Brains, Minds and Machines Summer Course 2016

Semester: 

Summer

Offered: 

2017

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. 

Set in the charming town of Woods Hole, there will be lectures and tutorials by leaders in the field. In addition, students will be working on cutting-edge projects with the help of faculty and teaching assistants. 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. Projects will be cross-disciplinary.

This course aims to cross-educate computer engineers and neuroscientists; it is appropriate for graduate students, postdocs, and faculty in computer science and/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.

All local costs of participating in this course (tuition, MBL room & board) are provided by an NSF Science and Technology Center award to the Center for Brains, Minds, and Machines, Grant No. CCF-1231216. Limited travel reimbursement may be available for admitted students.