Depending on the task, it’s quicker to verify the AI response than work through the blank page phase.
Because of I haven’t found anyone asking the same question on a search index, ChatGPT won’t tell me to just use Google or close my question as a duplicate when it’s not a duplicate.
They don’t give you the answer, they give you a rough idea of where to look for the answer.
I’ve used them to generate chunks of boilerplate code that was 80% of what I needed, because I knew what I needed and wanted to save time.
in my use case, the hallucinations are a good thing. I write fiction, in a fictional setting that will probably never actually become a book. If i like what gpt makes up, I might keep it.
Usually, I’ll have a conversation going into detail about a subject, this is me explaining the subject to gpt, then having gpt summarize everything it learned about the subject. I then plug that summary into my wiki of lore that nobody will ever see. Then move on to the next subject. Also gpt can identify potential connections between subjects that I didn’t think about, and wouldn’t have if it didn’t hallucinate them.
I just tried out Gemini.
I asked it several questions in the form of ‘are there any things of category x which also are in category y?’ type questions.
It would often confidently reply ‘No, here’s a summary of things that meet all your conditions to fall into category x, but sadly none also fall into category y’.
Then I would reply, ‘wait, you don’t know about thing gamma, which does fall into both x and y?’
To which it would reply ‘Wow, you’re right! It turns out gamma does fall into x and y’ and then give a bit of a description of how/why that is the case.
After that, I would say ‘… so you… lied to me. ok. well anyway, please further describe thing gamma that you previously said you did not know about, but now say that you do know about.’
And that is where it gets … fun?
It always starts with an apology template.
Then, if its some kind of topic that has almost certainly been manually dissuaded from talking about, it then lies again and says ‘actually, I do not know about thing gamma, even though I just told you I did’.
If it is not a topic that it has been manually dissuaded from talking about, it does the apology template and then also further summarizes thing gamma.
…
I asked it ‘do you write code?’ and it gave a moderately lengthy explanation of how it is comprised of code, but does not write its own code.
Cool, not really what I asked. Then command ‘write an implementation of bogo sort in python 3.’
… and then it does that.
…
Awesome. Hooray. Billions and billions of dollars for a shitty way to reform web search results into a coversational form, which is very often confidently wrong and misleading.
Idk why we have to keep re-hashing this debate about whether AI is a trustworthy source or summarizer of information when it’s clear that it isn’t - at least not often enough to justify this level of attention.
It’s not as valuable as the marketing suggests, but it does have some applications where it may be helpful, especially if given a conscious effort to direct it well. It’s better understood as a mild curiosity and a proof of concept for transformer-based machine learning that might eventually lead to something more profound down the road but certainly not as it exists now.
What is really un-compelling, though, is the constant stream of anecdotes about how easy it is to fool into errors. It’s like listening to an adult brag about tricking a kid into thinking chocolate milk comes from brown cows. It makes it seem like there’s some marketing battle being fought over public perception of its value as a product that’s completely detached from how anyone actually uses or understands it as a novel piece of software.
copilot did the same with basic math. just to test it I said “let’s say I have a 10x6 rectangle. what number would I have to divide width and height by, in order to end up with a rectangle that’s half the area?”
it said “in order to make it half, you should divide them by 2. so [pointlessly lengthy steps explaining the divisions]”
I said “but that would make the area 5x3 = 15 units which is not half the area of 60”
it said “you’re right! in order to … [fixing the answer to √2 using approximation”
I don’t know if I said it then, or after some other fucking nonsense but when I said “you’re useless” it had the fucking audacity to take offense and end the conversation!
like fuck off, you don’t get to have fake pride if you don’t have basic fake intelligence but use it in your description.
And then more money spent on adding that additional garbage filter to the beginning and the end of the process which certainly won’t improve the results.
sigh people do talk about this, they complain about it non-stop. These same people probably aren’t using it as intended, or are deliberately trying to farm a “gotcha” response. AI is a very neat tool which can do a lot of things well, but it’s important to recognize its limitations. I don’t use it for things I don’t understand because I won’t recognize if it’s spitting out nonsense, but for topics I do understand it’s hard to overstate how efficient and time saving it is.
“Give me a vegan recipe using <ingredient>” has been flawless. The recipes are decent, although they tend to use the same spices over and over.
The FuckAI people are valid for their concerns.
Unfortunately, their anger seems to constantly be misdirected at the weirdest things, instead of root issues.
Oh, there is plenty of hate for the hype cycle in general which is about as close to the root of the issue as you can get.
I sometimes use it to “convert” preexisting bulletpoints or informal notes into a professional sounding business email. I already know all the information so proofreading the final product doesn’t take a lot of time.
I think a lot of people who shit on AI forget that some people struggle with putting their thoughts into words. Especially if they aren’t writing in their native language.
Efficiency depends on the cost doesnt it?
The cost to me, the user, is nothing
Sorry to hear that you consider your time worthless. Have you tried therapy for that?
Probably because they’re not checking them
I’m convinced people who can’t tell when a chat bot is hallucinating are also bad at telling whether something else they’re reading is true or not. What online are you reading that you’re not fact checking anyway? If you’re writing a report you don’t pull the first fact you find and call it good, you need to find a couple citations for it. If you’re writing code, you don’t just write the program and assume it’s correct, you test it. It’s just a tool and I think most people are coping because they’re bad at using it
Yeah. GPT models are in a good place for coding tbh, I use it every day to support my usual practice, it definitely speeds things up. It’s particularly good for things like identifying niche python packages & providing example use cases so I don’t have to learn shit loads of syntax that I’ll never use again.
In other words, it’s the new version of copying code from Stack Overflow without going to the trouble of properly understanding what it does.
I know how to write a tree traversal, but I don’t need to because there’s a python module that does it. This was already the case before LLMs. Now, I hardly ever need to do a tree traversal, honestly, and I don’t particularly want to go to the trouble of learning how this particular python module needs me to format the input or whatever for the one time this year I’ve needed to do one. I’d rather just have something made for me so I can move on to my primary focus, which is not tree traversals. It’s not about avoiding understanding, it’s about avoiding unnecessary extra work. And I’m not talking about saving the years of work it takes to learn how to code, I’m talking about the 30 minutes of work it would take for me to learn how to use a module I might never use again. If I do, or if there’s a problem I’ll probably do it properly the second time, but why do it now if there’s a tool that can do it for me with minimum fuss?
Pft you must have read that wrong, its clearly turning them into master programmer one query at a time.
They’re trying not to lose money on the developments
Because in a lot of applications you can bypass hallucinations.
- getting sources for something
- as a jump off point for a topic
- to get a second opinion
- to help argue for r against your position on a topic
- get information in a specific format
In all these applications you can bypass hallucinations because either it’s task is non-factual, or it’s verifiable while promoting, or because you will be able to verify in any of the superseding tasks.
Just because it makes shit up sometimes doesn’t mean it’s useless. Like an idiot friend, you can still ask it for opinions or something and it will definitely start you off somewhere helpful.
All LLMs are text completion engines, no matter what fancy bells they tack on.
If your task is some kind of text completion or repetition of text provided in the prompt context LLMs perform wonderfully.
For everything else you are wading through territory you could probably do easier using other methods.
Also just searching the web in general.
Google is useless for searching the web today.
Not if you want that thing that everyone is on about. Don’t you want to be in with the crowd?! /s
so, basically, even a broken clock is right twice a day?
Yes, but for some tasks mistakes don’t really matter, like “come up with names for my project that does X”. No wrong answers here really, so an LLM is useful.
great value for all that energy it expends, indeed!
How is that faster than just picking a random name? Noone picks software based on name.
And yet virtually all of software has names that took some thought, creativity, and/or have some interesting history. Like the domain name of your Lemmy instance. Or Lemmy.
And people working on something generally want to be proud of their project and not name it the first thing that comes to mind, but take some time to decide on a name.
No, maybe more like, even a functional clock is wrong every 0.8 days.
https://superuser.com/questions/759730/how-much-clock-drift-is-considered-normal-for-a-non-networked-windows-7-pcThe frequency is probably way higher for most LLMs though lol
I only use it for complex searches with results I can usually parse myself like ‘‘list 30 typical household items without descriptions or explainations with no repeating items’’ kind of thing.
great value for all that energy it expends, indeed!
it’s because everyone stopped using it, right?
at least months ago?
It’s usually good for ecosystems with good and loads of docs. Whenever docs are scarce the results become shitty. To me it’s mostly a more targeted search engine without the crap (for now)
Because most people are too lazy to bother with making sure the results are accurate when they sound plausible. They want to believe the hype, and lack critical thinking.
I don’t want to believe any hype! I just want to be able to ask “hey Chatgtp, I’m looking for a YouTube video by technology connections where he discusses dryer heat pumps.” And not have it spit out "it’s called “the neat ways your dryer heat pumps save energy!”
And it is not, that video doesn’t exist. And it’s even harder to disprove it on first glance because the LLM is mimicing what Alex would have called the video. So you look and look with your sisters very inefficient PS4 controller-to-youtube interface… And finally ask it again and it shy flowers you…
But I swear he talked about it ?!?! Anyone?!?
He hasn’t
I think in a recent video he mentioned he will soon, but he hasn’t done a video with even a segment on best pumps in dryers yet
Fairly confident in this, recently finished a rewatch of basically all his content
This sound awfully familiar, like almost exactly what people were saying about Wikipedia 20 years ago…
Those people were wrong because wikipedia requires actual citations from credible sources, not comedic subreddits and infowars. Wikipedia is also completely open about the information being summarized, both in who is presenting it and where someone can confirm it is accurate.
AI is a presented to the user as a black box and tries to be portray it as equivalent to human with terms like ‘hallucinations’ which really mean ‘is wrong a bunch, lol’.
Pretty weak analogy. Wikipedia was technologically trivial and did a really good job of avoiding vested interests. Also the hype is orders of magnitude different, noone ever claimed Wikipedia was going to lead to superhuman intelligences or to replacement of swathes of human creative/service workers.
Actually since you mention it, my hot take is that Wikipedia might have been a more significant step forward in AI than openAI/latest generation LLMs. The creation of that corpus is hugely valuable in training and benchmarking models of natural language. Also it actually disrupted an industry (conventional encyclopedias) in a way that I’m struggling to think of anything that LLMs has replaced in the same way thus far.
Remember when you had to have extremely niche knowledge of “banks” in a microcontroller to be able to use PWM on 2 pins with different frequencies?
Yes, I remember what a pile of shit it was to try and find out why xyz is not working while x and y and z work on their own. GPT usually gets me there after some tries. Not to mention how much faster most of the code is there, from A to Z, with only little to tweak to get it where I want (since I do not want to be hyper specific and/or it gets those details wrong anyway, as would a human without massive context).
Gippity is pretty good at getting me 90% of the way there.
It usually sets me up with at least all the terms and etc I now know to google, whereas before I wouldnt even know what I am looking for in the first place.
Also not gonna lie, search engines are even worse than gippity for accuracy often.
And Ive had to fight with so many cases of garbage documentation lately that gippity genuinely does the job better, because it has all the random comments from issues and solutions in its data.
Usually once I have my sort of key terms I need to dig into, I can use youtube/google and get more specific information though, and thats the last 10%
