There’s this linguistic problem where one word is used for two different things, it becomes difficult to tell them apart. “Training” or “learning” is a very poor choice of word to describe the calibration of a neural network. The actor and action are both fundamentally different from the accepted meaning. To start with, human learning is active whereas machining learning is strictly passive: it’s something done by someone with the machine as a tool. Teachers know very well that’s not how it happens with humans.
When I compare training a neural network with how a trained to play clarinet, I fail to see any parallel. The two are about as close as a horse and a seahorse.
Phanatik@kbin.social 5 months ago
A comedian isn't forming a sentence based on what the most probable word is going to appear after the previous one. This is such a bullshit argument that reduces human competency to "monkey see thing to draw thing" and completely overlooks the craft and intent behind creative works. Do you know why ChatGPT uses certain words over others? Probability. It decided as a result of its training that one word would appear after the previous in certain contexts. It absolutely doesn't take into account things like "maybe this word would be better here because the sound and syllables maintains the flow of the sentence".
Baffling takes from people who don't know what they're talking about.
frog@beehaw.org 5 months ago
I wish I could upvote this more than once.
What people always seem to miss is that a human doesn’t need to billions of examples to be able to produce something that’s kind of “eh, close enough”. Artists don’t look at billions of paintings. They look at a few, but do so deeply, absorbing not just the most likely distribution of brushstrokes, but why the painting looks the way it does. For a basis of comparison, I did an art and design course last year and looked at about 300 artworks in total (course requirement was 50-100). The research component on my design-related degree course is one page a week per module (so basically one example from the field the module is about, plus some analysis). The real bulk of the work humans do isn’t looking at billions of examples: it’s looking at a few, and then practicing the skill and developing a process that allows them to convey the thing they’re trying to express.
If the AI models were really doing exactly the same thing humans do, the models could be trained without any copyright infringement at all, because all of the public domain and creative commons content, plus maybe licencing a little more, would be more than enough.
Phanatik@kbin.social 5 months ago
Exactly! You can glean so much from a single work, not just about the work itself but who created it and what ideas were they trying to express and what does that tell us about the world they live in and how they see that world.
This doesn't even touch the fact that I'm learning to draw not by looking at other drawings but what exactly I'm trying to draw. I know at a base level, a drawing is a series of shapes made by hand whether it's through a digital medium or traditional pen/pencil and paper. But the skill isn't being able replicate other drawings, it's being able to convert something I can see into a drawing. If I'm drawing someone sitting in a wheelchair, then I'll get the pose of them sitting in the wheelchair but I can add details I want to emphasise or remove details I don't want. There's so much that goes into creative work and I'm tired of arguing with people who have no idea what it takes to produce creative works.
frog@beehaw.org 5 months ago
It seems that most of the people who think what humans and AIs do is the same thing are not actually creatives themselves. Their level of understanding of what it takes to draw goes no further than “well anyone can draw, children do it all the time”. They have the same respect for writing, of course, equating the ability to string words together to write an email, with the process it takes to write a brilliant novel or script. They don’t get it, and to an extent, that’s fine - not everybody needs to understand everything. But they should at least have the decency to listen to the people that do get it.
Marsupial@quokk.au 5 months ago
Children learn by watching others. We are trained from millions of examples from before birth.
Even_Adder@lemmy.dbzer0.com 5 months ago
When people say that the “model is learning from its training data”, it means just that, not that it is human, and not that it learns exactly humans. It doesn’t make sense to judge boats on how well they simulate human swimming patterns, just how well they perform their task.
Every human has the benefit of as a baby training on things around them and being trained by those around them, building a foundation for all later skills. Generative models rely on many text and image pairs to describe things to them because they lack the ability to poke, prod, rotate, and disassemble for themselves.
For example, when a model takes in a thousand images of circles, it doesn’t “learn” a thousand circles. It learns what circle GENERALLY is like, the concept of it. That representation, along with random noise, is how you create images with them. The same happens for every concept the model trains on. Everything from “cat” to more complex things like color relationships and reflections or lighting. Machines are not human, but they can learn despite that.
ParsnipWitch@feddit.de 5 months ago
In general I agree with you, but AI doesn’t learn the concept of what a circle is. AI reproduces the most fitting representation of what we call a circle. But there is no understanding of the concept of a circle. This may sound not picking, but I think it’s important to make the distinction.
That is why current models aren’t regarded as actual intelligence, although people already call them that…
Eccitaze@yiffit.net 5 months ago
It makes sense to judge how closely LLMs mimic human learning when people are using it as a defense to AI companies scraping copyrighted content, and making the claim that banning AI scraping is as nonsensical as banning human learning.
But when it’s pointed out that LLMs don’t learn very similarly to humans, and require scraping far more material than a human does, suddenly AIs shouldn’t be judged by human standards? I don’t know if it’s intentional on your part, but that’s a pretty classic example of a motte-and-bailey fallacy. You can’t have it both ways.
teawrecks@sopuli.xyz 5 months ago
What you count as “one” example is arbitrary. In terms of pixels, you’re looking at millions right now.
The ability to train faster using fewer examples in real time, similar to what an intelligent human brain can do, is definitely a goal of AI research. But right now, we may be seeing from AI what a below average human brain could accomplish with hundreds of lifetimes to study.
I mean, no, if you only ever look at public domain stuff you literally wouldn’t know the state of the art, which is historically happening for profit. Even the most untrained artist “doing their own thing” watches Disney/Pixar movies and listens to copyrighted music.
frog@beehaw.org 5 months ago
If we’re going by the number of pixels being viewed, then you have to use the same measure for both humans and AIs - and because AIs have to look at billions of images while humans do not, the AI still requires far more pixels than a human does.
And humans don’t require the most modern art in order to learn to draw at all. Sure, if they want to compete with modern artists, they would need to look at modern artists (for which educational fair use exists, and again the quantity of art being used by the human for this purpose is massively lower than what an AI uses - a human does not need to consume billions of artworks from modern artists in order to learn what the current trends are). But a human could learn to draw, paint, sculpt, etc purely by only looking at public domain and creative commons works, because the process for drawing, say, the human figure (with the right number of fingers!) has not changed in hundreds of years. A human can also just… go outside and draw things they see themselves, because the sky above them and the tree across the street aren’t copyrighted. And in fact, I’d argue that a good artist should go out and find real things to draw.
OpenAI’s argument is literally that their AI cannot learn without using copyrighted materials in vast quantities - too vast for them to simply compensate all the creators. So it genuinely is not comparable to a human, because humans can, in fact, learn without using copyrighted material. If OpenAI’s argument is actually that their AI can’t compete commercially with modern art without using copyrighted works, then they should be honest about that - but then they’d be showing their hand, wouldn’t they?
Bene7rddso@feddit.de 5 months ago
Humans learn mostly from real life. Go touch some grass
intensely_human@lemm.ee 5 months ago
When you look at one painting, is that the equivalent of one instance of the painting in the training data? There is an infinite amount of information in the painting, and each time you look you process more of that information.
I’d say any given painting you look at in a museum, you process at least a hundred mental images of aspects of it. A painting on your wall could be seen ten thousand times easily.
DaDragon@kbin.social 5 months ago
That’s what humans do, though. Maybe not probability directly, but we all know that some words should be put in a certain order. We still operate within standard norms that apply to aparte group of people. LLM’s just go about it in a different way, but they achieve the same general result. If I’m drawing a human, that means there’s a ‘hand’ here, and a ‘head’ there. ‘Head’ is a weird combination of pixels that mostly look like this, ‘hand’ looks kinda like that. All depends on how the model is structured, but tell me that’s not very similar to a simplified version of how humans operate.
Phanatik@kbin.social 5 months ago
Yeah but the difference is we still choose our words. We can still alter sentences on the fly. I can think of a sentence and understand verbs go after the subject but I still have the cognition to alter the sentence to have the effect I want. The thing lacking in LLMs is intent and I'm yet to see anyone tell me why a generative model decides to have more than 6 fingers. As humans we know hands generally have five fingers and there's a group of people who don't so unless we wanted to draw a person with a different number of fingers, we could. A generative art model can't help itself from drawing multiple fingers because all it understands is that "finger + finger = hand" but it has no concept on when to stop.
DaDragon@kbin.social 5 months ago
And that’s the reason why LLM generated content isn’t considered creative.
I do believe that the person using the device has a right to copyright the unique method they used to generate the content, but the content itself isn’t anything worth protecting.
intensely_human@lemm.ee 5 months ago
I don’t choose my words man. I get a vague sense of the meaning I want to convey and the words just form themselves.
ParsnipWitch@feddit.de 5 months ago
As an artist you draw with an understanding of the human body, though. An understanding current models don’t have because they aren’t actually intelligent.
Maybe when a human is an absolute beginner in drawing they will think about the different lines and replicate even how other people draw stuff that then looks like a hand.
But eventually they will realise (hopefully, otherwise they may get frustrated and stop drawing) that you need to understand the hand to draw one. It’s mass, it’s concept or the idea of what a hand is.
This may sound very abstract and strange but creative expression is more complex than replicating what we have seen a million times. It’s a complex function unique to the human brain, an organ we don’t even scientifically understand yet.
hascat@programming.dev 5 months ago
That’s not the point though. The point is that the human comedian and the AI both benefit from consuming creative works covered by copyright.
Phanatik@kbin.social 5 months ago
Yeah except a machine is owned by a company and doesn't consume the same way. It breaks down copyrighted works into data points so it can find the best way of putting those data points together again. If you understand anything at all about how these models work, they do not consume media the same way we do. It is not an entity with a thought process or consciousness (despite the misleading marketing of "AI" would have you believe), it's an optimisation algorithm.
chahk@beehaw.org 5 months ago
It’s a glorified autocomplete.
vexikron@lemmy.zip 5 months ago
And human comedians regularly get called out when they outright steal others material and present it as their own.
The word for this is plagiarism.
And in OpenAIs framework, when used in a relevant commercial context, they are functionally operating at profiting off of the worlds most comprehensive plagiarism software.
intensely_human@lemm.ee 5 months ago
They get called out when they use others work as a template, not as training data.
teawrecks@sopuli.xyz 5 months ago
Neither is an LLM. What you’re describing is a primitive Markov chain.
You may not like it, but brains really are just glorified pattern recognition and generation machines. So yes, “monkey see thing to draw thing”, except a really complicated version of that.
Think of it this way: if your brain wasn’t a reorganization and regurgitation of the things you have observed before, it would just generate random noise. There’s no such thing as “truly original” art or it would be random noise. Every single word either of us is typing is the direct result of everything you and I have observed before this moment.
Ironic, to say the least.
The point you should be making, is that a corporation will make this above argument up to, but not including the point where they have to treat AIs ethically. So that’s the way to beat them. If they’re going to argue that they have created something that learns and creates content like a human brain, then they should need to treat it like a human, ensure it is well compensated, ensure it isn’t being overworked or enslaved, ensure it is being treated “humanely”. If they don’t want to do that, if they want it to just be a well built machine, then they need to license all the proprietary data they used to build it. Make them pick a lane.
Phanatik@kbin.social 5 months ago
My description might've been indicative of a Markov chain but the actual framework uses matrices because you need to be able to store and compute a huge amount of information at once which is what matrices are good for. Used in animation if you didn't know.
What it actually uses is irrelevant, how it uses those things is the same as a regression model, the difference is scale. A regression model looks at how related variables are in giving an outcome and computing weights to give you the best outcome. This was the machine learning boom a couple of years ago and TensorFlow became really popular.
LLMs are an evolution of the same idea. I'm not saying it's not impressive because it's very cool what they were able to do. What I take issue with is the branding, the marketing and the plagiarism. I happen to be in the intersection of working in the same field, an avid fan of classic Sci-Fi and a writer.
It's easy to look at what people have created throughout history and think "this looks like that" and on a point by point basis you'd be correct but the creation of that thing is shaped by the lens of the person creating it. Someone might make a George Carlin joke that we've heard recently but we'll read about it in newspapers from 200 years ago. Did George Carlin steal the idea? No. Was he aware of that information? I don't know. But Carlin regularly calls upon his own experiences so it's likely that he's referencing a event from his past that is similar to that of 200 years ago. He might've subconsciously absorbed the information.
The point is that the way these models have been trained is unethical. They used material they had no license to use and they've admitted that it couldn't work as well as it does without stealing other people's work. I don't think they're taking the position that it's intelligent because from the beginning that was a marketing ploy. They're taking the position that they should be allowed to use the data they stole because there was no other way.
pupbiru@aussie.zone 5 months ago
okay
yup
woah there! that’s where we disagree… your position is based on the fact that you believe that this is plagiarism - inherently negative
perhaps its best not use loaded language. if we want to have a good faith discussion, it’s best to avoid emotive arguments and language that’s designed to evoke negativity simply by their use, rather than the argument being presented
its understandable that it’s frustrating, but just because a machine is now able to do a similar job to a human doesn’t make it inherently wrong. it might be useful for you to reframe these developments - it’s not taking away from humans, it’s enabling humans… the less a human has to have skill to get what’s in their head into an expressive medium for someone to consume the better imo! art and creativity shouldn’t be about having an ability - the closer we get to pure expression the better imo!
the less you have to worry about the technicalities of writing, the more you can focus on pure creativity
i’d question why it’s unethical, and also suggest that “stolen” is another emotive term here not meant to further the discussion by rational argument
so, why is it unethical for a machine but not a human to absorb information and create something based on its “experiences”?
tryptaminev@feddit.de 5 months ago
You do know that comedians are copying each others material all the time though? Either making the same joke, or slightly adapting it?
So in the context of copyright vs. model training i fail to see how the exact process of the model is relevant? At the end copyrighted material goes in and material based on that copyrighted material goes out.
pupbiru@aussie.zone 5 months ago
you know how the neurons in our brain work, right?
because if not, well, it’s pretty similar… unless you say there’s a soul (in which case we can’t really have a conversation based on fact alone), we’re just big ol’ probability machines with tuned weights based on past experiences too
Phanatik@kbin.social 5 months ago
You are spitting out basic points and attempting to draw similarities because our brains are capable of something similar. The difference between what you've said and what LLMs do is that we have experiences that we are able to glean a variety of information from. An LLM sees text and all it's designed to do is say "x is more likely to appear before y than z". If you fed it nonsense, it would regurgitate nonsense. If you feed it text from racist sites, it will regurgitate that same language because that's all it has seen.
You'll read this and think "that's what humans do too, right?" Wrong. A human can be fed these things and still reject them. Someone else in this thread has made some good points regarding this but I'll state them here as well. An LLM will tell you information but it has no cognition on what it's telling you. It has no idea that it's right or wrong, it's job is to convince you that it's right because that's the success state. If you tell it it's wrong, that's a failure state. The more you speak with it, the more fail states it accumulates and the more likely it is to cutoff communication because it's not reaching a success, it's not giving you what you want. The longer the conversation goes on, the more crazy LLMs get as well because it's too much to process at once, holding those contexts in its memory while trying to predict the next one. Our brains do this easily and so much more. To claim an LLM is intelligent is incredibly misguided, it is merely the imitation of intelligence.
pupbiru@aussie.zone 5 months ago
but that’s just a matter of complexity, not fundamental difference. the way our brains work and the way an artificial neural network work aren’t that different; just that our brains are beyond many orders of magnitude bigger
there’s no particular reason why we can’t feed artificial neural networks an enormous amount of random information as well, but in order to be efficient and make them specialise in the things we want, we only feed them information that’s directly related to the specialty we want them to perform
there’s some… let’s say “pre training” or “pre-existing state” that exists with humans too, but i’d argue that’s as relevant to the actual task of learning, comprehension, and creating as a BIOS is to running an operating system (that is, a necessary precondition but not actually what you’d call the main function)
i’m also not claiming that an LLM is intelligent (or rather i’d prefer to use the term self aware because intelligent is pretty nebulous); just that the structure it has isn’t that much different to our brains just on a level that’s so much smaller and so much more generic that you can’t expect it to perform as well as a human
i guess the core of what i’m getting at is that the self awareness that humans have is definitely not present in an LLM, however i don’t think that self-awareness is necessarily a pre-requisite for most things that we call creativity. i think that’s it’s entirely possible for an artificial neural net that’s fundamentally the same technology that we use today to be able to ingest the same data that a human would from birth, and to have very similar outcomes… given that belief (and i’m very aware that it certainly is just a belief - we aren’t close to understanding our brains, but i don’t fundamentally thing there’s anything other then neurons firing that results in the human condition), just because you simplify and specialise the input data doesn’t mean that the process is different. you could argue that it’s less, for sure, but to rule out that it can create a legitimately new work is definitely premature
ParsnipWitch@feddit.de 5 months ago
“Soul” is the word we use for something we don’t scientifically understand yet. Unless you did discover how human brains work, in that case I congratulate you on your Nobel prize.
You can abstract a complex concept so much it becomes wrong. And abstracting how the brain works to “it’s a probability machine” definitely is a wrong description. Especially when you want to use it as an argument of similarity to other probability machines.
pupbiru@aussie.zone 5 months ago
that’s far from definitive. another definition is
but since we aren’t arguing semantics, it doesn’t really matter exactly, other than the fact that it’s important to remember that just because you have an experience, belief, or view doesn’t make it the only truth
of course i didn’t discover categorically how the human brain works in its entirety, however most scientists i’m sure would agree that the method by which the brain performs its functions is by neurons firing. if you disagree with that statement, the burden of proof is on you. the part we don’t understand is how it all connects up - the emergent behaviour. we understand the basics; that’s not in question, and you seem to be questioning it
it’s not abstracted; it’s simplified… if what you’re saying were true, then simplifying complex organisms down to a petri dish for research would be “abstracted” so much it “becomes wrong”, which is categorically untrue… it’s an incomplete picture, but that doesn’t make it either wrong or abstract
i laid out an a leads to b leads to c and stated that it’s simply a belief, however it’s a belief that’s based in logic and simplified concepts. if you want to disagree that’s fine but don’t act like you have some “evidence” or “proof” to back up your claims… all we’re talking about here is belief, because we simply don’t know - neither you nor i
and given that all of this is based on belief rather than proof, the only thing that matters is what we as individuals believe about the input and output data (because the bit in the middle has no definitive proof either way)
if a human consumes media and writes something and it looks different, that’s not a violation
if a machine consumes media and writes something and it looks different, you’re arguing that is a violation
the only difference here is your belief that a human brain somehow has something “more” than a probabilistic model going on… but again, that’s far from certain
SuperSaiyanSwag@lemmy.zip 5 months ago
Am I a moron? How do you have more upvotes than the parent comment, is it because you’re being more aggressive with your statement? I feel like you didn’t quite refute what the parent comment said. You’re just explaining how Chat GPT works, but you’re not really saying how it shouldn’t use our established media as a reference.
Phanatik@kbin.social 5 months ago
I don't control the upvotes so I don't know why that's directed at me.
The refutation was based on around a misunderstanding of how LLMs generate their outputs and how the training data assists the LLM in doing what it does. The article itself tells you ChatGPT was trained off of copyrighted material they were not licensed for. The person I responded to suggested that comedians do this with their work but that's equating the process an LLM uses when producing an output to a comedian writing jokes.
SuperSaiyanSwag@lemmy.zip 5 months ago
Sorry, I was essentially emphasizing on my initial point “am I a moron?”, lol, because I legitimately didn’t get your point at first like others do in this thread.
I get what you mean now after reading it couple more times
intensely_human@lemm.ee 5 months ago
Text prediction seems to be sufficient to explain all verbal communication to me. Until someone comes up with a use case that humans can do that LLMs cannot, and I mean a specific use case not general high level concepts, I’m going to assume human verbal cognition works the same was as an LLM.
We are absolutely basing our responses on what words are likely to follow which other ones. It’s literally how a baby learns language from those around them.
chaos@beehaw.org 5 months ago
If you ask an LLM to help you with a legal brief, it’ll come up with a bunch of stuff for you, and some of it might even be right. But it’ll very likely do things like make up a case that doesn’t exist, or misrepresent a real case, and as has happened multiple times now, if you submit that work to a judge without a real lawyer checking it first, you’re going to have a bad time.
There’s a reason LLMs make stuff up like that, and it’s because they have been very, very narrowly trained when compared to a human. The training process is almost entirely getting good at predicting what words follow what other words, but humans get that and so much more. Babies aren’t just associating the sounds they hear, they’re also associating the things they see, the things they feel, and the signals their body is sending them. Babies are highly motivated to learn and predict the behavior of the humans around them, and as they get older and more advanced, they get rewarded for creating accurate models of the mental state of others, mastering abstract concepts, and doing things like make art or sing songs. Their brains are many times bigger than even the biggest LLM, their initial state has been primed for success by millions of years of evolution, and the training set is every moment of human life.
LLMs aren’t nearly at that level. That’s not to say what they do isn’t impressive, because it really is. They can also synthesize unrelated concepts together in a stunningly human way, even things that they’ve never been trained on specifically. They’ve picked up a lot of surprising nuance just from the text they’ve been fed, and it’s convincing enough to think that something magical is going on. But ultimately, they’ve been optimized to predict words, and that’s what they’re good at, and although they’ve clearly developed some impressive skills to accomplish that task, it’s not even close to human level. They spit out a bunch of nonsense when what they should be saying is “I have no idea how to write a legal document, you need a lawyer for that”, but that would require them to have a sense of their own capabilities, a sense of what they know and why they know it and where it all came from, knowledge of the consequences of their actions and a desire to avoid causing harm, and they don’t have that. And how could they? Their training didn’t include any of that, it was mostly about words.
One of the reasons LLMs seem so impressive is that human words are a reflection of the rich inner life of the person you’re talking to. You say something to a person, and your ideas are broken down and manipulated in an abstract manner in their head, then turned back into words forming a response which they say back to you. LLMs are piggybacking off of that a bit, by getting good at mimicking language they are able to hide that their heads are relatively empty. Spitting out a statistically likely answer to the question “as an AI, do you want to take over the world?” is very different from considering the ideas, forming an opinion about them, and responding with that opinion. LLMs aren’t just doing statistics, but you don’t have to go too far down that spectrum before the answers start seeming thoughtful.