Demystifying Artificial Intelligence


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Length of Course

3 hours

Delivery Method



$425 CAD


This masterclass will explain what Generative AI is, how it works, and why so many people think it will disrupt technology governance, education, the economy, and society. At the end of the class, participants will understand how Large Language Models (LLMs) are trained and deployed; their strengths/opportunities and weaknesses/risks; how AI is integrated into organizational workflows and deployed at scale; and how generative AI may (or may not) evolve in the foreseeable future.


Mark HumphriesMark Humphries is as Professor of History and head of the Digital Humanities Program Committee at Wilfrid Laurier University where he’s spearheading one of the first university-level certificate programs in Generative AI. As an emerging expert in this new field, he runs training sessions for educators, institutions, and organizations, teaches digital humanities course on ChatGPT, and is actively involved in shaping post secondary governance around this new technology. Mark has nearly two decades of experience applying emerging and cutting-edge technologies to historical problems; his award-winning publications include seven books and more than two dozen articles. He writes about generative AI in research and higher education on Substack and his work has been featured in University Affairs and on the Cognitive Revolution Podcast.


The masterclass format is a concise summary of 4-5 sessions delivered over a 3-hour period. Upon completion of the masterclass learning objectives, participants will receive a BEI certificate.

Proposed Agenda

  • Session 1: Introducing ChatGPT, the Large Language Models (LLMs) at the core of generative AI technology
  • Session 2: How LLMs are trained and deployed and the issues this raises with bias, accuracy, intellectual property, and control
  • Break
  • Session 3: A hands-on introduction to integrating AI into organizational workflows, from prompt engineering to deploying AI at scale
  • Session 4: Thinking about AI risk from potential existential threats to practical concerns around data privacy, misinformation, and reputational harm
  • Break
  • Session 5: Preparing for an uncertain future: how to separate hype from reality and make reasonable, informed predictions about how AI is likely to evolve

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