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Insightful new paper reviewing #opensource #AI model characteristics, to better help the public, industry, and government choose which #generativeAI models they want to leverage. Some of my key thoughts and takeaways: ⚡ Virtually none of the 'open source' models have peer-reviewed academic papers on any components of their models. Current norms of 'pre-print' working papers help maintain IP rights & competitive advantage but evade safety and quality controls that come with the scrutiny of peer-review. ⚡ This paper provides a nice complement to Stanford Institute for Human-Centered Artificial Intelligence (HAI) Foundation Transparency Model Index, including a broader spectrum of models from smaller enterprises, academic initiatives, and large corporations. However, I have yet to see a good review that puts transparency scores and performance benchmarks of models side-by-side. Have you? ⚡ 'Open source' concept in the #EU is a give-and-take between industry and regulators, and its current definition may not fully benefit the public. Under the EU AI Act, a few characteristics require disclosure: weights, model architecture, and usage. However, models released as open source are exempt from disclosing training info and fine-tuning methods. This paper clearly shows that training datasheets are among the least publicized, but necessary, characteristics of open source. Which models do you use in your work and why? Where would you like to see increased transparency and for what purpose? #artificialintelligence #generativeai #transparency #opensource #opensourceai #science #aipolicy #ai

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