Deep Learning uses Deep Neural Networks to learn models that can then be applied to problems. These are mostly very focused, for example face recognition, emotion recognition, object recognition and speech recognition. That is why these AI systems are called narrow AI or “weak AI”. Most of the time, they are able to do a specially learned task quite reliably. The AI benefits from being able to learn from a lot of data and to process information quickly. One should not succumb to the fallacy that the system is really intelligent.
In a study, computer scientists found that artificial intelligence systems failed an eye test that a child could easily pass. In it, the researchers presented a computer vision system with a living room scene. It is able to correctly identify the objects: it has correctly recognized a chair, a person and books on a shelf. Then an abnormal object is introduced into the scene: the image of an elephant. The mere presence of the elephant confuses the system. It begins to recognize a chair as a couch and the elephant as a chair. However, other objects are no longer recognized.
This is an interesting behavior. A person perceives the scene with the elephant as a whole and is able to recognize the presence of the elephant in the room as “wrong”. In contrast, artificial intelligence creates visual impressions from individual information, as if one were reading a description in Braille. In principle it processes pixel by pixel and forms more complex representations from it, but unfortunately never recognizes the absurd presence of the elephant. Here the model reaches its limits.
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