Causal Bandits Podcast

Send us a textSupport the showVideo version of this episode is available on YouTubeRecorded on Aug 24, 2023 in Berlin, GermanyDoes Causality Align with Bayesian Modeling? Structural causal models share a conceptual similarity with the models used in probabilistic programming. However, there are important theoretical differences between the two. Can we bridge them in practice? In this episode, we explore Thomas' journey into causality and discuss how his experience in Bayesian modeling accelerated his understanding of basic causal concepts. We delve into new causally-oriented developments in PyMC - an open-source Python probabilistic programming framework co-authored by Thomas - and discuss practical aspects of causal modeling drawing from Thomas' experience. "It's great to be wrong, and this is how we learn" - says Thomas, emphasizing the gradual and iterative nature of his and his team's successful projects. Further down the road, we take a look at the opportunities and challenges in uncertainty quantification, briefly discussing probabilistic programming, Bayesian deep learning and conformal prediction perspectives. Lastly, Thomas shares his personal journey from studying computer science, bioinformatics, and neuroscience, to becoming a major open-source contributor and an independent entrepreneur.Ready to dive in?About The GuestThomas Wiecki, Phd is a co-author of PyMC - one of the most recognizable Python probabilistic programming frameworks - and the CEO of PyMC Labs. Connect with Thomas: Thomas Wiecki on LinkedInThomas Wiecki on TwitteInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.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

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.

Matej Zečević, an AI and causality researcher who co-organized the NeurIPS causal workshop, dives into the fascinating relationship between large language models (LLMs) and causality. He challenges the assumption that LLMs can genuinely understand causal structures, posing thought-provoking questions about their capabilities. Matej shares insights from his diverse journey, the role of transparency in AI, and emphasizes the importance of collaboration in advancing the field. His passion for literature and its influence on his work adds a delightful touch to the discussion.