Episode 9: Diederik Roijers: "What If We Optimized for Enough, Not More?"
Join Iman Mossavat as he speaks with Diederik Roijers, senior researcher at the AI Lab at Vrije Universiteit Brussel. Diederik specializes in Multi-Objective Reinforcement Learning (MORL), a framework that shifts focus from optimizing a single outcome to balancing multiple, sometimes competing, objectives. Drawing on the ancient idea of balance—akin to Yin and Yang—Diederik challenges the status quo of “maximizing more.” Instead, he advocates for pursuing outcomes that are "good enough", prioritizing practicality, ethics, and societal benefit over perfection. 💡 Key Insight: How can AI navigate trade-offs like maximizing rewards while minimizing risks? Diederik’s vision emphasizes deliberate choices in AI design, promoting transparency, maintainability, and alignment with societal values. Together, they explore how AI can serve everyone fairly—not just a select few—while balancing innovation and responsibility. Tune in for a refreshing perspective on building AI systems that prioritize balance, fairness, and shared benefit.
Diederik Roijers