CSC 290 Course Description

This course will look at the past, present, and future of AI and its impact, and will involve essays based on readings, as well as hands-on projects exploring and assessing the state of the art in selected subfields of AI. The emphasis will be on “strong AI”, i.e., the attempt to achieve human-level AI (and perhaps go beyond it). The course will be suitable for writing credit.
Prerequisites: (173 AND 240) or 242 or 280 or 282. (See comments below.)

Lectures:   Tuesday and Thursday, 11:05a.m. to 12:20 p.m., in CSB 601.

Prerequisites

Students should meet the prerequisite requirements (at least one previous course in AI or theory of computation) and should not undertake this course lightly. It is expected to be a significant amount of work. The course requires getting a broad overview of current leading-edge AI projects concerned with higher-level cognition, and this presupposes a prior course in AI introducing basic topics such as knowledge representation, problem solving, language understanding, etc. However, students who have taken a theory course such as CSC 280 or CSC 282 probably have sufficient maturity in the more formal aspects of computer science to be able to make up for lack of a prior AI course. The projects will typically require the use of multiple source files and libraries, so experience with programming utilities such as make and rcs/cvs/bitkeeper is an asset.

Readings and Essays

Nearly half of the course work will consist of essays (5 or 6) based on readings. These readings range from “popular science” books and articles to more technical articles. (In addition, you will be looking at the scientific literature in a subfield of AI for the purpose of the project -- see below.)

A strongly recommended item for preliminary reading is Prof. Michael Scott's wonderful 2003 commencement address for the CSC graduating class, Onward and Upward Forever?. This nicely anticipates and sets the stage for some of the themes that are the focus of this course.

Required books

At least the following 5 books in the popular science genre will be required reading:

Also one of the following books on the history of AI may be added:

Links to further books and to articles

The following are links to further books and articles from which some readings will be selected (for you or by you).

Essay guidelines

For specific assigned topics, see Assignments.

Essays will be approximately 1500-2500 words in length. An essay at the short end of this range is alright if you have the skill to make your claims and arguments succinctly, and without becoming cursory or superficial. There is no penalty for going over the upper limit, unless it is through repetitiveness or irrelevancies.

One kind of place to look for examples of the kind of writing to strive for is in book reviews, in refereed journals, of some of the readings for this course.

Essays will be graded on 3 dimensions, roughly as follows:

Project: Assessing the state of the art in a subfield

Close to half of the work in the course will revolve around a set of semester-long projects aimed at assessing and demonstrating the state of the art in AI in various subfields that seem particularly relevant to achieving human-level AI.

The subfields are those directly concerned with cognition, in the sense of higher-level mental functions such as:

and perhaps some others. The following is a link to a more detailed list of topics.

The class will be organized into teams of 4 or 5 students, and each team will attempt to assess and demonstrate the state of the art in a particular subfield in the following way:

  1. researching, understanding, and presenting the state of the art in the chosen subfield;
  2. seeking out (by web search, etc.), acquiring, modifying for local use, and demonstrating a state-of-the-art system in the chosen subfield; and
  3. assessing the demonstrated system as a step towards the long-range goals of the subfield, the major difficulties yet to be overcome, and possible approaches to the difficulties, and presenting these assessments and ideas.

Team members will present status reports to the rest of the class on a regular basis, via semi-formal discussion sessions in which team understanding, approaches, and progress will be critiqued and potentially modified. Teams will also prepare biweekly written reports, and will hand in a final project report prior to the final project presentation.

Teams and topics will be selected by the instructor and TA a couple of weeks into the course on the basis of survey information, class discussion, and student preferences. Our hope is that most students who are going to drop the course will have done so by that time, so we can have stable teams. Depending on class size, we may have more than one team on a project, in semi-competition.

Attendance

Attendance is mandatory. Gaining an oversight of the various facets of AI and its history, prospects and contentious issues are an essential part of the experience. There may be 5-minute quizzes on material covered in the previous class as a means of documenting attendance and attention. This could include the content of unscripted discussions.

Grading

Grading will be based about 40% on your essays, 40% on your project accomplishments and presentations (including at least one demonstration), and 20% on class attendance/participation. I do not anticipate giving any exams.

Essays and reports must be handed in in time. Exceptions will be made only under the most dire of circumstances.

Academic Honesty

Student conduct in CSC 290 is governed by the College Academic Honesty Policy, the Undergraduate Laboratory Policies of the Computer Science Department, and the Acceptable Use Policy of Academic Technology Services.

As in all intellectual endeavors, proper attribution of work is crucial in the essays and project reports you submit. Be sure to provide proper references to the software and documentation you acquire for the project (naturally, describing any additions or modifications you made), and in your essays and reports provide citations in standard form for any text and ideas taken from other sources. Use of unattributed material is plagiarism.

Students in CSC 290/04


Last Change: 04 March 2004 / schubert@cs.rochester.edu