Chris Wiggins, Chief Data Scientist at The New York Times and a Columbia University professor, discusses the transition from predictive to prescriptive analytics. He emphasizes the importance of actionable decision systems, highlighting how hospitals could benefit from prescription-based treatments. Wiggins introduces the AI Hierarchy of Needs, outlines strategies for scaling data teams, and underlines the necessity of empathy in data science for effective collaboration. His insights help bridge the gap between advanced technology and practical organizational applications.

Hilary Mason, a renowned data scientist and co-founder of Hidden Door, dives into the transformative landscape of data science amid the rise of AI. She emphasizes the crucial role of human judgment in guiding AI outputs and warns against over-reliance on prompts, advocating for rich contextual approaches. Highlighting her company's mission, Hilary discusses turning AI's challenges into creative storytelling opportunities. She also offers insights on navigating career paths in the evolving job market, stressing the need for empathy and critical skills in a world shaped by automation.

Gabriel Weintraub, the Amman Professor of Operations at Stanford, shares his wealth of experience from Uber and Mercado Libre. He discusses bridging the gap between leadership and tech teams to foster data-driven organizations. Gabriel emphasizes the importance of starting with foundational steps in AI adoption and creating a culture that celebrates experimentation. He also highlights the unique AI opportunities in Latin America and the transformative power of generative AI for smaller teams, advocating a problem-first approach to drive impact.

Ramesh Johari, a Professor at Stanford University, dives into the evolution of online experimentation, especially for tech companies and marketplaces. He discusses how organizations can shift to self-learning models and the common pitfalls they encounter, such as risk aversion. The conversation touches on the transformative impact of generative AI on experimentation processes. Ramesh also shares strategies for cultivating a culture of learning from failure and integrating data scientists to enhance business value, all while moving beyond traditional A/B testing methods.

Chiara Farronato, an Associate Professor at Harvard Business School specializing in digital platforms, shares insights on the transformation of sectors through companies like Airbnb and Uber. She highlights the critical need for effective communication between managers and data scientists to foster better collaboration. Chiara discusses the importance of bridging gaps in understanding, particularly in product management, and explores the challenges traditional industries face in adopting data-driven cultures. Her experiences offer valuable lessons for business leaders navigating platform-based innovation.

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.

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.