Compares Introspective RSI (I-RSI), where meta-cognition and meso-cognition occur in the same AI entity, with Extrospective RSI (E-RSI), using separate entities. Discusses differences in monitoring, latency, parallelization, and transferability. Highlights implications for AI automating R&D and transition risks.
Key Points
- 1.*Fewer monitoring opportunities in I-RSI due to internal processes
- 2.*Gradual transition continuity favors E-RSI over sudden I-RSI
- 3.*Better transferability of E-RSI improvements across models
Impact Analysis
AI safety researchers benefit from clearer RSI taxonomies to assess self-improvement risks. Enables strategies for safer, monitorable AI R&D automation. Could influence paths to rapid vs gradual AI progress.
Technical Details
I-RSI enables low-latency internal self-observation and modification within one AI. E-RSI uses external channels like APIs for distributed cognition, aiding human oversight and generalization from non-AI tasks.