Brains, Minds and Machines Summer course

Semester: 

Summer

Offered: 

2014

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 of the two weeks will focus on general theoretical foundations and methods; the second week will examine four key areas of research for understanding intelligence.

The theoretical foundations discussed in the first week will consist of:

Inverse problems & well-posedness as a unifying theme;
Signal processing;
Machine Learning;
Bayesian inference;
Computational vision;
Planning and motor control; and
Neuroscience: neurons and models.
These topics will be complemented in the first week by MathCamps and NeuroCamps, to refresh the necessary background for some of the students.

The four areas of research examined in the second week will be:

Development of Intelligence – Understanding intelligence requires discovering how it develops from the interplay of learning and innate structure.
Circuits for Intelligence – Understanding the physical machinery of intelligence requires analyzing brains across multiple levels of analysis, from neural circuits to large-scale brain architecture.
  Visual Intelligence – Visual intelligence goes beyond the narrow domains of face recognition or detecting pedestrians crossing the street to detailed scene understanding, including context, actions, inferences, predictions, linguistic associations, and narrative.
Social Intelligence – Intelligence emerges from the social interactions among individuals.
Core presentations will be given jointly by neuroscientists, cognitive scientists, and computer scientists who have worked together. In each of the two weeks, the first two days of intensive lectures will be followed by three days of morning lectures and 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 last two days will be reserved for 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 25 students.