Brains, Minds and Machines

The science of today enables engineering solutions of tomorrow. In this symposium we will discuss state-of-the-art results in the scientific understanding of intelligence and how these results enable new approaches to replicate intelligence in engineered systems.

Understanding intelligence and the brain requires theories at different levels, ranging from the biophysics of single neurons to algorithms, computations, and a theory of learning. In this symposium, we aim to bring together researchers from machine learning, artificial intelligence, neuroscience, and cognitive science to present and discuss state-of-the-art research that is focused on understanding intelligence at these different levels.

Central questions of the symposium include how intelligence is grounded in computation, how these computations are implemented in neural systems, how intelligence can be described via unifying mathematical theories, and how we can build intelligent machines based on these principles.

Our core goal is to develop a science of intelligence, which means understanding human intelligence and its basis in the circuits of the brain and the biophysics of neurons. We also believe that the engineering of tomorrow will need the science of today, in the same way as the basic research of Hubel and Wiesel in the ‘60s was the foundation for today's deep learning architectures.

The symposium will consist of talks by invited speakers and a panel discussion.
Invited speakers and panelists at the symposium include

- Surya Ganguli (Stanford University)
- Demis Hassabis (Google DeepMind)
- Christof Koch (Allen Institute for Brain Science)
- Gabriel Kreiman (Harvard University)
- Gary Marcus (New York University)
- Tomaso Poggio (MIT)
- Andrew Saxe (Harvard University)
- Terrence Sejnowski (Salk Institute)
- Joshua Tenenbaum (MIT)