suy

Aaron was facing charges, and a really bad deal, and he took his life. He was never sentenced because there was no time for that, but it's obvious that things weren't going well for him, and that he was attacked disproportionally.

Meta is still facing investigation and will go to court still, and will very likely face charges for torrenting the data, same as Anthropic. They have not been sentenced because there was been no trial yet, it's still under investigation, because it will be a harsher penalty if it can be proved that they uploaded in addition to downloaded.

I think this is inaccurate enough to consider it misinformation (IMHO).

Yes, it is obvious that we all should fight Meta and praise Aaron Swartz. And it's true that the legal system is fucked up, and someone with power and money is in total advantage. But as despicable as Meta is, I don't think that saying wrong things is going to be the way to fight for our rights. There are already good enough arguments if we stay true to the facts.

I don't know where you got that image from. AllenAI has many models, and the ones I'm looking at are not using those datasets at all.

Anyway, your comments are quite telling.

First, you pasted an image without alternative text, which it's harmful for accessibility (a topic in which this kind of models can help, BTW, and it's one of the obvious no-brainer uses in which they help society).

Second, you think that you need consent for using works in the public domain. You are presenting the most dystopic view of copyright that I can think of.

Even with copyright in full force, there is fair use. I don't need your consent to feed your comment into a text to speech model, an automated translator, a spam classifier, or one of the many models that exist and that serve a legitimate purpose. The very image that you posted has very likely been fed into a classifier to discard that it's CSAM.

And third, the fact that you think that a simple deep learning model can do so much is, ironically, something that you share with the AI bros that think the shit that OpenAI is cooking will do so much. It won't. The legitimate uses of this stuff, so far, are relevant, but quite less impactful than what you claimed. The "all you need is scale" people are scammers, and deserve all the hate and regulation, but you can't get past those and see that the good stuff exists, and doesn't get the press it deserves.

Which ones? Name one.

What's wrong with what Pleias or AllenAI are doing? Those are using only data on the public domain or suitably licensed, and are not burning tons of watts on the process. They release everything as open source. For real. Public everything. Not the shit that Meta is doing, or the weights-only DeepSeek.

It's incredible seeing this shit over and over, specially in a place like Lemmy, where the people are supposed to be thinking outside the box, and being used to stuff which is less mainstream, like Linux, or, well, the fucking fediverse.

Imagine people saying "yeah, fuck operating systems and software" because their only experience has been Microsoft Windows. Yes, those companies/NGOs are not making the rounds on the news much, but they exist, the same way that Linux existed 20 years ago, and it was our daily driver.

Do I hate OpenAI? Heck, yeah, of course I do. And the other big companies that are doing horrible things with AI. But I don't hate all in AI because I happen to not be an ignorant that sees only the 99% of it.

I've made several Qt apps (in C++) easily packaged using AppImage. Perhaps OBS is harder because they require some level of integration with the hardware (e.g. the virtual camera perhaps requires something WRT drivers, I don't know), but in the general case of a Qt app doing "normal GUI stuff" and "normal user stuff" is a piece of cake. To overcome the glibc problem, it's true that it's recommended using an old distro, but it's not a must. Depends on what you want to support.

As a user, I prefer a native package, though (deb in my case).

My bad, I forgot he doesn't have time to think.

Too busy being one of the best players at Path of Exile 2. Despite that he doesn't identify the valuable loot. Or how to use the map. Or how levels work. But he's top 50! All very believable.

Ah, a classic watch. :-)

Elon probably thinks that SQL is MS SQL Sever, MySQL, or some such.

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

Wow, thanks, I have not seen this comment, yet I hinted about this in some of my other replies that I've done before.

Yes, I think ML is fair use, but there it would also be fair to force something into the public domain/open source if, in order to be accrued, it has to make use of fair use at unseen amounts of scale.

This would be a difficult to make law, though. Current ML is very inefficient in the amount of data it requires, but it could (and should) be made better.

Now I sail the high seas myself, but I don’t think Paramount Studios would buy anyone’s defence they were only pirating their movies so they can learn the general content so they can produce their own knockoff.

We don't know exactly how they source their data (and that is definitely shady), but if I can gain access to a movie in a legal way, I don't see why I would not be able to gather statistics from said movie, including running a speech to text model to caption it, then make statistics of how many times a few words were used, and followed by which ones. This is an oversimplified explanation of what a LLM does, but it's the fairest I can come up, and it would be legal to do so. The models are always orders of magnitude smaller than the data they are trained on.

That said, I don't imply that I'm happy with the state of high tech companies, the AI hype, the energy consumption, or the impact on the humble people. But I've put a lot of thought into this (and learning about machine learning for real), and I think this is not a ML problem, but a problem in the economic, legal and political system. AI hype is just a symptom.