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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Kathryn Hume — Financial Models, ML, and 17th-Century Philosophy 52:08 Share

Gradient Dissent: Conversations on AI

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