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Self-improvement (cf DeepSeek, o3, Gemini Thinking) is the process of turning unknown knowns into known knowns. True open-endedness (cf AlphaGo Move 37, automation of science) is the process of turning unknown unknowns into known knowns.

Veo 3 tackling some real world locations but "in-game" 1. Paris - Eiffel Tower

maybe the most impt result in multiagent systems

Dan Roy UBC, Alberta, Montreal, Toronto, Waterloo, McGill, Simon Fraser … so many amazing universities in Canada for anyone wanting to study AI. There’s outstanding places in the UK too: Oxford, Cambridge, Imperial, UCL, … and tons in Europe. Frankly from any of these places you can

Fantastic news!

**When AIs Start Rewriting Themselves** Darwin G​​ödel Machine: Open-Ended Evolution of Self-Improving Agents The Darwin G​​ödel Machine can: 1. Read and modify its own code 2. Evaluate if the change improves performance 3. Open-endedly explore the solution space 🧵👇

"If you can’t actually hack being genuinely creative, you’re not at the human level, no matter what else you can do. In some ways, the most salient thing that we leave behind is our legacy of ideas. We’re not remembered for our test scores. We’re remembered for the inventions we

Schmidhuber's Gödel Machine: AI "rewriting its code" if provably useful showed the dream of recursive self-improvement 🔄 Thrilled to share our practical realization, inspired by Darwinian evolution! Done with the amazing Jenny Zhang, Shengran Hu, Robert Lange Jeff Clune 😍

Veo 3 lands in the UK and is now also available on the Gemini app. Sound on!

Welcome to Google DeepMind's Open-Endedness Team 🚀🧑‍🔬

Revisiting Louis Kirsch et al.’s general-purpose ICL by meta-learning paper and forgot how great it is. It's rare to be taken along on the authors' journey to understand the phenomenon they document like this. More toy dataset papers should follow this structure.

And just like that, Interdimensional Cable 1 became reality. What a time to be alive.

how do we get truly novel outputs from AI? major labs including DeepMind and OpenAI have had teams dedicated to open-endedness. makes sense given the impact it could have on enabling discovery and self-improvement. doing today's deep dive thread on this field:

Great post by Mikael Henaff (after ascending, what an achievement!) on what makes The NetHack Learning Environment so extremely difficult for AI (even LLMs: balrogai.com). "While NetHack is complex in comparison to other RL benchmarks, it still contains only a tiny fraction of the

Great work by UCL DARK PhD student Nathan Herr in collaboration with Roberta Raileanu on going beyond breath-first search, tree of thoughts, and MCTS for LLM exploration for difficult reasoning problems.

Welcome back to Google DeepMind's Open-Endedness Team, Ashley! 🥳🎙️🎉🚀

Hello Gemini 2.5 Flash-Lite! So fast, it codes *each screen* on the fly (Neural OS concept 👇). The frontier isn't always about large models and beating benchmarks. In this case, a super fast & good model can unlock drastic use cases. Read more: blog.google/products/gemin…

The progress of Gemini over the last year +

The next seminar is this Friday (June 20) and starts at 12pm midday UK time! Roberta Raileanu from Google deepmind is going to talk about “Automating Scientific Discovery: How Far Are We?”! ucl.zoom.us/j/99748820264 ucl-ellis.github.io/dm_csml_semina…