DeepMindOvjeren akaunt

@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
Vrijeme pridruživanja: siječanj 2016.

Medijski sadržaj

  1. 28. sij

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

  2. 20. sij

    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.

  3. 16. sij

    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:

  4. 15. sij

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

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  5. 15. sij

    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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  6. 30. pro 2019.

    If you’re looking for something to listen to this week, try DeepMind: The podcast. Hear from , , and many others from our talented teams. 🔊 🔊

  7. 18. pro 2019.

    [2/2] Find out more about Google’s Project Euphonia and Tim’s story in the new Originals learning series , out today:

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  8. 17. pro 2019.

    After being named as new and noteworthy by , we’re proud that DeepMind: The podcast with is also one of the top shows in both the Science and Technology categories. 🎧 and subscribe here:

  9. 16. pro 2019.

    DeepInsight is a decoding framework for discovering and characterising the neural correlates of behaviour and stimuli in unprocessed biological data:

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  10. 16. pro 2019.

    What does it mean to understand language? We argue that human-like understanding requires complementary memory systems and rich representations of situations. A roadmap for extending ML models toward human-level language understanding:

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  11. 13. pro 2019.

    We’re in the final stretch of ! Thanks for so many engaging discussions so far this week. Today and tomorrow, we’ll be wrapping up by co-organising the following workshops:

  12. 13. pro 2019.
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  13. 13. pro 2019.

    We present ReQueST: a method for training RL agents from human feedback in the presence of unknown unsafe states. By , , , , Paper: Code:

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  14. 12. pro 2019.

    Good morning ! See below for today’s lineup of posters & presentations:

  15. 12. pro 2019.

    Training data is often collected through a biased process. Models trained on such data are inherently biased. We demonstrate how adversarial training through disentangled representations can reduce the effect of spurious correlations present in datasets:

  16. 11. pro 2019.

    We're looking forward to today's sessions at - see below for our schedule of presentations:

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  17. 10. pro 2019.

    It's been great to meet so many of you at so far! For those curious to learn more about how we think about AI and our approach to science, check out in The Economist for “The World in 2020”: Today's schedule:

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  18. 9. pro 2019.

    - the 33rd Conference on Neural Information Processing Systems - is now under way! We’re looking forward to this week’s tutorials, workshops, and sessions. Come say hello to the team @ our booth. We'll post schedules and updates here throughout the week!

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  19. 3. pro 2019.

    We introduce LOGAN, a game-theory motivated algorithm, which improves the state-of-the-art in GAN image generation by over 30% measured in FID: Here are samples showing higher diversity:

  20. 29. stu 2019.

    If you’re thinking about doing a Masters in Computer Science, listen to 's story and find out if a is the right fit for you:

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