Why Machine Intelligence is NOT Artificial [Human] Intelligence? AI is NOT Machine Learning, ML is NOT AHI, and Artificial NNs are NOT Human NNs. AA/AI rule 2: All Artificial Intelligence is Artificial Human Intelligence (AHI), divided as Narrow AI, Artificial General Intelligence (AGI), or Artificial Superintelligence (ASI). AA/AI rule 3: Real Machine Intelligence (MI) is NOT Artificial Human Intelligence (AHI), the capability of a computer system to mimic human intelligence/cognitive functions/behavior such as perception, learning, reasoning and problem-solving or NL communication. AA/AI rule 4: Machine Learning is NOT AHI.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What is Real #AI for Everything and Everybody Platform? The mainstream human-centric AI has some fundamental problems needing for fundamental solutions. First, it is philosophy, or rather lack of any philosophy, and blindly relying on statistics, its processes, algorithms, and inductive inferences, needing a large volume of big data as the "fuel" to train the model for the special tasks of the classifications and the predictions in very specific cases. Second, it is not a scientific AI agreed with the rules, principles, and method of science. Today's AI is failing to deal with reality and its causality and mentality strictly following a scientific method of inquiry depending upon the reciprocal interaction of generalizations (hypothesis, laws, theories, and models) and observable/experimental data. Third, there is no common definition of AI, and each one sees AI in its own way.

This article reviews the "Once learning" mechanism that was proposed 23 years ago and the subsequent successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches of AI, some classification standards and methods are discussed: 1) Human-oriented, machine-oriented, and biological-oriented AI R&D; 2) Information input processed by Dimensionality-up or Dimensionality-reduction; 3) The use of one/few or large samples for knowledge learning.