用可计算性构建通用预测模型，但被证明无法实现。
Solomonoff induction
- 基于可计算性设计通用预测框架
- 证明该方法存在根本性缺陷
- 适合研究理论人工智能的学者
本章探讨了索洛莫诺夫的通用预测方法。该方法的核心在于可计算性概念，其主要思想是试图满足两个合理的可计算性要求。然而，通过推广普特南的对角化论证，证明这一尝试失败。随后，文章批判性分析了该方法的所谓优势，特别是其为奥卡姆剃刀提供基础，以及作为机器学习方法的理论理想。结果表明，这些宣称的优势并不成立。
原文摘要 · Abstract (English)
This chapter discusses the Solomonoff approach to universal prediction. The crucial ingredient in the approach is the notion of computability, and I present the main idea as an attempt to meet two plausible computability desiderata for a universal predictor. This attempt is unsuccessful, which is shown by a generalization of a diagonalization argument due to Putnam. I then critically discuss purported gains of the approach, in particular it providing a foundation for the methodological principle of Occam's razor, and it serving as a theoretical ideal for the development of machine learning methods.
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