AI for AI Systems

As systems become increasingly complicated, cater to large geographical areas, have to seamlessly utilize an incredibly diverse array of computational resources and serve real-time, safety and mission-critical applications there is an emerging need for them to be self-aware or self-tuning in nature. Advances in machine learning and artificial intelligence have recently led to algorithms which can learn high-performance policies over extremely large state spaces (e.g. solving games like Ms. Pacman, Go, Poker or learn self-driving policies for autonomous cars, drones, etc).

Just as the growth of cheap abundant computing and specialized systems (e.g. dedicated accelerators for deep learning) has led to rapid advances in machine learning and artificial intelligence, there is an emerging opportunity for machine learning to help systems back. In this session, we want to explore the technical opportunities and unique challenges that surface when applying machine learning to optimize large-scale distributed systems.

Specifically, we want to explore challenges in developing systems which are self-tunable, resource-aware and use machine learning to dynamically optimize a running system to achieve desired latency, throughput, and other system-dependent utility functions. Making significant progress in this area requires multiple disciplines coming together, namely: machine learning, decision-making, distributed systems, and optimization.

Speaker Bios

Researcher Debadeepta Dey

Technical Fellow & Director, MSR Labs & AI Eric Horvitz

Eric Horvitz is a technical fellow and director at Microsoft Research. He has made contributions in areas of machine learning, perception, natural language understanding, decision making, and human-AI collaboration. His efforts and collaborations have led to fielded systems in healthcare, transportation, ecommerce, operating systems, and aerospace. He received the Feigenbaum Prize and the Allen Newell Prize for contributions to AI. He has been elected fellow of the National Academy of Engineering (NAE), the Association of Computing Machinery (ACM) , Association for the Advancement of AI (AAAI), and the American Academy of Arts and Sciences. He has served as president of the AAAI, and on advisory committees for the National Science Foundation, National Institutes of Health, President’s Council of Advisors on Science and Technology, DARPA, and the Allen Institute for AI. Beyond technical work, he has pursued efforts and studies on the influences of AI on people and society, including issues around ethics, law, and safety. He established the One Hundred Year Study on AI and served as a founder and co-chair of the Partnership on AI to Support People and Society. Eric received PhD and MD degrees at Stanford University. More information can be found on his home page.

Virginia Smith

Behnaz Arzani

I am a sixth year PhD candidate at The University of Pennsylvania, in the department of Computer and Information Science. I got my B.Sc. in Electrical Engineering at Sharif University Of Technology in 2010 where I worked with Prof. Javad Salehi. I started at Penn in January 2011, working under the guidence of Prof. Roch Guerin and Dr. Alejandro Ribeiro in the department of Electrical Engineering. After Prof. Guerin»s departure from Penn, I started working with him and Dr. Boon Thau Loo on Multi-homing strategies for efficient use of MPTCP and transferred to Computer Science. I was on an internship in the summer of 2013 at NEC Laboratories of America where I worked with Dr. Hui Zhang. I also spent the summer of 2015 and 2016 interning at Microsoft where I worked with Dr. Jitu Padhye, Dr. Yibo Zhu, Dr. Hongqiang Liu, Dr. Selim Ciraci, Dr. Geoff Outhred and Prof. Assaf Schuster.