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Res.9-003 Brains, Minds and Machines

Res.9-003 Brains, Minds and Machines (Summer 2015, MIT OCW). Instructors: Prof. Tomaso Poggio (Course Director, MIT) and Prof. Gabriel Kreiman (Course Director, Harvard). This course explores the problem of intelligence - its nature, how it is produced by the brain and how it could be replicated in machines - using an approach that integrates cognitive science, which studies the mind; neuroscience, which studies the brain; and computer science and artificial intelligence, which study the computations needed to develop intelligent machines. (from ocw.mit.edu)

Lecture 16 - Jessica Sommerville - Infants' Sensitivity to Cost and Benefit

Instructor: Jessica Sommerville. Infants' registration of effort-related costs in the actions of others, and its correlation with activation in sensorimotor cortex. Infants' use of costs and benefits of actions to guide their own prosocial behavior.


Go to the Course Home or watch other lectures:

Unit 1. Neural Circuits of Intelligence
Lecture 01 - Nancy Kanwisher - Human Cognitive Neuroscience
Lecture 02 - Gabriel Kreiman - Computational Roles of Neural Feedback
Lecture 03 - James DiCarlo - Neural Mechanisms of Recognition, Part 1
Lecture 04 - James DiCarlo - Neural Mechanisms of Recognition, Part 2
Lecture 05 - Winrich Freiwald - Primates, Faces, and Intelligence
Lecture 06 - Matt Wilson - Hippocampus, Memory, and Sleep, Part 1
Lecture 07 - Matt Wilson - Hippocampus, Memory, and Sleep, Part 2
Lecture 08 - Larry Abbott - Mind in the Fly Brain
Unit 2. Modeling Human Cognition
Lecture 09 - Josh Tenenbaum - Computational Cognitive Science, Part 1
Lecture 10 - Josh Tenenbaum - Computational Cognitive Science, Part 2
Lecture 11 - Josh Tenenbaum - Computational Cognitive Science, Part 3
Unit 3. Development of Intelligence
Lecture 12 - Liz Spelke - Cognition in Infancy, Part 1
Lecture 13 - Liz Spelke - Cognition in Infancy, Part 2
Lecture 14 - Alia Martin - Developing an Understanding of Communication
Lecture 15 - Laura Schulz - Children's Sensitivity to Cost and Value of Information
Lecture 16 - Jessica Sommerville - Infants' Sensitivity to Cost and Benefit
Lecture 17 - Josh Tenenbaum - The Child as Scientist
Lecture 18 - Debate: Tomer Ullman and Laura Schulz
Unit 4. Visual Intelligence
Lecture 19 - Shimon Ullman - Development of Visual Concepts
Lecture 20 - Shimon Ullman - Atoms of Recognition
Lecture 21 - Aude Oliva - Predicting Visual Memory
Lecture 22 - Eero Simoncelli - Probing Sensory Representations
Lecture 23 - Amnon Shashua - Applications of Vision
Unit 5. Vision and Language
Lecture 24 - Boris Katz - Vision and Language
Lecture 25 - Andrei Barbu - From Language to Vision and Back Again
Lecture 26 - Patrick Winston - Story Understanding
Lecture 27 - Tom Mitchell - Neural Representations of Language
Unit 6. Social Intelligence
Lecture 28 - Nancy Kanwisher - Introduction to Social Intelligence
Lecture 29 - Ken Nakayama - The Social Mind
Lecture 30 - Rebecca Saxe - MVPA: Window on the Mind via fMRI, Part 1
Lecture 31 - Rebecca Saxe - MVPA: Window on the Mind via fMRI, Part 2
Unit 7. Audition and Speech
Lecture 32 - Josh McDermott - Introduction to Audition, Part 1
Lecture 33 - Josh McDermott - Introduction to Audition, Part 2
Lecture 34 - Nancy Kanwisher - Human Auditory Cortex
Lecture 35 - Hynek Hermansky - Auditory Perception in Speech Technology, Part 1
Lecture 36 - Hynek Hermansky - Auditory Perception in Speech Technology, Part 2
Lecture 37 - Panel - Vision and Audition
Unit 8. Robotics
Lecture 38 - Russ Tedrake - MIT's Entry in the DARPA Robotics Challenge
Lecture 39 - John Leonard - Mapping, Localization, and Self-Driving Vehicles
Lecture 40 - Tony Prescott - Control Architecture in Mammals and Robots
Lecture 41 - Stefanie Tellex - Human-Robot Collaboration
Lecture 42 - Giorgio Metta - Introduction to the iCub Robot
Lecture 43 - iCub Team - Overview of Research on the iCub Robot
Lecture 44 - Panel: Robotics
Unit 9. Theory of Intelligence
Lecture 45 - Tomaso Poggio - iTheory: Visual Cortex and Deep Networks
Lecture 46 - Surya Ganguli - Statistical Physics of Deep Learning
Lecture 47 - Haim Sompolinsky - Sensory Representations in Deep Networks