GENED 1188: Rise of the Machines? Understanding and Using Generative AI

C_Stacked_STS.pngHarvardCollege_SSLeft_tagline_compressed.png

1188 image.png

Welcome to Gen Ed 1188!

Are you curious about ChatGPT, Claude, Google Gemini, Dall-E, Midjourney, and other forms of Generative AI?

Do you want to learn how to use these tools effectively, and understand how they work?

Are you excited and/or concerned about the impact that AI will have in our future?

Then this course might be for you!

Open to all—we expect no background in computer science or AI. Just a sense of curiosity and a spirit of adventure.

Course Schedule:

The course is divided into 4 parts, each 3 weeks long:

  1. Introduction to Generative AI (GAI)
  2. Near-term impacts of GAI across the disciplines
  3. Technological aspects of GAI
  4. The speculative, long-term future of GAI

A detailed weekly schedule is available from the Course Modules page.

Course goals:

Students will gain proficiency in the following areas:

  1. Foundational Understanding: Grasp the fundamental distinctions and principles behind machine learning and generative artificial intelligence (GAI).
  2. Ethical Awareness: Recognize the ethical challenges and implications of GAI, from truth validation to academic integrity and societal impacts.
  3. Technological Mechanics: Understand the inner workings of large language models (LLMs) and other GAI systems, including neural networks and transformer architecture.
  4. Multimodal AI: Gain insights into the realm of multimodal GAI tools, including creative opportunities and deepfakes in image, audio, and video formats.
  5. Practical Applications: Acquire hands-on experience with GAI tools, exploring their capabilities and limitations. Learn to validate and verify GAI outputs.
  6. GAI in Disciplines: Analyze the influence and potential of GAI across arts and humanities, social sciences, and science/engineering/medicine domains.
  7. Historical Context: Examine the evolution of GAI against the backdrop of past technological revolutions and anticipate its trajectory.
  8. Regulation and Governance: Understand the historical and current frameworks for technological regulation, speculating on the needs and challenges specific to GAI.
  9. Philosophical Inquiry: Engage in debates surrounding the consciousness, existential significance, and broader philosophical ramifications of advanced GAI systems.
  10. Future Preparedness: Reflect on and strategize for a future shaped by GAI, emphasizing responsible usage, regulation, and coexistence.

These goals aim to provide students with a comprehensive understanding of GAI and its implications, allowing them to engage thoughtfully with the technology both now and in the future.

Course format:

  1. Lectures (attendance required):
    • Held on Mondays and Wednesdays from noon to 1:15pm.
    • Participatory in nature, where students are encouraged to engage and contribute during the lecture sessions.
    • With required attendance, GenEd courses are not eligible for course-wide simultaneous enrollment waivers.
  2. Mandatory Weekly Discussion Sections:
    • Small-group sessions designed for deeper discussion and debate on topics covered in the lectures.
    • These sections are mandatory, and missing more than two unexcused sessions could result in a substantial reduction of the student's grade.

Typical enrollees:

As a General Education course we welcome all students regardless of background. We expect the course will be particularly appealing to students who want to learn to use generative AI tools, students who want to understand more about how these tools work (at a general non-technical level), and students who want to engage in thoughtful dialogue about how these AI tools will shape the future, and the philosophical, ethical, and regulatory questions that society will face.

When is the course typically offered?

We expect that this course will be offered roughly every other spring (2025, 2027, 2029…).

What can students expect from you as an instructor?

  • Dynamic Learning Experience: We're passionate about Generative AI, offering a blend of lectures, hands-on sessions, and discussions to foster deep understanding.

  • Access to Tools & Resources: You'll have direct access to AI tools throughout the semester, supported by our guidance and real-world application projects.

  • Open Dialogue & Collaboration: We value diverse perspectives, promoting open discussions in class, panel debates, and active interactions on our Slack channel.

  • Hands-on & Experiential Approach: Our teaching style emphasizes active participation, where you'll experiment with AI tools, engage in group projects, and explore AI's broader implications through multimedia.

  • Guidance & Support: We're here for you, with open office hours and a commitment to guiding you through both the technical and philosophical aspects of Generative AI.

Assignments and grading:

  • Class attendance and in-class quizzes (20%): Regular attendance at lectures and discussion sections, along with quizzes conducted during class.
  • Homework (20%): Includes section follow-ups and weekly assignments; typically due 11:59pm Thursdays.
  • Paper (20%): A 10-page research or exploration paper on a topic related to Generative AI. The use of GAI is allowed but must be appropriately acknowledged and cited.
  • Final Project (20%): Focuses on real-world applications of GAI methods; may be done as individual or small team project (2-3 people).
  • Final Exam (20%): A regular seated 3-hour cumulative exam, which includes sections that require the use of GAI and other sections that prohibit the use of GAI.

In translating numerical scores to final letter grades, we will use "conventional" grade cutoffs as a starting point: 93 A, 90 A-, 87 B+, 83 B, 80 B-, etc. We pledge that our actual grade cutoffs will never be more strict than these, so there are no a priori limits to the grade distribution. We may at our discretion be more generous (e.g. with students who are a bit below a particular cutoff).

Required Texts:

There are two required texts for the course:

Co-Intelligence: Living and Working with AI
Author: Ethan Mollick
Publisher: Portfolio
ISBN-13 ‏ : ‎ 978-0593716717

Frankenstein (1818 text preferred; 1831 text also OK)
Author: Mary Shelley
Publisher ‏ : ‎ Penguin Classics; also available free online from Project Gutenberg
ISBN-13 ‏ : ‎ 978-0143131847

Additional short stories and short articles will be assigned, TBA.

Absence, late work, and collaboration policies:

Attendance at lectures and sections is required. Students may miss up to 3 lectures and up to 2 sections without penalty. There are no excused absences from lecture or section for any reason—all absences count against the 5 total permissible absences. Late work will be downgraded 20% for each day it is late.

Unless otherwise specified in an assignment, all academic work is expected to be completed individually. When explicitly allowed (e.g. on the final project), students who collaborate must include the names of all collaborators and a brief description of each student's contribution to the assignment.

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 tablet to participate in class exercises. We expect that you’ll have or have access to a computer with a simple text editor, Excel or the equivalent, a web browser, and WiFi.

AI Policy:

This course encourages students to explore the use of generative artificial intelligence (GAI) tools for components of assignments and assessments. Any such use must be appropriately acknowledged and cited. It is each student’s responsibility to assess the validity and applicability of any GAI output that is submitted; you bear the final responsibility. Assignments will include sections that we strongly recommend be completed without GAI tools, since the final exam will be undertaken without access to GAI.  Violations of this policy will be considered academic misconduct. We draw your attention to the fact that different classes at Harvard could implement different AI policies, and it is the student’s responsibility to conform to expectations for each course. 

Academic Accommodations:

Harvard University values inclusive excellence and providing equal educational opportunities for all students. Our goal is to remove barriers for disabled students related to inaccessible elements of instruction or design in this course. If reasonable accommodations are necessary to provide access, please contact the Disability Access Office (DAO). Accommodations do not alter fundamental requirements of the course and are not retroactive. Students should request accommodations as early as possible, since they may take time to implement. Students should notify DAO at any time during the semester if adjustments to their communicated accommodation plan are needed