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  "documentTitle": "2025 Air Street Capital The State of AI Report 2025",
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      "text": "This proof-of-concept suggests a very exciting potential paradigm where superhuman AI systems could become \"teachers\" rather than just \"tools\", and help advance human knowledge in domains beyond chess.",
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      "text": "Researchers developed a method to discover \"dynamic concepts\" (concepts that motivate sequences of moves) by analyzing AlphaZero's neural network activations, filtering for teachability and novelty.\nAll four grandmasters improved their performance after studying concept prototypes (chess puzzles exemplifying each concept), with an average improvement of 0.85 puzzles solved correctly out of 4.\nNew concepts often involved counterintuitive plans that violated conventional chess principles, such as sacrificing the queen for long term strategic gain, or playing quiet positional moves over immediate attacks.\nThis proof-of-concept suggests a very exciting potential paradigm where superhuman AI systems could become \"teachers\" rather than just \"tools\", and help advance human knowledge in domains beyond chess.",
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      "text": "Researchers extracted novel chess concepts from AlphaZero (an AI system that mastered chess via self-play without human supervision) and successfully taught them to 4 world champion grandmasters, demonstrating that superhuman AI systems can advance human knowledge at the highest expert levels. This paper demonstrates an exciting process for mining superhuman knowledge and proving humans can learn them.",
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      "text": "Fig. 1. Learning from machine-unique knowledge. The pink circle represents what machines know (M) and the blue circle represents what humans know (H). Our work focuses on (M - H)—knowledge that is unique to machines. One prominent example of machine-unique knowledge is move 37 in the AlphaGo-Lee Sedol match.",
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