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AIs Improving Themselves? Automated Design of Agentic Systems
Manage episode 444202208 series 3605861
A tartalmat a Brian Carter biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Brian Carter vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
The paper proposes a new research area called Automated Design of Agentic Systems (ADAS), which aims to automatically create powerful AI systems, including inventing new components and combining them in novel ways. The authors introduce Meta Agent Search, an algorithm that uses a meta agent to iteratively program increasingly sophisticated agents by learning from a growing archive of previously discovered agents. The paper demonstrates that Meta Agent Search can discover agents that outperform hand-designed systems across various domains, including coding, science, and math, showing promise for efficiently developing increasingly capable AI systems. Importantly, the discovered agents demonstrate robustness and generalizability, exhibiting strong performance even when transferred across domains and models.
Read the paper here: https://arxiv.org/pdf/2408.08435
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