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Panel Discussion / Workshop: Dr. James A. Crowder & Dr. John N. Carbone

 

 

Current Artificial Intelligent learning models utilizing classical Neural Networks are generally homogenous in respect to learning algorithms utilized within the artificial neural structure. Current human neuro-physiological observations/analysis indicates that human neuronal synapses undergo changes according to local learning rules.  This results in learning characteristics that depend on the dendritic sites.  This workshop will explore utilizing more localized sensory information to form local rules which form differential Hebbian and plain Hebbian learning constructs within the same neuron.  This will allow the artificial neural network to create synapses with very different learning properties within the same network.

This enables a new and powerful computational artificial neural framework (based on a multiple-discipline intelligent agent approach) based on the concept a new learning model: Self-Supervised Machine Learning.  We will provide examples of human and artificial learning models and how these can be merged to form artificial computational networks that truly learn more like biological entities.

Biography

Dr. James A. Crowder is a Chief Engineer with Raytheon’s Intelligence, Information, and Services (IIS) Business Unit, at the Aurora, Colorado facility. With patents pending for his Artificial Cognitive Neural Framework, and the Artificial, Continuously Recombinant Neural Fiber Network, Dr. Crowder has spent the last 30 years working in artificial intelligence, genetic algorithms, and fuzzy systems across a variety of aerospace companies. He serves as technical advisor and mentor to a STEM school in Douglas Country, Colorado, the Alexandria School of Innovation. With over 90 publications and 2 book chapters, and 3 books, including “Artificial Cognition Architectures,” Springer, 2013, “Systems Engineering, Agile Design Methodologies,” Springer, 2013, he continues to push the envelope in artificial intelligence and autonomous cognitive systems. His current work involves the theory of Artificial Neurogenesis.

Dr. John N. Carbone has served Raytheon Company and the defense industry for ~28 years.  He currently serves as a Chief Science Advisor and Raytheon Engineering Fellow within the Intelligence, Information and Services (IIS) Division after previously serving as Product Area Technology Director and Chief Engineer for Innovation. Dr. Carbone has also served in various Chief Engineer, System, Software Architect Roles and more recently as Chief Data Architect/Scientist for major Cloud programs.

Dr. Carbone received his undergraduate Bachelor of Science degree in Computer Science from Baylor University, Masters of Engineering from Texas Tech University, Masters Equivalent in Software and Systems Engineering from University of Texas at Austin, and his PhD in Mechanical Engineering at Texas Tech University with focus on enhancing information theory research by combining cognition based frameworks and processes using various adaptations of multi-dimensional space-time relationship mechanics.

Dr. Carbone began his career developing real-time embedded voice, cyber, and data communications software solutions for satellite tracking and switching systems. Subsequently, Dr. Carbone has been forging bridges between High Performance Computing and Big Data architectures within distributed enterprise software engineering for ~15 years. During this tenure, Dr. Carbone was instrumental in providing architecture, design, implementation, and fielding of software systems which have lead to the development of a number of strategic ISR programs considered paradigm shifts for the Department of Defense and Intelligence Community.

Dr. Carbone has submitted and received national and international software patents, and continuously publishes and edits scientific content for books, journals, conferences and symposia. Topics include dynamic discovery, dynamically provisionable workflow of multiple disparate static and streaming data sources, geospatial intelligence enterprise systems, Big Data and Analytics, Cyber Security, disruption tolerant networking technology, and cognitive computing based frameworks for enhancing decision making of self-learning autonomous systems. Recent book publishing efforts for Elsevier include Mining Big Data to Improve National Security. Recent Springer Scientific published books include topics on Multi-Disciplinary Systems Engineering, Artificial Cognition Architectures, Applied Cyber Physical Systems and Artificial Psychology in late 2015.

Lastly, Dr. Carbone’s academic and professional society efforts include Baylor University, Texas Tech University and Texas A&M Commerce Computer Science and Cyber Security Boards and University of Texas at Dallas School of Management, Board Member for the Journal of Integrated Design and Process Science (JIDPS), Education Director and member of the Society of Design and Process Science (SDPS) ~19 years, Association for Computing Machinery (ACM) and recently VP for development of the Software Engineering Society (SES). His responsibilities include developing lasting relationships between academia and industry and jointly developing transformational K-20 curriculum for education including how to teach computational thinking and Science, Technology, Engineering, and Math (STEM).

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