Did you realize something? When analysts and media write about Artificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. (), most of them unfortunately only talk about machine . In doing so, they mention and machine in the same breath and thus equal with one single technology. This is wrong and a concerning progress.
In 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 machine and consists of several different components that provide intelligent solutions.
Machine 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 machine An algorithm is a fixed set of instructions for a computer. It can be very simple like "as long as the incoming number is smaller than 10, print "Hello World!". It can also be very complicated such as the algorithms behind self-driving cars.. And since some machine use cases succeeded, machine is hyped by the media.
However, machine 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 machine as a single concept. Machine needs lots of sample data or data in general to learn and be able to find valuable information respectively results in patterns. […]