Game Changer - AlphaZero's Groundbreaking Chess Strategies and the Promise of AI

Game Changer - AlphaZero's Groundbreaking Chess Strategies and the Promise of AI

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Game Changer - AlphaZero's Groundbreaking Chess Strategies and the Promise of AI

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Preface


This book is about an exceptional chess player, a player whose published games at the time of writing total just 10, but whose name already signifies the pinnacle of chess ability. A powerful attacker, capable of defeating even the strongest handcrafted chess engines with brilliant sacrifices and original strategies; and a player that developed its creative style solely by playing games against itself.

That player is AlphaZero, a totally new kind of chess computer created by British artificial intelligence (AI) company DeepMind.

Through learning about AlphaZero we can harness the new insights that AI has uncovered in our wonderful game of chess and use them to build on and enhance our human knowledge and skills. We talk to the people who created AlphaZero, and discover the struggles that brilliant people face when aiming for goals that have never before been achieved.

The authors feel extremely privileged to have worked with the creators of AlphaZero on this project. We recognise this as a defining moment, being right at the cutting edge of fastdeveloping technology that will have a profound effect on all areas of human life.

Our collaboration arose following the publication of 10 AlphaZero games during the December 2017 London Chess Classic tournament. The previous year, Matthew and Natasha had won the English Chess Federation (ECF) Book of the Year award for Chess for Life, a compilation of interviews with icons of chess, highlighting themes and core concepts of their games. We knew we could take a similar approach to AlphaZero, offering critical insight into how the AI thinks and plays, and sharing key learnings with the wider chess-playing community.

Who should read this book?
• keen chess players, looking to learn new strategies
AlphaZero’s chess is completely self-taught, stemming from millions of games played against itself. Much of its play matches the accepted human wisdom gathered over the past 200 years, which makes AlphaZero’s play intuitive, allowing humans to learn from it. This book brings out AlphaZero’s exquisite use of piece mobility and activity, with guidance from Matthew through the simple, logical, schematic ways in which AlphaZero builds up attacks against the opponent’s king’s position. We believe these techniques will inspire professionals and club players alike.

• artificial intelligence enthusiasts

As Demis Hassabis, CEO of DeepMind, explains, the application of AI to games is a means to something greater: ‘We’re not doing this to just solve games, although it’s a fun endeavour. These are challenging and convenient benchmarks to measure our progress against. Ultimately, it’s a stepping stone for us to build general-purpose algorithms that can be deployed in all sorts of ways and in all sorts of industries to achieve great things for society.’

Our interviews with the creative people who designed and built AlphaZero are full of insights that, using chess as an example, help us to better understand the opportunities and challenges afforded by AI.

• chess enthusiasts

As well as providing instructional material, this book is also a collection of fascinating games of astonishing quality, featuring dashing attacks, unexpected strategies, miraculous defences and crazy sacrifices. Matthew compared playing through these games to uncovering the lost notebooks of a great attacking player of the past, such as his hero Alexander Alekhine, and finding hundreds of hitherto unpublished ideas.

How to read this book
The chess content of this book is arranged in discrete chapters and designed to be read out of sequence, so it is perfectly possible to pick a theme you are interested in and start in the middle of the book. The chess content is not too heavy, with an emphasis on explanations rather than variations. We would recommend playing through the games with a chessboard. In our opinion, this promotes a measured pace of reading most conducive to learning.

Acknowledgements
We would like to thank DeepMind, and in particular Demis Hassabis, for the wonderful opportunity to study the games of AlphaZero, and for his personal involvement in making this project a success. We would like to thank Dave Silver, Lead Researcher on AlphaZero, as well as Thore Graepel, Matthew Lai, Thomas Hubert, Julian Schrittwieser and Dharshan Kumaran for their extensive technical explanations and their assistance in running test games and test positions on AlphaZero. Nenad Tomasev deserves a special mention for reviewing the chess content and giving us plenty of great feedback!

A big debt of gratitude is owed to Lorrayne Bennett, Sylvia Christie, Jon Fildes, Claire McCoy, Sarah-Jane Allen and Alice Talbert for all their amazing work in keeping this project running and helping us with all the things we needed (and the things we didn’t know we needed!). We’d also like to thank everybody at DeepMind for making us feel so welcome during our visits to the London office.

Thanks are also due to Allard Hoogland and the team at New in Chess who have published this book. They have supported our unique project and have ensured that the book is beautifully presented.

We would like to thank our families for their enthusiasm and support and, in the case of Matthew Selby, also for his technical expertise in extracting whatever we wanted from our data files.

All of these amazing people contributed to what has been a madly enjoyable and memorable project.

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Main feature:
Đây là hình máy tính bàn – Play Computer.
Important note: You go first. For example: When you see “White goes first” and you click Đây là hình máy tính bàn then you are the White side. When you see “Black goes first” and you click Đây là hình máy tính bàn then you are the Black side.
In short, when you click Đây là hình máy tính bàn you go first.
Additional features:
Đây là hình ảnh Start-on – Start
Đây là hình ảnh previous-on – Previous
Đây là hình ảnh next-on – Next
Đây là hình ảnh end-on – End
Đây là hình ảnh play-on – Play
Đây là hình ảnh pause-on – Pause
Đây là hình ảnh flip-on – Flip

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