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

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

"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."- Isaac Newton

"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". - Azamat Abdoullaev

The 3 biggest unknown unknowns are described and analyzed in terms of human intelligence and machine intelligence.

It is the best ideas, constructs, or concepts humans ever created:

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 ofReal AI Project Confidential Report: How to Engineer Man-Machine Superintelligence 2025: AI for Everything and Everyone (AI4EE).

Real AIis designed as a true AI, a new generation of intelligent machines, thatsimulates/models/represents/maps/understands the world of reality, as objective and subjective worlds, digital reality and mixed realities, its cause and effectrelationships, to effectively interact with any environments, physical, mental or virtual.

It overrules the fragmentary models of AI, as narrow and weak AI vs. strong and general AI, statistic ML/DL vs. symbolic logical AI.

Universal Artificial Intelligence, and how much might cost Real AI Model

The scope and scale of the world as the entity of entities, the totality of totalities, the system of systems, or the network of networks, as represented with human minds and machine mentality, may be determined by the relationships among several key knowledge domains:

what man/machine knows, aware and understand;

what man/AI does not know, or what man/AI does not like to know, aware but don't understand;

what man/AI cannot know, understand but not aware of

what man/AI does not know what it does not know, neither understand nor aware of

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.

The World as a Whole as theGreatest Unknown Unknowns

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.

Some best attempts to define the world could be found in philosophy/metaphysics, as

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. Armstrong, A World of States of Affairs) ,

"World of physical objects and events, including, in particular, biological beings; World of mental objects and events; World of objective contents of thought" (K. Popper, the model of three interacting worlds), etc.

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". String theory predicts an enormous number of potential universes, of which our particular universe or cosmos (with its particles and four fundamental forces of nature, gravity, the weak force, electromagnetism, the strong force, governing everything that happens in the universe) represents only one.

In all, the world is the totality of all entities and relationships, substances (actual or mental), structures (actual and conceptual), states (actual or mental) changes and processes (past, present and future), relationships (actual and conceptual) or phenomena, whether observable or not, actual, mental, digital or virtual.

This includes as really existing physical objects, immaterial things, unobservable entities posited by scientific theories like dark matter and energy, minds, God, numbers and other abstract objects, all the imaginary objects as abstractions, literary concepts, or fictional scenarios, virtual and digital objects, the internet, cyberspace, multiverses, metaverses, and all possible worlds. As to scientific realism, they all are parts of the real world having some ontological status or certain causal power.

Our world conception encompasses theWordNet's andImageNet'sentity, "that which is perceived or known or inferred to have its own distinct existence (living or nonliving)", with all its content and classifications.

As it is systematically and consistently presented inUniversal standard entity classification system [USECS]for human minds and machine intelligence:

The World Entities global reference

The Intelligent Content for iPhones, X.0 Web, Future Internet and Smart People

The Language of the World of Things;

THE CATALOG OF THE WORLD; THE WORLD CATALOGUE OF SUBSTANCES; THE WORLD CATALOGUE OF STATES

THE WORLD CATALOGUE OF CHANGES; THE WORLD CATALOGUE OF RELATIONS

THE ENCYCLOPEDIC KNOWLEDGE REFERENCE; SMART KNOWLEDGE WEB;

INTELLIGENT INTERNET OF EVERYTHING

https://www.slideshare.net/ashabook/universal-standard-entity-classification-system-usecs

There are few basic levels of the world of reality, material, ideal and mixed:

I-WORLD, IWORLD, iWORLD, iWorld: AI World: Intelligent, Innovative, Interconnected, Instrumented, Inclusive, Green WORLD: Smart CONTINENTS, COUNTRIES, CITIES, AND COMMUNITIES

As such,the World of Reality is Actuality - Mentality - Virtuality Continuum

There is no such thing as a mixed reality (MR), is a hybrid of reality and virtual reality, but a mixed actuality, the merging of physical and virtual worlds producing new environments and visualizations, where physical and digital objects co-exist and interact in real time.

There is no such thing as an augmented reality (AR), but an augmented actuality (AA), an interactive experience of a physical environment where the objects that reside in the actual world are enhanced by computer-generated perceptual information, as across multiple sensory modalities, visual, auditory, haptic, somatosensory and olfactory.

There is no such thing as a computer-mediated reality, but a computer-mediated actuality, which refers to the ability to manipulate one's perception of actuality through the use of a digital technology, a wearable computer or a smartphone.

There is no such thing as a simulated reality, but a simulated actuality, that actuality, physicality, or materiality could be simulatedas by quantum computer simulationto a degree indistinguishable from "true" actuality, as in human dreams.

'METAVERSO', the sum of all virtual worlds, augmented reality, and the Internet.

Then conscious minds may or may not know that they are inside a natural simulation, a mentally generated world, as if created by theevil demon, also known asDescartes' demon,malicious demonandevil genius, and described as the Transformation of Things.

Again, the essence of Reality, its Actuality, Mentality and Virtuality, is Causality or Causation, the second biggest unknown unknowns.

Reality, Universal Ontology and Knowledge Systems: Toward the Intelligent World

The World of Causality as a Great Unknown Unknowns

All the world's knowledge derives fromcausation driving the world as the engine of the universe.

Humans develop an ability to understand causality, causal power and mechanisms, making inferences and predictions and forecasting based on cause and effect, at an early age.Understanding causality is equal to a deep learning about the world, its mechanisms, forces, processes, phenomena, laws and rules and the behavior of things. There is an increasing awarenesshow the understanding of causality revolutionizes science and the world[The Book of Why: The New Science of Cause and Effect].

It defies any common knowledge and understanding, but the real nature and mechanisms of cause-effect relationships is still the greatest unknown known. Still we are completely unaware that we are unaware of its major features

Due to process philosophy, ontology of becoming, orprocessism, it is now a known unknown that "every cause and every effect is respectively some process, event, becoming, or happening"

But if"Ais the cause andBthe effect" or "Bis the cause andAthe effect" is true is the unknown unknown.

A standard narrative of causality is wrong and misleading the following:

"Causal relationships suggest change over time; cause and effect are temporally related, and the cause precedes the outcome. It is a unidirectional relationship between acause and itseffect [the final consequence of asequenceof actions or events expressed qualitatively or quantitatively, the result, the output]. All events are determined completely by previously existing causes, known as causal determinism.

Causality (causation, or cause and effect)is influence by which one event, process, state or object (a cause) contributes to the production of another event, process, state or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future".

The law, principle, or rule of universal causation is generally asserted arbitrarily, in every loose ways, namely:

"everything in the universe has a cause and is thus an effect of that cause"

"every change in nature is produced by some cause"

"everyeffecthas a specific and predictablecause"

"the universal law of successive phenomena is the Law of Causation"

"every event or phenomenon results from, or is the sequel of, some previous event or phenomenon, which being present, the other is certain to take place"

"relationships where a change in one variable necessarily results in a change in another variable"...

"relation between two temporally simultaneous or successive events when the first event (the cause) brings about the other (the effect)".

Causation could be an example of a regularity analysis, counterfactual analysis, manipulation analysis, statistical analysis, or probabilistic analysis.

A real and true causality MUST be a symmetrical, circular, bidirectional and reversible productive relationship, like as formalized by Bayes rule/theorem/law, describings the probability of an event, based on prior knowledge of conditions that might be related to the event:

P(X/Y) P(Y) = P (Y/X) P (Y)

By its very design, Bayes rule/theorem/law is essentially about Real Causality, or Interactive Causation, Circular Causality, modeling the Universal Law of Causal Reversibility in probabilistic terms.

It is a basic law in real statistics and causal probability theory. In statistical classification, two main approaches are commonly called the generative approach and the discriminative approach, withgenerative classifiers("generate" random instances (outcomes), a model of the conditional probability of the observableX, given a targetY =y,orjoint distribution) anddiscriminative classifiers(conditional distribution or no distribution).

Given a model of one conditional probability, and estimated probability distributions for the variables X and Y, denoted P(X) and P(Y), one can estimate the opposite conditional probability using Bayes' rule:

P(X|Y)P(Y)=P(Y|X)P(X)

For example, given a generative model for P(X|Y), one can estimate:

P(Y|X)=P(X|Y)P(Y)/P(X)

And given a discriminative model for P(Y|X), one can estimate:

P(X|Y)=P(Y|X)P(X)/P(Y)

Both types of classifiers or models or learning or algorithms, generative and discriminative, are covering such types of models, as Hidden Markov Model(HMM), Bayesian network (e.g. Naive bayes, Autoregressive model) or Neural networks, all underpinned by direct and reversed causal processes, X > Y and Y > X.

And statistic models are known for their wide applications to statistical physics, thermodynamics, statistical mechanics, physics, chemistry, economics, finance, signal processing, information theory, pattern recognition (speech, handwriting, gesture recognition, part-of-speech tagging, etc.), AI, ML, DL and bioinformatics.

Twentieth century definitions of causality derive upon statistics/probabilities/associations. One event (X) is said to cause another if it raises the probability of the other (Y):

P (Y/X) > P (Y)

Then a real Bayes' network is a causally reversible probabilistic network, presented as a probabilistic graphical model representing a set of causal variables and their conditional dependencies via a bi-directional cyclic graph (DCG). It can model any stochastic processes and random phenomena in the world.

For example, a real Bayesian network could represent the probabilistic causal relationships between diseases and symptoms, and vice versa. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Given diseases, the network can be used to compute the probabilities of the presence of various symptoms.

It embraces as a special case a Bayesian network (belief network, or decision network) as a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).

So, it is still a big unknown that a[true and real] causality is a sufficient and necessary relationship between cause and effect, or change in a cause changes an effect, and vice versa.

Cause effects Effect, if and only if the Effect inversely causes the Cause

Or,X causes, affects, influences, produces, or changes Y, if and only if Y affects (produces) X.

Changes in one variable X cause changes in others Y, IFF changes in Y cause changes in X.

The Principle of Reversible Causation (PRC) implies the structuralcausal models of reality operating with the most substantial general statements and observations about it.

All things act on and react upon by means of causal mechanisms and causal pathways. All entities receive and respond to stimuli from their environments. All organisms receive/detect/perceive and react/respond to stimuli from their environments. All cells receive and respond to signals from their surroundings/micro-environments. And signaling agents could be physical agents, chemical agents, biological agents, as antigens, or environmental agents, as in the immune system.

Again,Popper's three worldsof realityinvolvingthreeinteracting worlds, calledworld1,world2 andworld 3, are subject to the PRC.

World 1: the world of physical objects and events, including biological entities

World 2: the world of individual mental processes, the world of subjective or personal experiences

World 3: the world of the products of the human mind, having an effect back on world 2 through their representations in world 1.

It is like world 3 as a world of objective knowledge(languages, songs, paintings, mathematical constructions, theories, culture) is CAUSALLY acting on world 1 through world 2, having a CAUSAL effect back on world 2 through their representations in world 1.

Popper strongly advocates not only the existence of the products of the human mind, but also their being real rather than fictitious.As long as these have a causal effect upon us, they ought to be real. Products of the human mind, for example scientific theories, have proven to have an impact on the physical world by changing the way humans build things and utilize them. Popper believes that the causal impact of world 3 is more effective than scissors and screwdrivers (Popper, K. R. (1978). Three worlds. The Tanner Lecture on Human Values. The University of Michigan. Ann Arbor).

It is most critical to recognize the scope and scale of an interactive [cause-effect] relationship and how it differs from a non-causal relationship, link, or correlation, spatial contiguity or temporary sequence.

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