Ask a question of ChatGPT and other, similar chatbots and there’s a good chance you’ll be impressed at how adeptly it comes up with a good answer — unless it spits out unrealistic nonsense instead. Part of what’s mystifying about these kinds of machine learning systems is that they are fundamentally black boxes. No one knows precisely how they arrive at the answers that they do. Given that mystery, is it possible that these systems in some way truly understand the world and the questions they answer? In this episode, the computer scientist Yejin Choi of the University of Washington and host Steven Strogatz discuss the capabilities and limitations of chatbots and the large language models, or LLMs, on which they are built. 

Listen on Apple Podcasts, Spotify,  TuneIn or your favorite podcasting app, or you can stream it from Quanta.