alphago

This arXiv paper introduces a network-derived 'strategic tension' metric based on spectral radius to compare human and AI decision-making in Go, finding that while tension decays with human skill, elite play after AlphaGo's 2016 breakthrough shows a surge in sustained tension, framing AI as cognitive scaffolding that elevates human handling of complexity.

作者以 AlphaGo 之后围棋界的发展为镜，探讨数学家应如何适应 AI 生成的难以理解的证明：像围棋高手那样转向研究 AI 结果来提升人类集体认知，并接受 AI 让数学知识民主化的未来。

A tweet recommending Demis Hassabis's 1-hour Cambridge lecture as the clearest end-to-end explanation of what AI is really trying to solve, covering chess, DeepMind, AlphaGo, AlphaFold, digital biology, and AGI.

A former Google DeepMind researcher reflects on AlphaGo's Move 37 and argues that today's LLMs still lack genuine, auditable reasoning, calling for new architectures inspired by AlphaGo's search-and-reason approach in a new MIT Technology Review piece.

MIT Technology Review argues that unlike AlphaGo, which combined intuitive networks with explicit game-tree search to reason about future consequences, today's LLMs only perform fast, System-1-style next-token pattern completion—and that genuine deliberative reasoning must be built in for AI to be trustworthy in science and medicine.

Summary of David Silver's Reinforcement Learning Lecture 8 on integrating learning and planning, covering model-based RL and AlphaGo's use of policy and value networks with Monte Carlo Tree Search.

Eric Jang announces he has been working on a from-scratch implementation of AlphaGo, the 2016 AI breakthrough that inspired him to enter deep learning.

A thread sharing a video of self-play RL training with lidar and PPO in Unity, followed by a lecture on building AlphaGo from scratch.

Recommend a Chinese-subtitled tutorial on building AlphaGo from scratch, suitable for learning AI and reinforcement learning.

A detailed discussion on reinforcement learning and its connection to modern AI, using the reconstruction of AlphaGo with modern tools as a clear example of search and self-play. Key takeaways include neural network amortization of search, credit assignment challenges in LLMs vs AlphaGo, and implications for automated research.

Eric Jang releases AutoGo, a from-scratch tutorial for implementing AlphaGo, including code and a playable bot, demonstrating that frontier capabilities can now be replicated affordably.

A blackboard lecture by Eric Jang walks through building AlphaGo from scratch with modern AI tools, covering RL, MCTS, self-play, and connecting to LLM training, along with a discussion on automated AI research.

Opinion piece arguing that AlphaGo and ChatGPT are the two most significant AI breakthroughs, with ChatGPT having the greatest everyday impact by making AI accessible to the masses.

DeepMind reflects on the 10th anniversary of AlphaGo, highlighting its role in kickstarting the modern AI era and its subsequent impact on scientific research and the pursuit of AGI.

Demis Hassabis spoke at the Google for KOREA event, reflecting on AlphaGo's 10th anniversary and AlphaFold winning the Nobel Prize, while offering advice for Korea's AI future, leaders, and next-generation education.

This article reviews the history of AlphaGo's historic victory over Lee Sedol in 2016, analyzes its technical principles combining deep learning and search, and explores the profound impact of this event on the development of AI.