Artificial Intelligence for Social Impact
(CPSC 532L/530L, Term 2, Session 201, 2018-19)

Overview

Grading Scheme

Schedule

 

Overview

Course Description: This course will focus on the application of cutting-edge technologies from artificial intelligence to solve practical problems faced by a nonprofit partner in our own local community. In 2019 our partner will be CityStudio Vancouver, "an innovation hub at the heart of City Hall where city staff, students, faculty and community work together to design experimental projects that make Vancouver more sustainable, liveable and joyful."

 

This course takes a group of students with existing technical skill in AI and helps them to examine everything else that is required to make a real world impact. In particular, we will take an experiential learning approach to examine building a relationship with a client, problem identification, refining goals as new information becomes available, and communicating project progress. The course will also emphasize the use of modern tools and workflows for remote collaboration. Putting these tools into practice, the course will be remotely co-taught by Prof. Scott Kominers of the Harvard Business School. He will help to guide us in using the Case Method for some of our initial class discussions.

 

Prerequisites: Critical to the idea of the course is the premise that all enrollees have existing AI expertise that enables them to focus on choosing the right techniques for our partner's problem and applying these ideas in practice rather than learning the techniques themselves. Such previous experience with AI may include courses, research/internship/course projects, hobbies, work experience, etc. Additionally, an ability to speak, read and write fluently in English is essential for success in the class. All enrollees begin by signing up for the waitlist, contact the instructor personally, and are then transferred to the main course as appropriate.

 

Course number: The course is cross-listed as CPSC 532L (Topics in Artificial Intelligence, part of the department's "Computational Intelligence" stream) and CPSC 530L (Topics in Information Processing, part of the department's "Interdisciplinary Studies" stream). Students are free to enroll in whichever course better suits their needs.

 

Equity, Inclusion and Wellness: Please see the CS Department's resources on this topic.

 

Academic Honesty: Plagiarism is a serious offence (see the CS Department's statement) and will be dealt with harshly.  I consider plagiarism to be the unattributed use of an external source (e.g., another student, code or text from a web site, a book) in work for which a student takes credit, or the inappropriate use of an external source whether or not attribution is made. The seriousness of the offence depends on the extent to which the student relied upon the external source.  You must cite all external sources that you use, and write in your own words. Any text that you take verbatim from another source must be in quotation marks and followed by a citation.

Grading Scheme

This course is an experiment; we've never done this before, and the grading scheme is thus a work in progress. What follows may change considerably as the term goes on, and will certainly become more concrete.

 

Broadly speaking, grades will be divided into three categories:

1. Participation in class
2. Contributions to the project prototype (mathematical underpinnings, code, UI, etc)
3. Authorship of joint presentations and the final report

Participation in class (1) will be 25% of the final grade, determined through a mix of peer assessment and assessment by the instructors. (Active involvement in the peer assessment process is one form of participation expected of all students.) Each of contributions to the project (2) and authorship of presentations and the final report (3) will be allocated another 25%. Each student will be free to choose any way of splitting the remaining 25% between (2) and (3).

 

Schedule

The schedule will change considerably over the term; this initial draft is provided for your convenience.

 

Date	Topic	Milestone
Jan 3, 2019	Course overview; introductions
Jan 8, 2019	Cases
Jan 10, 2019	Cases
Jan 15, 2019	Cases
Jan 17, 2019	Cases
Jan 22, 2019	Prep for initial meeting with city
Jan 24, 2019	Initial meeting with city
Jan 29, 2019	Literature presentations	Presentations
Jan 31, 2019	Discuss feasible approaches
Feb 5, 2019	Subsequent presentations	Presentations
Feb 7, 2019	Discussion of what to pitch to the city, build a presentation
Feb 12, 2019	City - pitch initial idea	Joint presentation
Feb 14, 2019	Debrief, assign tasks for building initial prototype
Feb 19, 2019	READING WEEK - NO CLASS
Feb 21, 2019	READING WEEK - NO CLASS
Feb 26, 2019	Discuss progress over break; Case
Feb 28, 2019	Prep for presentations
Mar 5, 2019	Present to city - describe progress, hurdles, what we've learned	Joint presentation
Mar 7, 2019	Understand reaction, refocus, ...
Mar 12, 2019
Mar 14, 2019
Mar 19, 2019	Possible meeting with City
Mar 21, 2019
Mar 26, 2019
Mar 28, 2019	Workshopping of presentations	Feature-complete prototype
Apr 2, 2019	Dry run of presentations
Apr 4, 2019	Final presentations to city	Joint presentation
Apr 15, 2019		Joint writeup