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Understanding the Black Box in Decision-making
Decision-making processes can be likened to a black box, where certain inputs lead to specific outputs. By observing and understanding these patterns, one can gain insights into the probability distribution of outcomes for any given state. This concept is foundational to modern AI and machine learning, where large language models predict future states based on current inputs without necessarily understanding the language itself. The key lies in predicting and transitioning between states efficiently.