In this section, Prof. Ellen Hildreth shares how materials offered through this OCW course can be used to design full-semester courses with more focused content.
The course 9.523 Aspects of a Computational Theory of Intelligence was designed in part to expose students to the broad scope of research conducted in the Center for Brains, Minds, and Machines. The content of the Brains, Minds & Machines summer course is even broader (and evolves every year!), and far more extensive than could be covered in a single semester course. The summer course materials available through this OCW course provide a rich resource for the design of other courses that have a narrower intellectual scope. Two examples are elaborated below:
(1) The Development of Intelligence
From infancy to adulthood, how do we learn new concepts, and learn how to make intelligent inferences about objects, events, and relations in the world? Through many lectures and tutorial activities, students can explore behavioral studies of the cognitive capacities of infants and how they develop, neuroimaging studies of the development of the underlying brain mechanisms, and computational frameworks that capture potential learning mechanisms that make this development possible. Relevant components include:
- Lectures in Units 2 (Modeling Human Cognition) and 3 (Development of Intelligence) by Josh Tenenbaum, Liz Spelke, Alia Martin, Laura Schulz, and Jessica Sommerville
- Debate between Tomer Ullman and Laura Schulz in Unit 3 (Development of Intelligence)
- Lectures by Shimon Ullman in Unit 4 (Visual Intelligence)
- Lectures by Rebecca Saxe in Unit 6 (Social Intelligence)
- Tutorial by Tomer Ullman on the implementation of probabilistic models in the Church programming language, supplemented with the probmods.org electronic text
(2) Visual Intelligence
How do humans derive an understanding of objects, scenes, actions and events in the world, from dynamic visual images? Through lectures and tutorials throughout this OCW course, students can explore many aspects of high-level vision from the perspectives of perceptual behavior, brain mechanisms, and computational models, and also examine connections to audition and language, and state-of-the-art vision systems for applications such as self-driving vehicles, mobile robots, and aids for the visually impaired. Relevant components include:
- Lectures in Unit 1 (Neural Circuits of Intelligence) by James DiCarlo, Gabriel Kreiman, and Winrich Freiwald
- Components of Josh Tenenbaum’s lectures in Units 2 (Modeling Human Cognition) and 3 (Development of Intelligence) related to the use of visual cues in learning and inference
- Lectures in Unit 4 (Visual Intelligence) by Shimon Ullman, Aude Oliva, Eero Simoncelli, and Amnon Shashua
- Lecture by Andrei Barbu in Unit 5 (Vision and Language)
- Lecture by Ken Nakayama in Unit 6 (Social Intelligence)
- Panel on similarities and differences between hearing and vision in Unit 7 (Audition and Speech)
- Lectures in Unit 8 (Robotics) by Russ Tedrake, John Leonard, Stefanie Tellex, Giorgio Metta and the iCub team
- Lecture by Tomaso Poggio in Unit 9 (Theory of Intelligence)
