Learning Semantically Meaningful and Actionable Representations with Ashutosh Saxena – TWiML…

In this episode I’m joined by Ashutosh Saxena, a veteran of Andrew Ng’s Stanford Machine Learning Group, and co-founder and CEO of Caspar.ai. Ashutosh and I discuss his RoboBrain project, a computational system that creates semantically meaningful and actionable representations of the objects, actions and…

AI Innovation for Clinical Decision Support with Joe Connor – TWiML Talk #169

In this episode I speak with Joe Connor, Founder of Experto Crede. Joe’s been listening to the podcast for a while and he and I connected after he reached out to discuss an article I wrote regarding AI in the healthcare space. In this conversation,…

Dynamic Visual Localization and Segmentation with Laura Leal-Taixé -TWiML Talk #168

In this episode I’m joined by Laura Leal-Taixé, Professor at the Technical University of Munich where she leads the Dynamic Vision and Learning Group, and 2017 recipient of prestigious Sofja Kovalevskaja Award. In our conversation, we discuss several of her recent projects including work on…

Conversational AI for the Intelligent Workplace with Gillian McCann – TWiML Talk #167

In this episode I’m joined by Gillian McCann, Head of Cloud Engineering and AI at Workgrid Software.   Workgrid offers an intelligent workplace assistant that integrates with a variety of business tools and systems. In our conversation, which focuses on Workgrid’s use of cloud-based AI…

Computer Vision and Intelligent Agents for Wildlife Conservation with Jason Holmberg – TWiML…

In this episode, I’m joined by Jason Holmberg, Executive Director and Director of Engineering at WildMe. Wildme’s Wildbook and Whaleshark.org are both open source computer vision based conservation projects, that have been compared to a facebook for wildlife. Jason kicks us off with the interesting…

Pragmatic Deep Learning for Medical Imagery with Prashant Warier – TWiML Talk #165

In this episode I’m joined by Prashant Warier, CEO and Co-Founder of Qure.ai, a company building AI-powered software for radiology. In our conversation, Prashant and I discuss the company’s work building products for interpreting head CT scans and chest x-rays. Prashant shares with us some…

Taskonomy: Disentangling Transfer Learning for Perception (CVPR 2018 Best Paper Winner) with…

In this episode I’m joined by Amir Zamir, Postdoctoral researcher at both Stanford & UC Berkeley. Amir joins us fresh off of winning the 2018 CVPR Best Paper Award for co-authoring “Taskonomy: Disentangling Task Transfer Learning.” In this work, Amir and his coauthors explore the…

Zak Costello Interview – Predicting Metabolic Pathway Dynamics w/ Machine Learning

In today’s episode I’m joined by Zak Costello, post-doctoral fellow at the Joint BioEnergy Institute. Zak joins me to discuss his recent paper, “A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data.” In our conversation, we start with an overview of…

Nathan Kutz Interview – Machine Learning to Discover Physics and Engineering Principles

In this episode, I’m joined by Nathan Kutz, Professor of applied mathematics, electrical engineering and physics at the University of Washington. Nathan and I met a few months ago at the Prepare.AI conference in St. Louis where he gave a talk on “Machine Learning to…

Adji Bousso Dieng Interview – Designing Better Sequence Models with RNNs

In this episode, i’m joined by Adji Bousso Dieng, PhD Student in the Department of Statistics at Columbia University. In this interview, Adji and I discuss two of her recent papers, the first, an accepted paper from this year’s ICML conference titled “Noisin: Unbiased Regularization…