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OpenAI’s comment to the NTIA on open model weights

Apr 02, 2024 - openai.com
OpenAI discusses its approach to AI development and deployment, emphasizing the importance of safety, innovation, and free market competition. They highlight their past experiences with models like GPT-2 and GPT-3, which were released in stages to assess potential misuse and societal implications. OpenAI also emphasizes the importance of their Preparedness Framework, a science-based approach to continuously assess and mitigate any catastrophic risks posed by their AI models. They believe in the potential of AI to improve lives and unlock a better future, but also acknowledge the need for careful management of potential risks.

The article also discusses the need for a better science of AI risks, as the field is still nascent. OpenAI suggests that governments can play a crucial role in maturing AI risk and capability evaluation practices. They also argue that AI developers should assess their model’s potential to pose catastrophic risks and put appropriate mitigations in place before deployment or release. The article concludes by emphasizing the need for flexibility and adaptability in any government policy approach to AI, given the rapidly changing landscape of AI technology.

Key takeaways:

  • OpenAI believes in the potential of AI to improve lives and unlock a better future, and has been a pioneer in grappling with the question of how to distribute the benefits of AI models safely and effectively.
  • OpenAI has developed a Preparedness Framework, a science-based approach to continuously assess and mitigate any catastrophic risks that might be posed by their AI models.
  • OpenAI suggests that developers of highly capable AI should assess their model’s potential to pose catastrophic risks, and put appropriate mitigations in place before deploying or releasing it.
  • OpenAI emphasizes the need for a better science of AI risks and believes that governments have an important role to play in helping the AI ecosystem mature its risk and capability evaluation practices.
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