Self-Improvement and Recursive Enhancement

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  "llmSummary": "Comprehensive analysis of AI self-improvement from current AutoML systems (23% training speedups via AlphaEvolve) to theoretical intelligence explosion scenarios, with expert consensus at ~50% probability that software feedback loops could drive accelerating progress and task completion horizons doubling every 7 months (2019-2025). Quantifies key uncertainties including software feedback multiplier r=1.2 (range 0.4-3.6), timeline estimates of 5-15 years to recursive self-improvement, and critical compute bottleneck debate determining whether cognitive labor alone enables explosion.",
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