The conversation delves into Victor Dibia's career journey, global experiences, transition to a PhD, and strategic career planning. It also explores his focus on AI tooling and frameworks, as well as the evolution of Autogen and the Microsoft Agent Framework. The conversation delves into the actor-first paradigm in multi-agent systems and the concept of ensembling in machine learning. It explores the benefits of the actor-first approach and the considerations for using multiple agents in complex tasks. Additionally, it discusses the power of ensembling in complementing the biases of individual models and the potential for mixture of experts in achieving better performance.

Topics

• Career progression through diverse experiences
• Actor-first paradigm in multi-agent systems
• Autogen and Semantic Kernel

Chapters

• 00:00 Career Journey and Global Experiences
• 11:03 Focus on AI Tooling and Frameworks
• 19:11 Evolution of Autogen and Microsoft Agent Framework
• 28:59 Actor-First Paradigm in Multi-Agent Systems
• 36:39 Ensembling in Machine Learning