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
Jan 14, 2019 • 35min
Building a Recommender System from Scratch at 20th Century Fox with JJ Espinoza - TWiML Talk #220
Today we’re joined by JJ Espinoza, former Director of Data Science at 20th Century Fox.
In this talk we dig into JJ and his team’s experience building and deploying a content recommendation system from the ground up. In our conversation, we explore the design of a couple of key components of their system, the first of which processes movie scripts to make recommendations about which movies the studio should make, and the second processes trailers to determine which should be recommended to users.
Jan 10, 2019 • 47min
Legal and Policy Implications of Model Interpretability with Solon Barocas - TWiML Talk #219
Today we’re joined by Solon Barocas, Assistant Professor of Information Science at Cornell University.
Solon and I caught up to discuss his work on model interpretability and the legal and policy implications of the use of machine learning models. In our conversation, we explore the gap between law, policy, and ML, and how to build the bridge between them, including formalizing ethical frameworks for machine learning. We also look at his paper ”The Intuitive Appeal of Explainable Machines.”
Jan 7, 2019 • 33min
Trends in Computer Vision with Siddha Ganju - TWiML Talk #218
In the final episode of our AI Rewind series, we’re excited to have Siddha Ganju back on the show.
Siddha, who is now an autonomous vehicles solutions architect at Nvidia shares her thoughts on trends in Computer Vision in 2018 and beyond. We cover her favorite CV papers of the year in areas such as neural architecture search, learning from simulation, application of CV to augmented reality, and more, as well as a bevy of tools and open source projects.
Jan 3, 2019 • 52min
Trends in Reinforcement Learning with Simon Osindero - TWiML Talk #217
In this episode of our AI Rewind series, we introduce a new friend of the show, Simon Osindero, Staff Research Scientist at DeepMind.
We discuss trends in Deep Reinforcement Learning in 2018 and beyond. We’ve packed a bunch into this show, as Simon walks us through many of the important papers and developments seen this year in areas like Imitation Learning, Unsupervised RL, Meta-learning, and more.
The complete show notes for this episode can be found at https://twimlai.com/talk/217.
Dec 31, 2018 • 53min
Trends in Natural Language Processing with Sebastian Ruder - TWiML Talk #216
In this episode of our AI Rewind series, we’ve brought back recent guest Sebastian Ruder, PhD Student at the National University of Ireland and Research Scientist at Aylien, to discuss trends in Natural Language Processing in 2018 and beyond.
In our conversation we cover a bunch of interesting papers spanning topics such as pre-trained language models, common sense inference datasets and large document reasoning and more, and talk through Sebastian’s predictions for the new year.
Dec 27, 2018 • 51min
Trends in Machine Learning with Anima Anandkumar - TWiML Talk #215
In this episode of our AI Rewind series, we’re back with Anima Anandkumar, Bren Professor at Caltech and now Director of Machine Learning Research at NVIDIA.
Anima joins us to discuss her take on trends in the broader Machine Learning field in 2018 and beyond. In our conversation, we cover not only technical breakthroughs in the field but also those around inclusivity and diversity.
For this episode's complete show notes, visit twimlai.com/talk/215.
Dec 24, 2018 • 1h 8min
Trends in Deep Learning with Jeremy Howard - TWiML Talk #214
In this episode of our AI Rewind series, we’re bringing back one of your favorite guests of the year, Jeremy Howard, founder and researcher at Fast.ai.
Jeremy joins us to discuss trends in Deep Learning in 2018 and beyond. We cover many of the papers, tools and techniques that have contributed to making deep learning more accessible than ever to so many developers and data scientists.
Dec 20, 2018 • 55min
Training Large-Scale Deep Nets with RL with Nando de Freitas - TWiML Talk #213
Today we close out both our NeurIPS series joined by Nando de Freitas, Team Lead & Principal Scientist at Deepmind. In our conversation, we explore his interest in understanding the brain and working towards artificial general intelligence. In particular, we dig into a couple of his team’s NeurIPS papers: “Playing hard exploration games by watching YouTube,” and “One-Shot high-fidelity imitation: Training large-scale deep nets with RL.”
Dec 20, 2018 • 23min
Making Algorithms Trustworthy with David Spiegelhalter - TWiML Talk #212
David Spiegelhalter, Chair of the Winton Center at Cambridge and President of the Royal Statistical Society, dives into why trustworthiness is crucial for AI systems. He highlights the difference between being trusted and being trustworthy, stressing rigorous evaluations akin to drug development. Spiegelhalter discusses empowering patients through transparent AI in healthcare, enhancing decision-making with personalized explanations. Throughout, he advocates for integrating diverse expert insights to improve fairness and transparency in algorithmic practices.
Dec 18, 2018 • 56min
Designing Computer Systems for Software with Kunle Olukotun - TWiML Talk #211
Today we’re joined by Kunle Olukotun, Professor in the department of EE and CS at Stanford University, and Chief Technologist at Sambanova Systems. Kunle was an invited speaker at NeurIPS this year, presenting on “Designing Computer Systems for Software 2.0.” In our conversation, we discuss various aspects of designing hardware systems for machine and deep learning, touching on multicore processor design, domain specific languages, and graph-based hardware. This was a fun one!
