This Week In Machine Learning & Artificial Intelligence (ai) Podcast

In 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.

In 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 and energy.

In 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 game theor

In 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.

In 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/tal

In 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 Accra, Ghana.

In 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, & much m

In 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/talk/18

In 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!

In 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.

In 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. 

In 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.

In 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/182.

In 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://twimlai.com/talk/18

In 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.”

In 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.

In 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.

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.