The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence‪)‬ Sam Charrington

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.

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

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.

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.

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.

More than any system before it, ChatGPT has tapped into our enduring fascination with artificial intelligence, raising in a more concrete and present way important questions and fears about what AI is capable of and how it will impact us as humans. One of the concerns most frequently voiced, whether sincerely or cloaked in jest, is how ChatGPT or systems like it, will impact our livelihoods. In other words, “will ChatGPT put me out of a job???” In this episode of the podcast, I seek to answer this very question by conducting an interview in which ChatGPT is asking all the questions. (The questions are answered by a second ChatGPT, as in my own recent Interview with it, Exploring Large Laguage Models with ChatGPT.) In addition to the straight dialogue, I include my own commentary along the way and conclude with a discussion of the results of the experiment, that is, whether I think ChatGPT will be taking my job as your host anytime soon. Ultimately, though, I hope you’ll be the judge of that and share your thoughts on how ChatGPT did at my job via a comment below or on social media.