Present classical AI models, mainly based on supervised and unsupervised learning, may be appropriate to simulate human brain learning processes corresponding to the intelligence stage of children, when learning basics about their surroundings, but they are completely insufficient and totally inadequate to simulate highly sophisticate intelligence processes in the human brain when finding extremely innovative solutions or when making breakthrough scientific discoveries. Therefore, in order to do both, to decrypt codes embedded in the structure of Nature, and to simulate sophisticate and complex human brain processes, the present AI models should either be replaced or they should be extended and completed with new enhanced intelligence models, taking into account the randomness factor involved in those sophisticate thinking processes. An enhanced intelligence model (EI model) is defined by author as an intelligence model which takes into account of an increased degree of randomness, because when solving highly complicated problems, the information is not purely procedurally processed, but it depends of a serious degree of randomness, which is seriously disregarded by the present classical AI models
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