Course Date: August 15 – September 5, 2016
Deadline: March 14, 2016 | Apply here
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
- 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.
Previous Summer Course Details
2015 Invited Course Faculty:
DiCarlo, Jim, MIT
Freiwald, Winrich, Rockefeller University
Kanwisher, Nancy, MIT
Katz, Boris, MIT
Kreiman, Gabriel, Children’s Hospital Boston, Harvard University
McDermott, Josh, MIT
Oliva, Aude, MIT
Poggio, Tomaso, MIT
Rosasco, Lorenzo, Italian Institute of Technology
Spelke, Liz, Harvard University
Saxe, Rebecca, MIT
Schulz, Laura, MIT
Tenenbaum, Joshua, MIT
Wilson, Matthew, MIT
Winston, Patrick, MIT