Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Pradip Bose

Pradip Bose
Distinguished Researcher and Manager, IBM T. J. Watson Research Center

Website

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.

Sessions

4:05pm4:45pm Wednesday, April 17, 2019
Implementing AI
Location: Mercury Rotunda
Secondary topics:  AI case studies, Deep Learning and Machine Learning tools, Reliability and Safety
Pradip Bose (IBM T. J. Watson Research Center), Augusto Vega (IBM T. J. Watson Research Center), Nandhini Chandramoorthy (IBM T. J. Watson Research Center)
We will describe the fundamentals of a next generation AI research project. It is focused on creating future "self-aware" AI systems that have built-in autonomic detection and mitigation facilities to avoid faulty or undesirable behavior in the field: in particular, cognitive bias and inaccurate decisions that are perceived as being unethical. Software-hardware system architectures are discussed. Read more.