The Future of Markets is Cognitive
Markets are entering a new period — the cognitive era.
Cognitive computing offers fundamental differences in how systems are built and interact with humans. Cognitive-based systems are able to build knowledge and learn, understand natural language, and reason and interact naturally with human beings.
They are also able to put content into context with confidence-weighted responses and supporting evidence. Cognitive systems can quickly identify new patterns and insights. Over time, they will simulate even more closely how the brain actually works. In doing so, they could help us solve the world’s most complex problems by penetrating the complexity of big data and exploiting the power of natural language processing and machine learning.
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At least three types of capabilities can be identified for cognitive systems. They are not mutually exclusive, but can take advantage of more than one. The three areas are:
These systems fundamentally change the way humans and systems interact and significantly extend the capabilities of humans by taking advantage of peoples’ ability to provide expert assistance and to understand. These systems provide expert assistance by developing deep domain insights and presenting the information in a timely, natural and usable way. Here, cognitive systems play the role of an assistant, although one who is tireless, can consume vast amounts of structured and unstructured information, can reconcile ambiguous and even self-contradictory data, and can learn. Humans and computers working together are more effective than either one alone.
These systems have decision-making capabilities. Decisions made by cognitive systems are evidence-based and continually evolve based on new information, outcomes and actions. Currently, cognitive computing systems perform more as advisors by suggesting a set of options to human users, who ultimately make the final decisions. To do so, the systems rely on confidence scores — a quantitative value that represents the merit of a decision after evaluating multiple options — to help users make the best possible choice, including why a particular recommendation was made.
These systems can discover insights that perhaps could not be discovered by even the most brilliant human beings. Discovery involves finding insights and connections and understanding the vast amounts of information available around the world. With ever-increasing volumes of data, there is a clear need for systems that help exploit information more effectively than humans could on their own. While still in the early stages, some discovery capabilities have already emerged, and the value propositions for future applications are compelling. Advances in this capability area have been made in specific domains, such as medical research, where robust amounts of information exist.
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