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Feb 2 3 tweets 5 min read
Introducing #AlphaCode: a system that can compete at average human level in competitive coding competitions like @codeforces. An exciting leap in AI problem-solving capabilities, combining many advances in machine learning!

Read more: dpmd.ai/Alpha-Code 1/
Below is an example of a problem #AlphaCode can successfully solve, using the exact information seen on @codeforces, & the program that #AlphaCode writes.

By @liyuajia, David Choi, @junyoungch, @NateKushman, @Mononofu, @RemiLeblond, Tom Eccles, James Keeling, @FelixAxelGimeno 2/
Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, @pushmeet, @NandoDF, @koraykv & @OriolVinyalsML. 3/3

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More from @DeepMind

Sep 29, 2021
From packing an umbrella to preparing for extreme conditions, predicting short term weather patterns is crucial for daily life.

New research with the @metoffice and SOTA model advances the science of Precipitation Nowcasting - the prediction of rain: dpmd.ai/nowcasting 1/4
Today’s weather systems provide planet-scale predictions several days ahead, but often struggle to generate high-resolution predictions for short lead times. Nowcasting fills this performance gap, with predictions on rainfall within the next 1-2 hours. 2/4
Compared to widely used-nowcasting methods, meteorologists from @metoffice rated this method as their 1st choice 89% of the time.

There's more to do but our researchers hope this will act as a base for future work & promote greater integration of ML & environmental science. 3/4
Read 4 tweets
Sep 9, 2021
Introducing the '21 DeepMind x @ai_ucl Reinforcement Learning Lecture Series, a comprehensive introduction to modern RL.

Follow along with our researchers are they explore Markov Decision Processes, sample-based learning algorithms & much more: dpmd.ai/2021RLseries 1/2 Image
Also find the full series via the DeepMind @YouTube channel: dpmd.ai/DeepMindxUCL21
In the first lecture of the series, Research Scientist Hado introduces the course and explores the fascinating connection between reinforcement learning and artificial intelligence: dpmd.ai/RLseries1

#DeepMindxUCL @ai_ucl Image
Read 10 tweets
Jul 27, 2021
Reinforcement learning typically trains & tests agents on the same game. New work shows how our team trains generally capable agents on huge game spaces, resulting in agents that generalise to held-out test games, & learn behaviours like experimentation dpmd.ai/open-ended-blog 1/
Rather than training on a limited number of tasks, our team defines a whole universe of tasks that can be procedurally generated, from simple object finding games to complex strategic games like Capture the Flag. 2/
By constructing a hierarchical learning process with an open-ended and iteratively refined objective, it was possible to train agents that never stop learning, and develop increasingly general behaviour across games. 3/
Read 5 tweets
Jul 26, 2021
Mixed Integer Programming is an NP-hard optimisation problem arising in planning, logistics, resource allocation, etc.

Presenting a solver with neural heuristics that learns to adapt to the problem domain, outperforming SCIP on Google-scale MIPs: dpmd.ai/13349 (1/) Image
Practical applications often focus on finding good solutions fast rather than proving optimality. In follow-up work, Neural Neighborhood Selection finds better solutions even faster by learning heuristics for large neighborhood search: dpmd.ai/10201 (2/) Image
The neural solver learns even on single problem instances, improving the best known solutions to three open MIPLIB problems.

Milo-v12-6-r1-75-1: dpmd.ai/milo-v12-6-r1-…
Neos-1420790: dpmd.ai/neos-1420790
xmas10-2: dpmd.ai/xmas10-2

(3/) Image
Read 4 tweets
Jul 23, 2021
Yesterday we announced early collaborations using the #AlphaFold Protein Structure Database, which offers the most complete and accurate picture of the human proteome to date. So how is AlphaFold helping these organisations with their work…? 1/ Image
The Drugs for Neglected Diseases initiative (@DNDi) has advanced their research into life-saving cures for diseases that disproportionately affect the poorer parts of the world. 2/ Image
The @CEI_UoP is using #AlphaFold's predictions to help engineer faster enzymes for recycling some of our most polluting single-use plastics. 3/ Image
Read 4 tweets
Jul 22, 2021
Today with @emblebi, we're launching the #AlphaFold Protein Structure Database, which offers the most complete and accurate picture of the human proteome, doubling humanity’s accumulated knowledge of high-accuracy human protein structures - for free: dpmd.ai/alphafolddb 1/
We’re also sharing the proteomes of 20 other biologically-significant organisms, totalling over 350k structures. Soon we plan to expand to over 100 million, covering almost every sequenced protein known to science & the @uniprot reference database.

dpmd.ai/alphafold-blog 2/
We’re excited to see how this will enable and accelerate research for scientists around the world. We've already seen promising signals from early collaborators using #AlphaFold in their own work, including @DNDi, @CEI_UoP, @UCSF & @CUBoulder: dpmd.ai/alphafold-case… 3/
Read 5 tweets

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