AlphaGo is a computer program that plays the board game Go. It was made by DeepMind Technologies (Google affiliate). This program became famous due to the victories against professional players.
Powered versions[change | change source]
After the release of AlphaGo, DeepMind Technologies has made powered versions such as the AlphaGo Zero and the AlphaZero: AlphaZero is a self-taught program. This means that it became powerful without human guidance.
Details[change | change source]
The following table is the summary of AlphaGo achievements (including its variants).
|AlphaGo versus Fan Hui||176 GPUs,distributed||3,144||Oct 2015||5:0 against Fan Hui (professional player)|
|AlphaGo versus Lee Sedol||48 Tensor processing units (TPUs), distributed||3,739||Mar 2016||4:1 against Lee Sedol (former Korean & world champion)|
|AlphaGo Master||4 TPUs, single machine||4,858||May 2017||60:0 against professional players|
|AlphaGo Zero (40 block)||4 TPUs, single machine||5,185||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||Dec 2017||60:40 against AlphaGo Zero (20 block)|
Rivals[change | change source]
After the appearance of AlphaGo, several research groups have created computer Go programs with similar technical viewpoints.
Darkforest[change | change source]
DeepZenGo[change | change source]
Related pages[change | change source]
References[change | change source]
- 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.
- Chen, J. X. (2016). The evolution of computing: AlphaGo. Computing in Science & Engineering, 18(4), 4-7.
- Chang, H. S., Fu, M. C., Hu, J., & Marcus, S. I. (2016). Google Deep Mind's AlphaGo. OR/MS Today, 43(5), 24-29.
- 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.
- Borowiec, S. (2016). AlphaGo seals 4-1 victory over Go grandmaster Lee Sedol. The Guardian.
- 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.
- 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.
- 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.
- 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)
- 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.
- Marcus, G. (2018). Innateness, alphazero, and artificial intelligence. arXiv preprint arXiv:1801.05667.
- 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.
- Bratko, I. (2018). AlphaZero–what’s missing?. Informatica, 42(1).
- Dalgaard, M., Motzoi, F., Sorensen, J. J., & Sherson, J. (2020). Global optimization of quantum dynamics with AlphaZero deep exploration. npj Quantum Information, 6(1)
- 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.
- The New Yorker, How the Artificial-Intelligence Program AlphaZero Mastered Its Games, By James Somers, December 28, 2018.
- "【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.
- "AlphaGo Zero: Learning from scratch". DeepMind official website. 18 October 2017. Archived from the original on 19 October 2017. Retrieved 19 October 2017.
- 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.
- "AlphaZero Science paper supplementary material, Data S1, figure1_elos.json, max elo attained".
- Tian, Yuandong; Zhu, Yan (2015). "Better Computer Go Player with Neural Network and Long-term Prediction". arXiv:1511.06410v1 [cs.LG].
- "facebookresearch/darkforestGo". Facebook Research. 16 March 2021.
- 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.
- 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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