Kathryn Hume — Financial Models, ML, and 17th-Century Philosophy

Kathryn Hume — Financial Models, ML, and 17th-Century Philosophy

Author: Lukas Biewald December 16, 2021 Duration: 52:08

Kathryn Hume is Vice President Digital Investments Technology at the Royal Bank of Canada (RBC). At the time of recording, she was Interim Head of Borealis AI, RBC's research institute for machine learning.

Kathryn and Lukas talk about ML applications in finance, from building a personal finance forecasting model to applying reinforcement learning to trade execution, and take a philosophical detour into the 17th century as they speculate on what Newton and Descartes would have thought about machine learning.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-kathryn-hume

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Connect with Kathryn:

📍 Twitter: https://twitter.com/humekathryn

📍 Website: https://quamproxime.com/

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Timestamps:

0:00 Intro

0:54 Building a personal finance forecasting model

10:54 Applying RL to trade execution

18:55 Transparent financial models and fairness

26:20 Semantic parsing and building a text-to-SQL interface

29:20 From comparative literature and math to product

37:33 What would Newton and Descartes think about ML?

44:15 On sentient AI and transporters

47:33 Why casual inference is under-appreciated

49:25 The challenges of integrating models into the business

51:45 Outro

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Lukas Biewald hosts Gradient Dissent: Conversations on AI, a series that moves beyond theoretical discussions to examine how artificial intelligence is actually built and deployed. Each episode features a direct, unscripted talk with a leading practitioner-you’ll hear from engineers and researchers at places like NVIDIA, Meta, Google, Lyft, and OpenAI. The focus is on the tangible challenges and breakthroughs they encounter, from initial research to the complex reality of putting models into production. This isn't about abstract futures; it's a grounded look at the decisions shaping the field right now. Biewald, bringing his perspective from Weights & Biases, steers conversations toward the practical trade-offs and collaborative efforts that define modern AI work. For anyone in technology or business who wants to understand the mechanics behind the headlines, this podcast offers a rare, candid window into the process. You’ll come away with a clearer sense of how ideas become functional systems and what it really takes to operate at the cutting edge.
Author: Language: English Episodes: 100

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