draft syllabus Gen Ed Spring 2024

Catching the Tsunami- Riding the GPT Wave

Description:

Generative artificial intelligence (GAI) systems such as Chat-GPT have caught the entire world off-guard. They are evolving at a pace that is overwhelming the ability of individuals, organizations, and societies to understand, adjust to, and regulate them. Current-generation GAI tools can write narrative and music, can generate original art, and can write computer programs, all from natural language requests. This class will help you understand the basics of how these generative AI systems work under the hood, and will teach you how to (guardedly) bring them to bear on problems that interest you. We will explore a range of AI applications across the domain of liberal arts and science, and will illustrate ways in which we can harness GAI to enhance learning. We will pay particular attention to the limitations and pitfalls of these tools, both technical and ethical. We will also explore the likely impact this disruptive technology will have on the economy and the challenges it poses to sustaining a participatory democracy.

One-sentence summary:

We will take stock of how we are all living through the early stages of the adoption of a highly disruptive technology that promises to impact virtually all aspects of our lives.

Learning goals.

Students will:

Be able to describe the basic technology that underpins large language model (LLM) generative artificial intelligence, and how they digest, regurgitate, and fabricate information.
Be adept with the operation of Chat-GPT4, including the construction of appropriate prompts and be able to guide an iterative progression towards a desired result.
Appreciate and be able to describe the ethical considerations that surround the use of generative AI tools, including academic and personal integrity, appropriate attribution, and demonstrate a keen awareness of biases that reflect the training set, and have an appreciation of privacy issues.
Be able to use generative AI tools for a diverse range of applications, including support for text generation, programming, comparative literature, sociology.
Be able to describe, on the basis of personal experience, the strengths and weaknesses of generative-AI-produced human-like text.
Be able to describe the evolution of AI tools in the historical context of comparable disruptive technologies, and make educated conjectures about the likely progression of AI capabilities.
Gain experience in assessing the validity and provenance of results produced by generative AI tools.
Critically assess the output of AI-tools in terms of biases, ethical considerations, and equity.
Be able to use generative-AI tools in team-based projects, and incorporate the output into a final work product.
Be able to describe the likely impact of generative AI tools across the scope of human endeavor.

Course Structure:

The course will comprise a combination of participatory lectures (Tuesdays and Thursdays), weekly small-group discussion sections, as well as assignments, and projects. We will provide registered students with licenses for the AI tools we use in the course, for the duration of the semester. The course will culminate in a capstone project where students will work in teams to advance their skills in real-world applications of these methods. We’ll also host movie-and-pizza evenings every few weeks to explore how AI is treated in cinema.

Prerequisites:

We expect students to be proficient with the operation and use of personal computers, including simple spreadsheets, word processing, and web browsing. No programming experience is necessary. Bring an open mind, a healthy skepticism, and a willingness to explore. You’ll need a laptop or smartphone to participate in class exercises.

Assessments:

This is an active-participation learning experience. Attendance in lectures and discussion sections is mandatory, with the exception of two unexcused missed class sessions (in any combination of lecture and/or sections). Missing class will result in a substantial reduction of your grade. The course paper will be submitted in two iterations. Students will undertake final projects in groups of 2-3, on topics of shared interest. A cumulative in-class final will be an integral part of the course.

Assessment Weighting

Class participation and attendance: 20%

Homework: 20%

Papers (one short and one long) 20%

Final Project 20%

Final Exam 20%

AI policy:

Unless otherwise noted, the use of AI tools (with appropriate attribution) is encouraged. For many of our assignments it’s essential.

Alignment with the goals of the General Education program

We propose that this course be designated as fulfilling the STS criteria. The topic of generative artificial intelligence (GAI) is an outstanding framework for meeting the goals of the “Science, Technology, and Society” component of the general education program. The rapid evolution of the capabilities of GAI has caught the entire world off-guard, with a pace that is overwhelming the ability of organizations and societies to understand it, adjust to it, and regulate it. The ability of these systems to generate fake information (either intentionally or unintentionally!) has led to calls for the high-tech sector to suspend development until society can catch up. It has also shaken the foundations of our educational system, calling into question the ways we select, teach, assess, and communicate with students.

We are unaware of any other gen ed course that is centered on generative AI, with the breadth that we envision for this class.

Resources needed:

Instructional participation across the divisions

Experienced TFs

Laptops for students who don’t have one?

Licenses for Chat-GPT4, at $20 per student per month.

Classroom with projection capability, as well as smaller classrooms for sections of 15 students.

(We could run this course in the SEC building in Allston).

Draft Syllabus

Spring 2024 week	Lecture topics	Discussion section	Assignments (HW) and Readings ( R) Readings to be done before Tues of each respective week. HW due on Friday afternoons.
Jan 22	Tues: Course intro and GAI capability demo. Types of AI and natural language processing in a broader context. Training sets and the introduction of bias. Articulation of the perils. Example - summarizing uploaded questions from the class, as a basis for large-lecture discussion. Active learning session that invokes Chat-GPT4 Thurs: active learning session on critical examination of GPT output, initial assessment of validity. Evolving authorship and professional ethics.	Establishing an account, introduction to course framework and collaborative tools. Initial in-section active learning exercise. Make predictions and then compare to what it does. Play around in sandbox with guided iterative prompts.	R: Age of AI chap 1 and chap 2 (54 pgs) HW: Performance comparison of GPT3.5 to GPT4.0
Jan 29	Tues: Training, performance evolution, and projection into the future. Kick-off of a nano-GPT module with a limited training set. Thurs: Identifying biases, inaccuracies, and exploring limitations		R: Age of AI chap 3 (~40 pages) NYT article on training methods. HW: Experiments and evaluations on explicit and implicit bias in NLP results. Midway interrogation of nano-GPT system
Feb 5	Tues: Introduction to simple quantitative data analysis- lab results. Thurs: dealing with ill-structured data.	Analysis and extraction of summary statistics- median, mean, sigma	R: TBD HW exercise on data interpretation Final analysis of our GAI-trained model.
Feb 12	Tues: Extraction of information from a stack of reference papers Thurs: Extraction of information from qualitative survey data	Initial look at truth-assessment methods.	R: TBD HW:Comparing human and AI-generated text material
Feb 19	Tues: Application to creative writing and assessing AI generated text Thurs: Analyzing historical texts and data	GAI-assisted writing exercise, in section.	R: TBD HW: assessing the validity of AI-generated summaries.
Feb 26 Midpoint assessment of both students and of the course	Tues: in-class written assessment, blue books. Thurs: GAI and Harvard College- challenges and opportunities for enhancing student learning.	Roundtable discussion of academic integrity and AI tools.	R: TBD HW; paper 1 on predictions of impact on a sector of human society and suggestions on how to contend with it.
Mar 4	Tues: Philosophical and ethical aspects of AI in general and GAI in particular. Thurs: The Turing test, intellectual property, and the rights of AI systems.	Debate in section about good vs. evil aspect of AI.	R: Age of Ai chap 6 HW: short paper on ethical aspects
Mar 11	Spring break	Spring break	none
Mar 18 Papers returned to students Monday Mar 18	Tues: GAI-assisted language learning and translation Thurs: GAI-assisted generation and debugging of computer code	Break each section into 2 groups, based on interest. Exercises on	R: TBD HW: project proposals, online grouping into teams
Mar 25	Tues: AI and the nature of work. How might things be different, and how can you best prepare? Thurs: guest lecture- GAI and the law	Discussion of project selections, ruberic	R: Age of Ai chap 4 and 7. HW: work on revision of paper 1
Apr 1	Tues: guest lecture- GAI and medicine Thurs: GAI and higher education		R: readings on professional impacts HW: revisions of paper 1 due
Apr 8	Tues: guest lecture- GAI and democracy Thurs: AI misuse and intentional abuse. Implications for regulation, national security, warfare.	Final project work and assistance	R: Danielle Allen writings on democracy. Also some pessimistic narratives TBD. HW: Project outline submitted
Apr 15	Tues: Guarding against GAI hallucination and falsehoods: tools and methods Thurs:	Final project work and assistance	R: Trustworthy AI references TBD HW:
Apr 22	Partial week, classes end. Final projects due Monday April 22 Tues: guest lecture from US gov’t on regulatory aspects.	NA	Final projects due, GAI poster session/fair, and live demos.
Final exam	In-person, blue books.

Films to consider:

https://en.wikipedia.org/wiki/List_of_artificial_intelligence_films

2001, a Space Odyssey (“Open the pod bay doors Hal”, “I can’t do that, Dave”) (1968)

Ex Machina (2014)

A.I., Artificial Intelligence (2001)

Coded Bias (2020 documentary)

Terminator (1984)

The Matrix (1999)

Eagle Eye (2008)

Megan (2022)

References and reading materials:

Required text:

$14 in paperback.

Other potential readings:
Do Robots Dream of Electric Sheep, Philip K. Dick

I, Robot, Issac Asimov

Diaspora, Greg Egan

Potential faculty

Christopher Stubbs

Logan McCarty

Karim Lakhani (HBS)

Gary King

Xiao-Li Meng

Matthew Schwartz

SEAS CS