Apparently, stealing other peopleās work to create product for money is now āfair useā as according to OpenAI because they are āinnovatingā (stealing). Yeah. Move fast and break things, huh?
āBecause copyright today covers virtually every sort of human expressionāincluding blogposts, photographs, forum posts, scraps of software code, and government documentsāit would be impossible to train todayās leading AI models without using copyrighted materials,ā wrote OpenAI in the House of Lords submission.
OpenAI claimed that the authors in that lawsuit āmisconceive[d] the scope of copyright, failing to take into account the limitations and exceptions (including fair use) that properly leave room for innovations like the large language models now at the forefront of artificial intelligence.ā
A comedian isnāt 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 āmonkey see thing to draw thingā 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 doesnāt take into account things like āmaybe this word would be better here because the sound and syllables maintains the flow of the sentenceā.
Baffling takes from people who donāt know what theyāre talking about.
I wish I could upvote this more than once.
What people always seem to miss is that a human doesnāt need billions of examples to be able to produce something thatās kind of āeh, close enoughā. Artists donāt 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 isnāt looking at billions of examples: itās looking at a few, and then practicing the skill and developing a process that allows them to convey the thing theyāre 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.
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 doesnāt even touch the fact that Iām learning to draw not by looking at other drawings but what exactly Iām trying to draw. I know at a base level, a drawing is a series of shapes made by hand whether itās through a digital medium or traditional pen/pencil and paper. But the skill isnāt being able replicate other drawings, itās being able to convert something I can see into a drawing. If Iām drawing someone sitting in a wheelchair, then Iāll get the pose of them sitting in the wheelchair but I can add details I want to emphasise or remove details I donāt want. Thereās so much that goes into creative work and Iām tired of arguing with people who have no idea what it takes to produce creative works.
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 āwell anyone can draw, children do it all the timeā. 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 donāt get it, and to an extent, thatās fine - not everybody needs to understand everything. But they should at least have the decency to listen to the people that do get it.
Well, thatās not me. Iām a creative, and I see deep parallels between how LLMs work and how my own mind works.
Either youāre vastly overestimating the degree of understanding and insight AIs possess, or youāre vastly underestimating your own capabilities. :)
This whole AI craze has just shown me that people are losing faith in their own abilities and their ability to learn things. Iāve heard so many who use AI to generate āartworkā argue that they tried to do art āfor yearsā without improving, and hence have come to conclusion that creativity is a talent that only some have, instead of a skill you can learn and hone. Just because they didnāt see results as fast as theyād have liked.
Very well said! Creativity is definitely a skill that requires work, and for which there are no short cuts. It seems to me that the vast majority of people using AI for artwork are just looking for a short cut, so they can get the results without having to work hard and practice. The one valid exception is when itās used by disabled people who have physical limitations on what they can do, which is a point thatās brought up occasionally - and if that was the one and only use-case for these models, I think a lot of artists would actually be fine with that.
I started drawing seriously when I was 14. Looking at my old artwork, I didnāt start improving fast until I was around 19 or 20. Not to say I didnāt improve at all during those five to six years but the pace did get faster once I had ālearned to learnā so to say. That is to say it can take a lot of patience to get to a point where you actually start seeing improvement fast enough to stay motivated. But it is 100% worth it because at the end you have a lot of things you have created with your own two hands.
And regarding the point on physical limitations, I canāt blame anyone in a situation like that for using AI if they have no other chance for realising their imaginations. For others, it is completely possible and not reserved for people who have some mythical innate talent. Just grab a pen or a brush and enjoy the process of honing a fine skill regardless of the end result. ā¤ļø
Alternatively, you might be vastly overestimating human āunderstanding and insightā, or how much of it is really needed to create stuff.
Average humans, sure, donāt have a lot of understanding and insight, and little is needed to be able to draw a doodle on some paper. But trained artists have a lot of it, because part of the process is learning to interpret artworks and work out why the artist used a particular composition or colour or object. To create really great art, you do actually need a lot of understanding and insight, because everything in your work will have been put there deliberately, not just to fill up space.
An AI doesnāt know why itās put an apple on the table rather than an orange, it just does it because human artists have done it - it doesnāt know what apples mean on a semiotic level to the human artist or the humans that look at the painting. But humans do understand what apples represent - they may not pick up on it consciously, but somewhere in the backs of their minds, theyāll see an apple in a painting and itāll make the painting mean something different than if the fruit had been an orange.
Interestingly, LLMs seem to show emerging semiotic organization. By analyzing the activation space of the neural network, related concepts seem to get trained into similar activation patterns, which is what allows LLMs to zero shot relationships when executed at a ātemperatureā (randomness level) in the right range.
Pairing an LLM with a stable diffusion model, allows the resulting AI toā¦ well, judge by yourself: https://llm-grounded-diffusion.github.io/
Children learn by watching others. We are trained from millions of examples starting from before birth.
When people say that the āmodel is learning from its training dataā, it means just that, not that it is human, and not that it learns exactly humans. It doesnāt 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 doesnāt ālearnā 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 ācatā to more complex things like color relationships and reflections or lighting. Machines are not human, but they can learn despite that.
In general I agree with you, but AI doesnāt 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 nit picking, but I think itās important to make the distinction.
That is why current models arenāt regarded as actual intelligence, although people already call them thatā¦
I understand. I didnāt mean to imply any sort of understanding with the language I used.
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 itās pointed out that LLMs donāt learn very similarly to humans, and require scraping far more material than a human does, suddenly AIs shouldnāt be judged by human standards? I donāt know if itās intentional on your part, but thatās a pretty classic example of a motte-and-bailey fallacy. You canāt have it both ways.
I donāt understand what you mean, can you elaborate?
What you count as āoneā example is arbitrary. In terms of pixels, youāre 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 wouldnāt know the state of the art, which is historically happening for profit. Even the most untrained artist ādoing their own thingā watches Disney/Pixar movies and listens to copyrighted music.
If weāre 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 donāt 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 justā¦ go outside and draw things they see themselves, because the sky above them and the tree across the street arenāt copyrighted. And in fact, Iād argue that a good artist should go out and find real things to draw.
OpenAIās 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 OpenAIās argument is actually that their AI canāt compete commercially with modern art without using copyrighted works, then they should be honest about that - but then theyād be showing their hand, wouldnāt they?
Which is the literal goal of Dall-E, SD, etc.
They could definitely learn some amount of skill, I agree. Iād be very interested to see the best that an AI could achieve using only PD and CC content. It would be interesting. But youād agree that it would look very different from modern art, just as an alien who has only been consuming earth media from 100+ years ago would be unable to relate to us.
Yeah, Iād consider that PD/CC content that such an AI would easily have access to. But obviously the real sky is something entirely different from what is depicted in Starry Night, Star Wars, or H.P. Lovecraftās description of the cosmos.
Yeah, Iād consider that a strong claim on their part; what they really mean is, itās the easiest way to make progress in AI, and we wouldnāt be anywhere close to where we are without it.
And you could argue āconvenient that it both saves them money, and generates money for them to do it this wayā, but Iād also point out that the alternative is they keep the trained models closed source, never using them publicly until they advance the tech far enough that theyāve literally figured out how to build/simulate a human brain that is able to learn as quickly and human-like as youāre describing. And then we find ourselves in a world where one or two corporations have this incredible proprietary ability that no one else has.
Personally, Iād rather live in the world where the information about how to do all of this isnāt kept for one or two corporations to profit from, I would rather live in the version where they publish their work publicly, early, and often, show that it works, and people are able to reproduce it, open source it, train their own models, and advance the technology in a space where anyone can use it.
You could hypothesize of a middle ground where they do the research, but arenāt allowed to profit from it without licensing every bit of data they train on. But the reality of AI research is that it only happens to the extent that it generates revenue. Itās been that way for the entire history of AI. Douglas Hofstadter has been asking deep important questions about AI as it relates to consciousness for like 60 years (ex. GEB, I am a Strange Loop), but thereās a reason he didnāt discover LLMs and tech companies did. Thatās not to say his writings are meaningless, in fact I think theyāre more important than ever before, but he just wasnāt ever going to get to this point with a small team of grad students, a research grant, and some public domain datasets.
So, itās hard to disagree with OpenAI there, AI definitely wouldnāt be where it is without them doing what theyāve done. And Iām a firm believer that unless we figure our shit out with energy generation soon, the earth will be an uninhabitable wasteland. Weāre playing a game of climb the Kardashev scale, we opted for the āburn all the fossil fuels as fast as possibleā strategy, and now weāre a the point where either spent enough energy fast enough to figure out the tech needed to survive this, or we suffocate on the fumes. The clock is ticking, and AI may be our best bet at saving the human race that doesnāt involve an inordinate number of people dying.
OpenAI are not going to make the source code for their model accessible to all to learn from. This is 100% about profiting from it themselves. And using copyrighted data to create open source models would seem to violate the very principles the open source community stands for - namely that everybody contributes what they agree to, and everything is published under a licence. If the basis of an open source model is a vast quantity of training data from a vast quantity of extremely pissed off artists, at least some of the people working on that model are going to have a āare we the baddies?ā moment.
The AI models are also never going to produce a solution to climate change that humans will accept. We already know what the solution is, but nobody wants to hear it, and expecting anyone to listen to ChatGPT and suddenly change their minds about using fossil fuels is ludicrous. And an AI that is trained specifically on knowledge about the climate and technologies that can improve it, with the purpose of innovating some hypothetical technology that will fix everything without humans changing any of their behaviour, categorically does not need the entire contents of ArtStation in its training data. AIs that are trained to do specific tasks, like the ones trained to identify new antibiotics, are trained on a very limited set of data, most of which is not protected by copyright and any that is can be easily licenced because the quantity is so small - and you donāt see anybody complaining about those models!
OpenAI isnāt the only company doing this, nor is their specific model the knowledge that Iām referring to.
It is already being used to further fusion research beyond anything weāve been able to do with standard algorithms
Then itās not a solution. Thatās like telling your therapist, āI know how to fix my relationship, my partner just wonāt do it!ā
Lol. Yeah, I agree, thatās never going to work.
Thatās a strong claim to make. Regardless of the ethics involved, or the problems the AI can solve today, the fact is we seeing rapid advances in AI research as a direct result of these ethically dubious models.
In general, Iām all for the capitalist method of artists being paid their fair share for the work they do, but on the flip side, I see a very possible mass extinction event on the horizon, which could cause suffering the likes of which humanity has never seen. If we assume that is the case, and we assume AI has a chance of preventing it, then I would prioritize that over peopleās profits today. And I think itās perfectly reasonable to say Iām wrong.
And then thereās the problem of actually enforcing any sort of regulation, which would be so much more difficult than people here are willing to admit. Thereās basically nothing you can do even if you wanted to. Your Carlin example is exactly the defense a company would use: āI guess our AI just happened to create a movie that sounds just like Paul Blart, but we swear itās never seen the film. Great minds think alike, I guess, and we sell only the greatest of mindsā.
Personally I think the claim that the entire contents of ArtStation will lead to working technology that fixes climate change is the bolder claim - and if there was any merit to it, there would be some evidence for it that the corporations who want copyright to be disapplied to artists would be able to produce. And if weāre saying that getting rid of copyright protections will save the planet, then perhaps Disney should give up theirs as well. Because thatās the reality here: weāre expecting humans to be obliterated by AI but are not expecting the rich and powerful to make any sacrifices at all. And art is part of who we are as a species, and has been for hundreds of thousands of years. Replacing artists with AI because somehow that will fix climate change is not only a massive stretch, but what would we even be saving humanity for at that point? So that everybody can slave away in insecure, meaningless work so the few can hoard everything for themselves? Because the Star Trek utopia where AI does all the work and humans can pursue self-enrichment is not an option on the table. The tech bros just want you to think it is.
It isnāt wrong to use copyrighted works for training. Let me quote an article by the EFF here:
and
What you want would swing the doors open for corporate interference like hindering competition, stifling unwanted speech, and monopolization like nothing weāve seen before. There are very good reasons people have these rights, and we shouldnāt be trying to change this. Ultimately, itās apparent to me, you are in favor of these things. That you believe artists deserve a monopoly on ideas and non-specific expression, to the detriment of anyone else. If Iām wrong, please explain to me how.
Humans benefit from years of evolutionary development and corporeal bodies to explore and interact with their world before theyāre ever expected to produce complex art. AI need huge datasets to understand patterns to make up for this disadvantage. Nobody pops out of the womb with fully formed fine motor skills, pattern recognition, understanding of cause and effect, shapes, comparison, counting, vocabulary related to art, and spatial reasoning. Datasets are huge and filled with image-caption pairs to teach models all of this from scratch. AI isnāt human, and we shouldnāt judge it against them, just like we donāt judge boats on their rowing ability.
AI donāt require most modern art in order to learn to make images either, but the range of expression would be limited, just like a humanās in this situation. You can see this in cave paintings and early sculptures. They wouldnāt be limited to this same degree, but you would still be limited.
It took us 100,000 years to get from cave drawings to Leonard Da Vinci. This is just another step for artists, like Camera Obscura was in the past. Itās important to remember that early man was as smart as we are, they just lacked the interconnectivity to exchange ideas that we have.
I think the difference in artistic expression between modern humans and humans in the past comes down to the material available (like the actual material to draw with).
Humans can draw without seeing any image ever. Blind people can create art and draw things because we have a different understanding of the world around us than AI has. No human artist needs to look at a thousand or even at 1 picture of a banana to draw one.
The way AI sees and āunderstandsā the world and how it generates an image is fundamentally different from how the human brain conveys the object banana into an image of a banana.
That is definitely a difference, but even that is a kind of information shared between people, and information itself is what gives everyone something to build on. That gives them a basis on which to advance understanding, instead of wasting time coming up with the same things themselves every time.
Humans donāt need representations of things in images because they have the opportunity to interact with the genuine article, and in situations when that is impractical, they can still fall back on images to learn. Someone without sight from birth canāt create art the same way a sighted person can.
Thatās the beauty of it all, despite that, these models can still output bananas.
Humans learn mostly from real life. Go touch some grass
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.
Iād 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.
Thatās 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. LLMās just go about it in a different way, but they achieve the same general result. If Iām drawing a human, that means thereās a āhandā here, and a āheadā there. āHeadā is a weird combination of pixels that mostly look like this, āhandā looks kinda like that. All depends on how the model is structured, but tell me thatās not very similar to a simplified version of how humans operate.
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 Iām 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 thereās a group of people who donāt so unless we wanted to draw a person with a different number of fingers, we could. A generative art model canāt help itself from drawing multiple fingers because all it understands is that āfinger + finger = handā but it has no concept on when to stop.
And thatās the reason why LLM generated content isnāt 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 isnāt anything worth protecting.
You say that yet I initially responded to someone who was comparing an LLM to what a comedian does.
There is no unique method because thereās hardly anything unique you can do. Two people using Stable Diffusion to produce an image are putting in the same amount of work. One might put more time into crafting the right prompt but thatās not work youāre doing.
If 90% of the work is handled by the model, and you just layer on whatever extra thing you wanted, that doesnāt mean you created the thing. That also implies you have much control over the output. Youāre effectively negotiating with this machine to produce what you want.
Wouldnāt that lead to the same argument as originally brought against photography, though?
A photographer is effectively negotiating with the sun, the sky and everything else to hopefully get the result they are looking for on their device.
One difference is that the photographer has to go the places theyāre taking pictures of.
Another is that photography isnāt comparable to paintings and it never has been. Iām willing to bet photography and paintings have never coexisted in a contest. Except, when people say their generative art is comparable to what artists have been producing by hand, they are admitting that generative art has more in common with photography than it does with hand-crafted art but they want the prestige and recognition those artists get for their work.
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Thats not work to you? My company pays me to spend time to do the right thing, even though most of the work does the computer.
I see where you are going at, but your argument also invalidates other forms of human interaction and creating.
In my country copyright can only be granted if a certain amount of (human) work went into something. Any work.
The difficult part is finding out whats enough and what kind of work qualify to lead to some kind of protection, even if partial.
The difficult part was not to create something, but to prove someone did or didnt put enough work into it.
I think we can hold generated or assisted goods to the same standard.
Putting a simple prompt together should probably not be granted protection as no significant work went into it. But refining it, editing the resultā¦ maybe thats enough, thats really up to the society to decide.
At the same time we have to balance the power of machines against human work, so the human work doesnt get totally invalidated, but rather shifted and treated as sub-type.
Machines already replaced alot of work, also creative ones. Book-printing, forging, producing foodā¦ the scary part about generative AI is mainly the speed of them spreading.
So as a data analyst a lot of my work is done through a computer but I can apply my same skills if someone hands me a piece of paper with data printed on it and told me to come up with solutions to the problems with it. I donāt need the computer to do what I need to do, it makes it easier to manipulate data but the degree of problem solving required needs to be done by a human and thatās why itās my job. If a machine could do it, then they would be doing it but they arenāt because contrary to what people believe about data analysis, you have to be somewhat creative to do it well.
Crafting a prompt is an exercise in trial and error. Itās work but itās not skilled work. It doesnāt take talent or practice to do. Despite the prompt, you are still at the mercy of the machine.
Even by the case youāve presented, I have to ask, at what point of a human editing the output of a generative model constitutes it being your own work and not the machineās? How much do you have to change? Can you give me a %?
Machines were intended to automate the tedious tasks that we all have to suffer to free up our brains for more engaging things which might include creative pursuits. Automation exists to make your life easier, not to rob you of lifeās pursuits or your livelihood. It never shouldāve been used to produce creative work and I find the attempts to equate this abominationās outputs to what artists have been doing for years, utterly deplorable.
I donāt choose my words man. I get a vague sense of the meaning I want to convey and the words just form themselves.
As an artist you draw with an understanding of the human body, though. An understanding current models donāt have because they arenāt 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. Itās mass, itās 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. Itās a complex function unique to the human brain, an organ we donāt even scientifically understand yet.
Thatās not the point though. The point is that the human comedian and the AI both benefit from consuming creative works covered by copyright.
Yeah except a machine is owned by a company and doesnāt 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 āAIā would have you believe), itās an optimisation algorithm.
Itās a glorified autocomplete.
Itās so funny that this is something new. This was Grammarlyās whole schtick since before ChatGPT so how different is Grammarly AI?
Here is the bigger picture: The vast majority of tech illiterate people think something is AI because duh its called AI.
Its literally just the power of branding and marketing on the minds of poorly informed humans.
Unfortunately this is essentially a reverse Turing Test.
The vast majority of humans do not know anything about AI, and also a huge majority of them can also barely tell the difference between, currently in some but not all forms, output from what is basically a brute force total internet plagiarism and synthesis software, from many actual human created content in many cases.
To me this basically just means that about 99% of the time, most humans are actually literally NPCs, and they only do actual creative and unpredictable things very very rarely.
I call it AI because itās artificial and itās intelligent. Itās not that complicated.
The thing we have to remember is how scary and disruptive AI is. Given that fear, it is scary to acknowledge that we have AI emerging into our world. Because it is scary, that pushes us to want to ignore it.
Itās called denial, and itās the best explanation for why people arenāt willing to acknowledge that LLMs are AI.
It meets almost none of the conceptions of intelligence at all.
It is not capable of abstraction.
It is capable of brute force understanding similarities between various images and text, and then presenting a wide array of text and images containing elements that reasonably well emulate a wide array of descriptors.
This is convincing to many people that it has a large knowledge set.
But that is not abstraction.
It is not capable of logic.
It is only capable of again brute force analyzing an astounding amount of content and then producing essentially the consensus view on answers to common logical problems.
Ask it any complex logical question that has never been answered on the internet before and it will output irrelevant or inaccurate nonsense, likely just finding an answer to a similar but not identical question.
The same goes for reasoning, planning, critical thinking and problem solving.
If you ask it to do any of these things in a highly specific situation even giving it as much information as possible, if your situation is novel or even simply too complex, it will again just spit out a non sense answer that is basically going to be very inadequate and faulty because it will just draw elements together from the closest things it has been trained on, nearly certainly being contradictory or entirely dubious due to being unable to account for a particularly uncommon constraint, or constraints that are very uncommonly faced simultaneously.
It is not creative, in the sense of being able to generate something novel or new.
All it does is plagiarize elements of things that are popular and have many examples of and then attempt mix them together, but it will never generate a new art style or a new genre of music.
It does not even really infer things, is not really capable of inference.
It simply has a massive, astounding data set, and the ability to synthesize elements from this in a convincing way.
In conclusion, you have no idea what you are talking about, and you yourself literally are one of the people who has failed the reverse Turing Test, likely because you are not very well very versed in the technicals of how this stuff actually works, thus proving my point that you simply believe it is AI because of its branding, with no critical thought applied whatsoever.
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Current models arenāt intelligent. Not even by the flimsy and unprecise definition of intelligence we currently have.
Wanted to post a whole rant but then saw vexikron already did so I spare you xD
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 and profiting off of the worlds most comprehensive plagiarism software.
They get called out when they use others work as a template, not as training data.
Neither is an LLM. What youāre describing is a primitive Markov chain.
You may not like it, but brains really are just glorified pattern recognition and generation machines. So yes, āmonkey see thing to draw thingā, except a really complicated version of that.
Think of it this way: if your brain wasnāt a reorganization and regurgitation of the things you have observed before, it would just generate random noise. Thereās no such thing as ātruly originalā 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 thatās the way to beat them. If theyāre 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 isnāt being overworked or enslaved, ensure it is being treated āhumanelyā. If they donāt 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.
My description mightāve 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 didnāt 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. Iām not saying itās not impressive because itās 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.
Itās easy to look at what people have created throughout history and think āthis looks like thatā and on a point by point basis youād 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 weāve heard recently but weāll read about it in newspapers from 200 years ago. Did George Carlin steal the idea? No. Was he aware of that information? I donāt know. But Carlin regularly calls upon his own experiences so itās likely that heās referencing a event from his past that is similar to that of 200 years ago. He mightāve 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 theyāve admitted that it couldnāt work as well as it does without stealing other peopleās work. I donāt think theyāre taking the position that itās intelligent because from the beginning that was a marketing ploy. Theyāre taking the position that they should be allowed to use the data they stole because there was no other way.
okay
yup
woah there! thatās where we disagreeā¦ 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, itās best to avoid emotive arguments and language thatās designed to evoke negativity simply by their use, rather than the argument being presented
its understandable that itās frustrating, but just because a machine is now able to do a similar job to a human doesnāt make it inherently wrong. it might be useful for you to reframe these developments - itās not taking away from humans, itās enabling humansā¦ the less a human has to have skill to get whatās in their head into an expressive medium for someone to consume the better imo! art and creativity shouldnāt 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
iād question why itās unethical, and also suggest that āstolenā 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 āexperiencesā?
First of all, weāre not having a debate and this isnāt a courtroom so avoid the patronising language.
Second of all, my ābeliefā on the modelsā plagiarism is based on technical knowledge of how the models work and not how I think they work.
This would be impressive if it was true. An LLM is not intelligent simply through its appearance of intelligence.
Itās a chat bot thatās automated Google searches, letās be clear about what this can do. Itās taken natural language processing and applied it through an optimisation algorithm to produce human-like responses.
No, I disagree at a fundamental level. Humans need to compete against each other and ourselves to improve. Just because an LLM can write a book for you, doesnāt mean youāve written a book. Youāre just lazy. You donāt want to put in the work any other writer in existence has done, to mull over their work and consider the emotions and effect they want to have on the reader. To what extent can an LLM replicate the way George RR Martin describes his world without entirely ripping off his work?
If I take a book you wrote from you without buying it or paying you for it, what would you call that?
you know how the neurons in our brain work, right?
because if not, well, itās pretty similarā¦ unless you say thereās a soul (in which case we canāt really have a conversation based on fact alone), weāre just big olā probability machines with tuned weights based on past experiences too
You are spitting out basic points and attempting to draw similarities because our brains are capable of something similar. The difference between what youāve 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 itās designed to do is say āx is more likely to appear before y than zā. If you fed it nonsense, it would regurgitate nonsense. If you feed it text from racist sites, it will regurgitate that same language because thatās all it has seen.
Youāll read this and think āthatās what humans do too, right?ā 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 Iāll state them here as well. An LLM will tell you information but it has no cognition on what itās telling you. It has no idea that itās right or wrong, itās job is to convince you that itās right because thatās the success state. If you tell it itās wrong, thatās 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 itās not reaching a success, itās not giving you what you want. The longer the conversation goes on, the more crazy LLMs get as well because itās 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.
but thatās just a matter of complexity, not fundamental difference. the way our brains work and the way an artificial neural network work arenāt that different; just that our brains are beyond many orders of magnitude bigger
thereās no particular reason why we canāt feed artificial neural networks an enormous amount of ā¦ letās say tangentially related experiential information ā¦ as well, but in order to be efficient and make them specialise in the things we want, we only feed them information thatās directly related to the specialty we want them to perform
thereās someā¦ āpre trainingā or āpre-existing stateā that exists with humans too that comes from genetics, but iād argue thatās 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 to ensure the correct functioning of our body with our brain, but not actually what youād call the main function)
iām also not claiming that an LLM is intelligent (or rather iād prefer to use the term self aware because intelligent is pretty nebulous); just that the structure it has isnāt that much different to our brains just on a level thatās so much smaller and so much more generic that you canāt expect it to perform as well as a human - you wouldnāt expect to cut out 99% of a humans brain and have them be able to continue to function at the same level either
i guess the core of what iām getting at is that the self awareness that humans have is definitely not present in an LLM, however i donāt think that self-awareness is necessarily a pre-requisite for most things that we call creativity. i think thatās itās entirely possible for an artificial neural net thatās 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 outcomesā¦ given that belief (and iām very aware that it certainly is just a belief - we arenāt close to understanding our brains, but i donāt fundamentally thing thereās anything other then neurons firing that results in the human condition), just because you simplify and specialise the input data doesnāt mean that the process is different. you could argue that itās lesser, for sure, but to rule out that it can create a legitimately new work is definitely premature
āSoulā is the word we use for something we donāt 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 āitās a probability machineā definitely is a wrong description. Especially when you want to use it as an argument of similarity to other probability machines.
thatās far from definitive. another definition is
but since we arenāt arguing semantics, it doesnāt really matter exactly, other than the fact that itās important to remember that just because you have an experience, belief, or view doesnāt make it the only truth
of course i didnāt discover categorically how the human brain works in its entirety, however most scientists iām 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 donāt understand is how it all connects up - the emergent behaviour. we understand the basics; thatās not in question, and you seem to be questioning it
itās not abstracted; itās simplifiedā¦ if what youāre saying were true, then simplifying complex organisms down to a petri dish for research would be āabstractedā so much it ābecomes wrongā, which is categorically untrueā¦ itās an incomplete picture, but that doesnāt make it either wrong or abstract
*edit: sorry, it was another comment where i specifically said belief; the comment you replied to didnāt state that, however most of this still applies regardless
i laid out an a leads to b leads to c and stated that itās simply a belief, however itās a belief thatās based in logic and simplified concepts. if you want to disagree thatās fine but donāt act like you have some āevidenceā or āproofā to back up your claimsā¦ all weāre talking about here is belief, because we simply donāt 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, thatās not a violation
if a machine consumes media and writes something and it looks different, youāre arguing that is a violation
the only difference here is your belief that a human brain somehow has something āmoreā than a probabilistic model going onā¦ but again, thatās far from certain
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.
Am I a moron? How do you have more upvotes than the parent comment, is it because youāre being more aggressive with your statement? I feel like you didnāt quite refute what the parent comment said. Youāre just explaining how Chat GPT works, but youāre not really saying how it shouldnāt use our established media (copyrighted material) as a reference.
I donāt control the upvotes so I donāt know why thatās 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 thatās equating the process an LLM uses when producing an output to a comedian writing jokes.
Edit: Apologies if I do come across aggressive. Since the plagiarism machine has been in full swing, the whole discourse around it has gotten on my nerves. Iām a creative person, Iāve written poems and short stories, Iām writing a novel and I also do programming and a whole host of hobbies so when LLMs are used to put people like me out of a job using my own work, why wouldnāt that make me angry? What makes it worse is that Iām having to explain concepts to people regarding LLMs that they continue to defend. I canāt stand it so yes, I will come off aggressive.
Sorry, I was essentially emphasizing on my initial point āam I a moron?ā, lol, because I legitimately didnāt get your point at first like others do in this thread.
I get what you mean now after reading it couple more times
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, Iām 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. Itās literally how a baby learns language from those around them.
If you ask an LLM to help you with a legal brief, itāll come up with a bunch of stuff for you, and some of it might even be right. But itāll very likely do things like make up a case that doesnāt 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, youāre going to have a bad time.
Thereās a reason LLMs make stuff up like that, and itās 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 arenāt just associating the sounds they hear, theyāre 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 arenāt nearly at that level. Thatās not to say what they do isnāt impressive, because it really is. They can also synthesize unrelated concepts together in a stunningly human way, even things that theyāve never been trained on specifically. Theyāve picked up a lot of surprising nuance just from the text theyāve been fed, and itās convincing enough to think that something magical is going on. But ultimately, theyāve been optimized to predict words, and thatās what theyāre good at, and although theyāve clearly developed some impressive skills to accomplish that task, itās not even close to human level. They spit out a bunch of nonsense when what they should be saying is āI have no idea how to write a legal document, you need a lawyer for thatā, 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 donāt have that. And how could they? Their training didnāt 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 youāre 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 āas an AI, do you want to take over the world?ā is very different from considering the ideas, forming an opinion about them, and responding with that opinion. LLMs arenāt just doing statistics, but you donāt have to go too far down that spectrum before the answers start seeming thoughtful.