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
Aug 30, 2018 • 1h 35min
Deep Reinforcement Learning Primer and Research Frontiers with Kamyar Azizzadenesheli - TWiML Talk #177
Today we’re joined by Kamyar Azizzadenesheli, PhD student at the University of California, Irvine, who joins us to review the core elements of RL, along with a pair of his RL-related papers: “Efficient Exploration through Bayesian Deep Q-Networks” and “Sample-Efficient Deep RL with Generative Adversarial Tree Search.”
To skip the Deep Reinforcement Learning primer conversation and jump to the research discussion, skip to the 34:30 mark of the episode. Show notes at https://twimlai.com/talk/177
Aug 27, 2018 • 48min
OpenAI Five with Christy Dennison - TWiML Talk #176
Today we’re joined by Christy Dennison, Machine Learning Engineer at OpenAI, who has been working on OpenAI’s efforts to build an AI-powered agent to play the DOTA 2 video game. In our conversation we overview of DOTA 2 gameplay and the recent OpenAI Five benchmark, we dig into the underlying technology used to create OpenAI Five, including their use of deep reinforcement learning, LSTM recurrent neural networks, and entity embeddings, plus some tricks and techniques they use to train the models.
Aug 23, 2018 • 45min
How ML Keeps Shelves Stocked at Home Depot with Pat Woowong - TWiML Talk #175
Principal engineer Pat Woowong from The Home Depot discusses using ML to predict shelf-out scenarios, challenges in stock availability, implementing a smart list app for stock monitoring, complexity in deploying a button on shelves, utilizing BigQuery ML, and plans for expanding ML projects across all stores.
Aug 20, 2018 • 49min
Contextual Modeling for Language and Vision with Nasrin Mostafazadeh - TWiML Talk #174
Today we’re joined by Nasrin Mostafazadeh, Senior AI Research Scientist at New York-based Elemental Cognition.
Our conversation focuses on Nasrin’s work in event-centric contextual modeling in language and vision including her work on the Story Cloze Test, a reasoning framework for evaluating story understanding and generation. We explore the details of this task, some of the challenges it presents and approaches for solving it.
Aug 16, 2018 • 56min
ML for Understanding Satellite Imagery at Scale with Kyle Story - TWiML Talk #173
Today we’re joined by Kyle Story, computer vision engineer at Descartes Labs.
Kyle and I caught up after his recent talk at the Google Cloud Next Conference titled “How Computers See the Earth: A Machine Learning Approach to Understanding Satellite Imagery at Scale.” We discuss some of the interesting computer vision problems he’s worked on at Descartes, and the key challenges they’ve had to overcome in scaling them.
Aug 13, 2018 • 38min
Generating Ground-Level Images From Overhead Imagery Using GANs with Yi Zhu - TWiML Talk #172
Today we’re joined by Yi Zhu, a PhD candidate at UC Merced focused on geospatial image analysis. In our conversation, Yi and I take a look at his recent paper “What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks.” We discuss the goal of this research and how he uses conditional GANs to generate artificial ground-level images.
Aug 9, 2018 • 44min
Vision Systems for Planetary Landers and Drones with Larry Matthies - TWiML Talk #171
Today we’re joined by Larry Matthies, Sr. Research Scientist and head of computer vision in the mobility and robotics division at JPL. In our conversation, we discuss two talks he gave at CVPR a few weeks back, his work on vision systems for the first iteration of Mars rovers in 2004 and the future of planetary landing projects.
For the complete show notes, visit https://twimlai.com/talk/171.
Aug 6, 2018 • 46min
Learning Semantically Meaningful and Actionable Representations with Ashutosh Saxena - TWiML Talk #170
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 observations that a robot experiences in its environment, and allows these to be shared and queried by other robots to learn new actions.
For complete show notes, visit https://twimlai.com/talk/170.
Aug 2, 2018 • 42min
AI Innovation for Clinical Decision Support with Joe Connor - TWiML Talk #169
In this episode I speak with Joe Connor, Founder of Experto Crede.
In our conversation, we explore his experiences bringing AI powered healthcare projects to market in collaboration with the UK National Health Service and its clinicians, some of the various challenges he’s run into when applying ML and AI in healthcare, as well as some of his successes. We also discuss data protections, especially GDPR, potential ways to include clinicians in the building of applications.
Jul 30, 2018 • 45min
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.
In our conversation, we discuss several of her recent projects including work on image-based localization techniques that fuse traditional model-based computer vision approaches with a data-driven approach based on deep learning, her paper on one-shot video object segmentation and the broader vision for her research.
