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Cognitive Computing Will Redefine IoT Landscape

With billions of interconnected devices throughout the globe and their numbers taking an exponential growth everyday – the real concern is, deriving the necessary and required value from the enormous of data generated by them within a small span of time.

SwissCognitivePredictions say that by 2020, the IoT network will comprise of nearly 30 billion devices. But today, almost 90 percent of the data generated by these devices go unused. At present, we are merely scratching the surface of the IoT. We are yet to utilize the real potential that IoT has to offer.

Match made in heaven

Research have pointed out the increasingly symbiotic relationship between the Internet of Things (IoT) and computing – the artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. capabilities of the latter makes a perfect combination for the speed and size of the former. IoT provides the data quantities needed to optimize the value and Return on Investment (ROI) of analytics solutions. Such studies are indicative of a trend which will establish the presence of IoT into the data world more strongly.

computing can be perceived as a simulation of human thought processes in a computerized model. computing involves self- systems that use data mining, pattern recognition, vision and to mimic the way the human brain works. computing systems use machine algorithmsAn algorithm is a fixed set of instructions for a computer. It can be very simple like "as long as the incoming number is smaller than 10, print "Hello World!". It can also be very complicated such as the algorithms behind self-driving cars. and neural networksNeural Networks are simplified abstract models of the human brain. Usually they have different layers and many nodes. Each layer receives input on which it carries out simple computations, and passes on the result to the next layer, by the final layer the answer to whatever problem will be produced. . Such systems continually learn and acquire knowledge from the data fed into them continuously. In this process, the system learns to accurately refine the way they look for patterns, as well as enhance their methods of processing data. As a result, they become capable of understanding and predicting new problems and modelling their possible solutions.

Making complex simple

computing addresses complex problems – problems which are ambiguous and uncertain. In today’s dynamic and information – rich world, data is of various types and forms. At such a fast paced world, the way users use and interact with their surrounding device has changed drastically. Users expect to interact with the machine in a way they interact with other human beings. Users expect immediate insights from the massive amount of data generated in a fraction of a second. computing aids in keeping up with the pace by providing a synthesis of information, influence, context and insights. This requires systems to evaluate all the available data, algorithms, outcome required and suggest the best possible (not just the right) way to approach an analytical solution to produce the desired insights. […]

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