Beyond inspiration: Five lessons from biology on building intelligent machines

author: Bruno Olshausen, Helen Wills Neuroscience Institute, UC Berkeley
published: Aug. 23, 2016,   recorded: August 2016,   views: 441
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

The only known systems that exhibit truly intelligent, autonomous behavior are biological. If we wish to build machines that are capable of such behavior, then it makes sense to learn as much as we can about how these systems work. Inspiration is a good starting point, but real progress will require gaining a more solid understanding of the principles of information processing at work in nervous systems. Here I will focus on five areas of investigation that I believe will be especially fruitful: 1) the study of perception and cognition in tiny nervous systems such as wasps and jumping spiders, 2) developing good computational models of nonlinear signal integration in dendritic trees, 3) the use of sparse, overcomplete representations of sensory input, 4) understanding the computational role of feedback in neural systems, and 5) the use of active sensing systems for acquiring information about the world.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: