How the Artificial-Intelligence Program AlphaZero Mastered Its Games

How the Artificial-Intelligence Program AlphaZero Mastered Its Games

4/13/2019

link

https://www.newyorker.com/science/elements/how-the-artificial-intelligence-program-alphazero-mastered-its-games

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

computational intelligence ꞏ pattern recognition ꞏ computer vision ꞏ computer algorithms ꞏ alphazero ꞏ algorithmic optimization ꞏ shogi ꞏ game theory ꞏ human vs machine ꞏ natural language processing ꞏ algorithms ꞏ neural network training ꞏ deep learning ꞏ computational complexity ꞏ intelligent systems ꞏ game simulations ꞏ computational creativity ꞏ self-play ꞏ predictive modeling ꞏ cognitive computing ꞏ neural networks ꞏ big data ꞏ data-driven approaches ꞏ strategy ꞏ reinforcement learning ꞏ algorithmic thinking ꞏ neural network algorithms ꞏ chess ꞏ artificial intelligence ꞏ machine learning ꞏ learning algorithms ꞏ board games ꞏ neural network architecture ꞏ ai ꞏ data analysis ꞏ game playing ꞏ computational power ꞏ decision making ꞏ go ꞏ computational thinking ꞏ computer science