{"version":1,"type":"rich","provider_name":"Libsyn","provider_url":"https:\/\/www.libsyn.com","height":90,"width":600,"title":"LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis","description":"In this 58th episode of Learning Machines 101, I\u2019ll be discussing an important new scientific breakthrough published just last week for the first time in  the journal Econometrics&amp;nbsp; in the special issue on model misspecification titled \u201cGeneralized Information Matrix Tests for Detecting Model Misspecification\u201d. 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 &amp;nbsp;everyone will be using these methods so you might as well start using them now! ","author_name":"Learning Machines 101","author_url":"http:\/\/www.learningmachines101.com","html":"<iframe title=\"Libsyn Player\" style=\"border: none\" src=\"\/\/html5-player.libsyn.com\/embed\/episode\/id\/4855867\/height\/90\/theme\/custom\/thumbnail\/yes\/direction\/forward\/render-playlist\/no\/custom-color\/88AA3C\/\" height=\"90\" width=\"600\" scrolling=\"no\"  allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen><\/iframe>","thumbnail_url":"https:\/\/assets.libsyn.com\/secure\/content\/13416666"}