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...
Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what it takes to learn, teach, and build responsibly in the age of AI.
LINKS
The End of Programming as We Know It by Tim <--- Read this! (https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it/)
WTF? What’s the Future and Why It’s Up to Us (https://www.oreilly.com/tim/wtf-book.html)
The fundamental problem with Silicon Valley’s favorite growth strategy (https://qz.com/1540608/the-problem-with-silicon-valleys-obsession-with-blitzscaling-growth)
AI Engineering by Chip Huyen (https://www.oreilly.com/library/view/ai-engineering/9781098166298/)
Delphina's Newsletter (https://delphinaai.substack.com/)
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1:23:09
Episode 12: Your Machine Learning Solves The Wrong Problem
Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.
LINKS
Stefan's Stanford Website (https://www.gsb.stanford.edu/faculty-research/faculty/stefan-wager)
Machine Learning and Economics, Stefan and Susan Athey's lectures for the Stanford Graduate School of Business (https://www.youtube.com/@stanfordgsb)
Causal Inference: A Statistical Learning Approach (WIP!) (https://web.stanford.edu/~swager/causal_inf_book.pdf)
Mastering ‘Metrics: The Path from Cause to Effect by Angrist & Pischke (https://www.masteringmetrics.com/)
The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie (https://en.wikipedia.org/wiki/The_Book_of_Why)
Causal Inference: The Mixtape by Scott Cunningham (https://mixtape.scunning.com/)
A Technical Primer On Causality by Adam Kelleher (https://medium.com/@akelleh/a-technical-primer-on-causality-181db2575e41)
What Is Causal Inference? An Introduction for Data Scientists by Hugo Bowne-Anderson and Mike Loukides (https://www.oreilly.com/radar/what-is-causal-inference/)
The Episode on YouTube (https://www.youtube.com/watch?v=f9_Lt5p8avU&feature=youtu.be)
Delphina's Newsletter (https://delphinaai.substack.com/)
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54:40
Episode 11: What Comes After Code? The Role of Engineers in an AI-Driven Future
Peter Wang—Chief AI Officer at Anaconda and a driving force behind PyData—challenges conventional thinking about AI’s role in software development. As AI reshapes engineering, are we moving beyond writing code to orchestrating intelligence? Peter explores why companies are fixated on models instead of integration, how AI is breaking traditional software workflows, and what this shift means for open source. He also shares insights on the evolving role of engineers, the commoditization of AI models, and the deeper questions we should be asking about the future of software.
LINKS
Peter Wang on LinkedIn (https://www.linkedin.com/in/pzwang/)
Anaconda (https://www.anaconda.com/)
Mistral Saba (https://mistral.ai/news/mistral-saba)
Peter chatting with Hugo several years ago about the beginnings of PyData, NUMFOCUS, and Python for Data Science (https://vanishinggradients.fireside.fm/7)
Delphina's Newsletter (https://delphinaai.substack.com/)
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1:05:44
Episode 10: AI Won't Save You But Data Intelligence Will
Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps.
SHOW NOTES
Ari on LinkedIn (https://www.linkedin.com/in/arikaplan/)
The Data Intelligence Platform For Dummies by Ari and Stephanie Diamond (https://www.databricks.com/resources/ebook/maximize-your-organizations-potential-data-and-ai)
Databricks' AI/BI: Intelligent analytics for real-world data (https://www.databricks.com/product/ai-bi)
That time Ari spoke with Travis Kelce about how Travis and the Kansas City Chiefs use data and analytics! (https://www.linkedin.com/posts/arikaplan_wiley-databricks-genai-activity-7221214362575724545-RZwc/)
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59:42
Episode 9: Why 90% of Data Science Fails—And How to Fix It -- With Eric Colson
In this episode of High Signal, Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—breaks down why most companies fail to unlock the full potential of their data science teams. Drawing from years of experience leading data functions at top tech companies, Eric shares how organizations can shift from treating data scientists as a service function to empowering them as strategic drivers of business impact.
Key topics from the conversation include:
Data Science as a Strategic Function: Why many companies limit their data teams to answering business requests instead of leveraging their ideas for competitive advantage.
Beyond Skills—The Power of Cognitive Repertoires: How data scientists' unique ways of framing problems can lead to breakthrough innovations.
Trial and Error as a Competitive Advantage: Why most experiments fail—but scaling experimentation is the key to big wins.
Decoupling Algorithms from Applications: How separating data science from engineering enables rapid iteration and direct business impact.
Shifting from Cost Center to Revenue Generator: Practical steps for structuring data teams to drive measurable value and long-term success.
💡 Tune in to learn how leading companies structure their data teams for impact, why experimentation beats rigid planning, and how treating data science as a strategic function can unlock new business opportunities.
You can find more on our website: https://high-signal.delphina.ai/ (https://high-signal.delphina.ai/)
SHOW NOTES
Eric on LinkedIn (https://www.linkedin.com/in/ecolson/)
Beyond Skills: Unlocking the Full Potential of Data Scientists by Eric Colson (https://www.oreilly.com/radar/beyond-skills-unlocking-the-full-potential-of-data-scientists/)
MultiThreaded: Technology at StitchFix (https://multithreaded.stitchfix.com/)
A/B Testing with Fat Tails by Azevedo et al. (https://eduardomazevedo.github.io/papers/azevedo-et-al-ab.pdf)
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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