The rise of artificial intelligence
Denison Alumni
September 10, 2024
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Facilitator
Facilitator
Lew Ludwig
Lew Ludwig
Dr. Ludwig is a Professor of Mathematics and the director of the Center for Learning and Teaching. at Denison University. He is a nationally recognized speaker on generative AI, leading numerous webinars and workshops for the MAA, POD, and the GLCA. His work includes serving as a project leader on the MAA Instructional Practice Guide and presenting innovative teaching strategies at various national conferences.
How to get started with Generative AI
Subscribe to the 'On Tech A.I. Newsletter' from The New York Times to embark on your journey to becoming an AI expert. This weeklong series is just the beginning, with regular updates every few weeks to keep you informed throughout the year.
NYT April, 2023
A visual walk-through of how this type of artificial intelligence work by Seán Clarke, Dan Milmo, and Garry Blight, The Guardian, November 1, 2023
Watch this video for an easy-to-understand guide on common AI terms, an overview of how the GPT model functions, a comparison between ChatGPT 3.5 and 4, and an insight into some of its limitations, presented by Lew Ludwig, August 2023.
The importance of the liberal arts
Enjoy Joshua Rothman’s excellent article, "What Does It Really Mean to Learn?" in The New Yorker. Rothman explains into "educability"—the deep, ongoing human capacity to learn, contrasting sharply with the limitations of artificial intelligence. This piece explores the beautiful, often messy process of human learning, where knowledge isn't just acquired but woven into the rich tapestry of our experiences and insights. Read the article or listen to the nine-minute audio for a compelling perspective on the unique nature of human intellect. From a SLAC? Be sure to catch the "mic drop" sentence at the end.
Very recent pieces on AI
AI and the Case for Project-Based Teaching
In the age of ChatGPT, faculty members have no choice but to adjust course design from a focus on “what” to “why.” by Chad Raymond
Why We Should Normalize Open Disclosure of AI Use
It’s time we reclaim faculty-student trust through clear advocacy — not opaque surveillance. by Marc Watkins
Ryan Watkins discusses the impact of AI on professional agency, offering strategies to balance AI integration with maintaining human control and expertise.
In Teaching With Gen AI, Consider Sustainability
Faculty lack information about generative AI’s environmental impacts, and universities should prioritize sustainable computing, Susanne Hall writes.
Inspired by Tolkien's 'There and Back Again,' this column traces my journey with generative AI in the realm of higher education—a journey marked by initial skepticism and a quest for acceptance among my peers. Through personal insights and practical applications, I explore how this emerging technology can serve as a powerful ally in enhancing educational practices, offering a narrative of discovery and transformation that parallels the adventures of Bilbo Baggins." Lew Ludwig, 2024
Many things Mollick
An essential guide for educators on using AI as a transformative co-worker, co-teacher, and coach.
April, 2024
This blog/newsletter provides a research-based view on the implications of AI, Prof. Ethan Mollick of the Wharton School of Business
Working document
Some Technical Things to Consider with Generative AI
MIT Technology Review
MIT Technology Review
The article explores the remarkable and puzzling capabilities of large language models, which can perform impressive feats of generalization and reasoning that defy traditional statistical understanding, leaving AI researchers scratching their heads about the underlying mechanisms that drive these models' success.
March, 2024
Harvard Business School
Harvard Business School
The article explores how AI like GPT-4 can boost or trip up the work, depending on the task at hand. It's a deep dive into when AI is a help and when it's not!
September, 2023
The mathematics behind LLMs (technical)
A MUST FOR ALL MATH STUDENTS!
This is a continuation of the Neural Networks series, which introduces the mathematics and deep learning involved in transformers and their prerequisites.
3Blue1Brown April, 2024
A five part series from 2017 explaining neural networks, how they are trained and the concept of backpropagation. Requires a basic understadning of linear algerbra and mulit-variable calculus.
3Blue1Brown October, 2017
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