I’ve seen a lot of sentiment around Lemmy that AI is “useless”. I think this tends to stem from the fact that AI has not delivered on, well, anything the capitalists that push it have promised it would. That is to say, it has failed to meaningfully replace workers with a less expensive solution - AI that actually attempts to replace people’s jobs are incredibly expensive (and environmentally irresponsible) and they simply lie and say it’s not. It’s subsidized by that sweet sweet VC capital so they can keep the lie up. And I say attempt because AI is truly horrible at actually replacing people. It’s going to make mistakes and while everybody’s been trying real hard to make it less wrong, it’s just never gonna be “smart” enough to not have a human reviewing its’ behavior. Then you’ve got AI being shoehorned into every little thing that really, REALLY doesn’t need it. I’d say that AI is useless.
But AIs have been very useful to me. For one thing, they’re much better at googling than I am. They save me time by summarizing articles to just give me the broad strokes, and I can decide whether I want to go into the details from there. They’re also good idea generators - I’ve used them in creative writing just to explore things like “how might this story go?” or “what are interesting ways to describe this?”. I never really use what comes out of them verbatim - whether image or text - but it’s a good way to explore and seeing things expressed in ways you never would’ve thought of (and also the juxtaposition of seeing it next to very obvious expressions) tends to push your mind into new directions.
Lastly, I don’t know if it’s just because there’s an abundance of Japanese language learning content online, but GPT 4o has been incredibly useful in learning Japanese. I can ask it things like “how would a native speaker express X?” And it would give me some good answers that even my Japanese teacher agreed with. It can also give some incredibly accurate breakdowns of grammar. I’ve tried with less popular languages like Filipino and it just isn’t the same, but as far as Japanese goes it’s like having a tutor on standby 24/7. In fact, that’s exactly how I’ve been using it - I have it grade my own translations and give feedback on what could’ve been said more naturally.
All this to say, AI when used as a tool, rather than a dystopic stand-in for a human, can be a very useful one. So, what are some use cases you guys have where AI actually is pretty useful?
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
On the inverse I’ve found it to be quite bad at that. I can generally count on the AI answer to be wrong, fundamentally.
Might depend on your industry. It’s garbage at g code.
It probably depends how many good examples it has to pull together from stack overflow etc. it’s usually fine writing python, JavaScript, or powershell but I’d say if you have any level of specific needs it will just hallucinate a fake module or library that is a couple words from your prompt put into a function name but it’s usually good enough for me to get started to either write my own code or gives me enough context that I can google what the actual module is and find some real documentation. Useful to subject matter experts if there is enough training data would be my new qualifier.
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.
This is my one of 2 use cases for AI. I only recently found out after a life of being told I’m terrible at writing, that I’m actually really good at technical writing. Things like guides, manuals, etc that are quite literal and don’t have any soul or personality. This means I’m awful at writing things directed at people like emails and such. So AI gives me a platform where I can enter in exactly what I want to say and tell it to rewrite it in a specific tone or level of professionalism and it works pretty great. I usually have to edit what it gave me so it flows better or remove inaccurate language, but my emails sound so much better now! It’s also helped me put more personality into my resume and portfolio. So who knows, maybe it’ll help me get a better job?
Yeah, I’m really bad at structuring my writing and coming up with ways to phrase some things, especially when starting with a blank page. Having an existing base to work off of and edit helps me immensely.
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.
For one thing, they’re much better at googling than I am.
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.
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 just pass the create table statements after the instructions. It does pretty good up to 2 or 3 tables, but it will start to make mistakes when things get complicated
On the plus side, it’ll generate tedious code very well - double checking it is less draining than writing it yourself. Especially because I make more typos than it does - I often use it to get a starting point, then write the business logic myself
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)
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’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.
Any LLM I tried sucks using Rust. The book is great, you learn all of the essentials of Rust and it is also pretty easy to read.
I imagine that’s because Rust is still a relative newcomer to the industry and C/C++ have half a century of code out there.
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:
This is really cool, thanks for sharing.
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.
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.
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.
Woah cool! Can you share your prompt for that I’d like to try it
I don’t do anything too sophisticated, just something like:
Scan this image of a recipe and format it as JSON that conforms to the schema defined at https://schema.org/Recipe.
Sometimes it puts placeholders in that aren’t valid JSON, so I don’t have it fully automated… But it’s good enough for my needs.
I’ve thought that the various Nextcloud cookbook apps should do this for sites that don’t have the recipe object… But I don’t feel motivated to implement this myself.
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.
This is my current best use for it as well. Having a unique portrait for every named NPC helps them stand out quite a bit better and the players respond much more strongly to all of them.
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.
