The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Sam Charrington
Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.
Episodes
Mentioned books
Jul 26, 2018 • 37min
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. In our conversation, which focuses on Workgrid’s use of cloud-based AI services, Gillian details some of the underlying systems that make Workgrid tick, their engineering pipeline & how they build high quality systems that incorporate external APIs and her view on factors that contribute to misunderstandings and impatience on the part of users of AI-based products.
Jul 22, 2018 • 48min
Computer Vision and Intelligent Agents for Wildlife Conservation with Jason Holmberg - TWiML Talk #166
In this episode, I'm joined by Jason Holmberg, Executive Director and Director of Engineering at WildMe. Jason and I discuss Wildme's pair of open source computer vision based conservation projects, Wildbook and Whaleshark.org, Jason kicks us off with the interesting story of how Wildbook came to be, the eventual expansion of the project and the evolution of these projects’ use of computer vision and deep learning.
For the complete show notes, visit twimlai.com/talk/166
Jul 19, 2018 • 37min
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. We discuss the company’s work building products for interpreting head CT scans and chest x-rays. We look at knowledge gained in bringing a commercial product to market, including what the gap between academic research papers and commercially viable software, the challenge of data acquisition and more. We also touch on the application of transfer learning.
For the complete show notes, visit https://twimlai.com/talk/165.
Jul 16, 2018 • 48min
Taskonomy: Disentangling Transfer Learning for Perception (CVPR 2018 Best Paper Winner) with Amir Zamir - TWiML Talk #164
In this episode I'm joined by Amir Zamir, Postdoctoral researcher at both Stanford & UC Berkeley, who joins us fresh off of winning the 2018 CVPR Best Paper Award for co-authoring "Taskonomy: Disentangling Task Transfer Learning." In our conversation, we discuss the nature and consequences of the relationships that Amir and his team discovered, and how they can be used to build more effective visual systems with machine learning.
https://twimlai.com/talk/164
Jul 11, 2018 • 40min
Predicting Metabolic Pathway Dynamics w/ Machine Learning with Zak Costello - TWiML Talk #163
In today’s episode I’m joined by Zak Costello, post-doctoral fellow at the Joint BioEnergy Institute to discuss his recent paper, “A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data.” Zak gives us an overview of synthetic biology and the use of ML techniques to optimize metabolic reactions for engineering biofuels at scale.
Visit twimlai.com/talk/163 for the complete show notes.
Jul 9, 2018 • 43min
Machine Learning to Discover Physics and Engineering Principles with Nathan Kutz - TWiML Talk #162
In this episode, I’m joined by Nathan Kutz, Professor of applied mathematics, electrical engineering and physics at the University of Washington to discuss his research into the use of machine learning to help discover the fundamental governing equations for physical and engineering systems from time series measurements.
For complete show notes visit twimlai.com/talk/162
Jul 5, 2018 • 40min
Automating Complex Internal Processes w/ AI with Alexander Chukovski - TWiML Talk #161
In this episode, I'm joined by Alexander Chukovski, Director of Data Services at Munich, Germany based career platform, Experteer. In our conversation, we explore Alex’s journey to implement machine learning at Experteer, the Experteer NLP pipeline and how it’s evolved, Alex’s work with deep learning based ML models, including models like VDCNN and Facebook’s FastText offering and a few recent papers that look at transfer learning for NLP.
Check out the complete show notes at twimlai.com/talk/161
Jul 2, 2018 • 38min
Designing Better Sequence Models with RNNs with Adji Bousso Dieng - TWiML Talk #160
In this episode, I'm joined by Adji Bousso Dieng, PhD Student in the Department of Statistics at Columbia University to discuss two of her recent papers, “Noisin: Unbiased Regularization for Recurrent Neural Networks” and “TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency.” We dive into the details behind both of these papers and learn a ton along the way.
Jun 29, 2018 • 47min
Love Love: AI and ML in Tennis with Stephanie Kovalchik - TWiML Talk #159
In the final show in our AI in Sports series, I’m joined by Stephanie Kovalchik, Research Fellow at Victoria University and Senior Sports Scientist at Tennis Australia. In our conversation we discuss Tennis Australia's use of data to develop a player rating system based on ability and probability, some of the interesting products her Game Insight Group is developing, including a win forecasting algorithm, and a statistic that measures a given player’s workload during a match.
Jun 28, 2018 • 51min
Growth Hacking Sports w/ Machine Learning with Noah Gift - TWiML Talk #158
In this episode of our AI in Sports series I'm joined by Noah Gift, Founder and Consulting CTO at Pragmatic Labs and professor at UC Davis. Noah and I discuss some of his recent work in using social media to predict which players hold the most on-court value, and how this work could lead to more complete approaches to player valuation.
Check out the show notes at twimlai.com/talk/158
