From machine learning to IBM Watson in the kitchen – CBR explains cognitive computing.
There is no concrete definition assigned to cognitive computing, but it is broadly described as technology platforms which are based on Artificial Intelligence and Signal Processing. These platforms, billed as one of the key technologies for big data, can encompass machine learning, reasoning, speech, and language processing.
The term cognitive computing is usually used to describe hardware or software that acts or processes information in a similar way to how the human brain functions. This mimicking of the human brain has led to cognitive computing being recognised as a new type of computing, with IBM describing cognitive computing as ‘systems that learn at scale, reason with purpose and interact with humans naturally.’
The ultimate goal of cognitive computing systems is to create automated IT systems capable of solving problems without human supervision or assistance.
It is common for people to think of Artificial Intelligence and cognitive computing as one in the same, but cognitive systems differ in the fact that they comprise of separate components, from different disciplines, working together.
Cognitive computing platforms can consist of many different features depending on the application, but common features can grouped under the labels adaptive, interactive, stateful and contextual.
An adaptive feature of a cognitive system encompasses what is effectively machine learning – the system reacts to changing information and tailors goals and requirements to that information.