Building an artificial brain with neuro-inspired deep learning

SF Bay ACM Chapter
SF Bay ACM Chapter
Public group

Palo Alto Networks Inc

3000 Tannery Way · Santa Clara, CA

How to find us

Palo Alto Networks, 3000 Tannery Ave, Santa Clara, CA

Location image of event venue


By Dr. Chen-Ping Yu, Founder and CEO of Phiar


6:30 Doors Open, Snack & Networking
7:00 Presentation
Live Streaming on this link:

*** Please arrive by 7 PM due to Security ***
*** Bring PHOTO ID (passport, driver license, etc.) ***

The field of Computer Vision explores how to make machines understand visual data in various ways. Modern Computer Vision started out with optimizing statistical machine learning methods primarily using hand-crafted "features" for tasks such as Object Detection, Segmentation, and Tracking. Another inspiration behind Computer Vision techniques comes from looking into the biological vision systems, for example by mimicking the brain's deep layers of interconnected neurons as computational layers to accomplish the same tasks. AlexNet's win in the 2012 ImageNet competition solidified the practically of what is now called Deep Learning, and nowadays most techniques are based on such Deep Learning techniques.

Yet, just how biologically inspired are they? Would better knowledge of biological vision improve Deep Learning models further? And if so, what kind of direction would such a futuristic Neural Network take? In this talk, we will first review the recent progress of computer vision and related deep learning models, then we will go through a brief overview of how the biological visual system works, and discuss about building a more brain-inspired neural network, to explore if we are able to answer the three questions above.

Speaker Bio:
Dr. Chen-Ping Yu is the founder and CEO of Phiar, a company building the first AI-powered augmented reality smartphone navigation solution for driving. He was previously a postdoctoral fellow at Harvard University, researching neuro-inspired deep learning. Chen-Ping received his Ph.D. from Stony Brook University in Computer Vision and Machine Learning, and his M.S. from Penn State University. Chen-Ping's graduate research includes classical machine learning methods for image and video segmentation, neuro-image processing, computational models of the human visual system, and deep learning-based classification and detection models. Chen-Ping has been an NSF Fellow and the recipient of numerous honors and awards, and has published more than 15 scientific publications at top AI and cognitive science venues.