Welcome to High Signal, the podcast for data science, AI, and machine learning professionals.
High Signal brings you the best from the best in data science, mac...
Episode 5: The Hard Truth About Building AI Systems and What Most Leaders Miss About AI
In this episode of High Signal, Gabriel Weintraub (the Amman Professor of Operations, Information, and Technology at Stanford Graduate School of Business), brings his expertise in market design, data science, and operations, enriched by his experience with global platforms like Uber and Mercado Libre, to a conversation that spans practical strategies, cultural insights, and global perspectives on data and AI.
Highlights from the discussion include:
- Bridging the C-Level and Technical Divide: Gabriel emphasizes the importance of aligning leadership with on-the-ground teams to build effective, data-driven organizations.
- Starting with the Basics: From building pipelines to identifying high-ROI projects, Gabriel outlines foundational steps for companies adopting data science and AI.
- Cultural Transformation for Experimentation: He explains why fostering an experimentation culture, where negative results are valued for learning, is essential for success.
- Opportunities in Latin America: Gabriel shares insights on the unique challenges and immense potential of the Latin American tech ecosystem, including the critical role of startups and the need for local innovation systems.
- Generative AI’s Role in Driving Impact: Discussing generative AI’s transformative potential, Gabriel highlights its capacity to lower barriers for smaller teams while emphasizing the importance of problem-first approaches.
The conversation concludes with a forward-looking exploration of opportunities in government, education, and healthcare, and Gabriel’s optimism about building ecosystems where startups and local talent thrive.
🎧 Tune in to learn from Gabriel’s thoughtful perspectives on navigating the complexities of building data-driven cultures, the global AI landscape, and how to leverage data for impactful change.
You can find more on our website: https://high-signal.delphina.ai/
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Episode 4: How to Build an Experimentation Machine and Where Most Go Wrong
Ramesh Johari (Stanford, Uber, Airbnb, and more) explores the art and science of online experimentation, especially in the context of marketplaces and tech companies.
Ramesh shares insights on how organizations evolve from basic experimentation practices to becoming fast, adaptive, and self learning organizations. We dive into challenges like the risk aversion trap, the importance of learning from negative results, and how generative AI is reshaping the experimentation landscape.
We also talk about common failure modes and the types of things you're probably doing wrong, along with strategies to avoid these pitfalls. Plus, we discussed the role of incentives, the necessity of data driven decision making, and what it means to experiment in high stakes environments.
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Episode 3: Data Science Meets Management: Teamwork, Experimentation, and Decision-Making
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.
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Episode 2: Fooling Yourself Less: The Art of Statistical Thinking in AI
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.
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Episode 1: The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale
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.
Welcome to High Signal, the podcast for data science, AI, and machine learning professionals.
High Signal brings you the best from the best in data science, machine learning, and AI. Hosted by Hugo Bowne-Anderson and produced by Delphina, each episode features deep conversations with leading experts, such as Michael Jordan (UC Berkeley), Andrew Gelman (Columbia) and Chiara Farranato (HBS).
Join us for practical insights from the best to help you advance your career and make an impact in these rapidly evolving fields.
More on our website: https://high-signal.delphina.ai/
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