I'm embarrassed to admit this in my mid-forties, but I've never understood chess well enough to play a full game. My son and daughter both learned how to play in elementary school. I was glad they had that experience. I tried to pick up the game when they did, but, as a busy mom of three little kids, I just didn't have the time, the interest, or the stamina to really sit down and learn. Chess became more popular during the pandemic, and the boom has stuck around; according to a recent Yougov.com

Despite ever-growing interest in AI tools and assistants, it's worth remembering that they're still quite limited with numerous shortcomings. They are not as smart as they might seem on the surface. Case in point, ChatGPT is pretty useless when it comes to playing chess. As reported by Futurism, ChatGPT lost a chess game against the classic Atari 2600 gaming console. Robert Caruso, an engineer at Citrix, organised the game between the AI and a simple 1977 chess program released for the Atari 2600.

There exist several techniques for representing the chess board inside the computer. In the first part of this paper, the concepts of the bitboard-representation and the advantages of (rotated) bitboards in move generation are explained. In order to illustrate those ideas practice, the concrete implementation of the move-generator in FUSc# is discussed and we explain a technique how to verify the move-generator with the "perft"-command. We show that the move-generator of FUSc# works 100% correct. The second part of this paper deals with reinforcement learning in computer chess (and beyond). We exemplify the progress that has been made in this field in the last 15-20 years by comparing the "state of the art" from 2002-2008, when FUSc# was developed, with recent innovations connected to "AlphaZero". We discuss how a "FUSc#-Zero" could be implemented and what would be necessary to reduce the number of training games necessary to achieve a good performance. This can be seen as a test case to the general prblem of improving "sample effciency" in reinforcement learning. In the final part, we move beyond computer chess, as the importance of sample effciency extends far beyond board games into a wide range of applications where data is costly, diffcult to obtain, or time consuming to generate. We review some application of the ideas developed in AlphaZero in other domains, i.e. the "other Alphas" like AlphaFold, AlphaTensor, AlphaGeometry and AlphaProof. We also discuss future research and the potential for such methods for ecological economic planning.

The original version of this story appeared in Quanta Magazine. When Covid-19 sent people home in early 2020, the computer scientist Tom Zahavy rediscovered chess. He had played as a kid and had recently read Garry Kasparov's Deep Thinking, a memoir of the grandmaster's 1997 matches against IBM's chess-playing computer, Deep Blue. He watched chess videos on YouTube and The Queen's Gambit on Netflix. Despite his renewed interest, Zahavy wasn't looking for ways to improve his game.

That means I'm usually sorted into Generation X. But these days, looking back at the past 57 years, I think we should really rename it to Generation AI. It has been my generation having witnessed AI from its infancy to the breakthroughs we've seen in the past few years. And with a bit of luck most of us will witness how AI will be reshaping our societies – for good or bad – in the next 20 years. So let me give a recount of my encounters with AI throughout the decades.

In December 2017, AlphaZero, a chess-playing, artificial intelligence (AI) developed by Google, defeated Stockfish 8, the reigning world champion program at that time. AlphaZero calculates around 80,000 moves per second, according to The Guardian. Yet, out of 100 matches, AlphaZero won 28 and tied 72. Stockfish's open-source algorithm has been continually tweaked by human input over the years. The New Yorker reports that coders suggest an idea to update the algorithm, and the two versions are then pitted against each other for thousands of matches to see which comes out on top. Google claims that AlphaZero's machine learning algorithm had no human input beyond the programming of the basic rules of chess.

World's only artificial intelligence program that makes mistakes on purpose. Artificial intelligence is known for being accurate, leaving no room for error. But one artificial intelligence program is taking the road untouched, making errors on purpose. This AI program is called Maia. Maia is a chess program that uses cutting-edge AI from the best chess-playing programs.

Artificial intelligence is known for being accurate, leaving no room for error. But one artificial intelligence program is taking the road untouched, making errors on purpose. This AI program is called Maia. Maia is a chess program that uses cutting-edge AI from the best chess-playing programs. But instead of being the grandmaster of chess and making every move right, Maia aims to predict human moves, even the wrong ones.

Artificial intelligence is known for being accurate, leaving no room for error. But one artificial intelligence program is taking the road untouched, making errors on purpose. This AI program is called Maia. Maia is a chess program that uses cutting-edge AI from the best chess-playing programs. But instead of being the grandmaster of chess and making every move right, Maia aims to predict human moves, even the wrong ones.

The AI chess program, known as Maia, uses the kind of cutting-edge AI behind the best superhuman chess-playing programs. Alpha Zero broke from conventional AI chess programs by having computers learn, independent of any human instruction, how to play the game expertly. For chess, Alpha Zero is fed board positions and moves generated in practice games, and it tunes its neurons' firing to favor winning moves, an approach known as reinforcement learning . The result is a chess program capable of playing in a more human way. Well before that, AI that can predict and mimic human behavior could have immediate applications in chess and other games.