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.

Fake frames. Nvidia double benefits.

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

Amazing for reading docs

“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.

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.

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

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.

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 use it to re-tone and clarify corporate communications that I have to send out on a regular basis to my clients and internally. It has helped a lot with the amount of time I used to spend copy editing my own work. I have saved myself lots of hours doing something I don’t really like (copy-editing) and more time doing the stuff I do (engineering) because of it.

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.

There are some great use cases, for instance transcribing handwritten records and making them searchable is really exciting to me personally. They can also be a great tool if you learn to work with them (perhaps most importantly, know when not to use them - which in my line of work is most of the time).

That being said, none of these cases, or any of the cases in this thread, is going to return the large amounts of money now being invested in AI.

It’s pretty good at looking up readily available knowledge that doesn’t have a lot of nuance to it. There’s a lot of stuff you can look up but it always comes with a grain of salt.

Home remedies, bunch of baby facts like poop color meaning, recipes and adjustments, programming examples (requires very prompting skills).

Rewriting stuff into business English is another very nice use case. Tell the AI your qualitifations, ask to make a cover letter for “job description” then review. Drafting text and summarising also pretty good.

Adding modifiers to questions like “list of 20 for X” for a brainstorming or “include how scientifically reliable the claim is on scale of 1-10” really help with getting a good answer and some nuance to whatever claims.

It’s touted as the be all end all but in reality the use cases are very specific in my experience.

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

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).