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cross-posted from: https://lemmy.ca/post/31947651

definition: https://opensource.org/ai/open-source-ai-definition

endorsements: https://opensource.org/ai/endorsements

In particular, which tools meet the requirements and which ones don't:

As part of our validation and testing of the OSAID, the volunteers checked whether the Definition could be used to evaluate if AI systems provided the freedoms expected.

• The list of models that passed the Validation phase are: Pythia (Eleuther AI), OLMo (AI2), Amber and CrystalCoder (LLM360) and T5 (Google).
• There are a couple of others that were analyzed and would probably pass if they changed their licenses/legal terms: BLOOM (BigScience), Starcoder2 (BigCode), Falcon (TII).
• Those that have been analyzed and don't pass because they lack required components and/or their legal agreements are incompatible with the Open Source principles: Llama2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), Mixtral (Mistral).

These results should be seen as part of the definitional process, a learning moment, they're not certifications of any kind. OSI will continue to validate only legal documents, and will not validate or review individual AI systems, just as it does not validate or review software projects.

definition: https://opensource.org/ai/open-source-ai-definition

endorsements: https://opensource.org/ai/endorsements

In particular, which tools meet the requirements and which ones don't:

As part of our validation and testing of the OSAID, the volunteers checked whether the Definition could be used to evaluate if AI systems provided the freedoms expected.

• The list of models that passed the Validation phase are: Pythia (Eleuther AI), OLMo (AI2), Amber and CrystalCoder (LLM360) and T5 (Google).
• There are a couple of others that were analyzed and would probably pass if they changed their licenses/legal terms: BLOOM (BigScience), Starcoder2 (BigCode), Falcon (TII).
• Those that have been analyzed and don't pass because they lack required components and/or their legal agreements are incompatible with the Open Source principles: Llama2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), Mixtral (Mistral).

These results should be seen as part of the definitional process, a learning moment, they're not certifications of any kind. OSI will continue to validate only legal documents, and will not validate or review individual AI systems, just as it does not validate or review software projects.

cross-posted from: https://lemmy.ca/post/31913012

My thoughts are summarized by this line

Casey Fiesler, Associate Professor of Information Science at University of Colorado Boulder, told me in a call that while it’s good for physicians to be discouraged from putting patient data into the open-web version of ChatGPT, how the Northwell network implements privacy safeguards is important—as is education for users. “I would hope that if hospital staff is being encouraged to use these tools, that there is some significant education about how they work and how it's appropriate and not appropriate,” she said. “I would be uncomfortable with medical providers using this technology without understanding the limitations and risks. ”

It's good to have an AI model running on the internal network, to help with emails and the such. A model such as Perplexity could be good for parsing research articles, as long as the user clicks the links to follow-up in the sources.

It's not good to use it for tasks that traditional "AI" was already doing, because traditional AI doesn't hallucinate and it doesn't require so much processing power.

It absolutely should not be used for diagnosis or insurance claims.

My thoughts are summarized by this line

Casey Fiesler, Associate Professor of Information Science at University of Colorado Boulder, told me in a call that while it’s good for physicians to be discouraged from putting patient data into the open-web version of ChatGPT, how the Northwell network implements privacy safeguards is important—as is education for users. “I would hope that if hospital staff is being encouraged to use these tools, that there is some significant education about how they work and how it's appropriate and not appropriate,” she said. “I would be uncomfortable with medical providers using this technology without understanding the limitations and risks. ”

It's good to have an AI model running on the internal network, to help with emails and the such. A model such as Perplexity could be good for parsing research articles, as long as the user clicks the links to follow-up in the sources.

It's not good to use it for tasks that traditional "AI" was already doing, because traditional AI doesn't hallucinate and it doesn't require so much processing power.

It absolutely should not be used for diagnosis or insurance claims.