How the Artificial-Intelligence Program AlphaZero Mastered Its Games
How the Artificial-Intelligence Program AlphaZero Mastered Its Games
4/13/2019
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summary
This article explores the development and capabilities of AlphaZero, an artificial intelligence program developed by DeepMind. AlphaZero uses a reinforcement learning algorithm to teach itself how to play different games, including chess, shogi, and Go. The article discusses how AlphaZero was trained and explains its impressive ability to outperform human players and previously developed AI programs. It also delves into the strategies and techniques that AlphaZero employs to excel at these games. The article highlights the significance of AlphaZero's achievement in advancing the field of AI and its potential applications beyond games.
tags
artificial intelligence ꞏ ai ꞏ machine learning ꞏ alphazero ꞏ game playing ꞏ chess ꞏ go ꞏ shogi ꞏ deep learning ꞏ neural networks ꞏ algorithms ꞏ computer science ꞏ human vs machine ꞏ game theory ꞏ reinforcement learning ꞏ self-play ꞏ strategy ꞏ decision-making ꞏ computational intelligence ꞏ algorithmic thinking ꞏ board games ꞏ intelligent systems ꞏ computational thinking ꞏ data analysis ꞏ big data ꞏ computational power ꞏ computational creativity ꞏ algorithmic optimization ꞏ computer algorithms ꞏ cognitive computing ꞏ pattern recognition ꞏ predictive modeling ꞏ data-driven approaches ꞏ computer vision ꞏ natural language processing ꞏ learning algorithms ꞏ neural network training ꞏ neural network architecture ꞏ neural network algorithms ꞏ computational complexity ꞏ game simulations