Asklemmy

Here's some uses:

• skin cancer diagnoses with llms has a high success rate with a low cost. This is something that was starting to exist with older ai models, but llms do improve the success rate. source
• VLC recently unveiled a new feature of using ai to generate subtitles, i haven't used it but if it delivers then it's pretty nice
• for code generation, I agree it's more harmful than useful for generating full programs or functions, but i find it quite useful as a predictive text generator, it saves a few keystrokes. Not a game changer but nice. It's also pretty useful at generating test data so long as it's hard to create but easy (for a human) to validate.

"at worst it spits out garbage code that you spend more time debugging than if you had just written it by yourself."

I've not experienced this. Debugging for me is always faster than writing something entirely from scratch.

In the context of programming:

• Good for boilerplate code and variables naming when what you want is for the model to regurgitate things it has seen before.
• Short pieces of code where it's much faster to verify that the code is correct than to write the code yourself.
• Sometimes, I know how to do something but I'll wait for Copilot to give me a suggestion, and if it looks like what I had in mind, it gives me extra confidence in the correctness of my solution. If it looks different, then it's a sign that I might want to rethink it.
• It sometimes gives me suggestions for APIs that I'm not familiar with, prompting me to look them up and learn something new (assuming they exist).

There's also some very cool applications to game AI that I've seen, but this is still in the research realm and much more niche.

I have a very good friend who is brilliant and has slogged away slowly shifting the sometimes-shitty politics of a swing state's drug and alcohol and youth corrections policies from within. She is amazing, but she has a reading disorder and is a bit neuroatypical. Social niceties and honest emails that don't piss her bosses or colleagues off are difficult for her. She jumped on ChatGPT to write her emails as soon is it was available, and has never looked back. It's been a complete game changer for her. She no longer spends hours every week trying to craft emails that strike that just-right balance. She uses that time to do her job, now.

I need help getting started. I’m not an idea person. I can make anything you come up with but I can’t come up with the ideas on my own.

I’ve used it for an outline and then I rewrite it with my input.

Also, I used it to generate a basic UI for a project once. I struggle with the design part of programming so I generated a UI and then drew over the top of the images to make what I wanted.

I tried to use Figma but when you’re staring at a blank canvas it doesn’t feel any better.

I don’t think these things are worth the cost of AI ( ethically, financially, socially, environmentally, etc). Theoretically I could partner with someone who is good at that stuff or practice till I felt better about it.

People keep meaning different things when they say "Generative AI". Do you mean the tech in general, or the corporate AI that companies overhype and try to sell to everyone?

The tech itself is pretty cool. GenAI is already being used for quick subtitling and translating any form of media quickly. Image AI is really good at upscaling low-res images and making them clearer by filling in the gaps. Chatbots are fallible but they're still really good for specific things like generating testing data or quickly helping you in basic tasks that might have you searching for 5 minutes. AI is huge in video games for upscaling tech like DLSS which can boost performance by running the game at a low resolution then upscaling it, the result is genuinely great. It's also used to de-noise raytracing and show cleaner reflections.

Also people are missing the point on why AI is being invested in so much. No, I don't think "AGI" is coming any time soon, but the reason they're sucking in so much money is because of what it could be in 5 years. Saying AI is a waste of effort is like saying 3D video games are a waste of time because they looked bad in 1995. It will improve.

It's kinda handy if you don't want to take the time to write a boring email to your insurance or whatever.

Fake frames. Nvidia double benefits.

Note: Tis a joke, personally I think DLSS frame generation is cool, as every frame is "fake" anyway.

i’ve written bots that filter things for me, or change something to machine-readable formats

the most successful thing i’ve done is have a bot that parses a web page and figures out the date/time in standard format, gets a location if it’s listed in the description and geocodes it, and a few other fields to make an ical for pretty much any page

i think the important thing is that gen ai is good at low risk tasks that reduce but don’t eliminate human effort - changing something from having to do a bunch of data entry to skimming for correctness

AI saves time. There are few use cases for which AI is qualitatively better, perhaps none at all, but there are a great many use cases for which it is much quicker and even at times more efficient.

I'm sure the efficiency argument is one that could be debated, but it makes sense to me in this way: for production-level outputs AI is rarely good enough, but creates really useful efficiency for rapid, imperfect prototyping. If you have 8 different UX ideas for your app which you'd like to test, then you could rapidly build prototype interfaces with AI. Likely once you've picked the best one you'll rewrite it from scratch to make sure it's robust, but without AI then building the other 7 would use up too many man-hours to make it worthwhile.

I'm sure others will put forward legitimate arguments about how AI will inevitably creep into production environments etc, but logistically then speed and efficiency are undeniably helpful use cases.

making roguelike content. Mazes, monsters etc

Learning how to use Linux

I use it in a lot of tiny ways for photo-editing, Adobe has a lot of integration and 70% of it is junk right now but things like increasing sharpness, cleaning noise, and heal-brush are great with AI generation now.

I like using it to help get the ball rolling on stuff and organizing my thoughts. Then I do the finer tweaking on my own. Basically I kinda use a sliding scale of the longer it takes me to refine an AI output for smaller and smaller improvements is what determines when I switch to manual.

I generate D&D characters and NPCs with it, but that's not really a strong argument.

For programming though it's quite handy. Basically a smarter code completion that takes the already written stuff into account. From machine code through assembly up to higher languages, I think it's a logical next step to be able to tell the computer, in human language, what you actually are trying to achieve. That doesn't mean it is taking over while the programmer switches off their brain of course, but it already saved me quite some time.

I recently had to digitize dozens of photos from family scrapbooks, many of which had annoying novelty pattern borders cut out of the edges. Sure, I could have just cropped the photos more to hide the stupid zigzagged missing portions. But I had the beta version of Photoshop installed with the generative fill function, so I tried it. Half the time it was garbage, but the other half it filled in a bit of grass or sky convincingly enough that you couldn't tell the photo was damaged. +1 acceptable use case for generative AI, I guess.

I wish I could have an AI in my head that would do all the talking for me because socializing is so exhausting

There is no point. There are billions of points, because there are billions of people, and that's the point.

You know that there are hundreds or thousands of reasonable uses of generative AI, whether it's customer support or template generation or brainstorming or the list goes on and on. Obviously you know that. So I'm not sure that you're asking a meaningful question. People are using a tool to solve various problems, but you don't see the point in that?

If your position is that they should use other tools to solve their problems, that's certainly a legitimate view and you could argue for it. But that's not what you wrote and I don't think that's what you feel.

I use LLMs for search results when conventional search engines aren't providing relevant results, and then I can fact-check whatever answers the LLMs give me. Especially using them to ask questions that are easy to verify, like mathematical questions where I can check the validity of the answers. Or similarly programming questions where I can read through the solution, check the documentation for any functions used, and make sure the output is logical, and make any tweaks if the LLM gives a nearly-correct answer. I always ask LLMs to cite their sources so I can check those too.

I also sometimes use LLMs for formatting, like when I copy text off a PDF and the spacing is all funky.

I don't use LLMs for this, but I imagine that they would be a better replacement for previous automated translation tools. Translation seems to be one of the most obvious applications since LLMs are just language pattern recognition at the end of the day. Obviously for anything important they need to be checked by a human, but they would e.g. allow for people to participate in online communities where they don't speak the community's language.

Amazing for reading docs

I hate questions like this due to 1 major issue.

A generative ai with "error free" Output, is very differently useful than one that isn't.

Imagine an ai that would answer any questions objectively and unbiased, would that threaten job? Yeah. Would it be an huge improvement for human kind? Yeah.

Now imagine the same ai with a 10% bs rate, like how would you trust anything from it?

Currently generative ai is very very flawed. That is what we can evaluate and it is obvious. It is mostly useless as it produces mostly slop and consumes far more energy and water than you would expect.

A "better" one would be differently useful but just like killing half of the worlds population would help against climate change, the cost of getting there might not be what we want it to be, and it might not be worth it.

Current market practice, cost and results, lead me to say, it is effectively useless and probably a net negative for human kind. There is no legitimate usage as any usage legitimizes the market practice and cost given the results.

I think genAI would be pretty neat for ~~bit banging tests~~ fuzzing, aka. Throwing semi-random requests and/or signals at some device in the hopes of finding obscure edge-cases or security holes.

I treat it as a newish employee. I don't let it do important tasks without supervision, but it does help building something rough that I can work on.

In creative works like writing or art, it feels soulless and poor quality. In programming at best it’s a shortcut to avoid deeper learning, at worst it spits out garbage code that you spend more time debugging than if you had just written it by yourself.

I'd actually challenge both of these. The property of "soulessness" is very subjective, and AI art has won blind competitions. On programming, it's empirically made faster by half again even with the intrinsic requirement for debugging.

It's good at generating things. There are some things we want to generate. Whether we actually should, like you said, is another issue, and one that doesn't impact anyone's bottom line directly.

I'd say there are probably as many genuine use-cases for AI as there are people in denial that AI has genuine use-cases.

Top of my head:

• Text editing. Write something (e.g. e-mails, websites, novels, even code) and have an LLM rewrite it to suit a specific tone and identify errors.
• Creative art. You claim generative AI art is soulless and poor quality, to me, that indicates a lack of familiarity with what generative AI is capable of. There are tools to create entire songs from scratch, replace the voice of one artist with another, remove unwanted background noise from songs, improve the quality of old songs, separate/add vocal tracks to music, turn 2d models into 3d models, create images from text, convert simple images into complex images, fill in missing details from images, upscale and colourise images, separate foregrounds from backgrounds.
• Note taking and summarisation (e.g. summarising meeting minutes or summarising a conversation or events that occur).
• Video games. Imagine the replay value of a video game if every time you play there are different quests, maps, NPCs, unexpected twists, and different puzzles? The technology isn't developed enough for this at the moment, but I think this is something we will see in the coming years. Some games (Skyrim and Fallout 4 come to mind) have a mod that gives each NPC AI generated dialogue that takes into account the NPC's personality and history.
• Real time assistance for a variety of tasks. Consider a call centre environment as one example, a model can be optimised to evaluate calls based on language and empathy and correctness of information. A model could be set up with a call centre's knowledge base that listens to the call and locates information based on a caller's enquiry and tells an agent where the information is located (or even suggests what to say, though this is currently prone to hallucination).