Advancing AI through cognitive science

Instructor: Brenden Lake

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

Course numbers:
PSYCH-GA 3405.001 (Psychology)
DS-GA 3001.014 (Data Science)

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.

Please note that this syllabus is not final and there may be further adjustments.


This course is intended for graduate students in cognitive science or graduate students in data science / AI. Students are not expected to have a background in both cognitive science and AI. Instead, students may have experience in one field and the desire to learn about the other. Ideally, at the end of the course, students will have a deeper appreciation of contemporary issues in both fields and their potential for synergy. Programming is not a requirement for this course, although students may choose to incorporate programming in their final project.


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

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.

Overview of topics and schedule

Detailed schedule and readings

Please see below for the assigned readings for each class. Before each class, students will be asked to submit a reaction to the readings (three paragraphs).

2/1 Deep learning – Foundations

2/8 Intuitive physics (part 1: humans)

2/15 Intuitive physics (part 2: machines)

2/22 Intuitive psychology

3/1 Compositionality

3/8 Causality

3/15 NO CLASS. Spring Recess

3/29 Thinking fast

4/5 Critiques of “Building machines that learn and think like people” (with special guest Ernie Davis)

4/12 Response to critiques

4/19 Language and Culture

4/26 Emotion and Embodiment

5/3 Neuroscience