AI Industry Is Trying to Subvert the Definition of "Open Source AI"
The OSI's definition of "open source AI" faces criticism for permitting secrecy in AI development, prompting calls for clearer distinctions and emphasizing the need for genuine open source practices and public AI options.
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The Open Source Initiative (OSI) has released a definition of "open source AI" that has drawn criticism for allowing secretive practices in AI development, such as the use of undisclosed training data. Critics argue that this definition undermines the essence of open source, as the training data is integral to how neural networks function. Many AI models labeled as open source, like LLAMA, are considered open in name only. The OSI's stance appears to cater to corporate interests that prioritize secrecy while still wanting to benefit from the open source label. There is a call for a clearer distinction between true open source and models that may be partially open, suggesting terms like "open weights" to describe models that do not provide full transparency. The discussion also highlights the complexities of sharing data in fields like medical AI, where legal restrictions on data sharing exist. The need for a public AI option and genuine open source practices is emphasized, as well as the importance of addressing privacy concerns and the rights of individuals regarding their data.
- The OSI's definition of "open source AI" has been criticized for allowing secrecy in AI development.
- Many AI models labeled as open source do not meet the true criteria of openness.
- There is a suggestion to use the term "open weights" to differentiate between true open source and partially open models.
- Legal restrictions on data sharing complicate the definition of open source in fields like medical AI.
- A public AI option and genuine open source practices are deemed necessary for ethical AI development.
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Begun, the open source AI wars have.. This is going to be ugly. Really ugly.
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