Iterate to Innovate: Aligning AI with Business Needs

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Integrating machine learning into application stacks requires a profound understanding of both the business context and the capabilities of technology. The process is iterative, resembling a spaceship docking as teams refine their approach through testing hypotheses against real-world problems. When initial solutions fail, valuable insights about constraints and assumptions emerge, leading to revised strategies. This continual feedback loop fosters broader investigations into systemic challenges such as model explainability and the application of AI to diverse data types. By addressing these foundational issues, organizations can better harness AI's potential, ensuring that models function reliably and meet specific business requirements.

A major challenge in applied AI is out-of-distribution detection, or OOD, which is the task of detecting instances that do not belong to the distribution the classifier has been trained on. OOD data is often referred to as “unseen” data, as the model has not encountered it during training.

Bayan Bruss is the VP of AI Foundations at Capital One and in this role he works with academic researchers to translate the latest research to address fundamental problems in financial services. Bayan joins the show with Sean Falconer to talk about OOD, the importance of bringing AI research to real world applications, and more.

Full Disclosure: This episode is sponsored by Capital One

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from information visualization to quantum computing. Currently, Sean is Head of Marketing and Developer Relations at Skyflow and host of the podcast Partially Redacted, a podcast about privacy and security engineering. You can connect with Sean on Twitter @seanfalconer.