Long before neural networks and deep learning dazzled the public, pioneering research into games like poker, solitaire, and bridge quietly forged the foundations of AI’s ability to reason under uncertainty and incomplete information
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In the mid-20th century, the challenge of creating machines that could reason under uncertainty found fertile ground not only in theoretical math but also in classic card games such as poker, solitaire, and bridge
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While popular narratives around AI often emphasize big data and deep neural networks, the foundational strides in handling uncertainty and incomplete information trace back to classic card games—poker, solitaire, and bridge
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