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?
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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.
”allows me to actually make good-looking pictures (i’m bad at drawing & 3d modeling)”
omg bro just learn? i didnt know we had this many promptitutes on here
Well, given everything labeled AI one way or another, they are useful tools. LLMs in particular, on the other hand, seems to be little more than a way to fuck over the average consumer and funnel money in the pockets of a few con-men. I’m just keeping a list of every manufacturers pushing for “AI” as the next big thing, to make sure to avoid them like the plague. (Goodbye Nvidia, you won’t be missed)
The hate is a reactionary luddite impulse; most of the problems with the technology are symptomatic of capitalism (e.g. oil and gas partnerships for data centers, IP law as a concept). Opposition of these grotesque excesses should be grounded in anti-capitalism instead of this naive anti-AI sentiment. In a socialist society, one could foresee such tools helping with economic planning, for instance.
The only issue that is unique to AI separate from other new technologies is anthropomorphization/agreeability/psychosis. The ultimate safeguard would be for AI to be essentially a glorified statistical aearch engine, and never refer to itself as if it were a service-worker as it currently does. Asking AI a “personal” question should simply reflect back “Will Not Answer.”
how much do you really know about the luddites?
I’m just using it as an adjective to mean “anti-technology more or less on its own merit”. I’m aware that it was a social movement but frankly this is not how the word “luddite” is used in a modern linguistic context.
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 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.
i mean me personally i really dislike AI images and video, just both in concept and execution (obvs), and LLMs have had very little actual benefit for me. I do use them as a search engine now… but that’s only because search results have been fucked because of LLMs
Its intersection with capitalism is a technofeudalist nightmare that deserves a lot of critique. I am most concerned about the environmental damage, its military, policing and surveillance uses and the pressure it puts on workers in an already hellish economy.
However, it’s still a tool and it can have legit applications that genuinely benefit society. I also think that some of the backlash is overblown, especially the sloplist obsession with scanning everything for the faintest trace of AI use regardless of context. Also the clanker shit is just people loudly signaling that they are frothing at the mouth waiting for an opportunity to say a slur and I get unconfortable around these types.
I use it at work (software development) for boring, straightforward busywork, debugging assistance and the like. It’s good enough and saves time when used in a context I am already familiar with and I can easily review and verify what it spits out. I also use it at home to help me debug when something goes wrong with my Linux installation and 5 mins of google doesn’t cut it.
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
AI exists at my workplace but I am lucky enough that I can choose whether or not I use it. I did not. Some of my colleagues do use it to write their code for them and if they ever leave, I worry that I will be instructed to maintain their code
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.
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 first 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
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).
For coding I find LLMs legitimately revolutionary. I’ve tried letting DeepSeek & some local models like Qwen and Gemma loose on various projects to implement features and improvements 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.
AI is like fire. It’s a world-changing tool.
There are people that can’t see past the danger of how it could burn them. There are people that can’t see the potential in how it could be used productively.
There are people who are experimenting with it to try to find useful things to do with it, and there are people who are burning things down for shits and giggles, and most infuratingly of all there are people who can’t tell the difference between those last two groups and feel the need to soapbox about it anyway.
For the chat style AI, I have only found a few useful applications: looking up information in source languages I don’t speak, programming assistant, and linux troubleshooting.
The biggest issue I have with AI in my personal use is that I have to be weary about hallucinations. with programming, this is often easy to fix with testing. knowledge search and troubleshooting have the biggest chance of being impacted by hallucination since I would have to verify each item myself which defeats the benefit. I especially found that any sort of technical search will result in entirely wrong results presented with confidence, so I usually limit queries to general knowledge type searches, and use the results as reference only.
As such, for me, I think that chat AI is a neat tool that has a few uses, some of which are better than older methods. It is not a magic bullet and it cannot be trusted without human judgement.
When I was working, it was in a field that was mostly insulated from the worst impacts of AI. But our manager and IT department seemed to think it was the future and were trying to find places to shove it. But because of the work we do nothing stuck. so in that way, it didn’t really impact me too much in my professional work.
I think the biggest concern I have with chat style AI is deskilling myself. I worry that using it for programming has made me less able to read and write code. I try to use it collaboratively usually, so that I understand all the code, but I recently made a small program entirely with Deepseek without writing any code myself and it was a strange experience.
when it comes to non-chat style AI, I have dabbled in using some of the underlying algorithms to solve problems at work (gradient descent, evolutionary algorithms, etc.). But I’ve only used them to solve one off problems. I think that machine learning is a fascinating and highly useful field of research.
