OpenAI doesn’t produce LLMs only. People are gonna be paying for stuff like Sora or DallE. And people are also paying for LLMs (e.g. Copilot, or whatever advanced stuff OpenAI offers in their paid plan).

How many, and how much? I don’t know, and I am not sure it can ever be profitable, but just reducing it to “chains of bullshit” to justify that it has no value to the masses seems insincere to me. ChatGPT gained a lot of users in record time, and we know is used a lot (often more than it should, of course). Someone is clearly seeing value in it, and it doesn’t matter if you and I disagree with them on that value.

I still facepalm when I see so many people paying for fucking Twitter blue, but the fact is that they are paying.

I think that “exactly like” it’s absurd. Bubbles are never “exactly” like the previous ones.

I think in this case there is a clear economical value in what they produce (from the POV of capitalism, not humanity’s best interests), but the cost is absurdly huge to be economically viable, hence, it is a bubble. But in the dot com bubble, many companies had a very dubious value in the first place.

Lol. We’re as far away from getting to AGI as we were before the whole LLM craze. It’s just glorified statistical text prediction, no matter how much data you throw at it, it will still just guess what’s the next most likely letter/token based on what’s before it, that can’t even get it’s facts straith without bullshitting.

This is correct, and I don’t think many serious people disagree with it.

If we ever get it, it won’t be through LLMs.

Well… depends. LLMs alone, no, but the researchers who are working on solving the ARC AGI challenge, are using LLMs as a basis. The one which won this year is open source (all are if are eligible for winning the prize, and they need to run on the private data set), and was based on Mixtral. The “trick” is that they do more than that. All the attempts do extra compute at test time, so they can try to go beyond what their training data allows them to do “fine”. The key for generality is trying to learn after you’ve been trained, to try to solve something that you’ve not been prepared for.

Even OpenAI’s O1 and O3 do that, and so does the one that Google has released recently. They are still using heavily an LLM, but they do more.

I hope someone will finally mathematically prove that it’s impossible with current algorithms, so we can finally be done with this bullshiting.

I’m not sure if it’s already proven or provable, but I think this is generally agreed. just deep learning will be able to fit a very complex curve/manifold/etc, but nothing more. It can’t go beyond what was trained on. But the approaches for generalizing all seem to do more than that, doing search, or program synthesis, or whatever.

Yeah, you are not gonna be able to do that with an LLM. They will be able to quote only some passages, and only of popular books that have been quoted often enough.

You entirely ignored this part.

You basically proved my point in doing so, BTW. You cannot do what you claimed with an LLM. And I’m not saying, and I never said before “ChatGPT” or “OpenAI”. I don’t understand why you think that I might be “defending these hypocritical companies”, when I literally said the opposite at the end.

You are entirely fooled by the output of ChatGPT and you are not arguing in good faith (or you are entirely unable to understand what I said).

Edit/addendum: And to stress out my point, given that the person to whom I’ve replied to showed the output of ChatGPT as if it were any kind of proof, this is what other LLMs say. This is 4o mini:

Large Language Models (LLMs) like me do not have the ability to quote whole sections of copyrighted texts verbatim. While I can generate text based on patterns and information learned during training, I do not store or recall specific texts or books. Instead, I can provide summaries, analyses, or discuss themes and concepts related to a book without directly quoting it. If you have a specific topic or question in mind, feel free to ask!

And this is Llama 3.1 70B:

Large Language Models (LLMs) can generate text based on the patterns and structures they’ve learned from their training data, which may include books. However, whether they can quote whole sections of a book depends on several factors.

LLMs are typically trained on vast amounts of text data, including books, articles, and other sources. During training, they learn to recognize patterns, relationships, and context within the text. This allows them to generate text that is similar in style and structure to the training data.

However, LLMs do not have the ability to memorize or store entire books or sections of text. Instead, they use the patterns and relationships they’ve learned to generate text on the fly.

That being said, it’s possible for an LLM to generate text that is very similar to a section of a book, especially if the book is well-known or widely available. This can happen in a few ways:

1. Overlapping patterns: If the book’s writing style, structure, or content is similar to other texts in the training data, the LLM may be able to generate text that resembles a section of the book.
2. Memorization of key phrases: LLMs may memorize key phrases, quotes, or passages from the training data, which can be recalled and used in generated text.
3. Contextual generation: If the LLM is given a prompt or context that is similar to a section of the book, it may be able to generate text that is similar in content and style.

However, it’s unlikely that an LLM can quote a whole section of a book verbatim, especially if the section is long or contains complex or unique content. The generated text may be similar, but it will likely contain errors, omissions, or variations that distinguish it from the original text.

Feel free to give them a shot in: https://duck.ai

But then it does go on to quote materials verbatim, which shows it’s not “just” ‘extracting patterns’.

Is is just extracting patterns. Is making statistical samples of which token (“word”, informally speaking) is likely followed given the previous stream.

It can only reproduce passages of things it has seen many, many times. I cannot reproduce the whole work. Those two quotes can be seen elsewhere on the internet plenty of times. And it’s fair use there, so it would be fair use with a chat bot as well.

There have been papers published where researchers were able to regenerate an image that was present in the training set of Stable Diffusion. But they were only able to find that image (and others) in particular, because they were present in the training set multiple times, and the caption was the same (it was the portrait picture of some executive at a company).

when given the book and pages — quote copyrighted works

Yeah, you are not gonna be able to do that with an LLM. They will be able to quote only some passages, and only of popular books that have been quoted often enough.

Even if they started to use my service to literally copy entire books?

You cannot do that with an LLM.

Why are you defending massive corporations who could just pay up? Isn’t the whole “corporations putting profits over anything” thing a bit… seen already?

I hate that some corporations are burning money, resources and energy on this, and the solution is not to restrict fair use even further. Machine Learning is complex, but if I had to summarize in some way is “just” gathering statistics of which word comes next (in the case of a text model). This is no different than getting a large corpus of text, and sample it for word frequency, letter frequency, N-gram frequency, etc. It is well known that this is fair use. You only store the copyrighted works to run the software and produce a very transformative work that is a summary many orders of magnitude smaller than the copyrighted work. This is fair use, and it should still be. Changing that is gonna harm the public, small companies and independent researchers way more than big tech companies.

As I said in another comment, I would very much welcome a way to force big corpos to release their models. Make a model bigger than N parameters? You needed too much fair use in one gulp: your model has to be public, and in the public domain. I would fucking welcome that! But going in the opposite direction is just risky.

I don’t understand why small individuals think that copyright is their friend, and will protect them from big tech companies. Copyright will always harm the weak and protect the powerful as a net result. It’s already a miracle that we can enjoy free software and culture by licenses that leverage copyright in our favor.