Advancing AI through cognitive science

NYU PSYCH-GA 3405.001 / DS-GA 3001.014

This project is maintained by brendenlake

Advancing AI through cognitive science - Spring 2019

Instructor: Brenden Lake
Assistant Professor of Psychology and Data Science
New York University

Meeting time and location:
Thursday 4-5:50 PM
Meyer Room 465 (4 Washington Place)

Course numbers:
PSYCH-GA 3405.001 (Psychology)
DS-GA 3001.013 (Data Science)

Office hours:
Wednesdays 10-11:00 am, or by appointment; 60 5th Ave., Room 610

Summary: Why are people smarter than machines? This course explores how the study of human intelligence can inform and improve artificial intelligence. We will look to cognitive science, with special focus on cognitive development, to help elucidate a set of “key ingredients” that are important components of human learning and thought, but are either underutilized or absent in contemporary artificial intelligence. Through readings and discussion, we will cover ingredients such as “intuitive physics,” “intuitive psychology,” “compositionality,” “causality,” and “learning-to-learn,” although students will be encouraged to contribute other ingredients. Each ingredient will be discussed and compared from the perspectives of both cognitive science and AI, with readings drawn from both fields with roughly a 50/50 proportion.

This is a small discussion-based seminar, so please come ready to participate in the discussion. Please note that this syllabus is not final and there may be further adjustments.

Pre-requisites

Grading

The final grade is based on the final paper or project (50%), written reactions to the reading (25%), and participating in discussions (25%).

The final paper or project is done individually. For the final assignment, students may either write a final paper that proposes an additional ingredient of human intelligence that is underutilized in AI, or complete a project that implements one of the ingredients discussed in an algorithm. The final assignment proposal is due on Thursday, April 4 (one half page written). The final assignment is due on Tuesday, May 14.

What makes a good reaction post? There are many ways to write a good reaction post, and I would rather leave it up to you than impose a particular formula. Try to articulate an opinion about the readings, rather than write an exhaustive summary of the articles. I prefer to read your opinions than a summary! It’s great if you can put the articles in conversation with each other and with the theme of the class. It’s also okay to focus on one or two of the readings that interest you most, rather than talking about each of the readings equally.

The responses should be short – three short paragraphs is about right. I don’t want the reaction to take much of your time beyond reading the articles themselves (15 mins is reasonable for writing your response).

Course discussion

We will be using Piazza for reactions to readings and class discussion.

The signup link for our Piazza page is available here (https://piazza.com/nyu/spring2019/psychga3405001).

Once signed up, our class Piazza page is available here (https://piazza.com/nyu/spring2019/psychga3405001/home).

Final assignment

Course policies

Auditing: Please email instructor to see if there are available seats. Priority goes to registered students and then by date of audit request.

Overview of topics and schedule

Detailed schedule and readings

Please see below for the assigned readings for each class (to be read before class). Before each class, students will be asked to submit a reaction to the readings (three paragraphs). Reaction posts are submitted via Piazza. Papers are available for download on NYU Classes in the “Resources” folder. Reactions are due by midnight the day before class so that I have time to read the reactions.

1/31 Introduction and overview

2/7 Deep learning – Lecture

2/14 Deep learning - Discussion

2/21 Intuitive physics (part 1: humans)

2/28 Intuitive physics (part 2: machines)

3/7 Intuitive psychology (part 1: humans)

3/14 Intuitive psychology (part 2: machines)

3/21 NO CLASS. Spring Recess

3/28 Compositionality

4/4 Causality

4/11 Learning-to-learn

4/18 Critiques of “Building machines that learn and think like people”

4/25 Language and Culture

5/2 Emotion and Embodiment

5/9 Final assignment presentations