Yeah we all hear the main arguments… AI is bad because of slop content, stealing from creators, brain rot & brain damage, privacy concerns and most importantly… how billionaires are just using it for their own selfish reasons
But I’m asking about YOU 🫵 personally. The individual. What do you really think about AI? Do you care or are you indifferent? Has it actually affected your day to day life?
It’s a tool, and as such the class it serves depends on the mode of production and the class in power. It has some use cases, but it isn’t the supertool techies think it is. It also isn’t utterly worthless like some believe. Over time it will likely become more useful and better integrated.
First and foremost, I think it is egregiously misnamed. It is neither artificial nor intelligent; it is just math. There was a time when “knowledge based systems” was a popular moniker (albeit for a different approach) and I find it to be much more apt for the systems we call (generative) AI. If we’re going to repurpose a term, I think that one is more suitable. “Large language model” is good for its accuracy, but is also less evocative and descriptive, especially for the normies. Either way, it’s wild to me that we’re like “yeah AI is here now.” It’s frustrating to me because of how it impacts the way we interpret and use these systems.
Secondly, I think the way that Statesian companies are building these systems is exactly wrong, but I don’t suppose that should really be a surprise to anyone. The throwing-peas-at-the-wall and throwing-money-and-resources-at-it approach has netted results, sure, but wouldn’t it be neat if everyone was working together to more deliberately collate the entirety of human knowledge and create accessible tools for all to leverage? You know, instead of letting private companies extract the fruits of our labor, throw it into their equation, and then sell it back to us, over and over again? Anyway.
Outside of that, I think Cowbee’s succinct take reflects my view as well.
Ultimately, it is a tool. It’s impressive that hardware has developed to the point where we can throw so much language at these systems to get useful results. It is also true that these systems are both over- and underestimated. I think it’s also true that the current economic approach is intractable, and I look forward to the day when we more broadly understand how to build and use these tools more effectively.
The terminology can definitely be misleading. AI evokes anything ranging from a pathfinding algorithm to sci-fi sapient machine that takes over the world.
I think it can be accurately said that AI as in Artificial Intelligence is the end goal of the machine learning field, but it gets fuzzy fast on definitions whether the field has actually done that in any capacity.
Partly I guess because the concept of intelligence in the first place is largely a way of thinking about humans, not machines. Is a light “intelligent” if a sensor can detect when somebody within range and then the sensor triggers the light to turn off? It’s doing something that is useful, but it doesn’t know what it’s doing as a separate consciousness. I think it can be argued that what gets called generative AI is similar to that, but with a lot more complexity to the inference operations.
I would say the mistake is in thinking that if a tool becomes sufficiently complex, it is necessarily heading toward something distinct like what humans have as consciousness. But this is not taking into account form. Humans have a very specific biological form and if you simulate aspects of that form in a machine, you haven’t now recreated consciousness; you have created an advanced simulation of one or more facets of human-like cognition or processes. This can still have benefits. A blueprint for a building constructed from computed simulation could probably have use to an architect, even though it’s not the real building created yet.
So perhaps something like Simulated Cognition would be more appropriate for most of what gen “AI” is, in practice.
I agree with your points and perspective, but I also fell like “Simulated Cognition” is a bit too generous. I don’t think an LLM/what we currently have as generative “AI” is a simulation of cognition, though I acknowledge/concur that is the intent. Perhaps I’m splitting hairs too finely, but I see it instead as a statistical approximation of language processing.
I mean, I guess one could just say, “yeah, they’re a statistical approximation of language processing with the intent of simulating cognition”, and I’d have to acquiesce. So I guess my hang up hinges on how one interprets the word “simulated,” because I think its connotation tends to be more weighty than its literal definition. For example, if we said “Mock Cognition,” that’s more obviously fake cognition (to me, anyway). Whereas a mathematical simulation of something, for instance the flight trajectory of a satellite or rocket, is not the real thing, but is more or less expected to exactly model the real thing (at least in my selected example). And it makes me uncomfortable to apply that perception to the “Simulated Cognition” of our models that approximate language processing.
That’s fair. I’m definitely not married to either term. Mainly trying to work out something that is more accurate.
I will say, the reason I go for “simulated” is because for me, the connotation I think of is video game style simulation, i.e. something that is understood to be not real. But that may not be the takeaway most would have.
Either way, I get the concern of not overstating what gen AI is doing. Though on the other hand, I think it’s important not to understate it either. Like what models are doing now with complex code, or with reasoning layers, it seems almost trivializing to call it statistics, even if that is a component part of it.
We could also call them Bullshitting Machines, haha. They sure act like that sometimes. But yeah, I’m open to better ideas on better terminology for it. Precise terminology has never been my strongest area. I’m more apt to use language fluidly.
Virtual Cognition? 🤔 No, I don’t think we’re going to come up with anything better than Bullshitting Machines
I think it’s a really useful tool that’s made my life easier and allows me to explore a lot of ideas I was just too lazy to do before. I have like nearly a decade worth of half baked software project ideas, and I just never had the energy to work on them or finish ones I started. With LLMs, I can actually get them working to the point where I can see the idea in action which is really enjoyable for me. It’s also made my work easier where I can focus more on things I find interesting and delegate tedious tasks to the agent.
I do look forward to a time where we can run these tools entirely locally though. I do not like being dependent on company services or sending my data to them. And in general I see this as the real negative aspect of how this technology is being developed. We don’t want to end up in a situation where tools we rely on day to day are owned by a handful of corporations. Regular people need to own the means of production in the digital realm. Currently, anybody with a computer can do any type of digital work be it writing documents, design, programming, etc. But if we start relying on LLMs as a core part of our workflow, then that tool also needs to be run locally or we end up as digital serfs.
For coding I find LLMs to be legitimately revolutionary. I’ve tried letting DeepSeek & some local models like Qwen and Gemma loose on various projects to implement features and improvements for local use and so far most of the time it didn’t disappoint.
In the last couple of weeks I’ve been updating an old third party bot plugin for a game by prompting various behavior changes I’d like to see and it’s a night & day difference to its original state. If I had done this by hand the time it took would’ve been multiple magnitudes longer, it’d have been more error-prone (especially since it’s a C++ project written in classical C style, which is just UB galore) and I likely would’ve lost the enthusiasm to work on it by now.
Depends what you mean by AI. Because for the most part “AI” nowadays is just a pure marketing term. What the tech company marketing calls “AI” is not any kind of intelligence in the actual sense of the term. It’s just some machine learning algorithms and fancier CGI. We had “AI” back in the 90s too. Every computer game where the computer player makes autonomous decisions is a kind of AI. It’s the marketing that is the problem. The idea that it is some kind of revolutionary technology that can replace human labor. It’s not and it won’t be. It’s just another tool. Unfortunately too many people have fallen for the marketing hype, which has served to inflate the AI bubble that will pop sooner or later.
I’ve messed with neural nets over a decade ago and ever since I realized their limitations back then I’ve lost all interest.
It can never outgrow its own boundaries. If you train one too much then its output will just be the literal training data with some error, but never anything better, no matter how much data you throw at it.
That alone has always made the ideas that it can “create” things or “think”/“solve problems” absolutely ridiculous to me.
I’ve said this before on lemmy and have gotten downvotes from it, but neural nets are much closer to something like an ASIC.
And now there actually is a company that literally makes ASIC chips that implement neural nets! ;DI’m not denying that neural nets do have genuine uses, but the way most are being used in the 2020s just grosses me out.
The idea that it is some kind of revolutionary technology that can replace human labor.
Now people say that but 10+ years ago nobody cared. I had 2 chatbots talking to each other about random things that got picked from the Internet as an experiment all running 24/7 on a Pentium D PC, and whenever I showed this to someone they responded with a “uh cool.” Now these same people think they’re interacting with a “thinking” being that is capable of doing their job whenever they engage with some LLM-based bot, when in reality it’s not too different from what I had back then at its core.
I am an AI Luddite. Perhaps it is just the libertarian versions being offered up and pushed by people who should be in jail, not running governments. They are stealing data; something that used to be labeled piracy. Most of the crap that I have contact with as a consumer is either useless, or downright bad for society as it stifles creativity and promotes the surveillance state. The fact that there is a movement to poison data collection tells me I am not alone.
Depends on the game. seriously though. LLMs are not AI. They are not intelligent and they are not artificial. They are just a brute force algorithm to decode and respond in natural human language.
I often use deepseek instead of using search engines but only because SEO and LLM generated websites have ruined search results. I have used it to make a few simple programs which is great because I have no clue how to program.
Some people are using these tools for stupid shit but people always use tools for stupid shit. Atleast all the hype is going to break usa’s economy and maybe things will get bad enough that usaians will finally deal with their fascist overlords and become a normal country.
The USA cannot ever become a normal country. Settler colonies shall always be haunted by their birth.
This pretty much sums my thoughts.
They are basically summed up here: https://en.prolewiki.org/wiki/Essay:Intellectual_property_in_the_times_of_AI
I use it as a smart search tool. Whenever I want to do some exploring, or don’t remember a particular syntax, I use it. The output is usually 70% good, for simple things it works, most of the time it needs some tweaking, a few times it’s completely wrong. It’s interesting that when a problem is impossible to solve with a given approach, the LLM usually gives a wrong answer that seems to be correct.
So I think it’s a time saver for simple things, but it does not appear to me to be this revolutionary new tool. Although I use it, some days I completely forget about it. The advantage or disadvantage of having a legacy and non traditional code base to maintain is that the promised gains the tool provides are very minimal. But managers seem so bewitched about the tool, and I most of the time can’t get the hype. I genuinely don’t understand, and it seems to me that people are allucinating about it to a point that people seem to have lost their touch with reality.
The consequences I came to face as well. I feel overloaded and burnt out at work. If AI could do the work I’m doing today, I’d be glad to use it. If the CTO of the company does not worry about quality why the hell should I care, I’m not getting the stock bonus and it’s not my capital that is being increased in the process. The problem is lacking the bonus that AI promises while having to deal with the onus of having to deal with difficult timelines, impossible requirements, unreasonable PMs… Nowadays I hate working so much that sometimes I think about being laid off and even like the idea. I just don’t quit because I have a family to provide for.
I went through a phase where I was doing a lot of vibe coding. I thought that I was smart enough to be working around the known problems and limitations of it. I was not. Pretty much everything “I” had made turned into an unmaintainable nightmare once it passed a certain level of complexity, and in practice when you’re trying your best to actually make vibe coding work on a project larger than one file you spend so much time reviewing every little thing, rewording your specs etc that you might as well just write the fukken code yourself.
So I deleted all of those projects, and now I use AI for brainstorming (I talk into speech to text, usually while doing something else, then give the text to an LLM and say “organize these ramblings into a design document for me”) and simple specific task automation (“comment my code” and “update my readme”). I figure the one thing LLMs are demonstrably good at is summarization, so pretty much everything I ask them to do is along those lines.
That said, if I could press a button that would make all of the ai companies crash and burn but it meant that I would have to back to doing those things manually, I would press it in a heartbeat. The externalities that the tech world is forcing into all of us for this thing that makes summarizing documents more convenient are way too much.
As for anything else, generative images or video or whatever… I just have zero interest in making or consuming them. Sure it hits my feed like it does for everyone else but I’d say at this point my reaction is about 1/10 “haha” and 9/10 “ugh” when I see them. There was already so much human made art out there that you could be looking at new things all day every day and you wouldn’t be able to keep up.
edit: also, regarding the coding: if your goal isn’t “learn to code” but rather “make a quick python script that needs to work exactly one time”, then vibe coding can be a valid use case. but if you are trying to learn then prompting cannot teach you in the same way that you will never learn to make high quality digital art by prompting for image generator.
I find it’s perfectly possible to write large maintainable projects using these tools. I have a Rust project I built with LLMs that’s over 150k loc now, and it’s structure is a lot better than anything I would’ve ended up on my own. One of the things I do is ask the LLM to come up with a phased plan for introducing new features. I also ask the model to make mermaidjs diagrams I can inspect and come up with file layout up front. Then I get it to make a branch for each phase and implement a focused feature. Then I can review it and I have good context for what it’s supposed to be doing, and it’s scoped so that the code is manageable enough to fit in my head. Doing that alone gets you a long way. Another thing I do is ask it to make refactors by looking through the code base and finding repeating patterns in code that can be consolidated, or large files that need to be split up. If you do this regularly, you end up with much cleaner code, and the agent is much better at doing that at scale than you could by hand.
Testing is another really important aspect. I always ask the model to do TDD, and then add end to end integration tests for features. For web apps, using playwright storybooks is really effective. You can define exactly what the user workflow is and then have the model test it through a headless browser end to end. This creates a contract where you know what that functionality is actually working end to end.
As long as you don’t just let the model crap out tons of code unsupervised, and box it in sufficiently with a contract, then the code is no worse than what a human would produce. And I’d argue that it’s often better.
Openspec is along these lines and works well ime
Yeah it’s a nice tool, you really just need a bit of rails to keep the model on track I find. They’re good at doing well defined tasks, and if you have a plan where they just check steps off as they go, things tend to work well. I also found beads is a really handy tool for task tracking cause then you can just file stuff in there and instead of writing tasks in markdown you have a real history along with the status of the tasks. It solves the problem with markdown checklists getting stale.
good to know, I’ll check out beads. I was forced to start using Claude at work and had mixed impressions but once we started using openspec to keep it on rails as you say, it got hard to deny its capabilities.
I find I kind of look at the whole LLM + agentic harness setup as a genetic algorithm. Your tests and specs are the fitness function for the program you’re evolving, and the LLM is the mutator. At each step it generates some output, it gets tested against the fitness function, the LLM gets feedback and iterates on it. Eventually something working falls out in the end. The better you can define the selection criteria the more you box the agent in the better results you get.
I dont like gen AI because its really really bad for the environment
AI is not so useful as
peopletechbros want make us to believe. sure, it can help you to create a quick drawing or logo, or help you to structure a html webpage, maybe even help you to summarize a long text that you don’t want to read, but it’s not as futuristic or useful as we’ve been told.their cons (being too wasteful, too pollutant, stealing from creators, being an endless brainrot and fake news generator) overwhelms the usefulness. maybe if arrogant pricks like techbros weren’t behind it, most people would appreciate it
If you mean generative “AI”, I see very few and narrow uses for it. In my life it is a net negative and I despise its influence. Its a great way to destroy your critical thinking skills, self expression, and create really bad software and ugly images. I find out offensive when people shovel that slop to me; it contributes nothing, just fills the world with more hallucinations at the cost of the original authors and the environment
Editing to add: they are also incapable of ever producing anything truly novel. Generative applications of machine learning can remix and randomize training data in interesting ways, but it cannot do anything outside of that. Anyone claiming otherwise is selling you something or doesn’t understand how these things function. Not to mention it is one of the most brute-force forms of computation I’ve ever seen; I appreciate efficiency and elegance in computing and automation, and something like an LLM is the polar opposite. More efficient solutions almost always exist
Editing to add: they are also incapable of ever producing anything truly novel. Generative applications of machine learning can remix and randomize training data in interesting ways, but it cannot do anything outside of that. Anyone claiming otherwise is selling you something or doesn’t understand how these things function. Not to mention it is one of the most brute-force forms of computation I’ve ever seen; I appreciate efficiency and elegance in computing and automation, and something like an LLM is the polar opposite. More efficient solutions almost always exist
I’m not sure this is an accurate way to put it. I think I generally get what you’re going for, that their creativity is highly dependent on what they’ve seen in training. But saying it means they can’t do anything novel I think exaggerates what humans are doing, by comparison. Humans don’t reach into the ether and pull out something never seen before. They are deeply influenced by their inner and outer world from birth to death, and though they can combine things in a way that hasn’t quite been done in the same way before, it is still deeply dependent on what they have seen before (not entirely unlike AI training).
Where humans differ is 1) They can surely get a lot more creativity out of a lot less and 2) They are constantly learning on the fly, which makes them much more flexible and adaptable than a hard-trained LLM can be.
So are humans better at creativity? Absolutely, it’s not even close (especially when we collaborate on it effectively). But are humans creating wholly original works and gen AI isn’t? No, I don’t think so. Both humans and gen AI can create remixes of things they’ve seen that haven’t been seen before in quite the same way. But humans have a much higher ceiling on what they can do with their capability. Gen AI is a lot more hard-capped to training data and takes a lot of resources to learn more (and it can forget things / give different results from learning more - it won’t necessarily improve across the board).
Edit: downvoting me doesn’t make me incorrect. I’m an experienced writer and I can tell you with absolutely certainty that I have traced the line of inspiration before. Inspiration isn’t mysterious magic and it’s actually to our disadvantage ideologically to treat it as such. Part of how I became more conscious about sources of inspiration was in the process of questioning the unconscious ideology spilling into things I wrote. On top of this, there are cultural things that could be considered art yet are also very important to a person’s culture, not just playing around (like Hula dance, passing down stories); this example shows a way storytelling is combined with creativity in order to preserve history on purpose. I’ve found it more useful understanding this because it meant when I felt creatively dry, instead of cudgeling my brain, I’d go find somebody else’s work and experience it for the ideas.
I remain critical of their use cases and environmental impact but I am not opposed to it.
The consensus we reached with our party is that it is better to understand it and know how to use it because our enemy, the ruling class, is in control of it and is also using it. The party made the mistake with the rise of the internet to pass it off as some hype and years later when internet was widespread and common in use, they were behind on their knowledge and missed to boat. They won’t let it happen again.
Pros
- it’s free (I don’t have an income)
- no proprietary copyright bs that could get me in trouble for downloading it
- it’s on my computer, locally, doesn’t need internet
- comes from china (I always wanted something from there ever since i learned that it’s socialist)
- applied math & science
- gets actively developed
- allows me to actually make good-looking pictures (i’m bad at drawing & 3d modeling)
- pisses off the cultists that made me support copyright & made me believe illogical shit i’m embarrassed to have ever believed
- there’s so much i did learn and so much i can still learn
- the word GGUF sounds funny: g-g-g-g-g-g-g-g-gufff
Cons
- takes ages to download
- extremely brittle tooling
- LLMs trip me up. I don’t want machines to chat with me, I want them to shut up and do what I told them.
- needs a powerful computer (mine barely runs most of it)
- there’s so much I don’t understand (gets real “fun” when patching the buggy tooling)
- python update breaks everything
So, overall, I like it very much. But there’s Miraculous, and modded Minecraft, so it’s my number three interest.
there’s an online AI for turning an image (png raster) into a 3d model for like, your 3d printer: https://www.tripo3d.ai/
Not saying to use it, but it shows proof it works. If there isn’t something local for it yet, it’ll come out in a year or two. There’s already a local AI music gen tool that basically replaces Suno, and only needs 4GB of Vram.
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