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The World of Reality, Causality and Real Artificial Intelligence: Exposing the Great Unknown Unknowns

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"All men by nature desire to know." - Aristotle "He who does not know what the world is does not know where he is." - Marcus Aurelius "If I have seen further, it is by standing on the shoulders of giants." "The universe is a giant causal machine. The world is "at the bottom" governed by causal algorithms. Our bodies are causal machines. Our brains and minds are causal AI computers". The 3 biggest unknown unknowns are described and analyzed in terms of human intelligence and machine intelligence. A deep understanding of reality and its causality is to revolutionize the world, its science and technology, AI machines including. The content is the intro of Real AI Project Confidential Report: How to Engineer Man-Machine Superintelligence 2025: AI for Everything and Everyone (AI4EE). It is all a power set of {known, unknown; known unknown}, known knowns, known unknowns, unknown knowns, and unknown unknowns, like as the material universe's material parts: about 4.6% of baryonic matter, about 26.8% of dark matter, and about 68.3% of dark energy. There are a big number of sciences, all sorts and kinds, hard sciences and soft sciences. But what we are still missing is the science of all sciences, the Science of the World as a Whole, thus making it the biggest unknown unknowns. It is what man/AI does not know what it does not know, neither understand, nor aware of its scope and scale, sense and extent. "the universe consists of objects having various qualities and standing in various relationships" (Whitehead, Russell), "the world is the totality of states of affairs" (D. "World of physical objects and events, including, in particular, biological beings; World of mental objects and events; World of objective contents of thought" (K. How the world is still an unknown unknown one could see from the most popular lexical ontology, WordNet,see supplement. The construct of the world is typically missing its essential meaning, "the world as a whole", the world of reality, the ultimate totality of all worlds, universes, and realities, beings, things, and entities, the unified totalities. The world or reality or being or existence is "all that is, has been and will be". Of which the physical universe and cosmos is a key part, as "the totality of space and times and matter and energy, with all causative fundamental interactions".


Pinaki Laskar on LinkedIn: #AI #ml #robotics

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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 Create Causality of Machine Intelligence and Learning? Causation, and its powers and forces, processes and mechanisms, physical and mental, social and technological, is the engine of the world (as the positive and negative gravity, or dark energy, for the oscillating universe). Causal reasoning is the driving force of human mind/intelligence/intellect; for real causal mechanisms defines all the world's knowledge. The two opposing types of theories of causation, The Humean idealist theory (causation as regularities & invariants & patterns) the causal realist theory (causation as causal mechanism, causal processes, causal interactions, and causal laws, providing the mechanisms by which the world changes and machine works and human acts; to understand why something ever changes or events happen, we need to see how they are produced by these mechanisms). The Hume's theory holds that causation is entirely constituted by facts about empirical regularities among observable variables; there is no underlying causal nature, causal power, or causal necessity.


Pinaki Laskar on LinkedIn: #machinelearning #artificialintelligence #neuralnetworks

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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 the Mother Idea of All Ideas, Concepts, Rules, Laws or Algorithms? All in all, the idea of all ideas, the rule of all rules, or the law of all laws, the algorithms of all algorithms, or the Mother Discovery is certainly the concept of causality and causation as the real, circular, or reversible causality with the interactive causation, governing the causal order of the world and all its regions, domains and fields. The Six Layer Causal Hierarchy defines the Ladder of Reality, Causality and Mentality, Science and Technology, Human Intelligence and Non-Human Intelligence (MI or AI). The Causal World [levels of causation] is a basis for all real world constructs, as power, force and interactions, agents and substances, states and conditions and situations, events, actions and changes, processes and relations; causality and causation, causal models, causal systems, causal processes, causal mechanisms, causal patterns, causal data or information, causal codes, programs, algorithms, causal analysis, causal reasoning, causal inference, or causal graphs (path diagrams, causal Bayesian networks or DAGs). CAUSALITY AND GLOBAL ARTIFICIAL INTELLIGENCE We need a unifying model of reality/world in terms of causality/actuality, mentality/intelligence and computing/data /virtuality/cyberspace, where neural networks are brain-encoded causal networks.


Causal deep learning teaches AI to ask why

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Deep learning techniques do a good job at building models by correlating data points. But many AI researchers believe that more work needs to be done to understand causation and not just correlation. The field of causal deep learning -- useful in determining why something happened -- is still in its infancy, and it is much more difficult to automate than neural networks. Much of AI is about finding hidden patterns in large amounts of data. Soumendra Mohanty, executive vice president and chief data analytics officer at L&T Infotech, a global IT service company, said, "Obviously, this aspect drives us to the'what,' but rarely do we go down the path of understanding the'why.'"


What Is Causal Inference?

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We are immersed in cause and effect. Whether we are shooting pool or getting vaccinated, we are always thinking about causality. If I shoot the cue ball at this angle, will the 3 ball go into the corner pocket? What would happen if I tried a different angle? If I get vaccinated, am I more or less likely to get COVID? We make decisions like these all the time, both good and bad. Whenever we consider the potential downstream effects of our decisions, whether consciously or otherwise, we are thinking about cause. We're imagining what the world would be like under different sets of circumstances: what would happen if we do X? What would happen if we do Y instead? Judea Pearl, in The Book of Why, goes so far as to say that reaching the top of the "ladder of causation" is "a key moment in the evolution of human consciousness" (p. Human consciousness may be a stretch, but causation is about to cause a revolution in how we use data. In an article in MIT Technology Review, Jeannette Wing says that "Causality…is the next frontier of AI and machine learning." Get a free trial today and find answers on the fly, or master something new and useful. Causality allows us to reason about the world and plays an integral role in all forms of decision making. If we impose a fine on parents who are late picking up their children from daycare, will lateness decrease? Causality is essential in medicine: will this new drug reduce the size of cancer tumors? This kind of reasoning involves imagination: we need to be able to imagine what will happen if we do X, as well as if we don't do X. When used correctly, data allows us to infer something about the future based on what happened in the past.