The Rise of Machine Reasoning
Svetlana Yanushkevich
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Members: FreeCIS
IEEE Members: Free
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12 Nov 2021
Abstract:
The below citation from J. Pearls works expresses the current trend in Computational Intelligence (CI) well: Causal reasoning is an indispensable component of human thought that should be formalized ... toward achieving human-level machine intelligence. Causal reasoning is, perhaps, the most advanced type of inference used in decision support systems. It currently amalgamates the evolution of inference which started with a rule-based reasoning and continued to the case-based and probabilistic reasoning. It has emerged from a Bayesian approach, and advanced to efficient representation such as Bayesian belief networks. Their role in multiple CI applications will be reviewed in this talk. Most recently, the much-developed area of pattern recognition, also known as machine learning, and its most advanced implementation, deep learning, have come to understanding of importance of relations in pattern recognition, and even created the term deep reasoning. In this talk, we will focus on examples of causal reasoning for biometric-enabled decision support, and for the performance assessment of decision support systems.
Biography:
Dr. Svetlana Yanushkevich (SM IEEE 2004) is a full professor in the Department of Electrical & Software Engineering at Schulich School of Engineering (SSE), University of Calgary (UofC). She holds a Dr. Habilitated in Tech. Sci. (1999) from the Technical University of Warsaw, and joined the UofC in 2001. She is a founder of the Biometric Technologies Laboratory at the UofC. With her team in the Biometric Technologies Laboratory, she is developing novel decision support and risk assessment strategies based on machine reasoning, with applications to biometric-enabled access control and healthcare monitoring and diagnostics. She authored a monograph "Inverse Biometric Systems" (Taylor&Francis) and five other monographs and textbooks on the CI and digital design. She is also currently an Associate Dean, Equity, Diversity & Inclusion, and Research in the SSE at UofC.
The below citation from J. Pearls works expresses the current trend in Computational Intelligence (CI) well: Causal reasoning is an indispensable component of human thought that should be formalized ... toward achieving human-level machine intelligence. Causal reasoning is, perhaps, the most advanced type of inference used in decision support systems. It currently amalgamates the evolution of inference which started with a rule-based reasoning and continued to the case-based and probabilistic reasoning. It has emerged from a Bayesian approach, and advanced to efficient representation such as Bayesian belief networks. Their role in multiple CI applications will be reviewed in this talk. Most recently, the much-developed area of pattern recognition, also known as machine learning, and its most advanced implementation, deep learning, have come to understanding of importance of relations in pattern recognition, and even created the term deep reasoning. In this talk, we will focus on examples of causal reasoning for biometric-enabled decision support, and for the performance assessment of decision support systems.
Biography:
Dr. Svetlana Yanushkevich (SM IEEE 2004) is a full professor in the Department of Electrical & Software Engineering at Schulich School of Engineering (SSE), University of Calgary (UofC). She holds a Dr. Habilitated in Tech. Sci. (1999) from the Technical University of Warsaw, and joined the UofC in 2001. She is a founder of the Biometric Technologies Laboratory at the UofC. With her team in the Biometric Technologies Laboratory, she is developing novel decision support and risk assessment strategies based on machine reasoning, with applications to biometric-enabled access control and healthcare monitoring and diagnostics. She authored a monograph "Inverse Biometric Systems" (Taylor&Francis) and five other monographs and textbooks on the CI and digital design. She is also currently an Associate Dean, Equity, Diversity & Inclusion, and Research in the SSE at UofC.
