And insight applications use cognitive technologies—specifically advanced analytical capabilities such as machine learning—to uncover insights that can inform operational and strategic decisions across an organization. It may be valuable in this scenario to use natural language processing techniques to automate the process of reading and extracting the terms from a body of contracts.For certain business problems, cognitive technologies may be more than just viable and valuable. Examples of cognitive technologies include computer vision, machine learning, natural language processing, speech recognition, and robotics. 27 January 2015
Because of this, we think organizations will need the skills to design workflows, tasks, and interfaces to enable this kind of person-machine interaction.Recent and ongoing progress in the development and commercialization of cognitive technologies is creating valuable new opportunities for organizations. But these opportunities come with challenges and risks. The Georgia agency mentioned earlier—which has to process 40,000 campaign finance disclosure forms per month, many of which are handwritten—is an example of this. Computer vision has made great strides in recent years—Facebook claims it can recognize faces with 97 percent accuracy—but it still cannot generally recognize multiple objects in a scene or reliably understand what actions it is witnessing.Not only may cognitive systems produce imperfect results, they may also require a significant investment of human time to train or configure before they can do their work. These may be good automation candidates. Product applications embed the technology in a product or service to provide end-customer benefits. These insights influence the bank’s decisions on setting fees and offering consumer perks, and how customer service representatives should respond to certain customer inquiries about services and fees.Aetna and GNS Healthcare teamed up to use machine learning and other analytic techniques to improve the health of patients and reduce the cost of caring for them. Natural language process systems may require a time-consuming configuration process that defines the concepts and vocabulary that are most important to the systems’ users.For the foreseeable future, then, the use of cognitive technologies implies that humans will be very much “in the loop”—not only to develop, customize, and train the systems, but also to oversee, guide, and improve them. Another example is Twitter, which uses natural language processing to help advertisers understand when, why, and how its users post comments about television shows and TV advertising; this capability would not be possible without cognitive computing to analyze the language of the tweets.We do not intend to suggest, with our simple three Vs framework, that investing in cognitive technologies is a simple matter.
But there is a broad range of problems for which cognitive technologies can provide at least part of a solution.
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