How To Play Go The Ai Way

Download or read book How to Play Go the AI Way! written by Shinji Yamada and published by BoD – Books on Demand. This book was released on 2020-12-14 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for amateurs in go who would like to learn and employ the modern AI style. The AI style may seem confusing because there are so many tactics far away from traditional thinking. But the study of the new techniques introduced by AI has already lead to their rapid spread and adoption. Today they are applied by pros almost as a matter of course. This book summarizes the findings from the study of AI techniques and explains them in illustrative diagrams. "I wrote this book with a lot of enthusiasm and I hope that this way everybody can profit from the insights of my studies. I am very happy to be able to witness this important turning point towards a new era, in which an AI can defeat humans in the game of go. Engaging with the AI style has given me joy like I have never felt before in go. I hope this kind of joy will be conveyed and passed on through the book." Yamada Shinji

Download or read book Keep Fit! written by Masaaki Fukui and published by BoD – Books on Demand. This book was released on 2021-11-10 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some people may think that the 5×5 board is just a reduced version of Go. Fukui Masaaki's little creations show that much of what is complex and profound in Go can be found in the tight confines of the 5×5 board. The problems cover endgame moves, aspects of life and death, attacking and defending, judgments based on calculating territory, the presence or absence of ko threats, and even things pertaining to the realm of middle game fighting. Not only is this book useful for improving your understanding of shapes and techniques, it is also a lot of fun!

Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning

Download or read book Introduction to AI Safety, Ethics and Society written by Dan Hendrycks and published by Dan Hendrycks. This book was released on with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book 38 Basic Joseki written by Kiyoshi Kosugi and published by . This book was released on 1973 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handleiding voor beginnende go-spelers.

Download or read book Playing Smart written by Julian Togelius and published by MIT Press. This book was released on 2019-01-15 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: THE FUTURE OF GAME DESIGN IN THE AGE OF AI: Can games measure intelligence? And how will artificial intelligence inform games of the future? In Playing Smart, Julian Togelius explores the connections between games and intelligence to offer a new vision of future games and game design. Video games already depend on AI. We use games to test AI algorithms, challenge our thinking, and better understand both natural and artificial intelligence. In the future, Togelius argues, game designers will be able to create smarter games that make us smarter in turn, applying advanced AI to help design games. In this book, he tells us how. Games are the past, present, and future of artificial intelligence. In 1948, Alan Turing, one of the founding fathers of computer science and artificial intelligence, handwrote a program for chess. Today we have IBM’s Deep Blue and DeepMind’s AlphaGo, and huge efforts go into developing AI that can play such arcade games as Pac-Man. Programmers continue to use games to test and develop AI, creating new benchmarks for AI while also challenging human assumptions and cognitive abilities. Game design is at heart a cognitive science, Togelius reminds us—when we play or design a game, we plan, think spatially, make predictions, move, and assess ourselves and our performance. By studying how we play and design games, Togelius writes, we can better understand how humans and machines think. AI can do more for game design than providing a skillful opponent. We can harness it to build game-playing and game-designing AI agents, enabling a new generation of AI-augmented games. With AI, we can explore new frontiers in learning and play.

Download or read book Making AI Intelligible written by Herman Cappelen and published by Oxford University Press. This book was released on 2021 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Can humans and artificial intelligences share concepts and communicate? One aim of Making AI Intelligible is to show that philosophical work on the metaphysics of meaning can help answer these questions. Cappelen and Dever use the externalist tradition in philosophy of to create models of how AIs and humans can understand each other. In doing so, they also show ways in which that philosophical tradition can be improved: our linguistic encounters with AIs revel that our theories of meaning have been excessively anthropocentric. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. Many important decisions about human life are now influenced by AI. In giving that power to AI, we presuppose that AIs can track features of the world that we care about (e.g. creditworthiness, recidivism, cancer, and combatants.) If AIs can share our concepts, that will go some way towards justifying this reliance on AI. The book can be read as a proposal for how to take some first steps towards achieving interpretable AI. Making AI Intelligible is of interest to both philosophers of language and anyone who follows current events or interacts with AI systems. It illustrates how philosophy can help us understand and improve our interactions with AI.