Machine learning process allows processors to describe and use laws that were not explained clearly by the programmer.
Here is an effort to make a short view and description of how this thought is applied in different industry areas.
The initial point is that this procedure can help persons by helping them to find prototypes or dependencies that are not observable by humans.
The key idea here is that you should not explain each type of irregularity. You give a full-size list of diverse known cases to the structure and the system uses it for identifying the exceptions.
It allows assembling a large amount of data using an ample range of meaningful factors. An operator can’t control resourcefully with more than few hundreds of thing with many factors. A machine can do combining more efficiently.
The next point is that the system is the substitute of people. The system makes a study of people's actions, makes rules supporting this information and applies these laws acting instead of people.
It is for all types of standard decisions making. There are many activities which need for standard actions in standard circumstances. People create some "standard decisions" and eliminate cases which are not good. There are no causes, why devices can't do documents processing, calls, bookkeeping, customer support etc. Such scheme will save the money for business owners. Praiseworthy investigation of information will still be made by individuals.
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