How AI Learned to Think

When AI merged gameplay and language, everything changed...Sponsored by Brilliant | Use https://brilliant.org/artoftheproblem for 30-day free trial and 20% discount My goal here is to introduce model based learning and show how language understanding merged with gameplay AI strategies recently. From early chess engines to modern language models ( OpenAI o1, Google Gemini etc.). We examine key breakthroughs in game-playing AI—TD-Gammon, AlphaGo, and MuZero—and their contribution to current large language model architectures. Special focus on the convergence of Monte Carlo Tree Search (MCTS) with neural networks, and how these techniques transformed into today's chain-of-thought reasoning. Deepseek Deep Seek SUPPORT this work: https://www.patreon.com/artoftheproblem Timestamps: 00:00 intro 01:00 definition of reasoning 03:57 intuition 06:35 MCTS 07:40 AlphaGO 09:37 World Models 10:36 MuZero 12:45 Chain/Tree of Thought 14:03 RL on Reasoning 15:41 ARC AGI Test