Basically a deer with a human face. Despite probably being some sort of magical nature spirit, his interests are primarily in technology and politics and science fiction.
Spent many years on Reddit before joining the Threadiverse as well.
It’s weird how AI has turned so much of the internet from its generally anti-copyright stance. I’ve seen threads in piracy and datahoarding communities that were riddled with “won’t someone please think of the copyright!” Posts raging about how awful AI was.
I maintain the same view I always have. Copyright is indeed broken, because of how overly restrictive and expansive it has become. Most people long ago lost sight of what it’s actually for.
If they do it’s not by the actual training of AI.
Simple.wikipedia isn’t a summary of regular Wikpedia, it’s a whole separate thing. It’s intended to convey the same data, just in a simpler way.
The problem being discussed here is not the availability of Wikipedia’s data. It’s about the ongoing maintenance and development of that data going forward, in the future. Having a static copy of Wikipedia gathering dust on various peoples’ hard drives isn’t going to help that.
Wikipedia’s traditional self-sustaining model works like this: Volunteers (editors) write and improve articles for free, motivated by idealism and the desire to share knowledge. This high-quality content attracts a massive number of readers from search engines and direct visits. Among those millions of readers, a small percentage are inspired to become new volunteers/editors, replenishing the workforce. This cycle is “virtuous” because each part fuels the next: Great content leads to more readers which leads to more editors which leads to even better content. AI tools (like ChatGPT, Google AI Overviews, Perplexity, etc.) disrupt this cycle by intercepting the user before they reach Wikipedia.
A week or two back there was a post on Reddit where someone was advertising a project they’d put up on GitHub, and when I went to look at it I didn’t find any documentation explaining how it actually worked - just how to install it and run it.
So I gave Gemini the URL of the repository and asked it to generate a “Deep Research” report on how it worked. Got a very extensive and detailed breakdown, including some positives and negatives that weren’t mentioned in the existing readme.
Oh boy, I bet the comments on this one will be useful.
LLMs were trained on our social media feeds, after all…
Ah, low numbers of seeds. Must’ve just not wanted to wait.
Which, as I said, seems strange. Why don’t those businesses just download the torrents?
Seems strange. Anna’s Archive makes their collection available for bulk download as torrent files, they shouldn’t need to “cut a deal” for access to that. Just download the torrent and now you’ve got the whole collection available locally.
Only 12 percent reported both lower costs and higher revenue, while 56 percent saw neither benefit. Twenty-six percent saw reduced costs, but nearly as many experienced cost increases.
So 38% saw benefits from AI, whereas “nearly” 26% saw cost increases from it. One could just as easily write the headline “More companies experience increased benefits from AI than experience increased costs” based on this data but that headline wouldn’t get so many clicks.
Youtube isn’t the way you think it should be, though.
“Why did they take that feature away? I was busy abusing it!”
I have a sneaking suspicion that the vast majority of the people raging about AIs scraping their data are not raging about it being done inefficiently.
You’re thinking of “model decay”, I take it? That’s not really a thing in practice.
Raw materials to inform the LLMs constructing the synthetic data, most likely. If you want it to be up to date on the news, you need to give it that news.
The point is not that the scraping doesn’t happen, it’s that the data is already being highly processed and filtered before it gets to the LLM training step. There’s a ton of “poison” in that data naturally already. Early LLMs like GPT-3 just swallowed the poison and muddled on, but researchers have learned how much better LLMs can be when trained on cleaner data and so they already take steps to clean it up.
I have no idea what “established means” would be. In the particular case of the Fediverse it seems impossible, you can just set up your own instance specifically intended for harvesting comments and use that. The Fediverse is designed specifically to publish its data for others to use in an open manner.
And remember: if you’re not running her on your own hardware she’s not an AI girlfriend. She’s an AI prostitute.