FAGMA Media Research Solutions

Pattern matching is not enough

Pattern matching is not enough

Did you realize something? When analysts and media write about (), most of them unfortunately only talk about . In doing so, they mention and in the same breath and thus equal with one single technology. This is wrong and a concerning progress.

SwissCognitiveIn particular, it is confusing the market during a time when 58 percent of organizations worldwide (according to Forrester) are still researching . However, is more than just and consists of several different components that provide intelligent solutions.

Machine learning is not equal to

First of all, machines do not understand. This is by far the biggest misconception while discussing , in particular in the context of virtual private assistants like Amazon or Apple Siri. Machines match data to predefined data patterns of understanding. Thus, understanding is a question of the size of a data pool, because the more data is matched to something we can understand the more “understanding” a machine seems to have.

The biggest issue is that research has been an oscillating system between several techniques. Whenever one does not do “the job completely,” people get frustrated and turn to another one. And right now, the market is of the opinion that organizations do have tons of data that can be utilized together with algorithms. And since some use cases succeeded, is hyped by the media.

However, just helps to identify patterns within data sets and thus tries to make predictions based on existing data. It is most important to check the plausibility and correctness of the results since you can always find something in endless sets of data. And that’s also one of the drawbacks if you consider as a single concept. Machine learning needs lots of sample data or data in general to learn and be able to find valuable information respectively results in patterns. […]

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