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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.

Gather-Attend-Scatter (GATS), a novel module that combines pretrained foundation models operating at different rates into larger multimodal networks. Paper: arxiv.org/abs/2401.08525

Why do browsers ever lose what I typed in? Would be great (and so cheap space-wise) to have a storage of everything I recently typed for when a page glitches.

Check out my new startup, a music making app for everyone. And I mean everyone! Thrilled to see this finally in the open. What a wild ride! Wouldn't be possible without the amazing co-founders Charlie Nash, Conor Durkan, Andrew Sanchez and David Ding and all the

We are looking for a London-based researcher. During the last couple of years our team did AlphaTensor and FunSearch and our goal hasn't changed: using AI to discover novel algorithms. Join us! boards.greenhouse.io/deepmind/jobs/…

Can unsupervised transfer outperform zero-shot transfer? In our #ICML2024 paper we show it can! 🔥 We present 🐢TURTLE , a method for fully unsupervised transfer with any foundation model! TURTLE also achieves SOTA unsupervised performance 🔥 Kudos to Artyom Gadetsky Yulun Jiang

How can we discover fine-grained classes within a coarsely labeled dataset? Excited to present FALCON at #ICML2024, a method for uncovering fine-grained classes without fine-grained supervision. FALCON is versatile and works across various domains! W/ Matej Grcić Artyom Gadetsky

Our latest work AlphaProof, building on AlphaZero, LLMs and the Lean theorem prover, combined with AlphaGeometry 2 managed to solve 4 IMO problems and achieve silver-medalist level! 🚀 More at dpmd.ai/imo-silver

Try our 3D "Street View for Coral Reefs" prototype (3D Gaussian splatting)! We've hit a new milestone today! A small step towards our mission after months of work.. So stoked to tell you \o/ 🔵 Why? Coral Reefs are so precious, beautiful, incredibly complex and threatened

A clear step towards achieving my dream: building AI that assists competitive programmers 🧑‍💻 “This is an exciting approach to combine work of human competitive programmers and LLMs, to achieve results that neither would achieve on their own.” --Petr Mitrichev Details below! 🧵

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

Think you know Gemini? 🤔 Think again. Meet Gemini 2.5: our most intelligent model 💡 The first release is Pro Experimental, which is state-of-the-art across many benchmarks - meaning it can handle complex problems and give more accurate responses. Try it now →

Tired of manual prompt engineering to solve new task with your LLM? We introduce Joint Inference—a framework for fully unsupervised adaptation of large (vision) language models that often performs on par with supervised approaches 🔥 #ICLR2025 In collaboration with Amir Zamir

Our team at Google DeepMind is looking for a Student Researcher to work on AI for maths and algorithmic discovery (we did AlphaTensor and FunSearch before); if you've been accepted to the program and are looking for a team please reach out at [email protected]

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...

🧵Google DeepMind just dropped a bombshell: An AI agent that autonomously writes algorithms better than humans. It’s called AlphaEvolve, and it could completely change how we build software and solve problems. Here’s why this changes everything👇