LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis

In this 58th episode of Learning Machines 101, I’ll be discussing an important new scientific breakthrough published just last week for the first time in the journal Econometrics  in the special issue on model misspecification titled “Generalized Information Matrix Tests for Detecting Model Misspecification”. The article provides a unified theoretical framework for the development of a wide range of methods for determining if a learning machine is capable of learning its statistical environment. The article is co-authored by myself, Steven Henley, Halbert White, and Michael Kashner. It is an open-access article so the complete article can be downloaded for free! The download link can be found in the show notes of this episode at: www.learningmachines101.com . In 30 years  everyone will be using these methods so you might as well start using them now!