Theory and Practice of Artificial Intelligence

If you don't understand artificial intelligence, ask yourself because others don't understand it either. There is no generally applicable definition of artificial intelligence. The field is so vast that its actors will always have to develop a more precise definition of their own objectives in their context. Artificial intelligence is broadly defined as the simulation of human intelligence by machines programmed to learn, reason, and solve problems. Therefore, AI encompasses several subfields, including machine learning (ML), deep learning (DL), robots, natural language processing (NLP), and computer vision, etc., each with distinct applications and limitations. And yet, AI can be classified into two main categories: i-Narrowly defined AI (concrete definition of AI) Which is simply defined as an automated algorithm operating on the basis of statistical transition matrices, which allows it to increase the amount of existing information. ii- General AI (broad definition of AI): such as machine learning (ML), deep AI (DAI), image and voice recognition, etc. These broad AI systems that operate according to predefined parameters and lots of input data cannot be generalized beyond their designated functions. We believe that the term AI should be reserved for software capable of increasing the amount of information that is the function of only humans (and much less other creatures) and unitary 4D x-t statistical transition matrices. Note that the connection between artificial intelligence and the 4D x-t unitary space is revealed for the very first time. Some hypothetical AIs with human-like cognitive abilities including reasoning, writing and reading rules, pronunciation, specific tasks such as facial recognition, speech processing, etc. are usually called AI systems, which is not true. These systems operate in classic 3D+t space and obviously cannot increase the amount of existing information and therefore cannot be qualified as artificial intelligence. We can conveniently call these systems storage intelligence systems (SI systems) rather than artificial intelligence systems. On the other hand, concrete and narrow AI in 4D x-t unit space remains a well-defined theoretical and practical construct with many existing examples in physics and mathematics as well as in real daily life.