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

Learning Machines 101

Contenuto fornito da Richard M. Golden, M.S.E.E., and B.S.E.E.. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Richard M. Golden, M.S.E.E., and B.S.E.E. o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

9y ago 19:38

MP3•Pagina principale dell'episodio

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 episodi

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