If an organization runs a survey in 2024 on whether it should get into AI, then they’ve already bodged an LLM into the system and they’re seeing if they can get away with it. Proton Mail is a priva…
we appear to be the first to write up the outrage coherently too. much thanks to the illustrious @self
Mistral’s Mixtral-8x7B-Instruct-v0.1 produced copyrighted content on 22% of the prompts.
did you know that a lesser-known side effect of the infinite monkeys approach is that they will produce whole sections of copyright content abso-dupo-lutely by accident? wild, I know! totes coinkeedink!
I’d be glad to provide it once you’ve come to your senses and want to discuss things like an adult
jesus fucking christ you must be a fucking terrible person to work with
I’m too old to discuss against bad faith arguments.
Especially with people who won’t read the information I provide them showing their initial information was wrong.
One is a company that has something to sell, the other an article with citations showing why it’s not easy to determine what percentage of a data set is infringing on copyright, or whether exact reproduction via “fishing expedition” prompting is a useful metric to determine if unauthorized copyright was used in training.
The dumbest take though is attacking Mistral of all LLMs, even though it’s on an Apache 2.0 license.
Well since you want to use computers to continue the discussion, here’s also ChatGPT:
Determining the exact percentage of copyrighted data used to train a large language model (LLM) is challenging for several reasons:
Scale and Variety of Data Sources: LLMs are typically trained on vast and diverse datasets collected from the internet, including books, articles, websites, and social media. This data encompasses both copyrighted and non-copyrighted content. The datasets are often so large and varied that it is difficult to precisely categorize each piece of data.
Data Collection and Processing: During the data collection process, the primary focus is on acquiring large volumes of text rather than cataloging the copyright status of each individual piece. While some datasets, like Common Crawl, include metadata about the sources, they do not typically include detailed copyright status information.
Transformation and Use: The data used for training is transformed into numerical representations and used to learn patterns, making it even harder to trace back and identify the copyright status of specific training examples.
Legal and Ethical Considerations: The legal landscape regarding the use of copyrighted materials for AI training is still evolving. Many AI developers rely on fair use arguments, which complicates the assessment of what constitutes a copyright violation.
Efforts are being made within the industry to better understand and address these issues. For example, some organizations are working on creating more transparent and ethically sourced datasets. Projects like RedPajama aim to provide open datasets that include details about data sources, helping to identify and manage the use of copyrighted content more effectively【6†source】.
Overall, while it is theoretically possible to estimate the proportion of copyrighted content in a training dataset, in practice, it is a complex and resource-intensive task that is rarely undertaken with precision.
no, you utter fucking clown. they’re literally posting to take the piss out of you, the only person in the room who isn’t getting that everyone is laughing at them, not with them
god these weird little fuckers’ ability to fill a thread with garbage is fucking notable isn’t it? something about loving LLMs makes you act like an LLM. how depressing for them.
To think that when sneer club/techtakes migrated to lemmy, I was pretty sure we would not be getting a lot of incidental traffic to the communities. Just about as wrong as you can be.
Btw, you can just fact check my claim about what Mistral is licenced under. The article talks about copyright and AI detection in general, which to anyone with basic critical thinking skills could then understand would apply to other LLMs like Mistral.
You might want to look up what pearl clutching means as well. You’re using it wrong:
did you know that a lesser-known side effect of the infinite monkeys approach is that they will produce whole sections of copyright content abso-dupo-lutely by accident? wild, I know! totes coinkeedink!
jesus fucking christ you must be a fucking terrible person to work with
I’ve seen toddlers throw more mature tantrums
she wrote harry potter with an llm, didn’t she?
I’m too old to discuss against bad faith arguments.
Especially with people who won’t read the information I provide them showing their initial information was wrong.
One is a company that has something to sell, the other an article with citations showing why it’s not easy to determine what percentage of a data set is infringing on copyright, or whether exact reproduction via “fishing expedition” prompting is a useful metric to determine if unauthorized copyright was used in training.
The dumbest take though is attacking Mistral of all LLMs, even though it’s on an Apache 2.0 license.
chatgpt gets it
Well since you want to use computers to continue the discussion, here’s also ChatGPT:
Determining the exact percentage of copyrighted data used to train a large language model (LLM) is challenging for several reasons:
Scale and Variety of Data Sources: LLMs are typically trained on vast and diverse datasets collected from the internet, including books, articles, websites, and social media. This data encompasses both copyrighted and non-copyrighted content. The datasets are often so large and varied that it is difficult to precisely categorize each piece of data.
Data Collection and Processing: During the data collection process, the primary focus is on acquiring large volumes of text rather than cataloging the copyright status of each individual piece. While some datasets, like Common Crawl, include metadata about the sources, they do not typically include detailed copyright status information.
Transformation and Use: The data used for training is transformed into numerical representations and used to learn patterns, making it even harder to trace back and identify the copyright status of specific training examples.
Legal and Ethical Considerations: The legal landscape regarding the use of copyrighted materials for AI training is still evolving. Many AI developers rely on fair use arguments, which complicates the assessment of what constitutes a copyright violation.
Efforts are being made within the industry to better understand and address these issues. For example, some organizations are working on creating more transparent and ethically sourced datasets. Projects like RedPajama aim to provide open datasets that include details about data sources, helping to identify and manage the use of copyrighted content more effectively【6†source】.
Overall, while it is theoretically possible to estimate the proportion of copyrighted content in a training dataset, in practice, it is a complex and resource-intensive task that is rarely undertaken with precision.
“exact percentage”
just fuck right off. wasting my fucken time.
You’re the one who linked to an exact percentage, not me. Have a good day.
no, you utter fucking clown. they’re literally posting to take the piss out of you, the only person in the room who isn’t getting that everyone is laughing at them, not with them
Whatever makes you feel better buddy
re-read your chatgpt response and think about whether the percentages in my original link could be too high or too low.
trick question everyone knows this late on a friday you want a body high for that nice mellow low feeling
but, like, really think this time. at this point i’m not arguing with you, i’m trying to help you.
you should speak to a physicist, they might be able to find a way your density can contribute to science
I’ve read the article you’ve posted:. it does not refute the fucking datapoint provided, it literally DOES NOT EVEN MENTION MISTRAL AT ALL.
so all I can tell you is to take your pearlclutching tantrum bullshit and please fuck off already
god these weird little fuckers’ ability to fill a thread with garbage is fucking notable isn’t it? something about loving LLMs makes you act like an LLM. how depressing for them.
I bet they go to react conferences
snrk
brava.
“I said good day, sir. good day!” [walks through revolving door]
Meta:
To think that when sneer club/techtakes migrated to lemmy, I was pretty sure we would not be getting a lot of incidental traffic to the communities. Just about as wrong as you can be.
Where was it prior? @earthquake @self
previously we were r/SneerClub and r/TechTakes on Reddit. we’ve also got a local archive of old r/SneerClub posts which I’m currently redesigning to fix the, ah, everything about its web design
the dorks that come in and dumbpost are fun for the first few interactions, but, damn, they get annoying fast
Yes, clearly I’m the one throwing a tantrum 🙄
Btw, you can just fact check my claim about what Mistral is licenced under. The article talks about copyright and AI detection in general, which to anyone with basic critical thinking skills could then understand would apply to other LLMs like Mistral.
You might want to look up what pearl clutching means as well. You’re using it wrong:
https://dictionary.cambridge.org/us/dictionary/english/pearl-clutching
Considering I’ve done the opposite of a shocked reaction. While at it, maybe also look up “projection”
https://www.psychologytoday.com/us/basics/projection
Anyhow, have a good day.