Chiara Farronato (Harvard Business School) discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer marketplaces and the evolving role of data in decision-making. This episode offers key insights for business leaders working with technical teams and navigating platform-based innovation.
","summary":"Chiara Farronato (Harvard Business School) discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer marketplaces and the evolving role of data in decision-making. This episode offers key insights for business leaders working with technical teams and navigating platform-based innovation.","date_published":"2024-10-19T12:00:00.000-04:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/c6ce415b-4f49-41ae-b3db-700040f0674d.mp3","mime_type":"audio/mpeg","size_in_bytes":50126181,"duration_in_seconds":3132}]},{"id":"44cfa4bc-192f-4325-90d2-13b7b9e1d781","title":"Episode 2: Fooling Yourself Less: The Art of Statistical Thinking in AI","url":"https://highsignal.fireside.fm/2","content_text":"Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causal inference’s critical role in decision-making. This episode offers insights into balancing statistical theory with applied data analysis, making it a must-listen for both data practitioners and those interested in how statistics shapes our world.","content_html":"Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causal inference’s critical role in decision-making. This episode offers insights into balancing statistical theory with applied data analysis, making it a must-listen for both data practitioners and those interested in how statistics shapes our world.
","summary":"Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causal inference’s critical role in decision-making. This episode offers insights into balancing statistical theory with applied data analysis, making it a must-listen for both data practitioners and those interested in how statistics shapes our world.","date_published":"2024-10-19T11:00:00.000-04:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/44cfa4bc-192f-4325-90d2-13b7b9e1d781.mp3","mime_type":"audio/mpeg","size_in_bytes":58426015,"duration_in_seconds":3651}]},{"id":"c4068f54-90b5-4e7a-b036-07a08ac9e813","title":"Episode 1: The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale","url":"https://highsignal.fireside.fm/1","content_text":"Michael Jordan (UC Berkeley) on the future of machine learning as it extends to a planetary scale in \"The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale.\" In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI can scale to address planetary-level challenges.","content_html":"Michael Jordan (UC Berkeley) on the future of machine learning as it extends to a planetary scale in "The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale." In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI can scale to address planetary-level challenges.
","summary":"Michael Jordan (UC Berkeley) on the future of machine learning as it extends to a planetary scale in \"The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale.\" In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI can scale to address planetary-level challenges.","date_published":"2024-10-19T09:00:00.000-04:00","attachments":[{"url":"https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/c4068f54-90b5-4e7a-b036-07a08ac9e813.mp3","mime_type":"audio/mpeg","size_in_bytes":72201950,"duration_in_seconds":4512}]}]}