Outcome Optimization i.e. making optimal choices. In very complex systems such as traffic flow and financial markets, finding an optimal outcome is a complex task. This is especially true when the data model may be changing rapidly. Traditional programs can’t adapt fast enough. Decision support. Decision support applications have been around for as long as computing systems have been around. Over time, however, the decisions that people make have become more and more complicated. This not only makes it hard to find experts capable of making good decisions, it makes it hard to train these experts. Cognitive computing shows promise in helping to walk average people through complex decisions where there is too much data for an average human to process. Finding relevant patterns in large amounts of non-uniform data that is constantly being updated. Current data analytics is fine for looking at pre-determined patterns in situations where the data doesn’t change much such as transactional data. There may be more of the data but the data itself is the same. Even social media data is relatively uniform compared to other types of unstructured data. More sophisticated analysis, such as content clustering, relies on training the system once to find a pattern to detect in other documents. Cognitive computing does what humans can’t, such as sifting through huge amounts of ever growing information, while doing what humans do best, finding patterns that are shifting when new data is introduced. Highly sophisticated search within a dynamic domain. For the most part, searching through unstructured data such as web sites is a fairly simply affair. Text is examined for key words and techniques such as stemming are used to insure the search is not too literal. Cognitive computing opens up possibilities for learning patterns in the data that will help when searching for data. In some cases, searching will happen by traversi
Cognitive computing is the culmination of 40 years of computer science research that is finally reaching commercialization. Two trends have converged to make commercialization a reality. First, there is now a need. Knowledge workers are overwhelmed with enormous amounts of data and no clear way to make use of it. Current computing techniques are reaching the limits of what we can do to make all this data useful for the average knowledge worker.
Second, we now have hardware and software platforms that are capable of dealing with the requirements of cognitive computing. In the past, it would take so long to run the complex algorithms and process the massive amounts of data that AI was rendered impractical for commercial applications. Outside of the government or a university, there were few organizations with the computing power to make an AI application useful even for narrow applications. Hardware and software systems have finally caught up with the needs of cognitive computing rendering it practical.