In a world inundated with data, we rely more and more on machines to help us understand it all. However, despite advances in machine learning, the pattern-recognition and learning abilities of artificial brains still pales in comparison to even the simplest mammalian brains.

Today’s computer scientists are turning to neuroscientists and the human brain for inspiration to design computer systems that can interpret, analyze, and learn information as successfully as humans.

But do artificial intelligence and neuroscience really need each other? Is there a better way to model machine intelligence? Is the relationship reciprocal? Are simple, artificial brains changing our understanding of complex, organic ones?

This year's Institute for Applied Computational Science (IACS) symposium on the Future of Computation in Science and Engineering will explore these issues and others that have arisen from the collision between neuroscience, computer science and machine learning.  BRAIN + MACHINES will bring together machine learning experts, neuroscientists, and scholars from across several fields to explore questions like:

  • How can humans and intelligent machines work together?
  • How are advances in technology and computation impacting the health and safety of humans?
  • What does the future hold for brains and machines?

Speakers include David Cox, assistant professor of molecular and cellular biology and of computer science at The Harvard John A. Paulson School of Engineering and Applied Sciences; John Leonard, professor of mechanical and ocean engineering at MIT; Francesca Rossi, professor of computer science at the University of Padova; Jeff Lichtman, the Jeremy R. Knowles Professor of Molecular and Cellular Biology; and Nancy Kanwisher, professor of neuroscience at MIT.

The event is free and open to the public. For more information, visit