The Foundation of Cognitive Computing – Machine Learning
Cognitive computing has a lot of buzz behind it, but what does it really mean for IT? Traditional computer systems allow users to search for and access data quickly, giving people fast answers.
Cognitive systems analyze data and draw conclusions in near real time—and perhaps even faster. The idea of putting artificial intelligence into a computer system sounds like something out of science fiction, but there’s an explanation behind what makes these systems tick that’s rooted in science fact.
There are two fundamental principles that underlie every form of cognitive system: pattern recognition and probabilistic modeling.
Pattern recognition focuses on how computers identify relevant information based on past experience or knowledge; basically, when computers observe a situation (or stream of data), they can identify patterns.
Probabilistic modeling is more about determining cause-and-effect relationships within sets of information; again, using past experience or knowledge, computers can determine which factors influenced specific events or results and use those findings to predict future outcomes.
So how do these things relate to each other? Let’s say you have some raw data—say, spreadsheets with detailed sales figures by region.
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