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 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!
Dec 14, 2018 • 54min
Operationalizing Ethical AI with Kathryn Hume - TWiML Talk #210
Today we conclude our Trust in AI series with this conversation with Kathryn Hume, VP of Strategy at Integrate AI. We discuss her newly released white paper “Responsible AI in the Consumer Enterprise,” which details a framework for ethical AI deployment in e-commerce companies and other consumer-facing enterprises. We look at the structure of the ethical framework she proposes, and some of the many questions that need to be considered when deploying AI in an ethical manner.
Dec 12, 2018 • 46min
Approaches to Fairness in Machine Learning with Richard Zemel - TWiML Talk #209
Today we continue our exploration of Trust in AI with this interview with Richard Zemel, Professor in the department of Computer Science at the University of Toronto and Research Director at Vector Institute.
In our conversation, Rich describes some of his work on fairness in machine learning algorithms, including how he defines both group and individual fairness and his group’s recent NeurIPS poster, “Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer.”
Dec 11, 2018 • 46min
Trust and AI with Parinaz Sobhani - TWiML Talk #208
In today’s episode we’re joined by Parinaz Sobhani, Director of Machine Learning at Georgian Partners.
In our conversation, Parinaz and I discuss some of the main issues falling under the “trust” umbrella, such as transparency, fairness and accountability. We also explore some of the trust-related projects she and her team at Georgian are working on, as well as some of the interesting trust and privacy papers coming out of the NeurIPS conference.
