Thinking Machines
IBM
IBM Research
Cognitive computing systems learn and interact naturally with people to extend what either humans or machine could do on their own. They help human experts make better decisions by penetrating the complexity of Big Data.
Most data now comes in unstructured forms such as video, images, symbols and natural language - a new computing model is needed in order for businesses to process and make sense of it, and enhance and extend the expertise of humans.
Rather than being programmed to anticipate every possible answer or action needed to perform a function or set of tasks, cognitive computing systems are trained using artificial intelligence (AI) and machine learning algorithms to sense, predict, infer and, in some ways, think.
Cognitive computing systems get better over time as they build knowledge and learn a domain - its language and terminology, its processes and its preferred methods of interacting. Unlike expert systems of the past which required rules to be hard coded into a system by a human expert, cognitive computers can process natural language and unstructured data and learn by experience, much in the same way humans do. While they'll have deep domain expertise, instead of replacing human experts, cognitive computers will act as a decision support system and help them make better decisions based on the best available data, whether in healthcare, finance or customer service.
In traditional AI, humans are not part of the equation, yet in cognitive computing, humans and machines work together. To enable a natural interaction between them, cognitive computing systems use image and speech recognition as their eyes and ears to understand the world and interact more seamlessly with humans. It provides a feedback loop for machines and humans to learn from and teach one another. By using visual analytics and data visualization techniques, cognitive computers can display data in a visually compelling way that enlightens humans and ...
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