Wikipedia

AlphaGo

computer program developed by Google DeepMind to play the board game Go

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]

AlphaGo logo
AlphaGo logo

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

Powered versions change

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 change

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 change

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

Darkforest change

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

DeepZenGo change

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

Related pages change

References change

  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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