Exploring Dark Energy & Star Formation w/ ML with Viviana Acquaviva - TWiML Talk #184
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.
26 Sep 201840min
Document Vectors in the Wild with James Dreiss - TWiML Talk #183
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.
24 Sep 201840min
Applied Machine Learning for Publishers with Naveed Ahmad - TWiML Talk #182
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.
20 Sep 201839min
Anticipating Superintelligence with Nick Bostrom - TWiML Talk #181
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
17 Sep 201844min
Can We Train an AI to Understand Body Language? with Hanbyul Joo - TWIML Talk #180
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.”
13 Sep 201851min
Biological Particle Identification and Tracking with Jay Newby - TWiML Talk #179
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.
10 Sep 201845min
AI for Content Creation with Debajyoti Ray - TWiML Talk #178
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.
6 Sep 201855min
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
30 Aug 20181h 34min
