Chess has long been considered a battle of wits between two humans. But with the rise of artificial intelligence, this age-old perception is slowly being challenged. With the latest introduction of generative AI, the situation has become sharper. Deep learning algorithms and neural networks have allowed AI-powered chess engines to become increasingly sophisticated, becoming a new challenge for even the most skilled grandmasters. In this blog post, we will explore the world of generative AI in chess and how, in general, the domain of chess influenced the evolution of artificial intelligence – as well as how top players are responding to this new foe on the board.
AI and generative AI in chess
The first and most important thing to know is that generative AI is an area within the broader field of AI that specifically deals with creating new content, while AI encompasses a broader range of technologies and applications beyond generative tasks.
In the context of chess, AI is used in computer programs or algorithms that are designed to play chess at a high level, often surpassing human capabilities. But there are much more common applications of artificial intelligence in the chess game:
Chess engines: AI-powered programs that play chess at a high level, using algorithms and search techniques to analyze positions, calculate move possibilities, and make intelligent decisions.
Position evaluation: AI algorithms assess the strength of a chess position by considering factors like piece activity, pawn structure, king safety, and other positional aspects to determine positional superiority.
Move generation and search: AI algorithms employ search techniques, such as the alpha-beta algorithm, to explore the space of possible moves, analyze resulting positions, and determine the best move in a given position.
Cheat detection: AI-based systems can help detect cheating in chess by analyzing player moves, identifying suspicious patterns, and comparing them to known databases or expected behaviors.
Opening books: AI algorithms are used to create comprehensive opening books that contain well-analyzed moves and variations, providing players with guidance and recommendations for the initial phase of the game.
Game annotations & analysis: AI tools analyze completed chess games, providing annotations and insights into critical moments, mistakes, missed opportunities, and strategic evaluations, helping players improve their gameplay.
Chess databases: AI algorithms organize and search through vast collections of chess games and positions, enabling players, trainers, and researchers to study historical games, identify trends, explore the opening theory, and analyze specific positions.
Chess engines can be considered as predecessors for the development of AI in chess. Chess engines, with their ability to analyze positions, calculate move possibilities, and make intelligent decisions, paved the way for the broader field of AI in chess. Chess engines, such as Stockfish, Komodo, and others, were among the earliest examples of AI applications in chess.
Over time, AI in chess expanded beyond traditional chess engines to incorporate newer techniques, such as deep learning and generative AI. The introduction of generative AI, as demonstrated by AlphaZero, introduced new undiscovered approaches to playing and understanding chess. For example, in a series of 100 games against Stockfish, one of the strongest traditional chess engines at the time, AlphaZero won 28 games, lost none, and drew the remaining games and showcased unconventional, strategic moves that had never been used previously. Estimations based on these results suggest that AlphaZero’s playing strength was well above 3,400 Elo, while Stockfish is typically rated around the 3,300 Elo mark.*
FYI: For 2017, the highest Elo rating achieved by a human in classical chess was 2843 held by Magnus Carlsen, Norwegian chess grandmaster and World Chess Champion.
*The estimations may vary depending on the specific rating system used, the time of evaluation, and relative strength compared to human grandmasters.
Have you ever thought about it?
When generative AI creates chess puzzles, how does it create a lot of different puzzles while sometimes doing puzzles that seem very similar to ones we've seen before?
The mystery behind its ability to produce so many content pieces (puzzles) while occasionally presenting recognizable outcomes lies in the AI’s sophisticated learning algorithms. Through a vast library of chess puzzles and games, AI gains a deep understanding of the intricate patterns and strategies inherent in chess. In other words, AI employs advanced techniques such as machine learning and pattern recognition. It analyzes existing puzzles, studies successful tactics, and identifies recurring patterns, etc. Armed with the full picture and a big volume of data, AI then uses its generative capabilities to create puzzles that showcase originality while still adhering to the strategic foundations and aesthetic allure of chess, not proposing strategically poor moves and so on. And it’s AI’s responsibility not to generate senseless puzzles that wouldn’t bring training opportunities for users.
How does AI find a balance between making new and unique puzzles while also giving us a sense of déjà vu?"
To strike a so-called balance between novelty and already solved puzzles, the AI employs reinforcement learning and pattern recognition techniques. These algorithms help the AI incorporate elements from known chess patterns, tactics, and strategies into its puzzle-generation process.
The Role of Chess in the AI Technology Advancement
The role of chess in the AI technology revolution extends beyond the game itself, with its findings and advancements having a big impact on various other industries.
Some food for reflection:
Can we say that one of the first domains where AI was used is chess?
Yes, chess is often considered as one of the earliest spheres where AI was applied and demonstrated its capabilities. The development of AI chess programs has a rich history that spans several decades, beginning in the 1950s. The development of AI chess programs and the challenge to create computer foes able to defeat human grandmasters have been essential milestones in the history of AI. Why? Because chess provides a well-defined and rule-based domain that allows AI researchers to test and refine their algorithms and techniques. Its algorithms were instrumental in advancing search algorithms, heuristics, optimization techniques, and machine learning methods. The complexity of chess positions and the profound strategic aspects of the game have challenged AI researchers to develop innovative approaches to tackle these problems.
How did the introduction of AI in mid of XX century change the perception of technology?
The development of IBM’s Deep Blue and its famous matches against World Chess Champion Garry Kasparov in the late 1990s made a real buzz about the topic and influenced AI’s further advancement. First, Deep Blue’s victory highlighted the importance of computational power and hardware advancements in AI. The matches required immense computing resources, including custom-built hardware and parallel processing capabilities. The need for high-performance computing to support AI applications became evident and influenced the development of faster processors and specialized hardware for AI tasks.
Moreover, researchers looked at the technology from another angle: they introduced the concept of AI-human collaboration, combining human creativity and intuition and AI’s computational superpower, where humans leverage AI to enhance their capabilities.
Last but not least, It prompted discussions on ensuring transparency, accountability, and ethical guidelines in developing and using AI technology.
AI techniques developed for chess applied in other industries
Algorithms, techniques, and insights gained from AI research in chess have been successfully transferred and adapted to address challenges in other industries, enabling advancements in optimization, pattern recognition, decision-making, and strategic analysis. Here are the most prominent examples:
Search Algorithms and Optimization
AI techniques from chess, such as minimax search and alpha-beta pruning, have been adapted for solving optimization problems in logistics, supply chain management, and transportation. These techniques help in finding the most efficient routes, optimal allocation of resources, and scheduling of tasks. Additionally, metaheuristic algorithms inspired by chess algorithms, like simulated annealing and genetic algorithms, are used to solve complex optimization problems.
Machine Learning and Pattern Recognition
As in one of our examples above, AI relies on machine learning techniques for various tasks, including pattern recognition, position evaluation, and motion prediction. Similar techniques have been applied in image analysis, computer vision, and object recognition. For example, CNNs (Convolutional Neural Networks) that are used in chess have been adapted for image classification, object detection, and medical image analysis.
Natural Language Processing
NLP techniques, such as part-of-speech tagging, named entity recognition, and syntactic parsing, have been used to extract meaningful information from chess annotations. Similar techniques are employed in chatbots, virtual assistants, sentiment analysis, and text summarization.
Reinforcement Learning
Chess engines have utilized reinforcement learning techniques for self-play and improving their playing strength over time. These techniques have been applied in robotics, where robots learn from interactions with their environment. Reinforcement learning algorithms enable robots to acquire skills, make decisions, and optimize their actions based on rewards and penalties received during learning.
Game Theory and Strategic Analysis
Chess’s connections with game theory have influenced AI applications in economics and business. The strategic analysis techniques used in chess, such as predicting opponents’ moves and evaluating potential outcomes, have been adapted to model and optimize pricing strategies, market dynamics, and competitive behavior in economics and business scenarios.
Healthcare and Medical Diagnosis
AI techniques used in chess for decision-making and pattern recognition have been applied in healthcare for medical diagnosis, treatment planning, and drug discovery. Machine learning algorithms analyze medical data, such as patient records and imaging data, to identify patterns and assist in diagnosing diseases, predicting patient outcomes, and discovering potential drug targets.
