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.

Today we continue our re:Invent 2022 series joined by Kumar Chellapilla, a general manager of ML and AI Services at AWS. We had the opportunity to speak with Kumar after announcing their recent addition of geospatial data to the SageMaker Platform. In our conversation, we explore Kumar’s role as the GM for a diverse array of SageMaker services, what has changed in the geospatial data landscape over the last 10 years, and why Amazon decided now was the right time to invest in geospatial data. We discuss the challenges of accessing and working with this data and the pain points they’re trying to solve. Finally, Kumar walks us through a few customer use cases, describes how this addition will make users more effective than they currently are, and shares his thoughts on the future of this space over the next 2-5 years, including the potential intersection of geospatial data and stable diffusion/generative models.The complete show notes for this episode can be found at twimlai.com/go/607

Today we’re joined by Disha Singla, a senior director of machine learning engineering at Capital One. In our conversation with Disha, we explore her role as the leader of the Data Insights team at Capital One, where they’ve been tasked with creating reusable libraries, components, and workflows to make ML usable broadly across the company, as well as a platform to make it all accessible and to drive meaningful insights. We discuss the construction of her team, as well as the types of interactions and requests they receive from their customers (data scientists), productionized use cases from the platform, and their efforts to transition from batch to real-time deployment. Disha also shares her thoughts on the ROI of machine learning and getting buy-in from executives, how she sees machine learning evolving at the company over the next 10 years, and much more!The complete show notes for this episode can be found at twimlai.com/go/606

Today we’re excited to kick off our coverage of the 2022 NeurIPS conference with Johann Brehmer, a research scientist at Qualcomm AI Research in Amsterdam. We begin our conversation discussing some of the broader problems that causality will help us solve, before turning our focus to Johann’s paper Weakly supervised causal representation learning, which seeks to prove that high-level causal representations are identifiable in weakly supervised settings. We also discuss a few other papers that the team at Qualcomm presented, including neural topological ordering for computation graphs, as well as some of the demos they showcased, which we’ll link to on the show notes page. The complete show notes for this episode can be found at twimlai.com/go/605.

Today we’re excited to kick off our 2022 AWS re:Invent series with a conversation with Emad Mostaque, Founder and CEO of Stability.ai. Stability.ai is a very popular name in the generative AI space at the moment, having taken the internet by storm with the release of its stable diffusion model just a few months ago. In our conversation with Emad, we discuss the story behind Stability's inception, the model's speed and scale, and the connection between stable diffusion and programming. We explore some of the spaces that Emad anticipates being disrupted by this technology, his thoughts on the open-source vs API debate, how they’re dealing with issues of user safety and artist attribution, and of course, what infrastructure they’re using to stand the model up.The complete show notes for this episode can be found at https://twimlai.com/go/604.

Today we're joined by ChatGPT, the latest and coolest large language model developed by OpenAl. In our conversation with ChatGPT, we discuss the background and capabilities of large language models, the potential applications of these models, and some of the technical challenges and open questions in the field. We also explore the role of supervised learning in creating ChatGPT, and the use of PPO in training the model. Finally, we discuss the risks of misuse of large language models, and the best resources for learning more about these models and their applications. Join us for a fascinating conversation with ChatGPT, and learn more about the exciting world of large language models.The complete show notes for this episode can be found at https://twimlai.com/go/603

Are AI-generating algorithms the path to artificial general intelligence(AGI)? Today we’re joined by Jeff Clune, an associate professor of computer science at the University of British Columbia, and faculty member at the Vector Institute. In our conversation with Jeff, we discuss the broad ambitious goal of the AI field, artificial general intelligence, where we are on the path to achieving it, and his opinion on what we should be doing to get there, specifically, focusing on AI generating algorithms. With the goal of creating open-ended algorithms that can learn forever, Jeff shares his three pillars to an AI-GA, meta-learning architectures, meta-learning algorithms, and auto-generating learning environments. Finally, we discuss the inherent safety issues with these learning algorithms and Jeff’s thoughts on how to combat them, and what the not-so-distant future holds for this area of research. The complete show notes for this episode can be found at twimlai.com/go/602.

Today we’re joined by Cedric Cocaud, the chief engineer of the Wayfinder Group at Acubed, the innovation center for aircraft manufacturer Airbus. In our conversation with Cedric, we explore some of the technical challenges of innovation in the aircraft space, including autonomy. Cedric’s work on Project Vahana, Acubed’s foray into air taxis, attempted to leverage work in the self-driving car industry to develop fully autonomous planes. We discuss some of the algorithms being developed for this work, the data collection process, and Cedric’s thoughts on using synthetic data for these tasks. We also discuss the challenges of labeling the data, including programmatic and automated labeling, and much more.