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2.2. Artificial / Deep-learning Neural Networks

First of all, neurons are not essential to intelligence, in the same way as feathers and flapping wings are not essential to aviation. So, neurons are not the source of natural intelligence.

Scientists fail to define intelligence in a natural way (as a set of natural laws). Without a natural definition of intelligence, AI is limited to engineering: specific solutions to specific problems. Artificial Neural Networks (ANN) are engineered to store an average pattern, based on a training set of patterns. As a consequence, the use of ANNs is limited to pattern recognition. And the use of Deep-learning Neural Networks (DNN) is limited to perform trained tasks, based on pattern recognition.

ANNs are lacking the logic implemented by natural intelligence. As a consequence, human intelligence (natural intelligence) is required to select the patterns of the training set. Humans are therefore the only naturally intelligent factor in pattern recognition. Not the ANN. The word “learning” is therefore a misfit term when used in regard to an ANN. To illustrate:

We don't have to feed a child thousands of pictures of a cat before a child is able to recognize a cat. One example of a cat may be sufficient for a child to distinguish this type of animal from other types of animal. At the moment the child sees another cat, it will point to the animal and ask “Cat?”, in order to get a confirmation that it has learned to distinguish this type of animal from other types of animal correctly.

My father taught me: “Don't become a monkey that learns a trick”. DNNs are engineered to perform a trick, based on pattern recognition. DNNs are lacking natural intelligence. So, they don't understand the essence of the task. Therefore, they need to be trained. Human intelligence (natural intelligence) is required to design the algorithms that describe the essence of the task. After a lot of training runs, the DNN has mastered to perform that trick, without understanding the essence of the task. Having designed the training algorithms, humans are the only naturally intelligent factor in performing the trained trick of a DNN. Not the DNN itself. The word “learning” is therefore also a misfit term in regard to a DNN. To illustrate:

We don’t need to play a game thousands of times, before a child is able to play this game. Explaining the rules of the game may be sufficient for a child to play that game, while the rules of a game can't be explained to a DNN.


In our brain, pattern recognition doesn’t provide the intelligence itself. Pattern recognition only provides the input for the intelligent (=hard-coded) brain. Self-driving cars work in a similar way: Pattern recognition provides the input on which the programmed logic responds.

The only way to improve pattern recognition in machines: To identify individual parts of each object, like the left ear of a cat, its right ear, its nose, its whiskers, its mouth, its tail, each eye, each leg, and so on.