Perspectives on Cognitive Informatics and Cognitive Computing

Yingxu Wang, George Baciu, Yiyu Yao, Witold Kinsner, Keith Chan, Bo Zhang, Stuart Hameroff, Ning Zhong, Chu-Ren Hunag, Ben Goertzel, Duoqian Miao, Kenji Sugawara (+4 others)
2010 International Journal of Cognitive Informatics and Natural Intelligence  
Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by
more » ... nomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, Definition 3: Abstract Intelligence (αI) is the general mathematical form of intelligence as a natural mechanism that transfers information into behaviors and knowledge (Wang, 2009a) . Typical paradigms of αI are natural intelligence, artificial intelligence, machinable intelligence, and computational intelligence, as well as their hybrid forms. Definition 4: Denotational Mathematics (DM) is a category of expressive mathematical structures that deals with high-level mathematical entities beyond numbers and sets, such as abstract objects, complex relations, perceptual information, abstract concepts, knowledge, intelligent behaviors, behavioral processes, and systems (WangMany developments of the last century centered around adaptation and adaptive systems. The focus in this century appears to be shifting towards cognition and cognitive dynamical systems with emergence (Kinsner, 2007) . Although cognitive dynamical systems are always adaptive to various conditions in the environment, adaptive systems of the past have not been cognitive. The evolving formulation of cognitive informatics (CI) (Wang, 2002a;) has been an important step in bringing the diverse areas of science, engineering, and technology required to develop such cognitive systems. Current examples of various cognitive systems include autonomic computing, cognitive radio, cognitive radar, cognitive robots, cognitive networks, cognitive computers, cognitive cars, cognitive factories, as well as brain-machine interfaces for physically-impaired persons, and cognitive binaural hearing instruments. The phenomenal interest in this area may be due to the recognition that perfect solutions to large-scale scientific and engineering problems may not be feasible, and we should seek the best solution for the task at hand. The "best" means suboptimal and the most reliable (robust) solution, given not only limited resources (financial and environmental) but also incomplete knowledge of the problem and partial observability of the environment. Many new theoretical, computational and technological developments have been described at this conference and related journals. The challenges can be grouped into several categories: (a) theoretical, (b) technological, and (c) sociological. The first group of theoretical issues include modelling, reformulation of information and entropy, multiscale measures and metrics, and management of uncertainty. Modelling of cognitive systems requires radically new approaches. Reductionism has dominated our scientific worldview for the last 350 years, since the times of Descartes, Galileo, Newton, and Laplace. In that approach, all reality can be understood in terms of particles (or strings) in motion. A Nobel laureate physicist, Stephen Weinberg said "All explanatory arrows point downward, from societies to people, to organs, to cells, to biochemistry, to chemistry, and ultimately to physics." "The more we know of the universe, the more meaningless it appears." However, in this unfolding emergent universe with agency, meaning, values and purpose, we cannot prestate or predict all that will happen. Since cognitive systems rely on perceiving the world by agents, learning from it, remembering and developing the experience of self-awareness, feelings, intentions, and deciding how to control not only tasks but also communication with other agents, and to create new ideas, CI may not only rely on the reductionist approach of describing nature. In fact, CI tries to expand the modelling in order to deal with the emergent universe where no laws of physics are violated, and yet ceaseless unforeseeable creativity arises and surrounds us all the time. This new approach requires many new ideas to be developed, including reformulation of the concept of cognitive information, entropy, and associated measures, as well as management of uncertainty, and new forms of cognitive computing. As we have seen over the last decade, cognitive informatics is multidisciplinary, and requires cooperation between many subjects, including sciences (e.g.Software Engineering. He is the initiator of a number of cutting-edge research fields and/or subject areas such as cognitive informatics, abstract intelligence, denotational mathematics, cognitive computing, theoretical software engineering, coordinative work organization theory, cognitive complexity of software, and built-in tests. He has published over 105 peer reviewed journal papers, 193 peer reviewed conference papers, and 12 books in cognitive informatics, software engineering, and computational intelligence. He is the recipient of dozens international awards on academic leadership, outstanding contribution, research achievement, best paper, and teaching in the last 30 years. . Dr. Kinsner has been involved in research on algorithms and software/hardware computing engines for real-time multimedia, using wavelets, fractals, chaos, emergent computation, genetic algorithms, rough sets, fuzzy logic, neural networks. Applications included signal and data compression, signal enhancement, classification, segmentation, and feature extraction in various areas such as real-time speech compression for multimedia, wideband audio compression, aerial and space ortho image compression, biomedical signal classification, severe weather
doi:10.4018/jcini.2010010101 fatcat:zx37bo5m35hbveyln5gcol6p74