Methods and apparatus to obtain well-calibrated uncertainty in deep neural networks

This invention describes a system that helps artificial intelligence (AI) models accurately express how confident they are in their predictions. It works by using a special mathematical formula during training to ensure the AI's confidence levels are well-tuned, and then further refines this confidence, especially when the data it's analyzing shifts. The specific claims detail an apparatus that includes components for determining this formula, training the AI, and fine-tuning its confidence.

Why it matters: Since 2020, the widespread deployment of AI models in critical applications has significantly increased the demand for trustworthy systems that can accurately quantify their uncertainty. This invention's focus on improving calibration under distributional shift directly addresses a growing challenge as AI encounters more varied real-world data.

Status: ExpiredProtection ended and was never renewed
How hard to build: SpecializedSoftware logic, deep learning expertise, computational infrastructure

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