Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond

By Fei-yue Wang, J. Zhang, Xinhu Zheng, Xiao Wang, Yong Yuan, Xiaoxiao Dai, Jie Zhang, Liuqing Yang. IEEE/CAA Journal of Automatica Sinica, 2016

296 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/bef4ae975a0068484cfa62d3b006991d68716c04

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Haibo He
2. Ding Wang
3. Derong Liu
4. Dimitris Papailiopoulos
5. Jy-yong Sohn
6. Michael Gira
7. Ruisu Zhang
8. Shashank Rajput

In Computational Theory and Mathematics

1. Haibo He
2. Ding Wang
3. Derong Liu
4. Xiao Wang
5. Dimitris Papailiopoulos
6. Kangwook Lee
7. Pan Hui
8. Qinglai Wei

Citations in the corpus

• How do Data Science Workers Collaborate? Roles, Workflows, and Tools (2020, background): “In the hype surrounding AI, many fancy technology demos mark key milestones, such as IBM DeepBlue [15], which defeated a human chess champion for the first time, and Google’s AlphaGo demo, which defeated the world champion in Go [96].”
• Adaptive Critic Nonlinear Robust Control: A Survey (2017, methodology): “Deep neural networksbased learning has played a vital role in AlphaGo’s success [12].”
• Artificial Intelligence Techniques in Smart Grid: A Survey (2021, background): “AlphaGo [54] presents the success of DRL by applying the rich perception of high-dimension input and policy control.”
• Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management (2020, background): “I NTELLIGENT transportation systems (ITS) are typical complex systems and they integrate various IT technologies [1], [2].”
• All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, background): “The most famous game based on deep reinforcement learning is chess with AlphaGo developed by DeepMind in 2015 [266].”
• An Overview of Smart Contract: Architecture, Applications, and Future Trends (2018, background): “At present, the parallel society based on CPSS has already begun to emerge, its core and essential characteristics are virtual-real interaction and feedback regulation [29], [30].”
• Mastering the game of Go with deep neural networks and tree search (2019, background): “So perhaps, the pipeline introduced in AlphaGo bears the potential to be also applied to other domains with minor modifications [9].”
• LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks (2022, background): “1 Introduction Deep neural networks have been highly successful across a multitude of domains, from computer vision [1, 2] and natural language processing [3, 4], to game playing [5, 6].”