What is The Universal Machine Intelligence Model (UMIM), the #RealityAI? UMIM, as real autonomous artificial intelligence (RAAI) with deep causal learning, knowledge and understanding about the world of reality provided by its key components/modules: Machine World Model, world model engine (WME); Master Algorithm; World Data Framework World Data; Global Knowledge Base, World.Net; Domain Knowledge Base, Domain.Net; The project of creating human-like and human-level artificial intelligence (hlhlAI) as Artificial General Intelligence as machine intelligence and learning in three ways: Assuming that machine intelligence replicates/imitate only special parts of human intelligence, cognition or intelligent behavior, ML, DL, ANNs, developing #artificialintelligence ONLY for specific purposes (ANI); Assuming that machine intelligence is NOT identical to human intelligence, Weak AI; Assuming that machine intelligence is identical to human intelligence, Strong AI; My position is different, relying on science and its key subject of reality and interactions. First, we SHOULD not develop computers with human-like intelligence, but with human-level intelligence and beyond. Second, computer intelligence/power will never develop into human reason, the two are fundamentally different. Third, computers SHOULD have a causal model of reality, or machine intelligence and learning (MIL) must be Causal/Real MIL.

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 How to develop The Causal #AI Theory? The #CausalAI is analyze the public data sets taking the following programming steps: Finding appropriate empirical evidence to evaluate the degree to which a causal claim or theory is or is not supported; Developing a causal theory about the relationship between an independent variable (X) and a dependent variable (Y) from the data set; Identifying the credible causal mechanism that connects X to Y; Explaining its answers: (a) X causes some Y (b) Could Y cause X? (c) What other variables (Z) would you like to control for in your tests of this theory?

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 It is a common knowledge that current ML technology fails when applied to dynamic, complex systems. It produces static models that overfit to yesterday's world. The models are data-hungry and unintelligible to humans, as listed below, Static models Historic correlations Black box Observational data only Predictions only And Causal AI comes with the competitive features, Dynamic models Causal drivers Explainable Understands business context Predictions, interventions & counterfactuals Causal AI is a new category of intelligent machines that understand cause and effect ― a major step towards true AI. It is widely recognized that Understanding Causality Is the Next Challenge for Machine Learning. Deep neural nets do not interpret cause-and effect, or why the associations and correlations exist.