This session explores the progression from foundational machine learning concepts to the future of embodied artificial intelligence. We begin with the core of machine learning, which learns to predict answers, and move to the role of Large Language Models (LLMs) in understanding questions and responding to diverse instructions.

Building upon this, we examine the AI Agent, which utilizes its understanding of context and history from an LLM to plan and execute actions, either independently or with external tools. The discussion then advances to the next frontier: building AgenticAI to comprehend, plan, simulate, and act within the real-world environment, a concept we define as "PhysicalAI." This emerging field of PhysicalAI opens possibilities for sophisticated simulation and planning to recommend actions or guide a human-in-the-loop.