The rockefeller university

Event Details

Type

Center for Studies in Physics and Biology Seminars

Speaker(s)

Hava Siegelmann, Ph.D., provost professor, University of Massachusetts Amherst

Speaker bio(s)

How come drones are still mainly human controlled and have such limited autonomy? First, drones operate under significant constraints, including limited computational power, energy capacity, and communication bandwidth. Reinforcement Learning fail to maintain optimal performance under such constraints. We propose sequence AI algorithms that significantly improving compute and energy efficiency. Among the key features are rapid onboard responses and adaptability in dynamic environmental changes, robustness to missing inputs, minimization of sensor usage and the ability to use cheaper sensors to greater effect, as well as making possible the use of cheaper hardware while maintaining peak effectiveness. Second issue is the need of communication and cooperation among drones. Distributed AI is known to suffer explosion of communication needs, and this is not available in realistic swarms of drones. We propose a cooperative AI where the agents are lifelong learners. On the go, they are able to update, learn from failures, and become more expert with more experience. This paradigm enables both collaborative AI without explosive communication as well as a great reduction in the required labeled data (teacher), since the agents peer-teach each other. We suggest that these two directions of research will advance us towards true safe autonomy.

Open to

Public

Phone

(212) 327-8636

Sponsor

Melanie Lee
(212) 327-8636
leem@rockefeller.edu