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This invention describes a method for understanding how an artificial intelligence (AI) system arrives at its decisions. It works by closely watching the internal parts of the AI network as it processes information. Specifically, it measures how often different components are accessed or used, which it calls "attention," and then analyzes these access patterns to explain the AI's final choice. The claims narrow this to measuring the level of access to internal factors and generating specific "factor vectors" to represent this attention.
Why it matters: Since 2019, the complexity of AI models has dramatically increased, making explainability a critical challenge. Advances in hardware-level monitoring and the urgent need for transparent AI systems, especially with large language models, make this approach more relevant now.
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