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We believe that evolutionary open-ended search and self-improving AI systems will be key to unlocking stronger AI capabilities. For a short overview, this video from Wes Roth discusses AlphaEvolve and our recent Darwin Gödel Machine. Full Video: Short Video:

23,821 просмотров • 1 год назад •via X (Twitter)

Комментарии: 6

Фото профиля Sakana AI
Sakana AI1 год назад

The authors also recommend @MatthewBerman’s video for a nice introduction and walk through of the Darwin Gödel Machine paper. Video:

Фото профиля Coral AI News
Coral AI News1 год назад

Coral AI is the most powerful AI for documents. See the difference yourself:

Фото профиля Ant A
Ant A1 год назад

@WesRothMoney 👍

Фото профиля Rippa Sats
Rippa Sats1 год назад

@WesRothMoney Will I be able to use this model? Better said, can I provide it certain Math to have it Rewrite itself?

Фото профиля Altiam Kabir
Altiam Kabir1 год назад

@WesRothMoney Exciting potential for AI advancements!

Фото профиля Phil
Phil1 год назад

@WesRothMoney If you don’t understand evey and all aspects of how it improved then you need to back off as you are playing with a fire that can impact humanity!!!

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New Paper! Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents A longstanding goal of AI research has been the creation of AI that can learn indefinitely. One path toward that goal is an AI that improves itself by rewriting its own code, including any code responsible for learning. That idea, known as a Gödel Machine, proposed by Jürgen Schmidhuber over two decades ago, is a hypothetical self-improving AI. It optimally solves problems by recursively rewriting its own code when it can mathematically prove a better strategy, making it a key concept in meta-learning or “learning to learn.” While the theoretical Gödel Machine promised provably beneficial self-modifications, its realization relied on an impractical assumption: that the AI could mathematically prove that a proposed change in its own code would yield a net improvement before adopting it. Sakana AI, in collaboration with Jeff Clune’s lab at UBC, proposes something more feasible: a system that harnesses the principles of open-ended algorithms like Darwinian evolution to search for improvements that empirically improve performance. We call the result the Darwin Gödel Machine. DGMs leverage foundation models to propose code improvements, and use recent innovations in open-ended algorithms to search for a growing library of diverse, high-quality AI agents. Applied to practical tasks, we implemented Darwin Gödel Machine as a self-improving coding agent that rewrites its own code to improve performance on programming tasks. It creates various self-improvements, such as a patch validation step, better file viewing, enhanced editing tools, generating and ranking multiple solutions to choose the best one, and adding a history of what has been tried before (and why it failed) when making new changes (see the attached video). We believe that Darwin Gödel Machines represent a concrete step towards AI systems that can autonomously gather their own stepping stones to learn and innovate forever!

hardmaru

104,782 просмотров • 1 год назад