Crowder goes on, "Application developers across multiple industries recognize the value of leveraging cognitive capabilities to add insight to their digital interaction with their customers. Cognitive systems are now helping doctors, for example, make more informed treatment decisions by analyzing medical journals and medical images, and in the process revolutionizing healthcare. Our computers are becoming, as Thomas Watson, Jr. once envisioned, the most potent tool'for extending the powers of the human beings who use them.' Cognitive systems are defined by a few characteristics: They understand the world rather than automate it; they advise and provide evidence rather than output a binary answer; and they learn and improve rather than remain static. To achieve their goal of better understanding the world, cognitive systems glean insights from vast amounts of unstructured text, voice, and image data."
Regardless of industry, the companies that win in the digital era are those that take the shortest paths to the best results. That means getting the right information in the right hands at the right time. These realities are why more organizations are turning to cognitive solutions. Our market report, "The cognitive advantage: Insights from early adopters on driving business value," reveals that early adopters employ cognitive computing for competitive differentiation. In fact, 65 percent say that cognitive adoption is very important to their strategy and success, and more than half regard cognitive computing as a must-have to remain competitive.
Despite the prevalence of the Computational Theory of Mind and the Connectionist Model, the establishing of the key principles of the Cognitive Science are still controversy and inconclusive. This paper proposes the concept of Pattern Recognition as Necessary and Sufficient Principle for a general cognitive science modeling, in a very ambitious scientific proposal. A formal physical definition of the pattern recognition concept is also proposed to solve many key conceptual gaps on the field.
Since I'm a cognitive scientist and also something of a data scientist, I figured I'd weigh in on'Cognitive Computing'--what it is, what it isn't, and what is could (and should) be. The'cognitive' bit of Cognitive Computing is a marketing term. Cognitive computing describes technology platforms that broadly speaking, are based on the scientific disciplines of Artificial Intelligence and Signal Processing. These platforms encompass machine learning, reasoning, natural language processing, speech and vision, human-computer interaction, dialog and narrative generation and more. From the people I've talked to who work with this stuff, the converged-upon definition of Cognitive Computing seems to boil down to inference plus recommendation.