Data Sciences Institute - Industry Speaker Series

Talk title: From LLMs to AI Agents: From Words to Action

Description: This talk explores how large language models evolve from static text generators into dynamic, autonomous agents. We’ll trace the shift from simple input–output behavior to multi-turn systems that reason, use tools, and act in the world. Through intuitive visuals and real-world examples, we’ll examine how memory, planning, and code-driven execution enable agents to automate complex tasks—and what this transition means for the future of AI beyond next-word prediction.

Jay Alammar is Director and Engineering Fellow at Cohere (The Security-first Enterprise AI Company). In this role, he conducts applied research on improving LLM-backed agents for code generation and multi-step tool use. Through his popular AI/ML blog, Jay has helped millions of researchers and engineers visually understand machine learning concepts from Transformers to reasoning LLMs. Jay is also a co-creator of popular machine learning and natural language processing courses on Deeplearning.ai and Udacity. Jay is the co-author of the bestselling “Hands-On Large Language Models” book.

Step into the future of data science with the DSI Industry Speaker Series.
This dynamic series brings leaders from across industries to the University of Toronto’s Data Sciences Institute to share real-world challenges, innovative applications, and bold ideas shaping the data-driven world. Each session opens a window into how data science transforms sectors—from healthcare and finance to technology and beyond—while creating space for conversation, learning, and collaboration between industry and academia. Explore upcoming talks below and join us to connect, discover, and be inspired.

For more information, please visit: https://datasciences.utoronto.ca/industry_speakers/.

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