Causal Bandits Podcast

Andrew Lawrence, Director of Research at causaLens, shares his insights on the fascinating world of causality and modularity in AI. He discusses his journey from academia to industry, the role of Bayesian non-parametrics in understanding causal relationships, and the importance of collaboration in causal discovery. Andrew highlights the challenges of applying generative AI in high-stakes scenarios and underscores how teamwork is vital in translating research into practical applications. He also reflects on his supportive childhood and the significance of mentorship in tech.

Send us a textSupport the showVideo version of this episode available on YouTubeRecorded on Aug 14, 2023 in Frankfurt, GermanyAre Large Language Models (LLMs) causal? Some researchers have shown that advanced models like GPT-4 can perform very well on certain causal benchmarks. At the same time, from the theoretical point of view it's highly unlikely that these models can learn causal structures. Is it possible that large language models are not causal, but talk causality? In our conversation we explore this question from the point of view of the formalism proposed by Matej and his colleagues in their "Causal Parrots" paper. We also discuss Matej's journey from the dream of becoming a hacker to a successful AI and then causality researcher. Ready to dive in?Links EventsCausality Discussion Group (https://discuss.causality.link/) Eastern European Machine Learning Summer School (https://www.eeml.eu/home) Videos Prof. Moritz Helmstaedter Everyday AI: Your daily guide to grown with Generative AICan't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.Listen on: Apple Podcasts   SpotifySupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4