[RCAC Workshop] From Machine Learning to Agentic LLMs: The Intelligence Behind Modern AI Systems

In recent years, artificial intelligence has evolved from traditional machine learning models that make single predictions to powerful language models capable of reasoning, planning, and interacting with tools. But how did we get here, and what makes these systems capable of acting as “agents”? In this seminar, we will trace the evolution of AI from classical machine learning to deep learning and transformer-based large language models (LLMs). We will explore how early models relied on hand-crafted features and fixed tasks, how deep learning enabled automatic representation learning, and how transformers unlocked large-scale language understanding. Building on these foundations, we will explain how modern LLMs can perform multi-step reasoning, use external tools, and serve as the core intelligence behind agentic systems. By the end, attendees will understand the key ideas that power modern AI systems and how these ideas make agentic science possible. This session is designed for beginners with an interest in AI and machine learning!

Registration Link: https://purdue.ca1.qualtrics.com/jfe/form/SV_9HqQtOK6kw9FGf4