Brains, Minds and Machines

Course Information

Course Date: August 13 – September 3, 2017

Deadline: March 14, 2017 | Apply here

Tuition/Room and Board: All costs covered by funding agency

2016 Course Schedule

Directors: Gabriel Kreiman, Harvard University; and Tomaso Poggio, Massachusetts Institute of Technology
(L. Mahadevan, Harvard University, honorary director)

Course Description

The problem of intelligence – how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines – is arguably the greatest problem in science and technology. To solve it we will need to understand how human intelligence emerges from computation in neural circuits, with rigor sufficient to reproduce similar intelligent behavior in machines. Success in this endeavor ultimately will enable us to understand ourselves better, to produce smarter machines, and perhaps even to make ourselves smarter. Today’s AI technologies, such as Watson and Siri, are impressive, but their domain specificity and reliance on vast numbers of labeled examples are obvious limitations; few view this as brain-like or human intelligence. The synergistic combination of cognitive science, neurobiology, engineering, mathematics, and computer science holds the promise to build much more robust and sophisticated algorithms implemented in intelligent machines. The goal of this course is to help produce a community of leaders that is equally knowledgeable in neuroscience, cognitive science, and computer science.

The first half of the course will focus on the intersection between biological and computational aspects of learning and vision. The second half will focus on high-level social cognition and artificial intelligence, as well as audition, speech and language processing.

The class discussions will cover a range of topics, including:

  • Neuroscience: neurons and models
  • Computational vision
  • Biological vision
  • Machine learning
  • Bayesian inference
  • Planning and motor control
  • Memory
  • Social cognition
  • Inverse problems & well-posedness
  • Audition and speech processing
  • Natural language processing

These discussions will be complemented in the first week by MathCamps and NeuroCamps, to refresh the necessary background for some of the students. Throughout the course, students will participate in workshops and tutorials to gain hands-on experience with these topics.

Core presentations will be given jointly by neuroscientists, cognitive scientists, and computer scientists who have worked together. Throughout the course intensive lectures will be followed by afternoons of computational labs, with some additional evening research seminars. To reinforce the theme of collaboration between (computer science + math) and (neuroscience + cognitive science), exercises and projects often will be performed in teams that combine students with both backgrounds.

The course will culminate with student presentations of their projects. These projects provide the opportunity for students to work closely with the resident faculty, to develop ideas that grew out of the lectures and seminars, and to connect these ideas with problems from the students’ own research at their home institutions.

This course aims to cross-educate computer engineers and neuroscientists; it is appropriate for graduate students, postdocs, and faculty in computer science or neuroscience. Students are expected to have a strong background in one discipline (such as neurobiology, physics, engineering, and mathematics). Our goal is to develop the science and the technology of intelligence and to help train a new generation of scientists that will leverage the progress in neuroscience, cognitive science, and computer science. The course is limited to 30 students.

2016 Course Faculty & Lecturers

Allen, Kelsey, MIT
Barbu, Andrei, MIT
Ben-yosef, Guy, MIT
Brewer, Kris, Massachusetts Institute of Technology
Cadieu, Charles, Bay Labs Inc.
Chandrasekhar, Vijay, Institute for Infocomm Research
Chung, Sueyeon, Harvard University
Dicarlo James Massachusetts Institute of Technology
Flores, Francisco, MIT
Freiwald, Winrich, The Rockefeller University
Frogner, Charlie, MIT
Gershman, Samuel, Harvard University
Ghazanfar, Asif, Princeton University
Gottlieb, Jacqueline, Columbia University/NYSPI
Hale, Greg, MIT
Haxby, James, Dartmouth
Hildreth, Ellen, Wellesley College
Isik, Leyla, MIT
Kanwisher, Nancy, MIT
Katz, Boris, MIT
Kell, Alexander, MIT
Kim, Jiye, Boston Children’s Hospital
Koch, Christof, Allen Institute for Brain Science
Kosoy, Eliza, MIT
Kreiman, Gabriel, Harvard Medical School
Mackevicius, Emily, MIT
Mahadevan, L, Harvard University
McDermott, Josh, MIT
Mendoza-Holliday, Diego,MIT
Meyers, Ethan, Hampshire College
Mlynarski, Wiktor, MIT
Nakayama, Ken, Harvard University
Ng, Lydia, Allen Institute For Brain Science
Oliva, Aude, MIT
Olson, Joseph, Harvard University
Poggio Tomaso, MIT
Pompeo, Elisa, MIT
Premachandran Vittal Johns Hopkins University
Roig, Gemma, MIT
Rosasco, Lorenzo, MIT
Ross, Candace, MIT
Sassanfar, Mandana, MIT
Saxe, Rebecca, MIT
Schulz, Laura, MIT
Smith, Kevin, MIT
Socher, Richard, MetaMind
Sompolinsky, Haim, Edmond and Lily Safra Center for Brain Sciences
Spelke, Elizabeth, Harvard University
Sullivan, Kathleen, MIT
Tegmark, Max, MIT