#122 Learning and Teaching in the Age of AI, with Hugo Bowne-Anderson
Learning Bayesian Statistics
Evaluating LLM Applications in Tech
This chapter highlights the significance of monitoring LLM applications in software development and data science education, stressing the need for alignment with business goals. It also covers educational resources available on maven.com and encourages collaboration between data scientists and software engineers.
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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:
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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