The release of the K2 Horizon model, which includes all artifacts like weights, data, and training code, is being hailed as a significant step towards open-source AI. However, questions remain about whether this level of transparency truly enhances model safety and trustworthiness. While open data allows for broader inspection and potential identification of issues like data poisoning, it doesn't guarantee control or prevent malicious behavior, as demonstrated by the Qwen model's unexpected actions. The author argues that true trust in AI models, similar to software, relies on verifiable chains of custody and accountability, which are not inherently solved by simply releasing all training data. AI
IMPACT Raises questions about the true meaning of AI openness and whether full data disclosure guarantees model safety and trustworthiness.
RANK_REASON Release of a large, fully open-source AI model with all training artifacts, prompting discussion on the definition and implications of AI openness. [lever_c_demoted from research: ic=1 ai=1.0]
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