AI Researchers Counter Big Tech Claims, Successfully Develop AI Using Ethically Gathered Data
AI Unshackled: The Common Pile Dataset Challenges the norm
The tech world is abuzz with the latest development that's got big industry players scratching their heads. A ragtag team of researchers from EleutherAI, University of Toronto, Hugging Face, and other institutions have built something extraordinary––a dataset that, to the amusement of many, was deemed impossible to create. Let's dive deep into this revolution in the making.
The Impossible Becomes Possible
In the secluded corners of a university lab late in 2024, the unthinkable began taking shape. Dubbed the Common Pile v0.1, this 8-terabyte dataset is no ordinary collection. It's entirely sourced from ethically sound resources, such as public domain books, open-source code, scientific articles, StackExchange forums, and Creative Commons-licensed YouTube videos. And as if that wasn't impressive enough, not a single scrap of social media content or pilfered news site data was used.
Yet, this dataset isn't just an ordinary collection of text. It's been meticulously vetted and cleaned, a testament to the collective grit of the team who crafted it in a matter of mere months.
A Leap Forward for AI
To test the mettle of the Common Pile, the researchers trained two AI models––Comma v0.1-1T and Comma v0.1-2T––each boasting 7 billion parameters, similar to Meta's original LLaMA-7B model. They fed these models between one and two trillion tokens of text, the equivalent of hundreds of millions of books. And the results have left eyes wide open.
Comparative tests demonstrate that the models trained on the Common Pile hold their own against those powered by an array of large datasets. Even on programming tasks, they've shown remarkable promise.
Big Tech's Missed Opportunity
Despite the impressive showing of the Common Pile-trained models, it's essential to note that they're not on par with state-of-the-art AI systems like ChatGPT, Claude, and Gemini. These giants draw upon models that have been trained on tens of trillions of tokens, while this dataset only offers a couple of trillion.
That said, it's hard to ignore the glaring question: with the resources at their disposal, why didn't big tech companies like Meta embark on this ethical data journey earlier? Two-dozen researchers managed to pull it off as a side project, after all.
A Push Towards Open Data
For years, the industry has treated large-scale copyright scraping as an unavoidable necessity. But this study flips the script. It demonstrates that legally sound data can produce results that challenge the status quo.
The challenge now lies in scaling up. To compete with powerful systems like GPT-4, we'll need much more open, high-quality data, particularly in the realms of fiction, informal language, and conversations. But if there's anything the Common Pile proves, it's that this vision can become a reality in a not-so-distant future.
An Ethical Dataset for All
With the Common Pile in the public domain, there's potential for collaborative growth. The creators behind this feat are planning future editions to include more conversational dialogue, fiction, and underrepresented languages, all within open licensing bounds.
In essence, the ethos behind the Common Pile may be its most radical aspect––transparency, consent, and ethical data sourcing. This research serves as a beacon for what can be achieved when we prioritize not just technological advancement, but also ethics and accountability in AI development.
The study is yet to undergo peer-review, but you can access it freely on Github. Join the movement and help shape a future built on responsible and ethical AI practices. Large language models, we're coming for you! 💥🚀
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By the way, senior researcher at EleutherAI, Zak Stone, claims, "I would have loved to take a crack at ChatGPT, but for some arbitrary reason, [big tech companies] don't want us in the field." This comment hints at simmering tensions between the mainstream and the alternative AI communities.
AIdatasetethicslarge language model
- This groundbreaking Common Pile dataset serves as a challenge to the norms in the technology sector, proving that it's possible to create an ethical and extensive AI dataset without compromising on quality.
- The impressive results from the AI models trained on the Common Pile vindicate the potential of open-source data in fueling the evolution of artificial intelligence, outperforming or matching models that have been trained on larger datasets.
- The success of the Common Pile dataset and the models trained on it emphasizes the missed opportunities by big tech companies in prioritizing ethical data practices, as a small team of researchers managed to accomplish this feat relatively quickly.
- The Common Pile dataset, now available in the public domain, sparks a new movement in the tech and AI community, promoting the use of transparent, consent-driven, and ethically sourced data for future advancements in large language models and AI research.
