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DeepMind Retweeted
Enjoyed this
@NewInChess review on how#AlphaZero has influenced the phenomenal World Chess Champion@MagnusCarlsen, by his coach, the brilliant@PHChess It has loads of illustrative games from his incredible unbeaten run in 2019! https://www.newinchess.com/media/wysiwyg/product_pdf/873.pdf …pic.twitter.com/eeyJFOLIqd
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DeepMind Retweeted
Our
@DeepMind Scholars for 2019/20 reveal their aims for the future and what motivated them to study the Cambridge MPhil in Advanced Computer Science: http://ow.ly/KUAN50y7Xp3#AI#ComputerSciencepic.twitter.com/IlYRaw4Pui
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‘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
@Nature!
Read the blog: https://deepmind.com/blog/article/Dopamine-and-temporal-difference-learning-A-fruitful-relationship-between-neuroscience-and-AI …https://twitter.com/nature/status/1222604066796331008 …
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In “Artificial Intelligence, Values and Alignment” DeepMind’s
@IasonGabriel explores approaches to aligning AI with a wide range of human values: https://deepmind.com/research/publications/Artificial-Intelligence-Values-and-Alignment …pic.twitter.com/Zo81PSpwyF
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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: https://arxiv.org/abs/2001.08116
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DeepMind Retweeted
With the support of
@DeepMind, 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: https://app.trinethire.com/companies/23658-partnership-on-ai-to-benefit/jobs/22781-diversity-and-inclusion-research-fellow …Thanks. Twitter will use this to make your timeline better. UndoUndo -
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
@ThoreG. See you there!
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New lecture series
We've teamed up with @ai_ucl to bring you the#UCLxDeepMind 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:https://www.eventbrite.co.uk/o/ucl-x-deepmind-deep-learning-lecture-series-general-29078980901 …Show this threadThanks. Twitter will use this to make your timeline better. UndoUndo -
Given the smoothness of videos, can we learn models more efficiently than with
#backprop? 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. https://arxiv.org/pdf/2001.06232.pdf …pic.twitter.com/evbwULE0s2Thanks. Twitter will use this to make your timeline better. UndoUndo -
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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
@iclr_conf paper, we find markets impose useful structure on interactions between gradient-based learners: https://arxiv.org/abs/2001.04678 pic.twitter.com/IeLMcb9f2z
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Read our
@nature paper "A distributional code for value in dopamine-based reinforcement learning" online here: https://rdcu.be/b0mtAThanks. Twitter will use this to make your timeline better. UndoUndo -
Read our
@nature paper 'Improved protein structure prediction using potentials from deep learning' online here: https://rdcu.be/b0mtxThanks. Twitter will use this to make your timeline better. UndoUndo -
We worked with
@harvardbrainsci 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: https://deepmind.com/blog/article/Dopamine-and-temporal-difference-learning-A-fruitful-relationship-between-neuroscience-and-AI … (2/2)Show this threadThanks. Twitter will use this to make your timeline better. UndoUndo -
More exciting
@Nature news today: an example of how AI and neuroscience continue to propel each other forward. (1/2)pic.twitter.com/ba6ZWjF95o
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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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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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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. 2/4pic.twitter.com/1meRwkYKFI
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We have 2 papers published in
@nature 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. 1/4https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery …Show this threadThanks. Twitter will use this to make your timeline better. UndoUndo -
And read the paper in
@Nature today! https://rdcu.be/bZMRiShow this threadThanks. Twitter will use this to make your timeline better. UndoUndo
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