Part 11/14:
Overcoming Challenges in AI Adoption
Implementing AI solutions in real-world settings involves navigating regulatory, security, and operational challenges. Cooper discusses the importance of "model ops"—the practices of translating research from development into deployment and ensuring continuous performance monitoring.
He emphasizes rapid, agile cycles of testing and learning—"fail fast, learn fast"—to adapt AI models to changing data and environments. Recognizing that no model is perfect, human supervision remains crucial, especially for identifying outliers or addressing biases. Regular review processes help ensure models stay accurate, ethical, and relevant.