Generative AI: Forecasting Disruption

Overview

Class Central Tips

For centuries, society has considered revolutionary technologies like the printing press, the telegraph, and even the first computers major disruptors of work and life. “Generative AI: Forecasting Disruption” lays the foundation for understanding our reaction to the rise of generative artificial intelligence by looking to the past. We examine a brief history of previous “revolutionary” communication technologies and how their impact can inform the way we think about advances in generative AI. You’ll then explore the nature of generative AI, how it works, and what it can do. As the course concludes, you’ll learn how to improve your results when using generative AI, and recognize what AI can – and cannot – accomplish. This is the first course in “Navigating Disruption: Generative AI in the Workplace,” a course series on ways to respond to new advances in AI in the workplace and our lives.

Syllabus

Getting Started

Welcome to the course! In this module, we will begin with an overview of the Navigating Disruption series and the goals of this first course, Generative AI: Forecasting Disruption. After reflecting on our experiences with generative AI to date, we’ll work to situate generative AI as a disruptive technology by drawing parallels to past technological advances. Let’s get started!

Affordances of AI

In this module, we will focus in more detail on the affordances of AI. We will begin by differentiating various type of AI and examining the ways in which large language models operate. We’ll move on from there to explore how AI can generate visuals and consider the implications of how real those visuals appear in comparison to traditional visual media. Lastly, we’ll spend some time working to better understand the process of prompt engineering in an effort to optimize our own interactions with AI.

Limitations of AI

In this module, we will focus on the limitations of AI. These limitations are rooted in the ways AI learns and generates outputs, so we’ll first examine these two processes in detail. We’ll close the course by evaluating AI’s ability to generate outputs to achieve a particular goal.