Building effective AI products isn't just about using the latest large language model; it's about asking the right questions and solving real problems. In this episode, we’re joined byJonathan Burley, Director of AI of Bloomberg Industry Group, to explore his journey from modeling climate systems to leading AI strategy. Jonathan discusses why the scientific mindset is critical in machine learning, the value of the minimum viable experiment and how to avoid the pitfalls of generative AI demos.
Key Takeaways:
00:00 Introduction.
03:14 The evergreen skills of handling data nuances help scientists transition into industry.
08:57 The scientific method provides a foundational mindset for reasoning under uncertainty.
16:39 Frame conversations around concrete business problems instead of leading with new technology.
19:46 Focus on minimum viable experiments to test core assumptions before committing to a minimum viable product.
24:46 Find unexciting areas of the economy where AI tools can deliver rapid and measurable ROI.
38:20 Approach generative AI demos with caution because they easily disguise incomplete products.
41:51 Solve the most boring, thankless and repetitive tasks to build tools experts actually want to use.
Resources Mentioned:
Jonathan Burley
https://www.google.com/search?q=https://www.linkedin.com/in/jonathanburley/
Bloomberg Industry Group | LinkedIn
https://www.linkedin.com/company/bloomberg-industry-group/
Bloomberg Industry Group | Website
https://www.bloombergindustry.com/
Actifai | Website
https://www.actif.ai/index.html
Continuous Delivery — David Farley
https://www.youtube.com/c/ContinuousDelivery
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