Date
Meeting goals
- Explore, connect and learn about quantum computing
Purpose
- To collaborate and share information amongst groups engaged in emerging technologies by discussing and documenting best practices, challenges, and standards and by developing support structure, sharing resources and learning from each other
- To foster and support innovation by identifying and engaging forward thinkers interested in getting started and by promoting creative thinking and cross-disciplinary collaborations
- To publicize new initiatives and projects as an aggressive marketing strategy to invite industry partners and set up potential collaborations and/or to seek corporate sponsorship
Action items from previous meeting:
Today's Agenda
| Time | Item | Who | Notes | |
|---|---|---|---|---|
| 10 mins | Introduction, recap and new website | Parixit Davé | ||
| 80 mins | Presentation | Urs Muller Dir. of Developer Technology & Chief Software Architect, NVIDIA | The Future of Autonomous VehiclesUrs Muller's work focuses on the development of end-to-end solutions for autonomous vehicles. He has 20+ years of experience in robotics, computer vision, machine learning, and high-performance computing. He received Ph.Ds in Electrical Engineering and Computer Science from the Swiss Federal Institute of Technology in 1993. |
|
| NVIDIA background, history | NVIDIA - founded 1993, 11,000 employees, based in Santa Clara, CA focus on gaming, VR/AR, Data Center, Self driving cars all initiatives linked by the same underlying AI and visual computing architecture Drive-AGX stack:
NVIDIA rents space on former Bell Labs campus in Holmdel, NJ Bell Labs pioneered Deep Learning in 80's and 90's Deep learning allows us to solve problems that we don't know how to program led to development of neural networks Bell Works campus is ideal to test self-driving cars due to extensive private road network Convolutional Neural Networks access prior learning NVIDIA view of learning:
Lessons learned:
1995 - deployed Holmdel neural nets at Wachovia bank; eventually processed 20% of checks in US 1995 - AT&T Bell labs fractured 2002 - AT&T mass layoffs 2003 - Bell Labs work led to new programs at DARPA 2012 - Deep Learning becomes popular and commercially viable | |||
| Self driving cars | NJ Team Goal: solve the hard and unsolved autonomous vehicle problems using machine learning simple driving rules fail due to numerous variable road conditions and unwritten behaviors hard to write down every single rule of safe driving; relatively easy to collect training data through driving training data is gathered through pixel input (sensors) during actual human-driven path. Convolutional Neural Network prediction of path is compared to human path, and discrepancy is provided as feedback to the CNN for additional learning Open challenges: deal with ambiguous situations - there is often more than one correct answer (4-way stop signs) learn from imperfect behavior (several observations; most correct; some not correct) |
