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Wed Oct 14 2026 at 05:00 pm to 07:00 pm UTC-04:00

OMARI Seminar Series, Seminar I

  • Roger Guidon Hall, University of Ottawa · Ottawa
  • From USD 0.00
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OMARI Seminar Series, Seminar I
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INSIDE CHATGPT: HOW LARGE LANGUAGE MODELS WORK, LEARN AND RESPOND
About this Event

This Fall, the Ottawa Medical AI Research Institute (OMARI) is launching a new OMARI Seminar Series, bringing together, and in person, researchers, clinicians, medical students, faculty and others interested in the rapidly evolving field of AI in medicine. Join us!

Starting on October 14, 2026, the seminars will take place every two weeks on Wednesday afternoons (5 to 7 pm). The series will explore a broad range of topics across medical AI: from traditional machine learning to large language models, from tabular and imaging data to text, and from model development and clinical evaluation to implementation and emerging methods and applications.

The series will begin as a four-session pilot, giving us an opportunity to bring the Ottawa medical AI community together and learn what topics, formats and discussions are most valuable to participants. If there is strong interest, we hope to build on these first sessions and develop the seminar into a continuing forum for exchanging ideas, sharing research and connecting people working at the intersection of AI and medicine. For opening seminar, Prof. Khaled El Emam will introduce the series and Dr. Fida Dankar will join us as our first speaker.

This talk, titled Inside ChatGPT: How Large Language Models Work, Learn, and Respond, provides an accessible introduction to how ChatGPT and other large language models work. It begins by placing generative AI within the broader history of artificial intelligence and introducing transformers. Using simple examples, it explains how text is converted into numerical representations, how attention uses surrounding words to understand meaning, and how models such as GPT generate text by repeatedly estimating probabilities and selecting the next token. It also explains how language models learn through large-scale pretraining, instruction fine-tuning, and human feedback. Finally, it introduces practical prompting methods and shows how clear instructions, reliable evidence, and careful checking can improve model responses.

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Roger Guidon Hall, University of Ottawa, 451 Smyth Road, Ottawa, Canada

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