#122 Learning and Teaching in the Age of AI, with Hugo Bowne-Anderson

Dec 26, 2024

Hugo Bowne-Anderson is an independent data and AI consultant, known for his insights in education and podcasting. He discusses the challenges of building and deploying Large Language Model applications, stressing the importance of feedback in data science education. Hugo shares his journey in creating practical courses, highlighting the need for continuous learning in tech. He emphasizes collaboration between data scientists and software engineers and reflects on the evolving landscape of AI, calling for problem-solving over specific tools for aspiring data scientists.

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ANECDOTE

Early Bayesian Learning

Alexandre Andora learned Python programming, stats, and Bayesian stats through Hugo Bowne-Anderson’s DataCamp courses between 2017 and 2019.
They later met at PyData New York with Chris Fonnesbeck.

INSIGHT

Diverse Experiences

Hugo Bowne-Anderson’s diverse background in mathematics, biology, and data science has shaped his unique perspective.
His work now focuses on helping people build software that uses data effectively.

ANECDOTE

Bayesian Awakening

While working with biologists, Hugo Bowne-Anderson noticed their reliance on point estimates and t-tests, often without checking normality assumptions.
This experience, along with a colleague's explanation of the Bayesian workflow, sparked his interest in Bayesian statistics.

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Takeaways:

Effective data science education requires feedback and rapid iteration.
Building LLM applications presents unique challenges and opportunities.
The software development lifecycle for AI differs from traditional methods.
Collaboration between data scientists and software engineers is crucial.
Hugo's new course focuses on practical applications of LLMs.
Continuous learning is essential in the fast-evolving tech landscape.
Engaging learners through practical exercises enhances education.
POC purgatory refers to the challenges faced in deploying LLM-powered software.
Focusing on first principles can help overcome integration issues in AI.
Aspiring data scientists should prioritize problem-solving over specific tools.
Engagement with different parts of an organization is crucial for data scientists.
Quick paths to value generation can help gain buy-in for data projects.
Multimodal models are an exciting trend in AI development.
Probabilistic programming has potential for future growth in data science.
Continuous learning and curiosity are vital in the evolving field of data science.

Chapters:

09:13 Hugo's Journey in Data Science and Education

14:57 The Appeal of Bayesian Statistics

19:36 Learning and Teaching in Data Science

24:53 Key Ingredients for Effective Data Science Education

28:44 Podcasting Journey and Insights

36:10 Building LLM Applications: Course Overview

42:08 Navigating the Software Development Lifecycle

48:06 Overcoming Proof of Concept Purgatory

55:35 Guidance for Aspiring Data Scientists

01:03:25 Exciting Trends in Data Science and AI

01:10:51 Balancing Multiple Roles in Data Science

01:15:23 Envisioning Accessible Data Science for All

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric.

Links from the show:

Alex's last paper, “Unveiling True Talent: The Soccer Factor Model for Skill Evaluation”: https://arxiv.org/abs/2412.05911
Associated code and data: https://github.com/AlexAndorra/football-modeling/tree/main/40_submissions/MIT_Sloan_2025/01_Paper
Hugo on Blue Sky: https://bsky.app/profile/hugobowne.bsky.social
Hugo’s website: https://hugobowne.github.io/
Hugo on Linkedin: https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/
Hugo on GitHub: https://github.com/hugobowne
Vanishing Gradients podcast: https://vanishinggradients.fireside.fm/hosts/hugobowne
High Signal podcast: https://high-signal.delphina.ai/
25% discount on Hugo’s course on Building LLM Applications: https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=LEARNBAYES25
Lightning lessons if people want to get a sense of Hugo's teaching style and content:
https://maven.com/p/38a781/building-with-gen-ai-from-first-principles?utm_medium=ll_share_link&utm_source=instructor
https://maven.com/p/a6f9bf/mastering-llm-application-testing?utm_medium=ll_share_link&utm_source=instructor

Transcript:

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