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Aurora is a foundation model for the Earth system whose Nature paper, published on 21 May 2025 and promoted by Microsoft Research the same month, reports better than operational forecasts across four domains at far lower compute.
A February 2024 paper introduced DeepSeekMath 7B, a math-focused language model, and with it Group Relative Policy Optimization, a reinforcement-learning method that drops PPO's critic model.
On 17 January 2024 Google DeepMind announced AlphaGeometry, a system that pairs a language model with a symbolic deduction engine and solved 25 of 30 olympiad geometry problems in a benchmark, close to the 25.9 averaged by human gold medallists.
FunSearch, published in Nature in December 2023, used large language models for program search rather than direct solution search, and found results better than the previous best on the cap set problem and online bin packing.
On 14 November 2023, Google DeepMind announced GraphCast; its Science paper reported beating ECMWF HRES on most test variables, and a live version forecast Hurricane Lee's landfall earlier.
On 5 July 2023 Nature published Pangu-Weather, a deep-learning global weather model that reported better deterministic forecasts than ECMWF's operational system on every variable it tested, in the same issue as a deep-learning precipitation nowcasting paper.
On 4 April 2022, Google Research announced PaLM, a 540-billion-parameter language model trained on 6,144 TPU v4 chips with the Pathways system, and reported strong multi-step reasoning and above-average-human results on BIG-bench.
On 19 November 2019, DeepMind posted a MuZero preprint to arXiv: it plans with a learned model rather than rules, matched AlphaZero in Go, chess and shogi, and set a state of the art on 57 Atari games.
On 30 October 2019, DeepMind announced that AlphaStar reached Grandmaster level in StarCraft II for all three races, ranking above 99.8% of active players and playing under human-like interface limits.
A preprint posted to arXiv by DeepMind on 5 December 2017 described one general reinforcement-learning algorithm that, given only the rules, learned chess, shogi and Go by self-play and reached superhuman level in all three within 24 hours.
In 2017, DeepMind announced AlphaGo Zero, a Go system that started from random play, learned only by self-play, and outperformed earlier AlphaGo versions with one neural network and far less hardware.
On 27 January 2016 DeepMind announced AlphaGo alongside a paper in Nature reporting that the program had beaten European Go champion Fan Hui 5–0 in October 2015 — the first time a computer program had beaten a professional Go player.
On 19 December 2013, DeepMind researchers posted a paper showing that one convolutional network, trained with a variant of Q-learning, could learn Atari 2600 control policies directly from raw pixels and outperform prior methods on six of seven games.
