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AlphaGo

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AlphaGo is a computer program that plays the board game Go.[1][2] It was made by DeepMind Technologies (Google affiliate).[3] This program became famous due to the victories against professional players.[4][5]

Many new technologies were used to create AlphaGo, including deep learning,[6] optimization,[7] and the Monte Carlo algorithm.[8]

Powered versions

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After the release of AlphaGo, DeepMind Technologies has made powered versions such as the AlphaGo Zero[9][10] and the AlphaZero:[11][12][13][14][15] AlphaZero is a self-taught program.[16] This means that it became powerful without human guidance.

Details

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The following table is the summary of AlphaGo achievements (including its variants).

Strength of AlphaGo and its variants[17]
Versions Hardware Elo rating Date Results
AlphaGo versus Fan Hui 176 GPUs,[18]distributed 3,144[19] Oct 2015 5:0 against Fan Hui (professional player)
AlphaGo versus Lee Sedol 48 Tensor processing units (TPUs),[18] distributed 3,739[19] Mar 2016 4:1 against Lee Sedol (former Korean & world champion)
AlphaGo Master 4 TPUs,[18] single machine 4,858[19] May 2017 60:0 against professional players
AlphaGo Zero (40 block) 4 TPUs,[18] single machine 5,185[19] Oct 2017 100:0 against AlphaGo version that defeated Lee Sedol

89:11 against AlphaGo Master

AlphaZero (20 block) 4 TPUs, single machine 5,018

[20]

Dec 2017 60:40 against AlphaGo Zero (20 block)

Rivals

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After the appearance of AlphaGo, several research groups have created computer Go programs with similar technical viewpoints.

Darkforest

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This was made by Facebook.[21] The source codes are available on GitHub.[22]

DeepZenGo

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This was made in Japan.[23][24] Nihon Ki-in was also involved in its research and development.

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References

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  1. ↑ Wang, F. Y., Zhang, J. J., Zheng, X., Wang, X., Yuan, Y., Dai, X., ... & Yang, L. (2016). Where does AlphaGo go: From church-turing thesis to AlphaGo thesis and beyond. IEEE/CAA Journal of Automatica Sinica, 3(2), 113-120.
  2. ↑ Chen, J. X. (2016). The evolution of computing: AlphaGo. Computing in Science & Engineering, 18(4), 4-7.
  3. ↑ Chang, H. S., Fu, M. C., Hu, J., & Marcus, S. I. (2016). Google Deep Mind's AlphaGo. OR/MS Today, 43(5), 24-29.
  4. ↑ Chao, X., Kou, G., Li, T., & Peng, Y. (2018). Jie Ke versus AlphaGo: A ranking approach using decision making method for large-scale data with incomplete information. European Journal of Operational Research, 265(1), 239-247.
  5. ↑ Borowiec, S. (2016). AlphaGo seals 4-1 victory over Go grandmaster Lee Sedol. The Guardian.
  6. ↑ Granter, S. R., Beck, A. H., & Papke Jr, D. J. (2017). AlphaGo, deep learning, and the future of the human microscopist. Archives of pathology & laboratory medicine, 141(5), 619-621.
  7. ↑ Chen, Y., Huang, A., Wang, Z., Antonoglou, I., Schrittwieser, J., Silver, D., & de Freitas, N. (2018). Bayesian optimization in alphago. arXiv preprint arXiv:1812.06855.
  8. ↑ Fu, M. C. (2016, December). AlphaGo and Monte Carlo tree search: the simulation optimization perspective. In 2016 Winter Simulation Conference (WSC) (pp. 659-670). IEEE.
  9. ↑ Holcomb, S. D., Porter, W. K., Ault, S. V., Mao, G., & Wang, J. (2018, March). Overview on deepmind and its AlphaGo Zero AI. In Proceedings of the 2018 international conference on big data and education (pp. 67-71)
  10. ↑ Lapan, M. (2018). Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. Packt Publishing Ltd.
  11. ↑ Marcus, G. (2018). Innateness, alphazero, and artificial intelligence. arXiv preprint arXiv:1801.05667.
  12. ↑ Tian, Y., Ma, J., Gong, Q., Sengupta, S., Chen, Z., Pinkerton, J., & Zitnick, C. L. (2019). Elf opengo: An analysis and open reimplementation of alphazero. arXiv preprint arXiv:1902.04522.
  13. ↑ Bratko, I. (2018). AlphaZero–what’s missing?. Informatica, 42(1).
  14. ↑ Dalgaard, M., Motzoi, F., Sorensen, J. J., & Sherson, J. (2020). Global optimization of quantum dynamics with AlphaZero deep exploration. npj Quantum Information, 6(1)
  15. ↑ Xu, L. (2018, December). Deep bidirectional intelligence: AlphaZero, deep IA-search, deep IA-infer, and TPC causal learning. In Applied Informatics (Vol. 5, No. 1, p. 5). Springer Berlin Heidelberg.
  16. ↑ The New Yorker, How the Artificial-Intelligence Program AlphaZero Mastered Its Games, By James Somers, December 28, 2018.
  17. ↑ "【柯洁战败解密】AlphaGo Master最新架构和算法，谷歌云与TPU拆解" (in Chinese). Sohu. 24 May 2017. Retrieved 1 June 2017.
  18. ↑ 18.0 18.1 18.2 18.3 "AlphaGo Zero: Learning from scratch". DeepMind official website. 18 October 2017. Archived from the original on 19 October 2017. Retrieved 19 October 2017.
  19. ↑ 19.0 19.1 19.2 19.3 Silver, David; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis; Huang, Aja; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian; Chen, Yutian; Lillicrap, Timothy; Fan, Hui; Sifre, Laurent; Driessche, George van den; Graepel, Thore; Hassabis, Demis (19 October 2017). "Mastering the game of Go without human knowledge". Nature. 550 (7676): 354–359.
  20. ↑ "AlphaZero Science paper supplementary material, Data S1, figure1_elos.json, max elo attained".
  21. ↑ Tian, Yuandong; Zhu, Yan (2015). "Better Computer Go Player with Neural Network and Long-term Prediction". arXiv:1511.06410v1 [cs.LG].
  22. ↑ "facebookresearch/darkforestGo". Facebook Research. 16 March 2021.
  23. ↑ Lee, C. S., Wang, M. H., Ko, L. W., Kubota, N., Lin, L. A., Kitaoka, S., ... & Su, S. F. (2018) Human and smart machine co-learning: brain-computer interaction at the 2017 IEEE International Conference on Systems, Man, and Cybernetics. IEEE Systems, Man, and Cybernetics Magazine, 4(2), 6-13.
  24. ↑ Wu, T. R., Wu, I. C., Chen, G. W., Wei, T. H., Wu, H. C., Lai, T. Y., & Lan, L. C. (2018). Multi-labeled value networks for computer Go. IEEE Transactions on Games, 10(4), 378-389.

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