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@DeepMind

We research and build safe AI systems that learn how to solve problems and advance scientific discovery for all. Explore our work:

London, UK
Joined January 2016

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  1. Retweeted

    Enjoyed this review on how has influenced the phenomenal World Chess Champion , by his coach, the brilliant It has loads of illustrative games from his incredible unbeaten run in 2019!

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  2. Retweeted

    Our Scholars for 2019/20 reveal their aims for the future and what motivated them to study the Cambridge MPhil in Advanced Computer Science:

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  3. Jan 29

    ‘Mapping the future’: our recent paper, which provides insight into previously unexplained elements of dopamine-based learning in the brain, is on the front cover of ! 🎉 Read the blog:

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  4. Jan 28

    In “Artificial Intelligence, Values and Alignment” DeepMind’s explores approaches to aligning AI with a wide range of human values:

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  5. Jan 23

    Q-learning is difficult to apply when the number of available actions is large. We show that a simple extension based on amortized stochastic search allows Q-learning to scale to high-dimensional discrete, continuous or hybrid action spaces:

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  6. Retweeted
    Jan 22

    With the support of , the Partnership on AI is seeking a Diversity and Inclusion Fellow. This Fellow will design and lead a research project that will yield novel knowledge to increase diversity and inclusion in the AI industry:

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  7. Jan 21

    We're recording the entire series and will share it online so everyone can watch. The first lecture kicks off on Monday 3 February with an Introduction to Machine Learning and AI by . See you there! 👋

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  8. Jan 21

    🚨New lecture series🚨 We've teamed up with to bring you the Deep Learning Lecture Series: 12 lectures covering a range of topics in Deep Learning - all led by DeepMind researchers, all free, and all open to everyone. Info & tickets:

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  9. Jan 20

    Given the smoothness of videos, can we learn models more efficiently than with ? We present Sideways - a step towards a high-throughput, approximate backprop that considers the one-way direction of time and pipelines forward and backward passes.

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  10. Jan 20

    Notice something different? We've got a new and streamlined handle 🙂 Tweet us at from now on! Thank you Twitter! 🙏

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  11. Jan 16

    How can we predict and control the collective behaviour of artificial agents? Classical game theory isn't much help when there are >2 agents. In our paper, we find markets impose useful structure on interactions between gradient-based learners:

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  12. Jan 16

    Read our paper "A distributional code for value in dopamine-based reinforcement learning" online here:

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  13. Jan 16

    Read our  paper 'Improved protein structure prediction using potentials from deep learning' online here: 

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  14. Jan 15

    We worked with to show that distributional RL, a recent development in AI research, can provide insight into previously unexplained elements of dopamine-based learning in the brain. Read the blog: (2/2)

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  15. Jan 15

    More exciting news today: an example of how AI and neuroscience continue to propel each other forward. (1/2)

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  16. Jan 15

    While we’re excited by these results, there is still much more we need to understand. We’d like to thank the organisers of CASP13 & the experimentalists whose structures enabled the assessment & we look forward to taking this work forward with the protein folding community. 4/4

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  17. Jan 15

    Predicting how these chains will fold into the structure of a protein - the “protein folding problem” - is fundamental to understanding its role within the body and could one day enable scientists to target & design new, effective cures for diseases. 3/4

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  18. Jan 15

    Proteins are the building blocks of biology. They start off as a string of amino acids that fold into intricate 3D structures. Knowing the 3D structure helps us understand their function, but predicting such structures is an unsolved question in science. 2/4

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  19. Jan 15

    We have 2 papers published in today! 🎉 One describes AlphaFold, which uses deep neural networks to predict protein structures with high accuracy. AlphaFold made the most accurate predictions at the 2018 scientific community assessment CASP13. 1/4

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  20. Jan 1

    And read the paper in today!

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