image description (contains clarifications on background elements)
Lots of different seemingly random images in the background, including some fries, mr. crabs, a girl in overalls hugging a stuffed tiger, a mark zuckerberg “big brother is watching” poser, two images of fluttershy (a pony from my little pony) one of them reading “u only kno my swag, not my lore”, a picture of parkzer parkzer from the streamer “dougdoug” and a slider gameplay element from the rhythm game “osu”. The background is made light so that the text can be easily read. The text reads:
i wanna know if we are on the same page about ai.
if u diagree with any of this or want to add something,
please leave a comment!
smol info:
- LM = Language Model (ChatGPT, Llama, Gemini, Mistral, ...)
- VLM = Vision Language Model (Qwen VL, GPT4o mini, Claude 3.5, ...)
- larger model = more expensivev to train and run
smol info end
- training processes on current AI systems is often
clearly unethical and very bad for the environment :(
- companies are really bad at selling AI to us and
giving them a good purpose for average-joe-usage
- medical ai (e.g. protein folding) is almost only positive
- ai for disabled people is also almost only postive
- the idea of some AI machine taking our jobs is scary
- "AI agents" are scary. large companies are training
them specifically to replace human workers
- LMs > image generation and music generation
- using small LMs for repetitive, boring tasks like
classification feels okay
- using the largest, most environmentally taxing models
for everything is bad. Using a mixture of smaller models
can often be enough
- people with bad intentions using AI systems results
in bad outcome
- ai companies train their models however they see fit.
if an LM "disagrees" with you, that's the trainings fault
- running LMs locally feels more okay, since they need
less energy and you can control their behaviour
I personally think more positively about LMs, but almost
only negatively about image and audio models.
Are we on the same page? Or am I an evil AI tech sis?
IMAGE DESCRIPTION END
i hope this doesn’t cause too much hate. i just wanna know what u people and creatures think <3
I used to think image generation was cool back when it was still in the “generating 64x64 pictures of cats” stage. I still think it’s really cool, but I do struggle to see it being a net positive for society. So far it has seemed to replace the use of royalty free stock images from google more than it has replaced actual artists, but this could definitely change in the future.
There are some nicer applications of image generation too, like dlss upscaling or frame generation, but I can’t think of all that much else honestly.
I think we should avoid simplifying it to VLMs, LMs, Medical AI and AI for disabled people.
For instance, most automatic text capture ais (optical Character Recognition, or OCR) are powered by the same machine learning algorithms. Many of the finer-capability robot systems also utilize machine learning (Boston Dynamics utilizes machine learning for instance). There’s also the ability to ID objects within footage, as well as spot faces and referencing it with a large database in order to find the person with said face.
All these are Machine Learning AI systems.
I think it would also be prudent to cease using the term ‘AI’ when what we actually are discussing is machine learning, which is a much finer subset. Simply saying ‘AI’ diminishes the term’s actual broader meaning and removes the deeper nuance the conversation deserves.
Here are some terms to use instead
- Machine Learning = AI systems which increase their capability through automated iterative refinement.
- Evolutionary Learning = a type of machine learning where many instances of randomly changed AI models (called a ‘generation’) are run simultaneously, and the most effective is/are used as a baseline for the next ‘generation’
- Neural Network = a type of machine learning system which utilizes very simple nodes called ‘neurons’ for processing. These are often used for image processing, LMs, and OCR.
- Convolution Neural Network (CNN) = a Neural network which has an architecture of neuron ‘fliters’ layered over each other for powerful data processing capabilities.
This is not exhaustive but hopefully will help in talking about this topic in a more definite and nuanced fashion. Here is also a document related the different types of neural networks
Mr crabs would use unethical llms, very accurate
true, he would totally replace his workers with robots, and then complain about hallucinated recipes.
I wish people stopped treating these fucking things as a knowledge source, let alone a reliable one. By definition they cannot distinguish facts, only spit out statistically correct-sounding text.
Are they of help to your particular task? Cool, hope the model you’re using hasn’t been trained on stolen art, or doesn’t rely on traumatizing workers on the global south (who are paid pennies btw) to function.
Also, y’know, don’t throw gasoline to an already burning planet if possible. You might think you need to use a GPT for a particular task or funny meme, but chances are you actually don’t.
That’s about it for me I think.
edit: when i say “you” in this post i don’t mean actually you OP, i mean in general. sorry if this seems rambly im sleep deprived as fuckj woooooo
peeps who use these models for facts are obv not aware what the models are doing. they don’t know that these models are just guessing facts.
also yes, big sad about peeps in the south being paid very poorly.
can totally see your point, thank you for commenting! <3
There are so many different things that are called AI, the term AI doesn’t have any meaning whatsoever. Generally it seems to mean anything that includes machine learning somewhere in the process, but it’s largely a marketing term.
Stealing art is wrong. Using ridiculous amounts of power to generate text is ridiculous. Building a text model that will very confidently produce misinformation is pretty dumb.
There are things that are called AI that are fine, but most aren’t.
I’ll just repeat what I’ve said before, since this seems like a good spot for this conversation.
I’m an idiot with no marketable skills. I want to write, I want to draw, I want to do a lot of things, but I’m bad at all of them. gpt like ai sounds like a good way for someone like me to get my vision out of my brain and into the real world.
My current project is a wiki of lore for a fictional setting, for a series of books that I will never actually write. My ideal workflow involves me explaining a subject as best I can to the ai (an alien technology or a kingdom’s political landscape, or drama between gods, or whatever), telling the ai to ask me questions about the subject at hand to make me write more stuff, repeat a few times, then have the ai summarize the conversation back to me. I can then refer to that summary as I write an article on the subject. Or, me being lazy, I can just copy-pasta the summary and that’s the article.
As an aside, I really like chatgpt 4o for lore exploration, but I’d prefer to run an ai on my own hardware. Sadly, I do not understand github and my brain glazes over every time I look at that damn site.
It is way too easy for me to just let the ai do the work for me. I’ve noticed that when I try to write something without ai help, it’s worse now than it was a few years ago. generative ai is a useful tool, but it should be part of a larger workflow, it should not be the entire workflow.
If I was wealthy, I could just hire or commission some artists and writers to do the things. From my point of view, it’s the same as having the ai do the things, except it’s slower and real humans benefit from it. I’m not wealthy though, hell, I struggle to pay rent.
The technology is great, the business surrounding it is horrible. I’m not sure what my point is.
I’m sorry, but did you ever think of the option to try? To write a story you have to work on it and get better.
GPT or llms can’t write a story for you, and if you somehow wrangle it to write a story without losing it’s thread - then is it even your story?
look, it’s not going to be a good story if you don’t write it yourself. There’s a reason for why companies want to push it, they don’t want writers.
I’m sure you can write something, but that you have issues which you need to deal with before you can delve into this. I’m not saying it’s easy, but it’s worth it.
Also read books. Read books to become a better writer.
PPS. If you make an llm write it you’ll come across issues copyrighting it, at least last I heard.
I honestly am skeptical about the medical stuff. Machine learning can’t even do the stuff it should be good at reliably, specifically identifying mushrooms/mycology in general.
that is interesting. i know that there are plenty of plant recognition onces, and recently there have been some classifiers specifically trained on human skin to see if it’s a tumor or not. that one is better than a good human doctor in his field, so i wonder what happened to that mushroom classifier. Maybe it is too small to generalize or has been train in a specific environment.
I haven’t looked closely enough to know, but I recall medical image analytics being “better than human” well before the current AI/LLM rage. Like, those systems use machine learning, but in a more deterministic, more conventional algorithm sense. I think they are also less worried about false positives, because the algorithm is always assumed to be checked by a human physician, so my impression is that the real sense in which medical image analysis is ‘better’ is that it identifies smaller or more obscure defects that a human quickly scanning the image might overlook.
If you’re using a public mushroom identification AI as the only source for life-and-death choice, then false positives are a much bigger problem.
yes, that is what i have heard too. there was a news thing some days ago that this “cancer scanner” thing will be available in two years to all doctors. so that’s great! but yes, we very much still need a human to watch over it, so its out-of-distribution-generations stay in check.
Do not trust AI to tell you if you can eat a mushroom. Ever. The same kinds of complexity goes into medicine. Sure, the machine learning process can flag something as cancerous (for example), but will always and forever need human review unless we somehow completely change the way machine learning works and speed it up by an order of magnitude.
yeah, we still very much need to have real humans go “yes, this is indeed cancer”, but this ai cancer detection feels like a reasonable “first pass” to quickly get a somewhat good estimation, rather than no estimation with lacking doctors.
Sorry in advance for being captain obvious, but I feel like I can’t get over this. Your comment is *valuable and I completely agree with your take here, but then the elephant in the room is: how do the people with power actually choose to use these tools? It’s not like I can effect change on healthcare AI use on my own.
So yes, it really can be first pass, good sanity check type of tool. It could help a good doctor if it was employed in a sane and useful way. And if the people with power over the system choose to use that way, I believe it would be a genuine benefit to a majority of humanity, worth the cost of its creation and maintenance.
Or, it could be used to second guess the doctors, cram more cases through without paying them fairly, or “justify” not having enough qualified experts to match our collective need.
Just framing how it is used a little bit differently suddenly takes us from genuine benefit to humanity, into profit-seeking for the 1% and lower quality of life for the remainder of us. That is by far my largest concern with this. I suppose that’s my largest concern with a lot of things right now.
yes, currently ai is largely being marketed to evil businesses wanting to automate some humans away. and in healthcare, especially in the US i fear, this will likely catch on.
it’s simply more cost-effective, while also being generally more reliable (better than humans even) at very specific tasks. buuuuut not all tasks. so we still have to keep around a doctor since they are needed for physical tests and such.
this amount of exclusively profit-driven stuff is - really sad. u would expect “health” companies to actually want to make u well off… but no they jus wan ur moni. big sad.
i am very sorry for everyone who has to live in this reality.
From what little I know if it, it’s sorta twofold what it does:
-
It looks through documentation across a patient record to look for patterns a doctor might miss. For example, a patient comes in complaining of persistent headaches/fatigue. A doctor might look at that in isolation and just try to treat the symptoms, but an AI might see some potentially relevant lab results in their histories and recommend more testing to rule out a cancer diagnosis that the doctor might have thought unlikely without awareness of that earlier data.
-
Doctors have to do a lot of busywork in their record keeping that AIs can help streamline. A lot of routine documentation, attestations, statements, etc. Since so much of it is very template-heavy already, an AI might be able to streamline the process as well as tailor it better to the patient. E.g. the record indicates “assigned male at birth” and an ER doctor defaults to he/him pronouns looking only at the medical birth sex marker, but the patient is also being seen by a gender clinic at which she is receiving gender affirming treatment as a trans woman and brings up that earlier data to correct the documentation and make it more accurate and personalized for the patient.
In reality, I am sure that practices and hospital systems are just going to use this as an excuse to say “You don’t need to spend as much time on documentation and chart review now so you can see more patients, right?” It’s the cotton gin issue.
-
Having worked with ML in manufacturing, if your task is precise enough and your input normalized enough, it can detect very impressive things. Identifying mushrooms as a whole is already too grand a task, especially as it as to deal with different camera angles, lighting … But ask it to differentiate between a few species, and always offer pictures using similar angles, lighting and background, and the results will most likely be stellar.
Like I said, I’m just skeptical. I know it can do impressive things, but unless we get a giant leap forward, it will always need extensive human review when it comes to medicine (like my mycology example). In my opinion, it is a tool for quick and dirty analysis in the medical field which may speed things up for human review.
My biggest problem with AI is how it was pushed and marketed to us in ways that don’t make sense / are unethical. Even the environmental concerns would be ameliorated if AI weren’t being pushed into everything. (Using “AI” here to refer to things like LM, image, and art generators,etc.)
yes, i completely agree.
having some LM generate “comment suggestions” for content creators on youtube is such a genuine waste of compute and the environment. (yes this is a real thing)
it was marketed as this “smart machine” which ends up being too dum for most people wanting to use it.
This not relevant to your comment, really, but I would like to applaud you for word usage.
LMs give the appearance of understanding, but as soon as you try to use them for anything that you actually are knowledgable in, the facade crumbles.
Even for repetitive tasks, you have to do a lot of manual checking to ensure they did not start hallucinating half way through.
I haven’t really used AIs myself, however one of my brothers loves AI for boilerplate code which he of course looks over afterwards. If it saves time and you only have to do some minor editing then that seems like a win to me. Probably shouldn’t be used like this in any non-hobby project by people who aren’t adept at coding however
I’m a programmer as well. When ChatGPT & Co initially came out, I was pretty excited tbh and attempted to integrate it into my workflow, which kinda worked-ish? But was also a lot of me being amazed by the novelty, and forgiving of the shortcomings.
Did not take me long to phase them out again though. (And no, it’s not the models I used; I have tried again now and then with the new, supposedly perfect-for-programming models, same results). The only edgecase where they are generally useful (to me at least) are simple tasks that I have some general knowledge of (to double theck the LM’s work) but not have any interest in learning anything further than I already know. Which does occur here and there, but rarely.
For everything else programming-related, it’s flat out shit.I do not beleive they are a time saver for even moderately difficult programs. Bu the time you’ve run around in enough circles, explaining “now, this does not do what you say it does”, “that’s the same wring answer you gave me two responses ago”, “you have hallucinated that function”, and found out the framework in use dropped that general structure in version 5, you may as well do it yourself, and actually learn how to do it at the same time.
For work, I eventually found that it took me longer to describe the business logic (and do the above dance) than to just… do the work. I also have more confidence in the code, and understand it completely.
In terms of programming aids, a linter, formatter and LSP are, IMHO, a million times more useful than any LM.
this matches my experience too. good IDEs or editors with LSP support allll the way.
also wanna add that it’s weird to me that we turn to LLMs to generate mountains of boilerplate instead of… y’know, fixing our damn tools in the first place (or using them correctly, or to their fullest) so that said boilerplate is unnecessary. abstractions have always been a thing. it seems so inefficient.
Makes me feel warm around the heart to hear that it’s not just me 🫠
ikr, it makes the horrors just a little more bearable ✨
I also 100% agree with you. My work has a developer productivity team that tries to make sure we have access to good tools, and those folks have been all over AI like flies on shit lately. I’ve started to feel a bit like a crazy Luddite because I do not feel like Copilot increases my productivity. I’m spending like 90% of my time reading docs, debugging and exploring fucked up edge cases, or staring off into space while contemplating if I’m about to introduce some godawful race condition between two disparate systems running in kubernetes or something. Senior developers usually do shit that would take hours to properly summarize for a language model.
And yeah, if I have to write a shitload boilerplate then I’m writing bad code and probably need to add or fix abstraction. Worst case, there’s always vim macros or a quick shell oneliner to generate that shit. The barrier to progress is useful because it warns me that I’m being a dummy. I don’t want to get rid of that when the only benefit is that I get to context switch between code review mode and system synthesis mode.
Yeah, with seniors it’s even more clear how little LMs can help.
I feel you on the AI tools being pushed thing. My company is too small to have a dedicated team for something like that, buuuut… As of last week, we’re wasting resources on an internal server hosting Deepseek on absurd hardware. Like, far more capable than our prod server.
Oh, an we pride ourselves on being soooo environmentally friendly 😊🎉
for even moderately difficult programs.
My brother uses it to generate templates and basic structs and functions, not to generate novel code. That’s probably the difference here. I believe it’s integrated into his text editor as well? It’s the one github offers
Edit: Probably wouldn’t be useful if it wasn’t integrated into the editor and therefore the generation being just a click away or some sort of autofill. Actually writing a prompt does sound tedious
I’ve heard this argument so many fucking times and i hate genai but there’s no practical difference between understanding and having the appearance of such, that is just a human construct that we use to try to feel artificially superior ffs
No. I am not saying that to put man and machine in two boxes. I am saying that because it is a huge difference, and yes, a practical one.
An LLM can talk about a topic for however long you wish, but it does not know what it is talking about, it has no understanding or concept of the topic. And that shines through the instance you hit a spot where training data was lacking and it starts hallucinating. LLMs have “read” an unimaginable amount of texts on computer science, and yet as soon as I ask something that is niche, it spouts bullshit. Not it’s fault, it’s not lying; it’s just doing what it always does, putting statistically likely token after statistically liken token, only in this case, the training data was insufficient.
But it does not understand or know that either; it just keeps talking. I go “that is absolutely not right, remember that <…> is <…,>” and whether or not what I said was true, it will go "Yes, you are right! I see now, <continues to hallucinate> ".
There’s no ghost in the machine. Just fancy text prediction.
you’re right, it doesn’t do classification perfectly every time. but it drills down on the amount of human labour required to classify a large set of data.
about the knowledge: it really comes down to which model you are talking to. “generalist” models like GPT4o or claude 3.5 sonnet have been trained to know many things somewhat, but no single thing perfectly.
currently companies seem to train largely on IT-related things. these models are great at helping me program, but they are terrible at specifically writing GDScript (a niche game-programming language) since they forget all the methods and components the language has.
Even with LMs supposedly specialising in the areas that I am knowledgable (but by no means an expert) in, it’s the same. Drill down even slightly beyond surface-level, and it’s either plain wrong, or halucinated when not immediately disprovable.
And why wouldn’t it be? These things do not possess knowledge, they possess the ability to generate texts about things we’d like them to be knowledgable in, and that is a crucial difference.
Ultimately, the issue is our current societies being fucked. If AI were refined, sensibly monitored and generally used by people who can recognize mistakes (where it matters), and keep their fossil fuel usage in check, AI could be a big step towards gay space communism. Like, who wants to do menial labor? Let AI do it where sensible and pay the former workers the money that’s saved by doing that. But as it is, it’s mostly going to be used to further the agendas of authoritarians and capitalists.
yesyey, this very much. in the hands of people who know the capabilities of the models, they tend to use them well and speed up their work. gay space communism would be totally cool if shiddy jobs could slowly be automated away <3
but yea, big sad cuz evil capitalists go “yesyes we make ai for ur business” even tho world would be better without business ~ ~
A lot of those points boil down to the same thing: “what if the AI is wrong?”
If it’s something that you’ll need to check manually anyway, or where a mistake is not a big deal, that’s probably fine. But if it’s something where a mistake can affect someone’s well-being, that is bad.
Reusing an example from the pic:
- Predicting 3D structures of proteins, as in the example? OK! Worst hypothesis the researchers will notice that the predicted structure does not match the real one.
- Predicting if you have some medical problem? Not OK. A false negative can cost a life.
That’s of course for the usage. The creation of those systems is another can of worms, and it involves other ethical concerns.
of course using ai stuffs for medical usage is going to have to be monitored by a human with some knowledge. we can’t just let it make all the decisions… quite yet.
in many cases, ai models are already better than expert humans in the field. recognizing cancer being the obvious example, where the pattern recognition works perfectly. or with protein folding, where humans are at about 60% accuracy, while googles alphafold is at 94% or so.
clearly humans need to oversee AIs output, but we are getting to a point where maybe humans make the wrong decision, and deny an AIs correct generation. so: no additional lives are lost, but many more could be saved
I mostly agree with you, I think that we’re disagreeing on details. And you’re being far, far more level-headed than most people who discuss this topic, who pretend that AI is either e-God or Satanic bytes. (So no, you aren’t an evil AI tech sis. Nor a Luddite.)
That said:
For clinical usage, just monitoring it isn’t enough - because when people know that there’s some automated system to catch their mistakes, or that they’re just catching the mistakes of that system, they get sloppier. You need really, really good accuracy.
Like, 95% accuracy might look like a lot, right? If it involves death or life, it means a death for each 20 cases, it’s rather high. In the meantime, if AlphaFold got it wrong 60% of the time instead of just 6%, it wouldn’t be a big deal.
Also, note that we’re both talking about “AI” as if it was a single thing. Under the hood it’s a bunch of completely different things; pattern recognition AI, predictive AI, generative AI, they work so differently from each other that we’d need huge walls of text to decide how good or bad each of them is.
This list is missing: AI generated images are not art.
I disagree, but I can respect your opinion.
i also think that way, but it’s also true that generated images are being used all over the web already, so people generally don’t seem to care.
What does “AI for disabled people” entail? A lot of ‘good AI’ things I see are things I wouldn’t consider AI, e.g. VLC’s local subtitle generation.
true, we kinda move the barrier on what “AI” means all the time. back then TTS and STT surprised everyone by how it worked kinda good. Now we don’t even consider it AI, even tho STT is almost always driven by a neural network, and new models like OpenAIs whisper models are still releasing.
there are also some VLMs which let you get pretty good descriptions of some images, in case none were provided by a human.
i have heard some people actually being able to benefit off of that.
Yeah, the way ‘AI’ companies have played with term AI is annoying as heck. The fact AGI has been allowed to catch on at all is frankly a failure of the tech press. I do remember reading a good article on how stuff stops being ‘AI’ when it gains real world use, that I can’t find because Google sucks now.
I don’t enough about running AI locally to know if this applies, but I just can’t stomach any of it because I can’t help but think of what those companies put people in places like Kenya through in order to get the token data to make these models useful. It’s probably unfair to taint the whole field like that, like I’m sure there are some models that haven’t been trained like this, but I just can’t shake the association.
Sounds like a pretty nuanced opinion to me. AI’s biggest problem is that it’s being driven by profit seeking companies that, as usual, have no interest in the damaging “externalities” that are involved. But anyone that says it has no application and all it does is hallucinate and waste energy is not aware of the progress being made.
There is an over arching issue with most of the extant models being highly unethical in where they got their data, effectively having made plagiarism machines.
It is not ok to steal the content of millions of small independent creators to create slop that drowns them out. Most of them were already offering their work for free. And I am talking about LMs here, writing is a skill.
Say what ever you want about big companies being bad for abusing IP laws, but this is not about the laws, not even paying people for their work, this is about crediting people when they do work, acknowledging that the work they did had value, and letting people know where they can find more.
Also, I don’t really buy the “it’s good for disabled people” that feels like using disabled people as a shield against criticism, and I’ve yet to see it brought up in good faith.
A human can read examples of good articles to learn how to write a good article, but an AI can’t?
It seems kinda arbitrary, I don’t think you can say anything objective about whether AI is plagiarism or not besides the most literal definition of the law (which is impossible as it itself is made arbitrary through the idea of fair use)
