The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
717 episodes - English - Latest episode: about 1 month ago - ★★★★★ - 323 ratingsMachine 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.
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Episodes
Industrializing Machine Learning at Shell with Daniel Jeavons - TWiML Talk #202
November 21, 2018 16:32 - 45 minutesIn this episode of our AI Platforms series, we’re joined by Daniel Jeavons, General Manager of Data Science at Shell. In our conversation, we explore the evolution of analytics and data science at Shell, discussing IoT-related applications and issues, such as inference at the edge, federated ML, and digital twins, all key considerations for the way they apply ML. We also talk about the data science process at Shell and the importance of platform technologies to the company as a whole.
Resurrecting a Recommendations Platform at Comcast with Leemay Nassery - TWiML Talk #201
November 19, 2018 19:19 - 47 minutesIn this episode of our AI Platforms series, we’re joined by Leemay Nassery, Senior Engineering Manager and head of the recommendations team at Comcast. In our conversation, Leemay and I discuss just how she and her team resurrected the Xfinity X1 recommendations platform, including the rebuilding the data pipeline, the machine learning process, and the deployment and training of their updated models. We also touch on the importance of A-B testing and maintaining their rebuilt infrastructure.
Productive Machine Learning at LinkedIn with Bee-Chung Chen - TWiML Talk #200
November 15, 2018 20:05 - 47 minutesIn this episode of our AI Platforms series, we’re joined by Bee-Chung Chen, Principal Staff Engineer and Applied Researcher at LinkedIn. Bee-Chung and I caught up to discuss LinkedIn’s internal AI automation platform, Pro-ML. Bee-Chung breaks down some of the major pieces of the pipeline, LinkedIn’s experience bringing Pro-ML to the company's developers and the role the LinkedIn AI Academy plays in helping them get up to speed. For the complete show notes, visit https://twimlai.com/talk/200.
Scaling Deep Learning on Kubernetes at OpenAI with Christopher Berner - TWiML Talk #199
November 12, 2018 20:15 - 49 minutesIn this episode of our AI Platforms series we’re joined by OpenAI’s Head of Infrastructure, Christopher Berner. In our conversation, we discuss the evolution of OpenAI’s deep learning platform, the core principles which have guided that evolution, and its current architecture. We dig deep into their use of Kubernetes and discuss various ecosystem players and projects that support running deep learning at scale on the open source project.
Bighead: Airbnb's Machine Learning Platform with Atul Kale - TWiML Talk #198
November 08, 2018 20:17 - 49 minutesIn this episode of our AI Platforms series, we’re joined by Atul Kale, Engineering Manager on the machine learning infrastructure team at Airbnb. In our conversation, we discuss Airbnb’s internal machine learning platform, Bighead. Atul outlines the ML lifecycle at Airbnb and how the various components of Bighead support it. We then dig into the major components of Bighead, some of Atul’s best practices for scaling machine learning, and a special announcement that Atul and his team made at S...
Facebook's FBLearner Platform with Aditya Kalro - TWiML Talk #197
November 06, 2018 21:53 - 38 minutesIn the kickoff episode of our AI Platforms series, we’re joined by Aditya Kalro, Engineering Manager at Facebook, to discuss their internal machine learning platform FBLearner Flow. FBLearner Flow is the workflow management platform at the heart of the Facebook ML engineering ecosystem. We discuss the history and development of the platform, as well as its functionality and its evolution from an initial focus on model training to supporting the entire ML lifecycle at Facebook.
Geometric Statistics in Machine Learning w/ geomstats with Nina Miolane - TWiML Talk #196
November 01, 2018 16:40 - 43 minutesIn this episode we’re joined by Nina Miolane, researcher and lecturer at Stanford University. Nina and I spoke about her work in the field of geometric statistics in ML, specifically the application of Riemannian geometry, which is the study of curved surfaces, to ML. In our discussion we review the differences between Riemannian and Euclidean geometry in theory and her new Geomstats project, which is a python package that simplifies computations and statistics on manifolds with geometric str...
Milestones in Neural Natural Language Processing with Sebastian Ruder - TWiML Talk #195
October 29, 2018 20:16 - 1 hourIn this episode, we’re joined by Sebastian Ruder, PhD student studying NLP at National University of Ireland and Research Scientist at text analysis startup Aylien. We discuss recent milestones in neural NLP, including multi-task learning and pretrained language models. We also look at the use of attention-based models, Tree RNNs and LSTMs, and memory-based networks. Finally, Sebastian walks us through his ULMFit paper, which he co-authored with Jeremy Howard of fast.ai who I interviewed in e...
Natural Language Processing at StockTwits with Garrett Hoffman - TWiML Talk #194
October 25, 2018 21:22 - 50 minutesIn this episode, we’re joined by Garrett Hoffman, Director of Data Science at Stocktwits. Stocktwits is a social network for the investing community which has its roots in the use of the $cashtag on Twitter. In our conversation, we discuss applications such as Stocktwits’ own use of “social sentiment graphs” built on multilayer LSTM networks to gauge community sentiment about certain stocks in real time, as well as the more general use of natural language processing for generating trading ideas.
Advanced Reinforcement Learning & Data Science for Social Impact with Vukosi Marivate - TWiML Talk #193
October 23, 2018 19:30 - 46 minutesIn the final episode of our Deep Learning Indaba series, we speak with Vukosi Marivate, Chair of Data Science at the University of Pretoria and a co-organizer of the Indaba. My conversation with Vukosi falls into two distinct parts, his PhD research in reinforcement learning, and his current research, which falls under the banner of data science with social impact. We discuss several advanced RL scenarios, along with several applications he is currently exploring in areas like public safety ...
AI Ethics, Strategic Decisioning and Game Theory with Osonde Osoba - TWiML Talk #192
October 18, 2018 14:59 - 47 minutesIn this episode of our Deep Learning Indaba Series, we’re joined by Osonde Osoba, Engineer at RAND Corporation. Osonde and I spoke on the heels of the Indaba, where he presented on AI Ethics and Policy. We discuss his framework-based approach for evaluating ethical issues and how to build an intuition for where ethical flashpoints may exist in these discussions. We also discuss Osonde’s own model development research, including the application of machine learning to strategic decisions and g...
Acoustic Word Embeddings for Low Resource Speech Processing with Herman Kamper - TWiML Talk #191
October 16, 2018 16:47 - 1 hourIn this episode of our Deep Learning Indaba Series, we’re joined by Herman Kamper, lecturer at Stellenbosch University in SA and a co-organizer of the Indaba. We discuss his work on limited- and zero-resource speech recognition, how those differ from regular speech recognition, and the tension between linguistic and statistical methods in this space. We also dive into the specifics of the methods being used and developed in Herman’s lab.
Learning Representations for Visual Search with Naila Murray - TWiML Talk #190
October 12, 2018 16:52 - 41 minutesIn this episode of our Deep Learning Indaba series, we’re joined by Naila Murray, Senior Research Scientist and Group Lead in the computer vision group at Naver Labs Europe. Naila presented at the Indaba on computer vision. In this discussion, we explore her work on visual attention, including why visual attention is important and the trajectory of work in the field over time. We also discuss her paper “Generalized Max Pooling,” and much more! For the complete show notes, visit twimlai.com...
Evaluating Model Explainability Methods with Sara Hooker - TWiML Talk #189
October 10, 2018 18:24 - 1 hourIn this, the first episode of the Deep Learning Indaba series, we’re joined by Sara Hooker, AI Resident at Google Brain. I spoke with Sara in the run-up to the Indaba about her work on interpretability in deep neural networks. We discuss what interpretability means and nuances like the distinction between interpreting model decisions vs model function. We also talk about the relationship between Google Brain and the rest of the Google AI landscape and the significance of the Google AI Lab in ...
Graph Analytic Systems with Zachary Hanif - TWiML Talk #188
October 08, 2018 19:49 - 54 minutesIn this, the final episode of our Strata Data Conference series, we’re joined by Zachary Hanif, Director of Machine Learning at Capital One’s Center for Machine Learning. We start our discussion with a look at the role of graph analytics in the ML toolkit, including some important application areas for graph-based systems. Zach gives us an overview of the different ways to implement graph analytics, including what he calls graphical processing engines which excel at handling large datasets,...
Diversification in Recommender Systems with Ahsan Ashraf - TWiML Talk #187
October 04, 2018 17:28 - 44 minutesIn this episode of our Strata Data conference series, we’re joined by Ahsan Ashraf, data scientist at Pinterest. We discuss his presentation, “Diversification in recommender systems: Using topical variety to increase user satisfaction,” covering the experiments his team ran to explore the impact of diversification in user’s boards, the methodology his team used to incorporate variety into the Pinterest recommendation system and much more! The show notes can be found at https://twimlai.com/ta...
The Fastai v1 Deep Learning Framework with Jeremy Howard - TWiML Talk #186
October 02, 2018 16:13 - 1 hourIn today's episode we're presenting a special conversation with Jeremy Howard, founder and researcher at Fast.ai. This episode is being released today in conjunction with the company’s announcement of version 1.0 of their fastai library at the inaugural Pytorch Devcon in San Francisco. In our conversation, we dive into the new library, exploring why it’s important and what’s changed, the unique way in which it was developed, what it means for the future of the fast.ai courses, and much more!
Federated ML for Edge Applications with Justin Norman - TWiML Talk #185
September 27, 2018 21:40 - 47 minutesIn this episode we’re joined by Justin Norman, Director of Research and Data Science Services at Cloudera Fast Forward Labs. In my chat with Justin we start with an update on the company before diving into a look at some of recent and upcoming research projects. Specifically, we discuss their recent report on Multi-Task Learning and their upcoming research into Federated Machine Learning for AI at the edge. For the complete show notes, visit https://twimlai.com/talk/185.
Exploring Dark Energy & Star Formation w/ ML with Viviana Acquaviva - TWiML Talk #184
September 26, 2018 17:49 - 40 minutesIn today’s episode of our Strata Data series, we’re joined by Viviana Acquaviva, Associate Professor at City Tech, the New York City College of Technology. In our conversation, we discuss an ongoing project she’s a part of called the “Hobby-Eberly Telescope Dark Energy eXperiment,” her motivation for undertaking this project, how she gets her data, the models she uses, and how she evaluates their performance. The complete show notes can be found at https://twimlai.com/talk/184.
Document Vectors in the Wild with James Dreiss - TWiML Talk #183
September 24, 2018 18:13 - 40 minutesIn this episode of our Strata Data series we’re joined by James Dreiss, Senior Data Scientist at international news syndicate Reuters. James and I sat down to discuss his talk from the conference “Document vectors in the wild, building a content recommendation system,” in which he details how Reuters implemented document vectors to recommend content to users of their new “infinite scroll” page layout.
Applied Machine Learning for Publishers with Naveed Ahmad - TWiML Talk #182
September 20, 2018 20:56 - 39 minutesIn today’s episode we’re joined by Naveed Ahmad, Senior Director of data engineering and machine learning at Hearst Newspapers. In our conversation, we discuss into the role of ML at Hearst, including their motivations for implementing it and some of their early projects, the challenges of data acquisition within a large organization, and the benefits they enjoy from using Google’s BigQuery as their data warehouse. For the complete show notes for this episode, visit https://twimlai.com/talk...
Anticipating Superintelligence with Nick Bostrom - TWiML Talk #181
September 17, 2018 19:49 - 44 minutesIn this episode, we’re joined by Nick Bostrom, professor at the University of Oxford and head of the Future of Humanity Institute, a multidisciplinary institute focused on answering big-picture questions for humanity with regards to AI safety and ethics. In our conversation, we discuss the risks associated with Artificial General Intelligence, advanced AI systems Nick refers to as superintelligence, openness in AI development and more! The notes for this episode can be found at https://twimla...
Can We Train an AI to Understand Body Language? with Hanbyul Joo - TWIML Talk #180
September 13, 2018 19:46 - 51 minutesIn this episode, we’re joined by Hanbyul Joo, a PhD student at CMU. Han is working on what is called the “Panoptic Studio,” a multi-dimension motion capture studio used to capture human body behavior and body language. His work focuses on understanding how humans interact and behave so that we can teach AI-based systems to react to humans more naturally. We also discuss his CVPR best student paper award winner “Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies.”
Biological Particle Identification and Tracking with Jay Newby - TWiML Talk #179
September 10, 2018 18:08 - 45 minutesIn today’s episode we’re joined by Jay Newby, Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. Jay joins us to discuss his work applying deep learning to biology, including his paper “Deep neural networks automate detection for tracking of submicron scale particles in 2D and 3D.” He gives us an overview of particle tracking and a look at how he combines neural networks with physics-based particle filter models.
AI for Content Creation with Debajyoti Ray - TWiML Talk #178
September 06, 2018 19:09 - 55 minutesIn today’s episode we’re joined by Debajyoti Ray, Founder and CEO of RivetAI, a startup producing AI-powered tools for storytellers and filmmakers. Deb and I discuss some of what he’s learned in the journey to apply AI to content creation, including how Rivet approaches the use of machine learning to automate creative processes, the company’s use hierarchical LSTM models and autoencoders, and the tech stack that they’ve put in place to support the business.
Deep Reinforcement Learning Primer and Research Frontiers with Kamyar Azizzadenesheli - TWiML Talk #177
August 30, 2018 20:07 - 1 hourToday 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
OpenAI Five with Christy Dennison - TWiML Talk #176
August 27, 2018 19:20 - 48 minutesToday 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 mo...
How ML Keeps Shelves Stocked at Home Depot with Pat Woowong - TWiML Talk #175
August 23, 2018 18:37 - 45 minutesToday 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/tal...
Contextual Modeling for Language and Vision with Nasrin Mostafazadeh - TWiML Talk #174
August 20, 2018 19:59 - 49 minutesToday 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.
ML for Understanding Satellite Imagery at Scale with Kyle Story - TWiML Talk #173
August 16, 2018 17:18 - 56 minutesToday 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.
Generating Ground-Level Images From Overhead Imagery Using GANs with Yi Zhu - TWiML Talk #172
August 13, 2018 20:47 - 38 minutesToday 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.
Vision Systems for Planetary Landers and Drones with Larry Matthies - TWiML Talk #171
August 09, 2018 15:39 - 43 minutesToday 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.
Learning Semantically Meaningful and Actionable Representations with Ashutosh Saxena - TWiML Talk #170
August 06, 2018 20:26 - 45 minutesIn 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...
AI Innovation for Clinical Decision Support with Joe Connor - TWiML Talk #169
August 02, 2018 17:44 - 42 minutesIn 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.
Dynamic Visual Localization and Segmentation with Laura Leal-Taixé -TWiML Talk #168
July 30, 2018 19:52 - 44 minutesIn 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.
Conversational AI for the Intelligent Workplace with Gillian McCann - TWiML Talk #167
July 26, 2018 13:49 - 36 minutesIn 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.
Computer Vision and Intelligent Agents for Wildlife Conservation with Jason Holmberg - TWiML Talk #166
July 22, 2018 03:58 - 48 minutesIn 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
Pragmatic Deep Learning for Medical Imagery with Prashant Warier - TWiML Talk #165
July 19, 2018 17:52 - 36 minutesIn 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/...
Taskonomy: Disentangling Transfer Learning for Perception (CVPR 2018 Best Paper Winner) with Amir Zamir - TWiML Talk #164
July 16, 2018 16:27 - 47 minutesIn 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
Predicting Metabolic Pathway Dynamics w/ Machine Learning with Zak Costello - TWiML Talk #163
July 11, 2018 21:27 - 39 minutesIn 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.
Machine Learning to Discover Physics and Engineering Principles with Nathan Kutz - TWiML Talk #162
July 09, 2018 16:28 - 43 minutesIn 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
Automating Complex Internal Processes w/ AI with Alexander Chukovski - TWiML Talk #161
July 05, 2018 16:38 - 39 minutesIn 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/tal...
Designing Better Sequence Models with RNNs with Adji Bousso Dieng - TWiML Talk #160
July 02, 2018 17:36 - 38 minutesIn 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.
Love Love: AI and ML in Tennis with Stephanie Kovalchik - TWiML Talk #159
June 29, 2018 16:24 - 46 minutesIn 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.
Growth Hacking Sports w/ Machine Learning with Noah Gift - TWiML Talk #158
June 28, 2018 14:55 - 50 minutesIn 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
Fine-Grained Player Prediction in Sports with Jennifer Hobbs - TWiML Talk #157
June 27, 2018 16:08 - 42 minutesIn this episode of our AI in Sports series, I'm joined by Jennifer Hobbs, Senior Data Scientist at STATS, a collector and distributor of sports data, to discuss the STATS data pipeline and how they collect and store different types of data for easy consumption and application. We also look into a paper she co-authored, Mythbusting Set-Pieces in Soccer, which was presented at the MIT Sloan Conference this year. https://twimlai.com/talk/157
Targeted Ticket Sales Using Azure ML with the Trail Blazers w/ Mike Schumacher & Chenhui Hu - TWiML Talk #156
June 26, 2018 16:21 - 37 minutesIn today’s episode of our AI in Sports series I'm joined by Mike Schumacher, director of business analytics for the Portland Trail Blazers, and Chenhui Hu, a data scientist at Microsoft to discuss how the Blazers are using machine learning to produce better-targeted sales campaigns, for both single-game and season-ticket buyers.
AI for Athlete Optimization with Sinead Flahive - TWiML Talk #155
June 25, 2018 19:57 - 40 minutesThis week we’re excited to kick off a series of shows on AI in sports. In this episode I'm joined by Sinead Flahive, data scientist at Dublin, Ireland based Kitman Labs to discuss Kitman’s Athlete Optimization System, which allows sports trainers and coaches to collect and analyze data for player performance optimization and injury reduction. Enjoy!
Omni-Channel Customer Experiences with Vince Jeffs - TWiML Talk #154
June 21, 2018 17:25 - 43 minutesIn this, the final episode of our PegaWorld series I’m joined by Vince Jeffs, Senior Director of Product Strategy for AI and Decisioning at Pegasystems. Vince and I had a great talk about the role AI and advanced analytics will play in defining future customer experiences. We do this in the context provided by one of his presentations from the conference, which explores four technology scenarios from Pegasystems’ innovation labs. These look at a connected car experience, the use of deep learn...
Workforce Intelligence for Automation & Productivity with Michael Kempe - TWiML Talk #153
June 20, 2018 18:45 - 36 minutesIn this episode of our PegaWorld series, I’m joined by Michael Kempe, chief operating officer at global share registry and financial services provider Link Market Services. In the interview, Michael and I dig into Link’s use of workforce intelligence software to allow it to track and analyze the performance of its workforce and business processes. Michael and I discuss some of the initial challenges associated with implementing this type of system, including skepticism amongst employees, and ...