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Researchers at MIT present a paper on self-evolving AI scientists that can discover and adapt their own scientific vocabulary, using a categorical framework to mathematically quantify genuine novelty and separate discovery from mere search or retrieval.
This paper replicates the Picbreeder human-driven open-ended image evolution process using large vision-language models, analyzing differences and exploring factors like exploratory noise, behavioral diversity, and memory.
Richard Socher's new startup Recursive Superintelligence exits stealth with $650M in funding, aiming to build a recursively self-improving AI that can autonomously identify and fix its own weaknesses without human intervention.