Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world

Jevin West and I are professors of data science and biology, respectively, at the University of Washington. After talking to literally hundreds of educators, employers, researchers, and policymakers, we have spent the last eight months developing the course on large language models (LLMs) that we think every college freshman needs to take.

https://thebullshitmachines.com

This is not a computer science course; it’s a humanities course about how to learn and work and thrive in an AI world. Neither instructor nor students need a technical background. Our instructor guide provides a choice of activities for each lesson that will easily fill an hour-long class.

The entire course is available freely online. Our 18 online lessons each take 5-10 minutes; each illuminates one core principle. They are suitable for self-study, but have been tailored for teaching in a flipped classroom.

The course is a sequel of sorts to our course (and book) Calling Bullshit. We hope that like its predecessor, it will be widely adopted worldwide.

Large language models are both powerful tools, and mindless—even dangerous—bullshit machines. We want students to explore how to resolve this dialectic. Our viewpoint is cautious, but not deflationary. We marvel at what LLMs can do and how amazing they can seem at times—but we also recognize the huge potential for abuse, we chafe at the excessive hype around their capabilities, and we worry about how they will change society. We don't think lecturing at students about right and wrong works nearly as well as letting students explore these issues for themselves, and the design of our course reflects this.

Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
thebullshitmachines.com

Synopsis from the project's "instructor guide:

>This is not a computer science course, nor even an information science course—though naturally it could be used in such programs.

>Our aim is not to teach students the mechanics of how large language models work, nor even the best ways of using them in various technical capacities.

>We view this as a course in the humanities, because it is a course about what it means to be human in a world where LLMs are becoming ubiquitous, and it is a course about how to live and thrive in such a world.

Kudos; feels very timely!

I feel that one underappreciated nuance is why we cannot use human examinations to judge AI. I haven't seen this satisfactorily spelt out anywhere, so I recently wrote a Twitter thread [1], including an example with running -vs- biking. It might be worth making sure your students understand this. Happy to expand on any aspects if you seek.

[1] : https://x.com/ergodicthought/status/1887774722706063606