Can LLMs uncover new knowledge - the scientific equivalent of AlphaGo’s move 37? In a Nature paper today, we Google DeepMind unveil that FunSearch, our new LLM based approach for program search, has uncovered new results in Maths and Computing. See details below.

A super cool project from GDM's Science team! It's the 'highest surprisal' project for me in quite some time: I haven't thought LLMs + evolution could do such incredible stuff :) Huge congrats to the team.

Here's a great example of human-AI collaboration to obtain new results in mathematics. In this case, a team from DeepMind has used LLMs to obtain the best known lower bound for the cap-set problem. Of course, there's more to it than talking to ChatGPT. theguardian.com/science/2023/d…

FunSearch is one of the best examples of creativity in AI. I highly recommend reading this paper. Congratulations Alexander Novikov and team for this amazing result.

Imagine a future where AI discovers new algorithms to tackle hard computational problems with unprecedented efficiency. Or, better yet -- come build this future with us! Our team behind AlphaTensor and FunSearch is hiring a research scientist in London: boards.greenhouse.io/deepmind/jobs/…

✨🎨🏰Super excited to share our new paper Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness Inspired by biology we 1) get adversarial robustness + interpretability for free, 2) turn classifiers into generators & 3) design attacks on vLLMs 1/12

Our team has been working hard to harness the power of AI to make software more secure.✨🔐 Today we are excited to share a major milestone: our AI agent has discovered its first real-world security vulnerability! googleprojectzero.blogspot.com/2024/10/from-n… More info 🧵

The #AlphaFold 3 model code and weights are now available for academic use. We Google DeepMind are excited to see how the research community continues to use AlphaFold to address open questions in biology and new lines of research. github.com/google-deepmin…

In Nature Machine Intelligence, we show how our AI system AlphaTensor-Quantum can make quantum computing more efficient. 🖥️⚡ By optimizing quantum circuits, it’s helping run calculations faster to save resources and accelerate discoveries. ↓ goo.gle/4iHAITd

I’m happy to share our new white paper on AlphaEvolve, an evolutionary coding agent. It uses Large Language Models to discover and optimize complex algorithms, aiming to tackle challenging computational and scientific problems by iteratively improving code.

Introducing AlphaEvolve: a Gemini-powered coding agent for algorithm discovery. It’s able to: 🔘 Design faster matrix multiplication algorithms 🔘 Find new solutions to open math problems 🔘 Make data centers, chip design and AI training more efficient across Google. 🧵

Excited to announce AlphaEvolve A powerful AI coding agent developed by our team in Google DeepMind that is able to discover impactful new algorithms for important problems in Maths and Computing by combining the creativity of large language models with automated evaluators.

We introduce 🌸✨ AlphaEvolve ✨🌸, an evolutionary coding agent using LLMs coupled with automatic evaluators, to tackle open scientific problems 🧑‍🔬 and optimize critical pieces of compute infra ⚙️ deepmind.google/discover/blog/…

AlphaEvolve, our new Gemini-powered coding agent, can help engineers + researchers discover new algorithms and optimizations for open math + computer science problems.  We’ve used it to improve the efficiency of our data centers (recovering 0.7% of our fleet-wide compute

Knowledge begets more knowledge, algorithms optimising other algorithms - we are using AlphaEvolve to optimise our AI ecosystem, the flywheels are spinning fast...