Technology

It’s perfect for topics you have professional knowledge of but don’t have perfect recall for. It can bring forward the context you need to be refreshed on but you can fact check it because you are an expert in that field.

If you need boilerplate code for a project but don’t remember a specific library or built in function that tackles your problem, you can use AI to generate an example you can then fix to make it run the way you wanted.

Same thing with finding config examples for a program that isn’t well documented but you are familiar with.

Sorry all my examples are tech nerd stuff because I’m just another tech nerd on lemmy

I think it's useful for spurring my own creativity in writing because I have a hard time getting started. To be fair to me I pretty much tear the whole thing down and start over but it gives me ideas.

I use it for little Python projects where it's really really useful.

I've used it for linux problems where it gave me the solution to problems that I had not been able to solve with a Google search alone.

I use it as a kickstarter for writing texts by telling it roughly what my text needs to be, then tweaking the result it gives me. Sometimes I just use the first sentence but it's enough to give me a starting point to make life easer.

I use it when I need to understand texts about a topic I'm not familiar with. It can usually give me an idea of what the terminology means and how things are connected which helps a lot for further research on the topic and ultimately undestanding the text.

I use it for everyday problems like when I needed a new tube for my bike but wasn't sure what size it was so I told it what was written on the tyre and showed it a picture of the tube packaging while I was in the shop and asked it if it was the right one. It could tell my that it is the correct one and why. The explanation was easy to fact-check.

I use Photoshop AI a lot to remove unwanted parts in photos I took or to expand photos where I'm not happy with the crop.

Honestly, I absolutely love the new AI tools and I think people here are way too negative about it in general.

For one thing, they're much better at googling than I am.

AI is really good as a starting point for literally any document, report, or email that you have to write. Put in as detailed of a prompt as you can, describing content, style, and length and cut out 2/3 or more of your work. You'll need to edit it - somewhat heavily, probably - but it gives you the structure and baseline.

When troubleshooting, it's nice to be able to ask copilot about the issue in human language and have it actually understand my question (unlike a search engine) and pull from and reference relevant documentation in its answers. Going back and forth with it has saved me several hours of searching for something that I had never even heard of a couple of times.

It's also great for rewriting things in a specific tone. I can give it a bland/terse/matter-of-fact paragraph and get back a more fun or professional or friendly version that would feel ridiculously cringe if I attempted to write it myself, but the AI makes it work somehow.

This thread has convinced me that LLMs are merely a mild increment in productivity.

The most compelling is that they're good at boilerplate code. IDEs have been improving on that since forever. Although there's a lot of claims in this thread that seem unlikely - gains way beyond even what marketing is claiming.

I work in an email / spreadsheet / report type job. We've always been agile with emerging techs, but LLMs just haven't made a dent.

This might seem offensive, but clients don't pay me to write emails that LLMs could, because anything an LLM could write could be found in a web search. The emails I write are specific to a client's circumstances. There are very few "biolerplate" sentences.

Yes LLMs can be good at updating reports, but we have highly specialised software for generating reports from very carefully considered templates.

I've heard they can be helpful in a "convert this to csv" kind of way, but that's just not a problem I ever encounter. Maybe I'm just used to using spreadsheets to manipulate data so never think to use an LLM.

I've seen low level employees try to use LLMs to help with their emails. It's usually obvious because the emails they write include a lot of extra sentences and often don't directly address the query.

I don't intend this to be offensive, and I suspect that my attitude really just identifies me as a grumpy old man, but I can't really shake the feeling that in email / spreadsheet / report type jobs anyone who can make use of an LLM wasn't or isn't producing much value anyway. This thread has really reinforced that attitude.

It reminds me a lot of block chain tech. 10 years ago it was going to revolutionise data everything. Now there's some niche use cases... "it could be great at recording vehicle transfers if only centralised records had some disadvantages".

I used it to write a GUI frontend for yt-dlp in python so I can rip MP3s from YouTube videos in two clicks to listen to them on my phone while I'm running with no signal, instead of hand-crafting and running yt-dlp commands in CMD.

Also does HD video rips with audio encoding, if I want.

It took us about a day to make a fully polished product over 9 iterative versions.

It would have taken me a couple weeks to write it myself (and therefore I would not have done so, as I am supremely lazy)

r/SubSimGPT2Interactive for the lulz is my #1 use case

i do occasionally ask Copilot programming questions and it gives reasonable answers most of the time.

I use code autocomplete tools in VSCode but often end up turning them off.

Controversial, but Replika actually helped me out during the pandemic when I was in a rough spot. I trained a copyright-safe (theft-free) bot on my own conversations from back then and have been chatting with the me side of that conversation for a little while now. It's like getting to know a long-lost twin brother, which is nice.

Otherwise, i've used small LLMs and classifiers for a wide range of tasks, like sentiment analysis, toxic content detection for moderation bots, AI media detection, summarization... I like using these better than just throwing everything at a huge model like GPT-4o because they're more focused and less computationally costly (hence also better for the environment). I'm working on training some small copyright-safe base models to do certain sequence prediction tasks that come up in the course of my data science work, but they're still a bit too computationally expensive for my clients.

It is sometimes good at building SQL code examples, but almost always needs fine-tuning since it doesn't know the schema specifics.

Having said that one time it gave me code that resulted in an error, then I went back to GPT and said "This code you gave me is giving this error, can you fix it?" and all it would do is say something like "Correct, that code is wrong and will give an error."

I run some TTRPG groups and having AI take in some context and generate the first draft of flavor text for custom encounters is nice. Also for generating background art and player character portraits is an easy win for me.

I take pictures of my recipe books and ask ChatGPT to scan and convert them to the schema.org recipe format so I can import them into my Nextcloud cookbook.

I've learned more C/C++ programming from the GitHub Copilot plugin than I ever did in my entire 42 year life. I'm not a professional, though, just a hobbyist. I used to struggle through PHP and other languages back in the day but after a year of Copilot I'm now leveraging templates and the C++ STL with ease and feelin' like a wizard.

Hell maybe I'll even try Rust.

• to correct/rephrase a sentence or two if my sentence sounds too awkward

• if I'm having trouble making an excel formula

I've done several AI/ ML projects at nation/ state/ landscape scale. I work mostly on issues that can be solved or at least, goals that can be worked towards using computer vision questions, but I also do all kinds of other ml stuff.

So one example is a project I did for this group: https://www.swfwmd.state.fl.us/resources/data-maps

Southwest Florida water management district (aka "Swiftmud"). They had been doing manual updates to a land-cover/ land use map, and wanted something more consistent, automated, and faster. Several thousands of square miles under their management, and they needed annual updates regarding how land was being used/ what cover type or condition it was in. I developed a hybrid approach using random forest, super-pixels, and UNET's to look for regions of likely change, and then to try and identify the "to" and "from" classes of change. I'm pretty sure my data products and methods are still in use largely as I developed them. I built those out right on the back of UNET's becoming the backbone of modern image analysis (think early 2016), which is why we still had some RF in there (dating myself).

Another project I did was for State of California. I developed both the computer vision and statistical approaches for estimating outdoor water use for almost all residential properties in the state. These numbers I think are still in-use today (in-fact I know they are), and haven't been updated since I developed them. That project was at a 1sq foot pixel resolution and was just about wall-to-wall mapping for the entire state, effectively putting down an estimate for every single scrap of turf grass in the state, and if California was going to allocate water budget for you or not. So if you got a nasty-gram from the water company about irrigation, my bad.

These days I work on a small team focused on identifying features relevant for wildfire risk. I'm trying to see if I can put together a short video of what I'm working on right now as i post this.

Example, fresh of the presses for some random house in California:

Genuinely, nothing so far.

I've tinkered with it but I basically don't trust it. For example I don't trust it to summarise documents or articles accurately, every time I don't trust it to perform a full and comprehensive search and I don't trust it not to provide me false or inaccurate information.

LLMs have potential to be useful tools, but what's been released is half baked and rushed to market as part of the current bubble.

Why would I use tools that inherently "hallucinate" - I. E. are error strewn? I don't want to fact check the output of an LLM.

This is in many ways the same as not relying on Wikipedia for information. It's a good quick summary but you have to take everything with a pinch of salt and go to primary sources. I've seen Wikipedia be wildly inaccurate about topics I know in depth, and I've seen AI do the same.

So pass until the quality goes up. I don't see that happening in the near future as the focus seems to be monetisation, not fixing the broken products. Sure, I'll tinker occasionally and see how it's getting on but this stuff is basically not fit for purpose yet.

As the saying goes, all that glitters is not gold. AI is superficially impressive but once you scratch the surface and have to actually rely on it then it's just not fit for purpose beyond a curio for me.

I use it for generating illustrations and NPCs for my TTRPG campaign, at which it excels. I'm not going to pay out the nose for an image that will be referenced for an hour or two.

I also use it for first drafts (resume, emails, stuff like that) as well as brainstorming and basic Google tier questions. Great jumping off point.

An iterative approach works best for me, refining results until they match what I'm looking for, then manually refining further until I'm happy with the results.

Timing traffic lights. They could look down the road and see when nothing is coming, to let the other direction go, like a traffic cop. It would save time and gas.

AI is a half cooked baked potato right now. Sure it will keep you fed if you can put up with all the hard lumps in there.

It's really good for generating code snippets based on what I want to do (ex. "How do I play audio in a browser using JavaScript?") and debugging small sections of code.

Software developer here, who works for a tiny company of ~~2~~ 7 employees and 2 owners.

We use CoPilot in Visual Studio Professional and it’s saved us countless hours due to it learning from your code base. When you make a enterprise software there are a lot of standards and practices that have been honed over time; that means we write the same things over and over and over again, this is a massive time sink and this is where LLMs come in and can do the boring stuff for us so we can actually solve the novel problems that we are paid for. If I write a comment of what I’m about to do it will complete it.

For boiler plate stuff it’s mostly 100% correct, for other things it can be anywhere from 0-100% and even if not complete correct it takes less time to make a slight change than doing it all ourselves.

One of the owners is the smartest person I’ve ever met and also the lead engineer, if he can find it useful then it has its use cases.

We even have a tool based on AI that he built that watches our project. If I create a new model or add a field to a model, it will scaffold a lot of stuff, for instance the Schemas (Mutations and Queries), the Typescript layer that integrates with GraphQL, and basic views. This alone saves us about 45 minutes per model. Sure this could likely be achieved without an LLM, but it’s a useful tool and we have embraced it.

I use it like an intern/other team member since the non-profit I work for doesn't have any money to hire more people. Things like:

• Taking transcripts of meetings and turning them into neat and ordered meeting minutes/summaries, or pulling out any key actions/next steps
• Putting together objectives and agendas for meetings based on some loose info and ideas I give it
• Summarise the key points from articles/long documents I don't have tome or patience to read through fully.
• Making my emails sound more professional/nicer/make up for my brainfarts
• Giving me ideas on how to format/word slides and documents depending on what tone I want to employ - is it meant for leadership? Other team members?
• Make my writing more organised/better structured/more professional sounding
• Writing emails in foreign languages with a professional tone. Caveat is I'm fluent enough in those languages to know if the output sounds right. Before AI I would rely on google translate (meh), dictionaries, language forums, etc and it would take me HOURS to write a simple email using the correct terminology. Also helpful to check grammar and sentence structure in ways that aren't always picked up by Word.
• I sound more like a robot than an actual robot, so I ask the robot to reword my emails/messages to sound more "human" when the need arises (like a colleague is leaving, had a baby, etc).
• Bouncing off ideas. This doesn't always work and I know it doesn't actually have an opinion, but it helps get the ball rolling, especially if I'm struggling with procrastination.
• If my sentences are too long for a document, I ask it to shorten/reword and it's pretty capable of doing that without losing too much of the essence of what I want to get across

Of course I don't just take whatever it spits out and paste it. I read through everything, make sure it still sounds more or less like "me". Sometimes it'll take a couple of prompts to get it to go where I want it, and takes a bit of review and editing but it saves me literal hours. It's not necessarily perfect, but it does the job. I get it's not a panacea, and it's not great for the environment, but this tech is literally saving my sanity right now.

If you already kinda know programming and are learning a new language or framework it can be useful. You can ask it "Give me an if statement in Tcl" or whatever and it will spit something out you can paste in and see if it works.

But remember that AI are like the fae: Do not trust them, and do not eat anything offered to you.

I switched to Linux a few weeks ago and i'm running a local LLM (which was stupidly easy to do compared to windows) which i ask for tips with regex, bash scripts, common tools to get my system running as i prefer, and translations/definitions. i don't copy/paste code, but let it explain stuff step by step, consult the man pages for the recommended tools, and then write my own stuff.

i will extend this to coding in the future; i do have a bit of coding experience, but it's mainly Pascal, which is horrendly outdated. At least i already have enough basic knowledge to know when the internal logic of what the LLM is spitting out is wrong.

Spaced repetition, in particular Anki with FSRS. I don't think they advertise it as "AI" or even "ML" anywhere, but let's just say gradient descent over gigantic datasets is involved, all to predict the time when you're about to forget something so that Anki can prompt you just before that happens. The default predictor is generic, derived from that gigantic dataset, it's like two handful of tuning parameters, once you've gone through enough cards yourself it can be tuned to your mind and habits, in particular, how you use the "hard, good, easy" buttons.

It's the perfect sledge hammer for the application for the simple reason that we don't actually understand how memory works so telling the computer "here's data from millions of med students and language learners, figure out how to predict it" is our best shot. And, indeed, it's the best-performing algorithm even before you tune it at which point it becomes eerie.

Relatedly, as in "no LLM, no diffusion" Proxima Fusion is using machine learning to crunch through the design space of stellerators to figure out what to prototype in the real world. Actual engineers doing actual engineering.

Then, lastly, yes, playing around with SDXL is fun. Just make sure you can actually judge the images, developing an artistic eye by hitting generate I think is close to impossible. Definitely slower than picking up a pencil, or firing up blender and actually learning how to draw or sculpt.

New question: does anyone NOT IN TECH have a use case for AI?

This whole thread is 90% programming, 9% other tech shit, and like 2 or 3 normal people uses

OP seems to be talking about generative AI rather than AI broadly. Personally I have three main uses for it:

• It has effectively replaced google for me.
• Image generation enables me to create pictures I've always wanted to but never had the patience to practise.
• I find myself talking with it more than I talk with my friends. They don't seem interested in anything I'm but chatGPT atleast pretends to be

Good for softening language in professional environment.

For me, I use Whisper for transcribing/translating audio data. This has helped me to double check claims about a video's translation (there's a lot of disinformation going around for topics involving certain countries at war).

Nvidia's DLSS for gaming.

Different diffusion models for creating quick visual recaps of previous D&D sessions.

Tesseract OCR to quickly copy out text from an image (although I'm currently looking for a better one since this one is a bit older and, while it gets the text mostly right, there's still a decent amount that it gets wrong).

LLMs for brainstorming or in the place of some stack overflow questions when picking up a new programming language.

I also saw an interesting use case from a redditor:

I had about 80 VHS family home videos that I had converted to digital

I then ran the 1-4 hour videos through WhisperAI Large-v3 transcription and pasted those transcripts into a prompt which had a little bit of background information on my family like where we live and names of everyone who might show up in the videos, and then gave the prompt some examples of how I wanted the file names to look, for example:

1996 Summer - Jane's birthday party - Joe's Soccer game - Alaska cruise - Lanikai Beach

And then had Claude write me titles for all the home videos and give me a little word doc to put in each folder which catalogues all the events in each video. It came out so good I have been considering this as a side business

https://www.reddit.com/r/LocalLLaMA/comments/1gaz5kg/what_are_some_of_the_most_underrated_uses_for_llms/lthuxsu/

Well "AI" is a broad category. Usually used to refer to GenAI, so:

• Creating quick stand-in art for a game before I've got proper sprites for it (not because "muh art theft", just because the AI art I've generated does not look very good to me)

• Summarising articles, like you said so I can decide if I want to read them in full

• Formatting text I've copied from pdfs

• More complex searches that require comprehension of grammar and natural language syntax. Any answer I get to these I then fact check using search terms a classical search engine can understand.

I read a paper a while back that found that people who used AI assistants for coding, who only used the assistants to generate small functions where the prompt already included the function declaration and the programmer already knew how the function should be written but just wanted to save time, in these cases the use of an AI assistant did not negatively impact the "correctness" of the produced code. So I guess I might one day use an AI coding assistant like that, but thus far I've never felt the need to use AI-generated code.

Idk if it counts as GenAI but I use Waifu2x to remove jpg artifacts and upscale textures to a useable state.

i use it to autoblog about my love for the capitalist hellscape that is our green earth on linkedin dot com

I've been learning docker over the last few weeks and it's been very helpful for writing and debugging docker-compose configs. My server how has 9 different services running on it.

I use it for python development sometimes, maybe once per day. I'll paste in a chunk of code and describe how I want it altered or fixed and that usually goes pretty well. Or if I need a generic function that I know will have been coded a million times before I'll just ask ChatGPT for it.

It's far from "useless" and has made me somewhat more productive. I can't see it replacing anyone's job though, more of a supplemental tool that increases output.

AI isn't useless, but it's current forms are just rebranded algorithms with every company racing to get theirs out there. AI is a buzzword for tools that were never supposed to be labeled AI. Google has been doing summary excerpts for like a decade. People blindly trusted it and always said "Google told me". I'd consider myself an expert on one particular car and can't tell you how often those "answers" were straight up wrong or completely irrelevant to one type of car (hint, Lincoln LS does not have a blend door so heat problems can't be caused by a faulty blend door).

You cite Google searches and summarization as it's strong points. The problem is, if you don't know anything about the topic or not enough, you'll never know when it makes mistakes. When it comes to Wikipedia, journal articles, forum posts, or classes, mistakes are possible there too. However, those get reviewed as they inform by knowledgeable people. Your AI results don't get that review. Your AI results are pretending to be master of the universe so their range of results is impossibly large. That then goes on to be taken is pure fact by a typical user. Sure, AI is a tool that can educate, but there's enough it proves it gets wrong that I'd call it a net neutral change to our collective knowledge. Just because it gives an answer confidently doesn't mean it's correct. It has a knack for missing context from more opinionated sources and reports the exact opposite of what is true. Yes, it's evolving, but keep in mind one of the meta tech companies put out an AI that recommended using Elmer's glue to hold cheese to pizza and claimed cockroaches live in penises. ChatGPT had it's halluconatory days too, it just got forgotten due to Bard's flop and Cortana's unwelcome presence.

Use the other two comments currently here as an example. Ask it to make some code for you. See if it runs. Do you know how to code? If not, you'll have no idea if the code works correctly. You don't know where it sourced it from, you don't know what it was trying to do. If you can't verify it yourself, how can you trust it to be accurate?

The biggest gripe for me is that it doesn't understand what it's looking at. It doesn't understand anything. It regurgitates some pattern of words it saw a few times. It chops up your input and tries to match it to some other group of words. It bundles it up with some generic, human-friendly language and tricks the average user into believing it's sentient. It's not intelligent, just artificial.

So what's the use? If it was specifically trained for certain tasks, it'd probably do fine. That's what we really already had with algorithmic functions and machine learning via statistics, though, right? But sparsing the entire internet in a few seconds? Not a chance.

Edit: can't beleive I there'd a their

Troublehsooting technology.

I've been using Linux as my daily driver for a year and a half and my learning is going a lot quicker thanks to AI. It's so much easier to ask a question and get an answer instead of searching through stack overflow for 30 minutes.

That isn't to say that the LLM never gives terrible advice. In fact, two weeks ago, I was digging through my logs for a potential intruder (false alarm) and the LLM gave me instructions that ended up deleting journal logs completely.

The good far outweighs the bad for sure tho.

The Linux community specifically has an anti-AI tilt that is embarrassing at times. LLMs are amazing, and much like random strangers on the internet, you don't blindly trust/follow everything they say, and you'll be just fine.

The best way I think of AI is that it's going through a bubble not unlike the early days of the internet. There was a lot of overvalued companies and scams, but it still ushered in a new era.

Another analogy that comes to mind is how people didn't trust wikipedia 20 years ago because anyone could edit it, and now it is one of the most trusted sources for information out there. AI will never be as 'dumb' as it is today, which is ironic because a lot of the perspective I see on AI was formed around free models from 2023.

Be cautious about the results when using them for googling and summarizing. I had them tell misinformation to me more than once. You'll "learn" things that are counter-factual.

Translating is a very good use case. I also use them for that an it works very well. Better than any Google Translate. And I use it for roleplay, like a D&D campaign, just not with your friends, but alone and the AI narrates the story. And one-off things where I need some ideas to spark my creativity.

What I've tried apart from that are programming, re-phrasing my emails, ... But I've never got any good results for that. Everytime I did that, I ended up not liking the result, deleting it and starting over and doing it myself.