| David Silver Awarded 2019 ACM Prize In Computing |
| Written by Sue Gee | |||
| Wednesday, 01 April 2020 | |||
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David Silver, best-known for AlphaGo, is the latest recipient of the ACM Prize in Computing. It is awarded for his breakthrough advances in computer game-playing and contributions to the "growing and impactful area of deep reinforcement learning".
Established in 2007, when it was known as the ACM-Infosys Foundation Award in the Computing Sciences, the ACM Prize in Computing recognizes: an early to mid-career fundamental innovative contribution in computing that, through its depth, impact and broad implications, exemplifies the greatest achievements in the discipline. The award carries a prize of $250,000, with its financial support provided by Infosys Ltd.
David Silver is a Professor at University College London and a Principal Research Scientist at DeepMind, the London-based AI company owned by Google since 2014. In our report of the acquisition, Google Buys Unproven AI Company we speculated: Perhaps the best clue as to what might have interested Google in the company is a recent paper published by a team working at DeepMind that demonstrates using a deep neural network to play a range of Atari 2600 games, including Breakout. Referring to this paper, co-authored by David Silver, Mike James wrote: What is novel about this work is that it doesn't use supervised or unsupervised learning to train the network but reinforcement learning. The network is trained using a modified form of Q learning which is about the only valid approach to machine reinforcement learning. In this case the machine isn't told how well it is doing at each move or just left to find patterns in the data it learns according to the reward it gets during the game and particularly at the end when it gets a win/loss reward. This is the "deep reinforcement learning", which Silver and the team he leads had started with Atari Games and then went on to develop with AlphaGo, an algorithm that combined ideas from deep-learning, reinforcement-learning, traditional tree-search and large-scale computing and famously defeated Lee Seedol in the 2016 televised match of man versus machine, AlphGo Defeats World's Top Ranking Go Player, which can still be enjoyed as a freely available video. For our account of the importance of this breakthrough, see Why AlphaGo Changes Everything. The following year saw the Future of Go Summit in China at which AlphaGo took on and triumphed in several showcase matches. It also saw the machine's official retirement as a competitive player, announced in a blog post by Demi Hassabis, CEO of DeepMind and David Silver in which they said: This week’s series of thrilling games with the world’s best players, in the country where Go originated, has been the highest possible pinnacle for AlphaGo as a competitive program. For that reason, the Future of Go Summit is our final match event with AlphaGo. The research team behind AlphaGo will now throw their energy into the next set of grand challenges, developing advanced general algorithms that could one day help scientists as they tackle some of our most complex problems, such as finding new cures for diseases, dramatically reducing energy consumption, or inventing revolutionary new materials. If AI systems prove they are able to unearth significant new knowledge and strategies in these domains too, the breakthroughs could be truly remarkable. It soon emerged that AlphaGo was just a stepping stone. It had been initialized by training on expert human games followed by reinforcement learning to improve its performance. In order to achieve greater performance and generality, Silver went on to develop the AlphaZero algorithm that learned entirely by playing games against itself, starting without any human data or prior knowledge except the game rules. As reported in DeepMind's AlphaZero Triumphs At Chess, the new algorithm achieved superhuman performance in the games of chess, Shogi, and Go, demonstrating unprecedented generality of the game-playing methods. We have recently reported breakthroughs made by David Silver's team in applying the AlphaGo/AlphaZero algorithm to other problems,,see AlphaFold DeepMind's Protein Structure Breakthrough for a report of its application in the realm of biology. Commenting on the award of the 2019 ACM Prize in Computing to David Silver, ACM President Cherri M. Pancake said: "Few other researchers have generated as much excitement in the AI field as David Silver. Human vs. machine contests have long been a yardstick for AI. Millions of people around the world watched as AlphaGo defeated the Go world champion, Lee Sedol, on television in March 2016. But that was just the beginning of Silver's impact. His insights into deep reinforcement learning are already being applied in areas such as improving the efficiency of the UK's power grid, reducing power consumption at Google's data centers, and planning the trajectories of space probes for the European Space Agency." It is planned that Silver will formally receive the ACM Prize in Computing at ACM’s annual awards banquet on June 20, 2020 in San Francisco, California, along with the winners of other ACM Awards. More InformationACM Prize in Computing Awarded to AlphaGo Developer David Silver - ACM Prize in Computing Related ArticlesScott Aaronson Winner of 2020 ACM Prize In Computing Google Buys Unproven AI Company AlphaGo Defeats World's Top Ranking Go Player Why AlphaGo Changes Everything AlphaGo Zero - From Nought To Top In 40 Days DeepMind's AlphaZero Triumphs At Chess Go Master Retires Citing AI Supremacy Alpha Go - Documentary Now Freely Available AlphaFold DeepMind's Protein Structure Breakthrough Computer Graphics Pioneers Win 2019 Turing Award To be informed about new articles on I Programmer, sign up for our weekly newsletter, subscribe to the RSS feed and follow us on Twitter, Facebook or Linkedin. 
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| Last Updated ( Thursday, 15 April 2021 ) |
