An Overview of the Generalization Problem

The Generalization problemgeneralization problem is examined, providing motivations for the introduction of Information TheoryInformation Theory tools. The proposed analysis will suggest the following general picture: although Machine learning (ML)Machine Learning techniques are classically conceptualized as curve-fitting methods, protocols that generalize effectively respond to a partially different logic by implementing Coarse-grainingcoarse-graining procedures. In summary, the first paradigm provides quantitative guarantees about Generalizationgeneralization when the hypothesis space is predetermined. However, this approach inherits some relevant limitations. Consequently, the Compressioncompression-generalization trade-off can be adopted as an alternative. Without the compression process, the effectiveness of artificial intelligence protocols—particularly in the realm of Deep LearningDeep Learning—would likely be far less impressive than what we currently observe.