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.
How LLMs and Generative AI are Revolutionizing AI for Science with Anima Anandkumar
Today we’re joined by Anima Anandkumar, Bren Professor of Computing And Mathematical Sciences at Caltech and Sr Director of AI Research at NVIDIA. In our conversation, we take a broad look at the emerging field of AI for Science, focusing on both practical applications and longer-term research areas. We discuss the latest developments in the area of protein folding, and how much it has evolved since we first discussed it on the podcast in 2018, the impact of generative models and stable diffusion on the space, and the application of neural operators. We also explore the ways in which prediction models like weather models could be improved, how foundation models are helping to drive innovation, and finally, we dig into MineDojo, a new framework built on the popular Minecraft game for embodied agent research, which won a 2022 Outstanding Paper Award at NeurIPS.
The complete show notes for this episode can be found at twimlai.com/go/614
AI Trends 2023: Natural Language Proc - ChatGPT, GPT-4 and Cutting Edge Research with Sameer Singh
Today we continue our AI Trends 2023 series joined by Sameer Singh, an associate professor in the department of computer science at UC Irvine and fellow at the Allen Institute for Artificial Intelligence (AI2). In our conversation with Sameer, we focus on the latest and greatest advancements and developments in the field of NLP, starting out with one that took the internet by storm just a few short weeks ago, ChatGPT. We also explore top themes like decomposed reasoning, causal modeling in NLP, and the need for “clean” data. We also discuss projects like HuggingFace’s BLOOM, the debacle that was the Galactica demo, the impending intersection of LLMs and search, use cases like Copilot, and of course, we get Sameer’s predictions for what will happen this year in the field.
The complete show notes for this episode can be found at twimlai.com/go/613.
AI Trends 2023: Reinforcement Learning - RLHF, Robotic Pre-Training, and Offline RL with Sergey Levine
Today we’re taking a deep dive into the latest and greatest in the world of Reinforcement Learning with our friend Sergey Levine, an associate professor, at UC Berkeley. In our conversation with Sergey, we explore some game-changing developments in the field including the release of ChatGPT and the onset of RLHF. We also explore more broadly the intersection of RL and language models, as well as advancements in offline RL and pre-training for robotics models, inverse RL, Q learning, and a host of papers along the way. Finally, you don’t want to miss Sergey’s predictions for the top developments of the year 2023!
The complete show notes for this episode can be found at twimlai.com/go/612
Supporting Food Security in Africa Using ML with Catherine Nakalembe
Today we conclude our coverage of the 2022 NeurIPS series joined by Catherine Nakalembe, an associate research professor at the University of Maryland, and Africa Program Director under NASA Harvest. In our conversation with Catherine, we take a deep dive into her talk from the ML in the Physical Sciences workshop, Supporting Food Security in Africa using Machine Learning and Earth Observations. We discuss the broad challenges associated with food insecurity, as well as Catherine’s role and the priorities of Harvest Africa, a program focused on advancing innovative satellite-driven methods to produce automated within-season crop type and crop-specific condition products that support agricultural assessments. We explore some of the technical challenges of her work, including the limited, but growing, access to remote sensing and earth observation datasets and how the availability of that data has changed in recent years, the lack of benchmarks for the tasks she’s working on, examples of how they’ve applied techniques like multi-task learning and task-informed meta-learning, and much more.
The complete show notes for this episode can be found at twimlai.com/go/611.
Service Cards and ML Governance with Michael Kearns
Today we conclude our AWS re:Invent 2022 series joined by Michael Kearns, a professor in the department of computer and information science at UPenn, as well as an Amazon Scholar. In our conversation, we briefly explore Michael’s broader research interests in responsible AI and ML governance and his role at Amazon. We then discuss the announcement of service cards, and their take on “model cards” at a holistic, system level as opposed to an individual model level. We walk through the information represented on the cards, as well as explore the decision-making process around specific information being omitted from the cards. We also get Michael’s take on the years-old debate of algorithmic bias vs dataset bias, what some of the current issues are around this topic, and what research he has seen (and hopes to see) addressing issues of “fairness” in large language models.
The complete show notes for this episode can be found at twimlai.com/go/610.
Reinforcement Learning for Personalization at Spotify with Tony Jebara
Today we continue our NeurIPS 2022 series joined by Tony Jebara, VP of engineering and head of machine learning at Spotify. In our conversation with Tony, we discuss his role at Spotify and how the company’s use of machine learning has evolved over the last few years, and the business value of machine learning, specifically recommendations, hold at the company.
We dig into his talk on the intersection of reinforcement learning and lifetime value (LTV) at Spotify, which explores the application of Offline RL for user experience personalization. We discuss the various papers presented in the talk, and how they all map toward determining and increasing a user’s LTV.
The complete show notes for this episode can be found at twimlai.com/go/609.
A premier podcast on AI/ML
I have enjoyed listening to many of the episodes and had fun participating in one
Lots of potential but incompetent host
The guests are amazing and this could be such an amazing podcast for the ML community. Unfortunately, the host is both a poor conversationalist (interviews lack flow, feel disjointed and tortured), and comes to the interviews so poorly informed that he struggles to put follow up questions together or even understand what the guest is saying.
excellent machine learning perspective
Sam puts lot of attention to every episode. Information is high quality and easy to grasp.