Cognitive Computing and Big Data Analytics
With huge advancements in technology in the last 30 years, the ability to gain insights and actions from data hasn’t changed much. In general, applications are still designed to perform predetermined functions or automate business processes, so their designers must plan for every usage scenario and code the logic accordingly. They don’t adapt to changes in the data or learn from their experiences. Computers are faster and cheaper, but not much smarter. Of course, people are not much smarter than they were 30 years ago either. That is about to change, for humans and machines. A new generation of an information system is emerging that departs from the old model of computing as process automation to provide a collaborative platform for discovery. The fi rst wave of these systems is already augmenting human cognition in a variety of fi elds. Acting as partners or collaborators for their human users, these systems may derive meaning from volumes of natural language text and generate and evaluate hypotheses in seconds based on analysis of more data than a person could absorb in a lifetime. That is the promise of cognitive computing.
Human Intelligence + Machine Intelligence
Traditional applications are good at automating well‐defi ned processes. From inventory management to weather forecasting, when speed is the critical factor in success and the processes are known in advance, the traditional approach of defi ning requirements, coding the logic, and running an application is adequate. That approach fails, however, when we need to dynamically fi nd and leverage obscure relationships between data elements, especially in areas in which the volume or complexity of the data increases rapidly. Change, uncertainty, and complexity are the enemies of traditional systems. Cognitive computing—based on software and hardware that learns without reprogramming and automates cognitive tasks—presents an appealing new model or paradigm for application development. Instead of automating the way we already conduct business, we begin by thinking about how to augment the best of what the human brain can do with new application capabilities. We start with processes for ingesting data from inside and outside the enterprise, and add functions to identify and evaluate patterns and complex relationships in large and sometimes unstructured data sets, such as natural language text in journals, books, and social media, or images and sounds. The result is a system that can support human reasoning by evaluating data in context and presenting relevant fi ndings along with the evidence that justifi es the answers.
This approach makes users more effi cient—like a traditional application—but it also makes them more effective because parts of the reasoning and learning processes have been automated and assigned to a tireless, fast collaborator. Like the fundamentals of traditional computing, the concepts behind smart machines are not new. Even before the emergence of digital computers, engineers and scientists speculated about the development of learning machines that could mimic human problem solving and communications skills. Although some of the concepts underlying the foundation technologies—including machine intelligence, computational linguistics, artifi cial intelligence, neural networks, and expert systems—have been used in conventional solutions for a decade or more, we have seen only the beginning. The new era of intelligent computing is driven by the confl uence of a number of factors:
■ The growth in the amount of data created by systems, intelligent devices, sensors, videos, and such
■ The decrease in the price of computer storage and computing capabilities
■ The increasing sophistication of technology that can analyze complex data as fast as it is produced
■ The in‐depth research from emerging companies across the globe that are investigating and challenging long‐held beliefs about what the collaboration of humans and machines can achieve
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