Do we understand AI chess? – Searcher

[1712.01815] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (arxiv.org)

What about a Leela(Chess) Zero? · Issue #369 · leela-zero/leela-zero · GitHub

The exploration of limits in artificial intelligence (AI) naturally incorporates AI chess into broader discussions concerning the development of AI technologies. A notable paper I encountered discuss the continuous evolution of AI chess strategies, focusing on two significant generations of AI chess players: AlphaGo and AlphaZero. This paper highlights how coding within these systems has transitioned to a more generic form, thereby offering a higher degree of operational freedom. Originally domain-specific, these codes now incorporate broader, more versatile algorithms such as the Monte Carlo Tree Search (MCTS). The modern AI chess players, as described in the paper, employ prediction and estimation strategies over traditional single-goal methodologies, marking a significant shift in their operational paradigms. They are increasingly dependent on advanced statistical methods, including Bayesian analysis, moving away from the conventional deep learning approaches that once dominated the field. The effectiveness of these prediction algorithms surpasses that of the traditional binary, goal-driven algorithms, illustrating a critical shift in AI strategy development. This shift raises intriguing questions about the performance capabilities of goal-driven AI, with self-trained, estimation-driven models consistently outperforming their predecessors.

This evolution in AI design philosophy reflects a broader trend toward systems capable of autonomous iteration and improvement. The focus is shifting away from achieving predefined results to fostering continuous, self-directed learning and adaptation. AI systems are no longer shackled by fixed goals or rigid expectations but are characterized by an ever-expanding scope of capability, much like a snowball rolling downhill, gathering size and momentum. In practical terms, this is exemplified by the newest neural networks, which allow for an adjustable number of hidden layers. These systems can be set to continuously add layers and retrain, enhancing their complexity and effectiveness over time.

By examining chess AI, we begin to understand the broader implications of unrestricted, self-improving AI models. These discussions are not just about chess but about the potential of applying this continuous improvement model to various types of AI across multiple domains, without imposing limitations. The conversation initiated by the study of AI chess merely scratches the surface of what is possible with self-improving and iterative generative AI technologies. AlphaZero exemplifies this new frontier, operating without predetermined goals, engaging in continuous prediction and modification, and maintaining the capacity to train itself indefinitely. This blueprint provided by AlphaZero not only challenges our understanding of intelligent systems but also invites us to consider the future landscape of AI development where the limits are as undefined as the potential outcomes.


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