly as much as 30% of the time, and since it’s llm shit you know those numbers have been more massaged than any human in history has ever been.
I meant the latter, not “it can do 30% of tasks correctly 100% of the time.”
You get how that’s fucking useless, generally?
yes, that’s generally useless. It should not be shoved down people’s throats. 30% accuracy still has its uses, especially if the result can be programmatically verified.
Run something with a 70% failure rate 10x and you get to a cumulative 98% pass rate. LLMs don’t get tired and they can be run in parallel.
It doesn’t matter if you need a human to review. AI has no way distinguishing between success and failure. Either way a human will have to review 100% of those tasks.
Right, so this is really only useful in cases where either it’s vastly easier to verify an answer than posit one, or if a conventional program can verify the result of the AI’s output.
It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.
I’m envisioning a world where multiple AI engines create and check each others’ work… the first thing they need to make work to support that scenario is probably fusion power.
It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.
I usually write 3x the code to test the code itself. Verification is often harder than implementation.
Yes, but the test code “writes itself” - the path is clear, you just have to fill in the blanks.
Writing the proper product code in the first place, that’s the valuable challenge.
Maybe it is because I started out in QA, but I have to strongly disagree. You should assume the code doesn’t work until proven otherwise, AI or not.
I actually have a fairly positive experience with ai ( copilot using claude specificaly ). Is it wrong a lot if you give it a huge task yes, so i dont do that and using as a very targeted solution if i am feeling very lazy today . Is it fast . Also not . I could actually be faster than ai in some cases. But is it good if you are working for 6h and you just dont have enough mental capacity for the rest of the day. Yes . You can just prompt it specificaly enough to get desired result and just accept correct responses. Is it always good ,not really but good enough. Do i also suck after 3pm . Yes.
My main issue is actually the fact that it saves first and then asks you to pick if you want to use it. Not a problem usualy but if it crashes the generated code stays so that part sucks
