Proton Mail goes AI, security-focused userbase goes ‘what on earth’

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.

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.

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:

1. 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.

2. 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.

3. 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.

4. 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.