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

Learning Machines 101

Content provided by Richard M. Golden, M.S.E.E., and B.S.E.E.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Richard M. Golden, M.S.E.E., and B.S.E.E. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

9y ago 19:38

MP3•Episode home

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!

85 episodes

#Science #Tech #Artificialintelligence #Bigdata #Datamining #Imageprocessing #Machinelearning #Speechrecognition #Math #Richard M. Golden #B.S.E.E #M.S.E.E #Robot #Android