用信息不确定度直接分析数据，实现无需模型的通用机器学习。
A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
- 基于信息不确定度（惊喜值）直接处理原始数据，无需假设分布。
- 在生成、因果发现等任务中达到或接近顶尖性能。
- 适合需要可解释性与数据可编辑性的实际应用。
传统机器学习依赖显式模型和领域假设，限制了灵活性与可解释性。我们提出一种无模型框架，利用惊喜值（信息论不确定性）直接从原始数据中分析并推断，消除分布建模需求，降低偏差，并支持高效更新，包括直接编辑和删除训练数据。通过量化不确定性来评估相关性，该方法实现了跨任务的泛化推断，涵盖生成推理、因果发现、异常检测与时间序列预测。强调可追溯性、可解释性与数据驱动决策，提供统一且人类可理解的机器学习框架，在多数常见任务中达到或接近当前最优表现。数学基础构建了‘信息力学’，使该技术适用于多种复杂数据类型，包括缺失数据。实证结果表明，这可能是神经网络之外一种具备可扩展性的可理解人工智能路径。
原文摘要 · Abstract (English)
Traditional machine learning relies on explicit models and domain assumptions, limiting flexibility and interpretability. We introduce a model-free framework using surprisal (information theoretic uncertainty) to directly analyze and perform inferences from raw data, eliminating distribution modeling, reducing bias, and enabling efficient updates including direct edits and deletion of training data. By quantifying relevance through uncertainty, the approach enables generalizable inference across tasks including generative inference, causal discovery, anomaly detection, and time series forecasting. It emphasizes traceability, interpretability, and data-driven decision making, offering a unified, human-understandable framework for machine learning, and achieves at or near state-of-the-art performance across most common machine learning tasks. The mathematical foundations create a ``physics'' of information, which enable these techniques to apply effectively to a wide variety of complex data types, including missing data. Empirical results indicate that this may be a viable alternative path to neural networks with regard to scalable machine learning and artificial intelligence that can maintain human understandability of the underlying mechanics.
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