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

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---Connect with Kathryn:📍 Twitter: https://twitter.com/humekathryn📍 Website: https://quamproxime.com/---Timestamps: 0:00 Intro0:54 Building a personal finance forecasting model10:54 Applying RL to trade execution18:55 Transparent financial models and fairness26:20 Semantic parsing and building a text-to-SQL interface29:20 From comparative literature and math to product37:33 What would Newton and Descartes think about ML?44:15 On sentient AI and transporters47:33 Why casual inference is under-appreciated49:25 The challenges of integrating models into the business51:45 Outro---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

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Gradient Dissent is a machine learning podcast from Weights & Biases with hosts Lukas Biewald, Lavanya Shukla and Caryn Marooney. It takes you behind-the-scenes to learn how industry leaders are putting deep learning models in production at NVIDIA, Meta, Google, Lyft, OpenAI, and more.