Bahador KhaleghiinAnalytics VidhyaA critical overview of AutoML solutionsIn an earlier article, I discussed how enterprise machine learning (ML) faces several challenges when it comes to successful…Apr 2, 2020Apr 2, 2020
Bahador KhaleghiinThe StartupWhy enterprise machine learning is struggling and how AutoML can helpThere are some who warn us about machine learning (ML) taking over and replacing us in the future. As a person who has been working in the…Apr 2, 2020Apr 2, 2020
Bahador KhaleghiinTowards Data ScienceAn Explanation of What, Why, and How of eXplainable AI (XAI)The following is a written summary of a talk I gave at the Toronto Machine Learning Summit in November 2019. They filmed it, which you can…Mar 3, 20201Mar 3, 20201
Bahador KhaleghiinTowards Data ScienceThe How of Explainable AI: Post-modelling ExplainabilityIn the first two parts of our overview of the How of XAI, we looked into pre-modelling explainability and explainable modelling…Jul 31, 20195Jul 31, 20195
Bahador KhaleghiinTowards Data ScienceThe How of Explainable AI: Explainable ModellingIn the first part of our overview of the How of Explainable AI, we looked a pre-modelling explainability. However, the true scope of…Jul 31, 2019Jul 31, 2019
Bahador KhaleghiinTowards Data ScienceThe How of Explainable AI: Pre-modelling ExplainabilityAI explainability is a broad and multi-disciplinary domain, being studied in several fields including machine learning, knowledge…Jul 31, 20191Jul 31, 20191
Bahador KhaleghiinElement AI LabA taxonomy of AI trustability challengesIn the first part of this series we established trustability as a key requirement for mass adoption and thus overall success of enterprise…Nov 8, 20181Nov 8, 20181
Bahador KhaleghiinElement AI LabThe missing ingredient for mass adoption of AI: trustAI is so hot right now. That’s confirmed by studies such as a recent Gartner report that calls AI an emerging “mega-trend.” In spite of…Nov 5, 20181Nov 5, 20181
Bahador KhaleghiinElement AI LabNIPS’17 highlights and trends overviewThe emergence of Deep Learning 2.0 and the return of BayesFeb 12, 2018Feb 12, 2018
