Along with Machine Learning and Semantics technology, Cognitive Computing has given renewed impetus to Artificial Intelligence processes and has brought unique advantages to the business world and the general user. Cognitive computing can recognize human language and speech to make sense of human behaviour and offer intelligent solutions for businesses of any kind. AI personal assistants, for example, use cognitive processes to extract meaning out of phrases in text or even via facial and speech recognition and enhance the process of data mining to suggest recommendations for customers (based on an analysis of their search engine history) and of course businesses that cater to consumers. The Cognitive processes of AI mimic the neural pathways of the human brain and help businesses predict fluctuations in customer demand, anticipate future mass trends, and also detect a crisis before it can occur; they can thus also optimize the company infrastructure and realign it along with business policy to discover previously unknown strategies that can attract multiple audiences from different demographics, help company levels collaborate more efficiently (also thanks to the Cloud), or diversify to explore new avenues of opportunity, and thrive to be one step ahead of competitors (big or small). Cogito is an excellent example of a cognitive technology software offered by companies such as Expert System, and comprises all the necessary algorithms and application modules that have a high degree of technological compatibility with various systems.
Every organization becomes involved in the transition to the new cognitive computing technology epoch soon. This begs the question: "Does artificial intelligence need leadership?" We talked about future challenges in leadership and business with Udo Hornfeck, Vice President at LEONI, a tier-1-supplier in the automotive industry with approximately 70.000 employees worldwide and located in Kitzingen, Germany. Mr Hornfeck leads the Global Research & Development departments. Mark McGregor Leadership Center: Mr Hornfeck, how do you define leadership?
Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described. To this end, we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a generalized concept-cognitive learning (GCCL) from the point of view of machine learning. Finally, experiments on some data sets are conducted to verify the feasibility of concept formation and concept-cognitive process of GCCL.
Leading industry experts are launching a Cognitive Computing Consortium to focus on furthering innovation in cognitive computing. The consortium is an interactive forum for researchers, developers, and practitioners of cognitive computing and its allied technologies. The consortium was co-founded by Sue Feldman, CEO, Synthexis; and Hadley Reynolds, principal analyst at NextEra Research, to fill a gap in the industry. Its mission is to enable professionals to exchange ideas and insights to conduct research and to educate buyers, users and the public on cognitive computing technologies, their uses, and potential impacts. The group was inspired to form after vendors told various experts that they needed an unbiased source to which they can refer potential clients for validation, advice, and background information.
IBM is forming a new cognitive computing research center in partnership with the University of Illinois. Big Blue said Friday the planned Center for Cognitive Computing Systems Research (C3SR) will be housed within the College of Engineering on the University of Illinois Urbana campus. Set to open this summer, C3SR will work to build integrated cognitive computing systems modeled on IBM's Watson technology. The systems will ingest reams of data pertaining to college curriculum, including videos, lecture notes, homework, and textbooks. After reasoning through the vast datasets, the systems will eventually attempt to pass a college level exam.