Quoting Jeremy Howard
📰 Simon Willison's Blog
Jeremy Howard proposes a solution to slow down recursive AI self-improvement by restricting the top-ranked model's use for frontier AI research, while making it accessible to others
Action Steps
- Evaluate the current state of recursive AI self-improvement
- Assess the potential risks and benefits of restricting top-ranked models
- Develop guidelines for responsible AI development and sharing
- Implement access controls for top-ranked models
- Monitor and adjust the guidelines as needed to ensure safety and fairness
Who Needs to Know This
AI researchers and developers can benefit from this proposal as it aims to prevent a power imbalance and slow down the advancement of frontier AI, which can be crucial for ensuring safety and responsibility in AI development
Key Insight
💡 Restricting top-ranked models from frontier AI research can help prevent a power imbalance and slow down the advancement of recursive AI self-improvement
Share This
💡 Jeremy Howard's proposal: restrict top-ranked AI models from frontier research, but make them accessible to others #AI #Safety #Responsibility
Key Takeaways
Jeremy Howard proposes a solution to slow down recursive AI self-improvement by restricting the top-ranked model's use for frontier AI research, while making it accessible to others
Full Article
Easy solution to slow down recursive AI self improvement: The lab with the top-ranked model must agree THEY must not use it for working on frontier AI But everyone else should have access to it. By definition, this means the frontier doesn't advance. It also has the critical benefit of avoiding a dangerous power imbalance. Anthropic has chosen the opposite
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