In this presentation, we will focus on next-generation systems that are proficient in pursuing advanced artificial intelligence (AI) based computation and decision-making. The particular challenge addressed in this ongoing research endeavor is that of cognitive resilience and ethical, bias-free choices. Operating under changing in-field data patterns is difficult in current AI inference engines. Precipitous loss of in-field inferential accuracy can lead to potentially catastrophic errors in decisions. Also, inadequate safeguards in factory-trained model development can lead to decisions at the edge that are perceived as having unintended cognitive bias. How to ensure that the cognitive decision-making at the edge is bias-free, ethical and resilient to in-field data changes or inconsistencies – those are the AI systems challenges we are primarily focused on in this project.
Our research is centered around a visionary system architecture, where the cloud provides continuous, scalable support for a connected swarm of edge compute devices operating in the field of analytics and inferential decision-making at the edge. The particular use cases we are focusing on are: (1) autonomous (self-driving) vehicles; (2) network cyber security through a swarm of intelligent monitoring agents; and (3) distributed, autonomous resource and power management in a data center or server setting. In each of these use cases, we are relying on a novel swarm-AI mechanism to facilitate continuous, in-field learning. Including a simulated “self-awareness” technique within each intelligent edge device, allows interesting new modes of collaborative perception which are not possible with today’s smart robotics technology alone. We present simulation and modeling based results to show the promise of using self-aware swarm-AI technology for advanced cooperative compute-and-infer tasks in a cloud-backed mobile cognition setting.
After establishing the benefit of self-aware swarm-AI technology, we present our ongoing multi-year R&D activity in implementing it for prototype demonstration in a real-life setting for our sponsoring government client.
PRADIP BOSE is a Distinguished Research Staff Member and Manager of the Efficient & Resilient Systems Department at IBM T. J. Watson Research Center, Yorktown Heights, NY. He has been involved in the design and pre-silicon modeling of virtually all IBM POWER-series microprocessors, since the pioneering POWER1 (RS/6000) machine, which started as the Cheetah (and subsequently America) superscalar RISC project at IBM Research. From 1992-95, he was on assignment at IBM Austin, where he was the lead performance engineer in a high-end processor development project (POWER3). During 1989-90, Dr. Bose was on a sabbatical assignment as a Visiting Associate Professor at Indian Statistical Institute, India, where he worked on practical applications of knowledge-based (AI)systems. His current research interests are in high performance computers, artificial intelligence, power- and reliability-aware microprocessor architectures, accelerator architectures, pre-silicon modeling and validation. He is the author or co-author of over a hundred publications (including several book chapters) and he also serves as an Adjunct Professor ar Columbia University. He has received twenty five Invention Plateau Awards, several Research Accomplishment and Outstanding Innovation Awards from IBM. Dr. Bose served as the Editor-in-Chief of IEEE Micro from 2003-2006 and as the chair of ACM SIGMICRO from 2011-2017. He is an IEEE Fellow and a member of the IBM Academy of Technology.
Augusto Vega is a Research Staff Member at IBM T. J. Watson Research Center. He holds a Ph.D in Computer Architecture from UPC Barcelona in Spain. He is a lead contributor to the swarm-AI project at IBM Research – with specific interests in the self-driving car application space.
Nandhini Chandramoorthy is a post-doctoral researcher at IBM T. J. Watson Research Center, Yorktown Heights, NY. She holds a Ph.D degree from Penn State University. Her research interests are in computer architecture, artificial intelligence, power-aware design and modeling methodologies.
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