This page shows the most relevant public items for lab:deep-mind-ai, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Jun 7, 2023 • Nature • Daniel J. Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelpi, Marco Selvi, Alhussein Fawzi
Fundamental algorithms such as sorting or hashing are used trillions of times on any given day. As demand for computation grows, it has become critical for these algorithms to be as performant as p...
Paper • Oct 30, 2019 • Nature • Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, David Silver
The game of StarCraft II has emerged as a grand challenge for artificial intelligence research owing to its complex, multi-agent, and partially observable environment. Here we introduce AlphaStar, ...
Paper • Dec 8, 2021 • arXiv • Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Eleni Elia, Danilo J. Rezende, Vinyals, Simonyan
Language modelling provides a step towards intelligent communication systems by harnessing large datasets and expressive models. We provide an analysis of Transformer-based language model architect...
Paper • Feb 4, 2016 • arXiv • Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present as...
Paper • May 12, 2022 • arXiv • Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Nando de Freitas
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato,...
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