Dr. Leonid Perlovsky is CEO, LP Information Technology, Professor of Psychology at Northeastern University, previously Visiting Scholar at Harvard University School of Engineering and Applied Sciences and Harvard Medical School Athinoula Martinos Brain Imaging Center, Technical Advisor and Principal Research Scientist at the AF Research Lab. He created a new area of cognitive mathematical engineering, dynamic logic, which solved a number of Big Data problems unsolvable for decades. He leads research projects on mathematical models of the mind mechanisms, cognitive algorithms for automatic recognition, integration of disparate data; interaction between cognition and language, hierarchy of abstract models, predictive and diagnostic models of culture, applications of this models to military intelligence, data mining, genetic-phenotype correlation studies, cybersecurity, commercial analytics and predictions, value acceptance in social networks. In leading brain imaging labs around the world his models have been proven experimentally to explain the mind mechanisms not understood previously. As Chief Scientist at Nichols Research, a $500mm high-tech DOD contractor, he led the corporate research in intelligent systems. He served as professor at Novosibirsk University and New York University; as a principal in commercial startups developing tools for biotechnology, text understanding, and financial predictions. He is invited as a keynote plenary speaker and tutorial lecturer worldwide, including most prestigious venues such as the Nobel Forum at Karolinska Institutet Stockholm; published more than 495 papers, 17 book chapters, and 4 books including “Neural Networks and Intellect,” Oxford University Press, 2001 (currently in the 3rd printing) and “Emotional, Cognitive, Neural Algorithms with Engineering Applications,” Springer 2011; awarded 2 patents. Dr. Perlovsky participates in organizing conferences on Computational Intelligence, serves as Chair for the IEEE Task Force on The Mind and Brain, on the International Neural Network Society (INNS) Board of Governors; past Chair for the IEEE Boston Computational Intelligence Chapter, Chair of The INNS Award Committee. He serves on the Editorial Board of ten professional journals, has founded and serves as Editor-in-Chief for “Physics of Life Reviews,” the IF=9.5, ranked #4 in the world by Thomson Reuters. He received National and International awards including The Best Paper Award at Russian most prestigious magazine, Zvezda; the Gabor Award, the top engineering award from the INNS; and the John McLucas Award, the highest US Air Force Award for basic research.
Efficiently extracting information from huge amount of data becomes more and more challenging. A most promising approach is to model abilities of human mind - cognitive algorithms. The talk describes cognitive algorithms, their applications to various engineering problems, and their foundations in mathematical models of the mind including higher cognitive abilities. Mechanisms of the mind include concepts, emotions, dynamic logic, and deep hierarchy of mental abilities including language, cognition and their interaction. Big Data informatics requires algorithms modeling all these abilities. Machine learning, artificial intelligence, and modeling of the mind has been plagued by computational complexity, CC, since the 1960s. We discuss dynamic logic (DL) overcoming CC when analyzing Big Data. DL is a process-logic, which replaces static states of classical logic; it serves as a basis for cognitive algorithms and for a mathematical theory of learning, combining the mechanisms of the mind into a hierarchical system of mental processes. Each process proceeds "from vague to crisp," from vague representation-concepts to crisp ones. Brain imaging and psychological experiments (Bar et al 2006; Kveraga et al 2007; Price 2012; Masataka & Perlovsky 2012) confirmed this as an adequate model of the brain perception, cognition, and higher mental functions.
Computational difficulty is related to Gödelian problems in logic: computational complexity is a manifestation of Gödelian incompleteness in finite systems, such as computers or brains. The mind is "not logical." Dynamic logic overcomes this difficulty. Engineering applications demonstrate orders of magnitude improvement in Big Data informatics, finding objects of interest in oceans of data, information integration, financial predictions, genetic studies, cybersecurity.
The dual hierarchy learning model of interacting language and cognition integrates language, text, and sensor data. A number of "mysteries" in this interaction are explained: what is the role of language in cognition, why children can talk before they really understand, how much adults are different from children in this respect, etc. These are explained in the model, and explanations are confirmed in brain imaging experiments (Binder et al 2005; Price 2012). Much difficulties in developing Big Data algorithms are related to confusing language and cognition.
The knowledge instinct drives acquisition of cognitive ability and is a foundation of all our higher cognitive abilities. Its satisfaction is experienced as aesthetic emotions (experimentally confirmed in Cabanac et al 2010). Efficient engineering algorithms must model these emotional abilities (Perlovsky, Deming, Ilin, 2011). The hierarchy of aesthetic emotions is discussed from understanding of everyday objects, to understanding of abstract concepts throughout the hierarchy, to the near top of the mental hierarchy. Contents of these "highest" concepts are discussed and the corresponding aesthetic emotions are related to the beautiful. Actions realizing beauty in one's life are related to emotions of spiritually sublime. Experimental tests of this conjecture are for the near future.
Contradictions among knowledge are experienced as negative aesthetic emotions, cognitive dissonance. Development of robots and human-computer interactions require algorithms modeling this ability. Cognitive dissonance counteracts the knowledge instinct and would prevent accumulation of knowledge and the entire human evolution, if not a special ability evolved for overcoming these emotions. It follows from the dual hierarchy model that this mechanism is music. This theoretical prediction has been experimentally confirmed (Masataka et al 2012, 2013, Cabanac et al, 2013). This explains the origin and evolution of music, what Darwin called the greatest mystery.
