Why can't an AI play chess?
_______________
The other day I decided to play a game with DeepSeek, but the game was sooo weird.
After Nxd4 DeepSeek played... Qxd5!!
I was surprised and decided to understand: "Why can't an AI play chess?".
Why Can't an AI Play Chess?
AI has surpassed human grandmasters in chess, with engines like Stockfish and AlphaZero dominating the game. Yet, despite their immense computational power, AI chess engines occasionally make bizarre mistakes, violate rules, or fail in ways that seem illogical to humans. Why does this happen?
1. AI Doesn’t "Understand" Chess—It Calculates It
At its core, an AI chess engine doesn’t "know" what chess is—it only processes positions based on mathematical evaluations.
No true intuition – Humans recognize patterns (e.g., weak squares, king safety) intuitively, while AI relies on brute-force search and heuristic scoring.
Blind spots in evaluation – If a position hasn’t been deeply searched or lacks training data, the AI may misevaluate it.
Example of AI Misjudgment
In rare cases, engines have recommended moves that lead to forced losses because they misjudged the depth of a tactic beyond their search horizon.
2. Rule Violations: When AI Breaks Chess Laws
Chess has strict rules (e.g., no moving into check, en passant, castling restrictions), yet AI can still "break" them.
Why Does This Happen?
Buggy implementations – Some chess engines have had coding errors that allowed illegal moves.
Training on imperfect data – Neural networks (like AlphaZero) learn from self-play, but if a rare rule (e.g., underpromotion) isn’t encountered enough, the AI might mishandle it.
Adjudication errors – Some online platforms use AI to auto-resign or claim draws, sometimes incorrectly.
Famous Cases of AI Rule Breaks
Leela Chess Zero (LC0) once tried to castle through check – Because its neural net didn’t fully internalize the rule.
Stockfish’s early versions had en passant bugs – Due to incomplete move generation logic.
3. Horizon Effect: AI Can’t See Far Enough
Even the strongest engines have a search depth limit, meaning they might miss long-term consequences.
Sacrifices that don’t work – An AI might think a piece sacrifice is winning because it sees a short-term attack, but misses a deep defensive resource.
Endgame tablebase gaps – Some positions require perfect play, but if the AI hasn’t precomputed them (or doesn’t use tablebases), it blunders.
4. The Future: Can AI Ever Truly "Play" Chess?
Right now, AI plays chess like a superpowered calculator—not a strategist. To truly "understand" chess, future systems might need:
Better explainability – Not just showing the best move, but explaining why.
Rule-based safeguards – Hardcoding chess rules rather than purely learning them.
Human-like intuition – Combining neural networks with symbolic reasoning.
Conclusion: AI is Strong, But Not Flawless
AI chess engines are unbeatable in practice, but they still make mistakes, misunderstand rules, and lack true comprehension. The real challenge isn’t just making AI stronger—it’s making it play chess the way humans do: with creativity, adaptability, and deep understanding.
______________________
P.S I wrote this with the help of a translator. If there are mistakes, don't get mad.)
