Keynote Lecture: Dr. James A. Crowder

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James Crowder

The Psychology of Artificial Intelligence: A Conversation with SANDI

 Dr. James A. Crowder
Systems Fellow at Colorado Engineering, Inc., USA
Formerly at Raytheon Technologies. Recipient of technical achievement awards from
Lockheed Martin, General Dynamics, Northrup Grumman, & Raytheon Technologies.

 
Abstract

There has been much talk over the last few years about the perils of the use of artificial intelligence in virtually everything we touch. From our phones to our cars, and everything in between, artificial intelligence is an integral part of our existence. Many prominent people, like Elon Musk and Stephen Hawking, have warned about the potential for machines to take over and cause havoc in the lives and very existence of humans. Hollywood has made untold billions of dollars painting doom- and- gloom scenarios about artificial intelligence and robots within our society today and in the future. But what is the true reality? We continually push to create increasingly intelligent systems/machines that attempt to learn, think, and reason like humans. Therefore, our first question becomes, when presented with this challenge is:

Which types of people do we want robots to learn, think, and reason like?

Do you want a Stephen Hawking? Do you want a Charles Manson? Do you want any of a host of past or current world dictators? All these people learn and think and reason, but all of them do it very differently from one another. To say you want a system that learns, thinks, and reasons like people is to say you want to give the computer/robot the ability to self-adapt, to create (through experience and learning) an adult intelligence and capabilities that is people-like in nature. No matter which “version” of human thinking and reasoning is desirable, the main question to be asked is:

“How do we test it to know if the AI system is working correctly?”

The fundamental problem with testing learning, thinking, reasoning, artificial intelligent systems is:

“What does it mean for it to work correctly?”

Upon reviewing the limitations of classical system test theory and implementations, we begin to understand the conundrums of applying these well-known processes for testing AI systems. This presentation will explore the new world of “AI Psychology” to understand where we may or may not be headed as we continually improve upon artificial intelligence technologies.

Biography

Dr. Crowder has a B.S. in Electrical Engineering, an M.S. in Electrical Engineering in signal processing, an M.S. in Applied Mathematics in applied probability theory, and a Ph.D. in Electrical Engineering in stochastic processing and chaos theory.

Dr. Crowder is currently a Systems Fellow at Colorado Engineering, Inc. In this role, he leads teams of system and software engineers in the design and development of next generation artificial intelligence and machine learning architectures and algorithms. Prior to his current position, Dr. Crowder was a Senior Principal Systems Engineer at Raytheon Technologies for 14 years.  In that role, Dr. Crowder led multiple research and development projects, including those dealing with autonomous vehicle systems, collision avoidance, optimal route planning, and autonomous mission planning. Dr. Crowder has over 30 years of experience in aerospace engineering that includes 25 years in the design and development of artificial intelligence and machine learning technologies.  Dr. Crowder has received technical achievement awards from Lockheed Martin, General Dynamics, Northrup Grumman, and Raytheon Technologies.

Dr. Crowder has published over 120 journal and conference papers on Artificial Intelligence, Systems Engineering, and Aerospace Engineering.  Dr. Crowder has also written and published 5 books related to artificial intelligence and systems engineering with Springer International Publishing, and has published several book chapters, including cybersecurity and biomedical engineering. Dr. Crowder’s experience provides him with a strong cross disciplinary background in systems engineering, software development, artificial intelligence & cognitive systems, genetic algorithms, fuzzy systems, machine learning, and neural networks. His latest book explores how to adequately test cognitive systems.


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