The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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

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.

Today we’re joined by Pat Woowong, principal engineer in the applied machine intelligence group at The Home Depot. We discuss a project that Pat recently presented at the Google Cloud Next conference which used machine learning to predict shelf-out scenarios within stores. We dig into the motivation for this system and how the team went about building it, their use of kubernetes to support future growth in the platform, and much more. For complete show notes, visit https://twimlai.com/talk/175.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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.