Jeff Clune: Agent AI Needs Darwin
Published:Jan 4, 2025 02:43
•1 min read
•ML Street Talk Pod
Analysis
The article discusses Jeff Clune's work on open-ended evolutionary algorithms for AI, drawing inspiration from nature. Clune aims to create "Darwin Complete" search spaces, enabling AI agents to continuously develop new skills and explore new domains. A key focus is "interestingness," using language models to gauge novelty and avoid the pitfalls of narrowly defined metrics. The article highlights the potential for unending innovation through this approach, emphasizing the importance of genuine originality in AI development. The article also mentions the use of large language models and reinforcement learning.
Key Takeaways
- •Jeff Clune is working on open-ended evolutionary algorithms for AI.
- •The goal is to create "Darwin Complete" search spaces for continuous skill development and exploration.
- •"Interestingness" is a key focus, using language models to gauge novelty and avoid metric-based pitfalls.
Reference
“Rather than rely on narrowly defined metrics—which often fail due to Goodhart’s Law—Clune employs language models to serve as proxies for human judgment.”