Machine's Worldview: Standard Universal Ontology (SUO): General Machine Intelligence and Learning = Real/True/Interactive AI/ML/DL/NNs

"We are what we think. All that we are arises with our thoughts. With our thoughts we make the world". The Buddha (Byrom, 1976/1993, p. 1)

After all, what could be more important or influential than the way an individual, a family, a community, a nation, or an entire culture conceptualizes reality? Is there anything more profound or powerful than the shape and content of human consciousness and its primary interpretation of the nature of things? When it comes to the deepest questions about human life and existence, does anything surpass the final implications of the answers supplied by one's essential Weltanschauung?

D. K. Naugle, Worldview: The History of a Concept, Wm. B. Eerdmans Publishing, 2002, p.345.

The world forms the intelligence, the intelligence forms the world.

No world model, no intelligence and consciousness, and vice versa. Author

The Singularity of Philosophy for Worldviews, Human or Machine

Philosophy is the conception of the world, covering all the bodies of world knowledge,?reality and existence; reason and mind;?knowledge and data; values and meaning and language.

It is eclectic of all possible methods,?from Socratic questioning to rational argument to dialectics to logical analysis and mathematical symbolism.

Being the systematic study of the universals and?the fundamentals, philosophy involves all possible methods,?be it formal or non-formal, logical or intuitive, scientific or religious.

One might apply science or mathematics or logic or statistics or probability theory or computing simulation or experimental methods, still it is philosophy,?the "love for wisdom".

Ab initio, it had emerged as natural philosophy to focus?on astronomy and mathematics,?physics and medicine. With?Newton's Mathematical?Principles of Natural Philosophy, physics went its?own?experimental way,?while keeping metaphysical assumptions and presumptions.

What is still valid is its ancient troyka:

• Metaphysical philosophy (global ontology and ontologies, cosmology, formal sciences, mathematics, logic, computer science, epistemology, philosophy of science )
• Natural philosophy (the sciences, from physics to life sciences)
• Moral philosophy (ethics, esthetics, the social sciences)

When you start some big conceptual project, as "formal or scientific or computing philosophy", it is critical to follow the full conceptualization life cycle:

to observe the world of entities and interactions

to classify?the world of entities and interactions

to define the world the world of entities and interactions

to conceptualize the world of entities and interactions

to evaluate and validate and test the world of entities and interactions

to manipulate the world of entities and interactions

The formal philosophy project failed, regardless of having the proponents of logical positivism/empiricism, the Berlin/Vienna?circles, Wittgenstein, Carnap, Quine, Popper.?

Bottom line. Philosophy is not about some formal methods, such as formal logic or probability theory, but about the?universal and fundamental?things, about the world knowledge, the world information, or the world data as the data universe.

And this is its singularity,?as the search for absolute knowledge and intelligence.

There are very few things in the world, which are universal and fundamental at the same time.

Now why today's AI as ML and DL, particularly generative AI and LLMs models, are deeply defective by default, being without any worldviews (world models). AI models have no a?priori knowledge of the world of reality and existence, causality and mentality, humanity and values,?knowledge and data, programming and computing, etc.?

It is just data pattern?matching,?rote memory, probabilistic combinatorics and statistical associations, with no idea?how the world is structured, behaves and changes.

A Super Science of Universal Ontology for a Superintelligence

We like jumping on every buzzword and every fad and trendy?hype in tech, such as semantic web, blockchain, NFTs, metaverse, DL, LLMs, Chatbots, GNNs, while ignoring?age-old principles which could revolutionize the way we create new tech.

From one?side we?have the newly?created world of data; from another, the ancient science of ontology, as?the study of what is, of what exists or of what is real.?

The common ground?between ontology in philosophy and science and in computer science and AI is that both are to describe everything that is, i.e., entities, states, ideas, and events, with all the properties and relationships or interactions between these things, according to a standard system of categories.

?That means that data should not only exist in the form of digital data or hypertext documents and hyperlinks between them, or text data, image?data, video data, audio data, etc. Data should be viewed as what it represents — objects, people, places, events, ideas, activities, processes and so on. Or, data, as data points or data sets, should be viewed as real-world or ontological data,?the subject matter of

Standard Data Ontology = Standard Universal Ontology + Computer Science + Statistics/Data Science + AI/ML/DL > General Machine Intelligence and Learning?

Data ontology is a way of processing and interrelating data in various formats based on ontological models, categories and concepts.

So, Real AI as General Machine Intelligence and Learning should have as its major category Real/Ontological/Linked Data, with all its ontological classes and relationships, referring to reality, its entities, processes and interrelationships.

That means that any artificial intelligent systems, AI, ML, DL, or ANNs, to be such should have <standard data ontology> layers, as its input and output data standardizing algorithms/programs, limiting complexity and organizing data into information and knowledge and intelligence.

There is a fundamental way to essentially improve the quality of ANNs,?making them real intelligent,?embedding an ontology layer, for data ontologizing (DO).

DO = Intelligent Data (Information/Knowledge) = Data (Standardization/Normalization)?+ SDO (causal/scientific world-model/worldview; sense/meaning making of the world).

It is like the top and bottom of the neocortex, the?neopallium,?isocortex, or the?six-layered cortex, made up of six layers, labelled from the outermost inwards, I to VI, and involved in sensory perception, cognition, reasoning, language and motor commands. Neurons are good ontologists somehow identifying the nature of each individual stimulus responding differently to different types of punishments or rewards, as food or sugar water.

Thus you transform the weighted numerical networks of different architectures to causal intelligent data networks, the onto-mathematical base of real/true AI.??

Real AI = AI/ML/DL/NNs + Standard Universal Ontology (SUO): Reality > Data > Intelligence: General Machine Intelligence and Learning

It is like human intelligence. Missing general ontology, as world models/world systems/worldviews, is missing the general intelligence potential. Then one might?be a great mathematician?or physicist or economist or psychologist or sociologist or developer or engineer, but lacking general intelligence and knowledge, like narrow/weak artificial super intelligences doing ONLY one specific task, translating, chess gaming, driving. writing, painting, singing, etc. .

??Here is a sample of the Twitter HIN showing how ontology?could make?or break it all.?

An example heterogeneous information network (HIN) where |?? | = 8 and |??| = 9.?

There are four entity types (T): ‘User’, ‘Tweet’, ‘Advertiser’, and ‘Ad’.?

There are seven types of relationship (R): ‘Follows’, ‘Authors’, ‘Favorites’, ‘Replies’, ‘Retweets’, ‘Promotes’, and ‘Clicks’.?

?SUO: the Comprehensive, Consistent and Coherent Worldview

Standard Universal Ontology (SUO) is the Transdisciplinary Science of Reality involving the world data/information/knowledge.

It implies the Scientific Modeling of the World of all Possible Realities in the integrated context of Ontological, Physical, Conceptual, Mathematical, Statistical, and Computing Models, Representations and Simulations to interpret, explore, explain, and estimate or predict.

The SUO is an interactive causal model (ICM) of the causal mechanisms of reality and its systems, where?mathematical models and computing simulations represent causality and causation, providing learning and inferences, explanations and predictions about causal interrelationships from the data universe of data points and data sets.?

The SUO as a unified science of reality is the fundamental core of any intelligent structures, processes, and activities, mind and intelligence, philosophy, science and engineering, technology and industry, social order, economy and government.

The SUO is the Comprehensive, Consistent and Coherent Worldview.

"A worldview is a way of describing the universe and life within it, both in terms of what is and what ought to be. A given worldview is a set of beliefs that includes limiting statements and assumptions regarding what exists and what does not (either in actuality, or in principle), what objects or experiences are good or bad, and what objectives, behaviors, and relationships are desirable or undesirable. A worldview defines what can be known or done in the world, and how it can be known or done. In addition to defining what goals can be sought in life, a worldview defines what goals should be pursued. Worldviews include assumptions that may be unproven, and even unprovable, but these assumptions are superordinate, in that they provide the epistemic and ontological foundations for other beliefs within a belief system." (adapted from Koltko-Rivera, 2000, p. 2)

Worldview has gone by many names in the literature: “philosophy of life” (Jung, 1942/1954), “world hypotheses” (Pepper, 1942/1970), “world outlook” (Maslow, 1970a, p. 39), “assumptive worlds” (Frank, 1973), “visions of reality” (Messer, 1992, 2000), “self-and-world construct system” (Kottler & Hazler, 2001, p.361), and many others. In anthropology, it has been denoted as “cultural orientations” (Kluckhohn, 1950), “value orientations,” “unconscious systems of meaning,” “unconscious canons of choice,” “configurations,” “culture themes,” and “core culture” (Kluckhohn & Strodtbeck, 1961/1973, pp. 1

As such, the SUO embraces fundamental categories and concepts, classifications and taxonomies, theories and ontologies, semantic models and knowledge graphs, domain ontologies and scientific methodologies, information models and data schemas, data structures and intelligent algorithms, mathematical models and computing models, statistical techniques and data analytics models, etc.

The SUO is the fundamental framework for general intelligence, human or machine, commonsense knowledge and causal reasoning and automated data/information/knowledge learning and inference, integration and transfer across any domains.

It makes the algorithmic foundation for modeling real-world, high-quality, linked and coherent data because of its capacity to effectively represent reality and its domains, with all the key entities, properties, processes, relationships and interactions.

In its essence, the SUO/SDO is the Catalogue of the World, the Universal Schema of all Things, organized as USECS (Universal Standard Entity Classification System, 1717 pages):

USECS = The World Ladder: Being, Existence, Entity, Thing (the universal class, everything) > … > Nonbeing, Nonexistence, Nonentity, or Nothing (the null class) The Comprehensive Hierarchy of Things is the grouping (organization or classification) of things (beings, entities, realities) as hierarchically ordered divisions (categories, classes, kinds, types, differences, determinations, distinctions, modes, ways, and varieties) and levels (layers and strata).?

The SUO's USECS, as encoded and embedded, pre-trained or programmed, acts as Standard Data Ontology (SDO) functioning as General Machine Intelligence for AI and ML.

The SDO organizes structured/ordered data sets and structures the unstructured data, digital information stored in big data lakehouses, and sourced from the IoTs devices, mobile phones, sensors and actuators, email and text and instant messages, word documents, PowerPoint presentations, electronic health records (EHRs), digital images, audio files and videos, collaboration software, documents, books, social media posts, such as MP3 audio files, JPEG images and Flash video files, etc.

The mainstream?AI in the form of machine learning, deep learning, neural networks or large language models is?a weak and narrow, imitative or fake AI (FAI) due to a lack of SDO, the world/domain data modeling.

No Data/Algorithm/Model Standardization, No Machine Intelligence and Deep Learning

Data integration is a significant barrier to ML/DL adoption. Main challenge to integration is the lack of standardization. Different technologies and systems may use different data formats and structures, making it difficult to integrate them seamlessly. This can result in data inconsistencies and errors that can affect the accuracy and reliability of ML/DL models.

The lack of standardization is a significant challenge for businesses looking to adopt machine learning. Different technologies and systems may use different data formats and structures, making it challenging to integrate them seamlessly. For example, one system may use JSON while another system may use XML. This creates a compatibility issue that needs to be addressed before data can be integrated.

Moreover, the lack of standardization also affects the quality of the data being used in machine learning models. Inconsistent data structures can lead to data inconsistencies and errors that can affect the accuracy and reliability of machine learning models.

Fragmented ML/DL/AI applications could be properly standardized by the standard data ontology modeling.

Everything You Know about Machine Intelligence is Confusing or Wrong

The AI world has been flooded with a series of large language model (LLM) projects promoted as the last word in AI. First, OpenAI shocked the world with GPT-3-4-X. In turn, Google presented LaMDA and MUM, two narrow AIs as revolutionizing chatbots and the search engine. And now the Beijing Academy of Artificial Intelligence (BAAI) conference presented Wu Dao 2.0.

The mainstream AI/MLANN/DL is still fails to be a Real?Causal AI, be it large-scale language models as 17bn Turing-NLG, 175bn GPT-3, 1.75T Wu Dao 2.0, big tech ML platforms, recommending engines, digital assistants, self-driving transportation, or lethal autonomous weapon systems?(LAWS), autonomous weapon systems (AWS), robotic weapons, killer robots operating in the air, on land, on water, under water, or in space.

It is the lost cause to reach a real artificial intelligence or generalized machine intelligence and learning,?following the current nonscientific assumption of anthropomorphic and anthropocentric AI (AAAI), as “the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions”.

The truly scientific paradigm is a world model-based AI or Real AI or Causal Machine Intelligence and Learning, the ontological modeling and computing simulation of reality and mentality and causality in digital machines that are programmed to effectively interact with any complex environments, physical, mental, social, digital, or virtual.??

Today five top-performing tech stocks in the market, namely, Facebook, Amazon, Apple, Microsoft, and Alphabet’s Google, FAAMG, represent the U.S.'s Narrow/Weak AI technology leaders whose products span standard machine learning and deep learning or data analytics cloud platforms, with mobile and desktop systems, hosting services, online operations, and software products.

As to the modest Gartner's predictions, the total FAI-derived business value was expected to reach $3.9 trillion in 2022.

A human-like narrow/weak/fake AI/ML/DL technology might rapidly make entire industries obsolete, in either case triggering widespread mass unemployment, while over-enriching the FAI Big Tech.

A real solution here is not a fake and false, narrow and weak, acausal AI of ML and DL, relying on blind statistics and mathematics, imitating some specific functions of human cognition or intelligent behavior.

The world needs the Real AI Technology which must be developed as a digital general purpose technology, where collective intelligence and world knowledge will be integrated as the Human-Machine Intelligence and Learning (HMIL) Global AI Platform, or Hyperintelligent [Hyperautomation] AI:

Real Intelligence > SUO USECS > AI + ML + DL + NLU + AGI + ASI + 6G+ Bio-, Nano-, Cognitive engineering + Robotics + SC, QC + the Internet of Everything + Human Minds + MME, BCE + Digital Superintelligence = Hyperintelligent [Hyperautomation] AI = GAI = HMIL =Encyclopedic Intelligence = Real AI = Global AI

UFO as the Standard of Standards ?

I advance UFO in the form of Standard Data Ontology acting as the world knowledge and intelligence platform (WKIP) for AI and ML.

Since the first civilizations, humanity has been after standardization, normalization and regulations, as the standard weights and measures of the Indus Civilization.

?The goal of standardization is?to enforce/mandate/innovate consistency or uniformity,?quality and compatibility, interoperability and safety?to data and knowledge, logics and languages, practices or operations, processes, products and services, technologies and industries, all within certain environments/settings/conditions.?

Human languages were the first tools of standards, followed with numbers,?weights and measures.

Since the Industrial?Revolution, we have had the standard explosion, from the nuts and bolts to the standards for food and water, buildings and technologies.?

Standards for technologies can enforce the quality and consistency of technologies and ensure their compatibility, interoperability and safety.

What has been missing is the standards for the world as such, its key elements and components, structures and functions, processes and relationships. Call it as you prefer:

standard universal ontology (SUO)

single universal ontology (SUO)

global ontology

causal/mathematical ontology

universal formal ontology

total schema of things

universal science and technology

world learning and inference model

universal knowledge graph

general knowledge and intelligence

world knowledge and intelligence model

Standard?ontology creates?the?compatibility and interchangeability, commonality and reference, quality and similarity, integration and measurement, specifications and symbol standards for all specific standards, technical or industrial, operational or procedural, material or administrative.

As a common platform of knowledge and inference transfer (world knowledge and intelligence platform), such a standard ontology rules out the negative effects of sectoral standardization on science and technology, such as market/version fragmentation, as technology stacks, restricting new technology and innovation.

AI's World Models

Real AI is not after human?mental models?referred to mental representation?or mental simulation of external reality for cognition, reasoning and decision-making.

For generalized machine intelligence and learning, the term "world" refers to the totality of entities, to the whole world of?reality?or to everything that is, was and will be.

In various specific contexts and settings, it could take special senses and meaning associated, for example, with the?Earth?and all life on it, with humanity as a whole or with an international or intercontinental scope, the surrounding environment or the universe as a whole.?

In terms of scales and scopes, levels, complexity and extension, there are few models of world models, to be modeled, embedded and coded my general MIL systems:

1. Physical World: Scientific Cosmology World as the universe as a whole, the cosmos: "[t]he totality of all space and time with their contents and all forms of matter and energy, forces and interactions; all that is, has been, and will be".
2. Natural World, Nature, Geo-Chemo-Biological world of natural reality, the planet Earth, the mineral, vegetable, animal world, with all the geo-chemo-biological objects and phenomena, processes and natural control cycles. Examples: the?World3?system dynamics?model for?computer simulation?of interactions between population systems, industrial systems, agriculture/food production systems, pollution systems, non-renewable resources systems, with growth and limits, in the?ecosystems?of the earth.?
3. Mental World of individual personal realities, all the mentality, its states, events and processes, in all causal relationships' mechanisms and patterns, to represent the surrounding world, the relationships between its various parts and a person's perception about their own sense and feelings, thoughts and decisions, actions and reactions, and their consequences.
4. Social World of social/institutional realities, with all social objects, states, events and processes, causally interacting as social networks of various levels, scopes and scales
5. Information World of information realities, with all its components and complex interactions, as signs, symbols and signals, percepts and thoughts, data, information and knowledge, with all forms of processing, communication and networking, from the human brain to social media networks.
6. Digital World, Virtual Reality, Metaverse, from computing machinery to the cyberspace of internet and the WWW
7. Mixed World, Extended Reality
8. Technological World of intelligent cyber-physical systems
9. Human-AI World of all possible realities
10. Total Reality, the totality of all worlds, the sum total of all that was, is and will be.?

World Data Models and/or LLMs

The power of intelligence, natural or machine, consists in its integral power to make sense of the world, modeling and simulating the most general?categories of reality?and how they are interrelated,?to interact effectively with any environments of any scope, scale and complexity.

The real-world intelligent systems should be pre-programmed or trained to learn the?categories of being/existence/reality as?the highest?genera?or?kinds of entities,?the most fundamental and the broadest?classes?of entities, computed in terms of digital data, qualitative and quantitative, categorical and non-categorical.

Various systems of world's categories have been proposed, as global and local or domain-specific, all sorts of classifications, metaphysics, ontologies, typologies, taxonomies, metaphysic and scientific, semantic and lexical, industrial and computing.

Whatever, they often include primary categories for?Entity or Thing, Substance,?State, Chang and Relationship. Secondary categories are Object, Situation, Condition, Quantity and Quality, Event, Action, Process, Place, Time.

It is traditionally described by metaphysics or ontology?and?ontologies,?the science of reality/existence/being with domain theories within the science of the world as a whole.

On the most fundamental level there exists only one thing: the world as a whole, which is driven by?Interaction, The universal set of interacting or interdependent entities is forming an integrated dynamic world of reality, as the universal causal network of all dynamic systems, modeled as the?world hypergraph.

The fundamental interactions/forces rule the physical world at all its scopes and levels. There are four fundamental interactions or forces at work in the universe:?the strong force, the weak force, the electromagnetic force, and the gravitational force, with a speculative fifth force, dark energy.

There is no reality but for interaction, "sine qua non?causation", "but-for causation." Entities, universal or particular, substantial or non-substantial, interact with reality, being the result of there interactions.?Reality?is built up through the interplay of entities: particular entities instantiate universal entities, and non-substantial entities characterize substantial entities.

Such a interactive world model (IWM) is ordered by the universal ladder of realities, the framework for learning a hierarchical representation of the world:

• All, Everything, the World of Reality, all existence, the universe as a whole
• Interaction, Causation and Causality, Cause and Effect; Relationships of Entities, Patterns, Rules, Laws
• Entity or Thing and Interaction, Interactivity, Interplay, Reciprocity
• Substance, State, Change and Relationship
• Thing and Fact; Noumena and Phenomena
• Entities and Properties (Situation, Condition, Quantity and Quality), Action and Reaction, Process and Place and Time
• Object and Event; individuals, instances, objects, facts, properties, features, characteristics or parameters, classes, sets, collections, relations
• Matter and Form, Energy and Forces, Quantities and Laws, States and Processes
• Structures, Systems, Processes and Environments, States of Affairs, Associations, Correlations, Cause and Effect, Networks
• Data Entity, Data Items, Datum/Observation, Data Sets, Universal Data Set or Data Universe
• Machine models, AI models. ML models, DL models, NL models, LLM models

The IWM is reflected by the Data Universe of various data units, items, observations and sets, providing real-world interpretations for AI/ML/DL/NL models and algorithms.

There is no real intelligence without the comprehensive and consistent world modelling, or?universal computing ontology, which is coding the division of all reality and the classification of all its entities in terms of world's data modeling and processing.

The interactive world modeling is the Intelligence Engine of all general-purpose knowledge and language understanding models, reflecting, mirroring and mapping, encoding and decoding designated pieces of reality:

Causal Models, from material, formal, efficient and final causes to mathematical models representing causal relationships within an individual system or population, facilitating inferences about causal relationships from statistical data

Mathematical models, a?mathematical representation of reality, as?logical models, dynamical systems, statistical models, differential equations, or game theoretic models

Scientific Modelling, reflections of reality, representing?empirical?objects, phenomena, and physical processes in a?logical?and?objective?way?"conceptual models?to better understand, operational models to?operationalize,?mathematical models?to quantify,?computational models?to simulate, and?graphical models?to visualize the subject"

Probabilistic models, using probability theory , involving probability space or triple, discrete and continuous?random variables, probability distributions, and?stochastic processes. Examples: statistics or quantum mechanics

Stochastic/Random/Statistical Models, descriptive statistics and inference statistics

Language models, a probability distribution over words or word sequences, generating probabilities by training on?text corpora,?using statistical and probabilistic techniques,?Google's BERT, Microsoft's Transformer,?OpenAI's GPT-3, ChatGPT

Language language models (LLMs) may be categorized as probabilistic methods and neural network language models. The problems of LLMs stem from the following: 1) large amounts of text to build the models, mostly from the Web. If to use Wikipedia as a proxy, of 6,900 languages alive, only 291 have Wikipedia. 2) LLMs learn many social biases such as gender, race, religion, demographics, etc. 3) LLMs do not understand the semantics of the text they learn from or their generated text. 4) The context problem, texts have deep context influencing the choice of the next word. 5) As the size increases (n), the number of possible permutations rises uncontrollably.

Faulty, inadequately trained, poorly understood algorithms, data poisoning and incorrect statistical approximation producing?erroneous results?could be disastrous for people’s lives.

Besides, today's AI works on limited models and cannot mimic general human understanding, cognition and intelligence.

Meantime, lacking the meaning-, truth- and intelligence bearing world models, LLMs have taken the world by storm having many applications—from part-of-speech tagging to automatic text generation, machine translation, QAC, OCR to Speech Recognition, sentiment analysis, or chatbots, as?ChatGPT?"interacting in a conversational way using Reinforcement Learning from Human Feedback (RLHF).

From Narrow AIs to Trans-Intelligence

There are 5 AI models of increasing quality and generality, as simulating human mind/intelligence, statistic, synthetic and real world:

• Logical/Symbolic AI, as the Logic Theory Machine, if-then expert systems and RPA, dubbed as GOFAI;
• Statistic/Subsymbolic, as ML/DL/ANN Narrow/Weak AI;
• AGI, simulating the human intelligence in general;
• Synthetic AI, as synthetic drugs;
• Real/True AI, basing on the ontological model of reality, its prime entities, relations and fundamental laws, as the world's data knowledge and reasoning computing framework. Understanding Artificial Intelligence

In essence, any real intelligence involves real world models as the framework for knowing, thought and planning to provide the deep learning and understanding of things, both qualitatively and quantitatively.

Hyperintelligent AI could be described as the power/faculty/ability, state, act, process or product/object of

• knowing/understanding the world
• perceiving the world as data structures
• inferring data patterns/information
• learning data patterns relationships/knowledge
• interacting with the world/environment/setting/context.

In all, there are three generations of the future AI to be developed at the same time, ANI > AGI > ASI, and Real AI, or Causal/Scientific/True AI, driving the HHIT:

ANI, Narrow, human-like AI systems (ANI), imitating parts of human intelligence (all today’s AI/ML/DL are narrow and weak AI, as LLMs, ChatGPT), all subordinated as below.

AGI, General, human-like and human-level AI systems (HLAI, Full AI, Strong AI), imitating all human intelligence (Multi-modal and multi-task AI, OpenAI Project, DeepMind AI project, etc).

Trans-AI, Really intelligent, autonomous machines, augmenting and complementing humans (Causal Machine Intelligence and Learning, Man-Machine Hyperintelligence, Real Superintelligence).

In reality, there is nothing common between real machine intelligence and learning (MIL) and human intelligence and learning (HIL), with a human-like AI/ML/DL, if only both of them are black box data/information/knowledge systems.

Machines operate in terms of world models and causal patterns, computing power and algorithms, quantities and data, numbers and statistics, figures and digits, tokens and syntax, mathematics and probabilities, precision and accuracy.

It is a stimulus-response black box model, having its inputs and outputs (or transfer characteristics, a transfer function, system function, or network function) producing useful conclusions/information without showing any information about its internal workings, which mechanisms/explanations remain opaque/“black.”

Humans think in terms of world models but qualities, senses and meanings, concepts and ideas, thoughts and images, semantics and pragmatics, biases and prejudices.

In all, there are two complementary worlds, the ontological world of quantitative/physical/cybernetic/causal machines vs. the human world of qualitative/emotional/feeling/reasoning/live humans.

Machines are machines, with their virtually unlimited world simulations, humans are humans, with our naturally limited mental worlds, they can only complement each other, as Man-Machine Hyperintelligence, where the SUO's USECS makes the General Intelligence for AI and ML.

How intelligent is today's AI [ML/DL/NNs] without its machine ontology?

To be plain, it is as smart as its dump, dull and deficient creators (3d creators). Today’s quasi-AI is biased, black-boxed, oblique, weak and narrow-minded due to lacking the machine's worldviews embedded by the SUO engine.

It could blindly and unknowingly perform strictly what it was designed for, videogame/chess/strategic games playing, self-driving, language translation, face recognition, fraud detection, speech communication, product recommendation, pattern matching, generating poetry or music or images or faces or new molecules, etc.

It is all relying on statistical relationships in raw input data sets to generate some patterns that humans find useful.

Such 3d AI cannot bring inventions, follow rules, create from scratch, invent scientific tools, compose songs, and mathematical theorems, being devoid of any intelligence and understanding, learning or common sense knowledge and reasoning, not mentioning causality.

It is mere inductive statistical machines, relying on statistical patterns in its training internet data, like GPT-3, thus unable to answer any simple nonsensical question, like if I could walk the rainbow….

The mainstream AI/MI/DL is deeply defective lacking the world model, as encoded computing representations of reality, its objects, places, people, or all the basic facts and theories about the world.

Conclusion

The SUO is inherent in any domain fields and special sciences and technologies,?as basic categories and classes, axioms and rules, models and algorithms, mechanisms and algorithms, like your brain has deeply inherent knowledge organizing your sense data, thinking?and actions.

Its mathematical modelling involves?"a mathematical representation of reality" as far as mathematics is about mental?tools to?condense world's data/information/knowledge, facilitating the world data encoding for digital intelligence.?

To create a powerful AI applications, one?could pre-train, encode or program the SUO's model of reality in the form of causal data types and patterns, relationships and algorithms. It all depends on how mature your SUO, if it is codified as the USECS or if you need to resort to?web-scraping?on a huge petabyte data corpus, with all the data, algorithmic, and social biases.

Resources

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

USECS (Universal Standard Entity Classification System, 1717 pages)

THE BIRTH OF A CAUSAL ARTIFICIAL SUPERINTELLIGENCE (CASI) 2025: A HUMAN-MACHINE GENERAL PURPOSE TECHNOLOGY

The Integration Dilemma: Exploring the Barriers to Machine Learning Adoption

THE BIRTH OF A CAUSAL ARTIFICIAL SUPERINTELLIGENCE (CASI) 2025: A HUMAN-MACHINE GENERAL PURPOSE TECHNOLOGY

I plan to develop a proof-of-concept/mechanism/principle prototype to demonstrate the CASI feasibility to turn its concept into a reality for a full-scale global deployment.

To estimate a prospective CASI investment,?Microsoft is investing?$10 billion in OpenAI to support in?building?artificial general intelligence (AGI).?

The design, development, and deployment of human-machine superintelligent digital platforms could theoretically be worth trillions of US dollars.

But it has tremendous potential to benefit the world and its peoples, with immense value in growing a connected, intelligent and inclusive world (I-World) with a smart society and green economy,?enhancing global sustainability and securities and opening new frontiers in science, technology, education, medicine, communication and deep space exploration.

SUPPLEMENT: Additional reading

1) Servant Leadership: A Worldview Perspective

J. Randall Wallace, International Journal of Leadership Studies, Vol. 2 Iss. 2, 2007, pp. 114-132.

What is Worldview?

“Worldview comes from the German word ‘weltanschauung’ meaning a ‘look into the world.’ It refers to a wide world perception. It constitutes the framework through which an individual interprets the world and interacts in it” (Worldview, 2006, p. 1). Nash (1996) stated that the writings of philosophers identify assumptions about the make-up of reality or how the world works, conceptual schemes, or patterns of ideas or values and organizes them to form a worldview. In the same manner, religions offer a scheme for interpreting the world and, therefore, are recognized as worldviews as well (Nash, 1996). A worldview is used to interpret and make sense of the world. Perceptions of the world and reality can greatly differ between people or cultures since their assumptions of what is important and true differ. There are many types of worldviews vying for supremacy. These include religious systems (formal philosophic systems such as modernism or postmodernism), less formal systems including large group perspectives such as a particular culture, or personal systems.

A history of challenge, debate, and theorizing within the philosophic community demonstrates how worldviews may have inherent weaknesses, inconsistencies, or inabilities to account for various beliefs or practices. This is consistent with Kuhn (1970) who; in explaining the history of scientific advancement; identified the challenges, shifts, and transformations associated with comparing belief systems and selecting the most stable or cohesive.

The whole article may be found by clicking here.

=================================

2) What is a worldview?

Vidal, C. (2008) Wat is een wereldbeeld? (What is a worldview?), in Van Belle, H. & Van der Veken, J., Editors, Nieuwheid denken. De wetenschappen en het creatieve aspect van de werkelijkheid, in press. Acco, Leuven.

?After all, what could be more important or influential than the way an individual, a family, a community, a nation, or an entire culture conceptualizes reality? Is there anything more profound or powerful than the shape and content of human consciousness and its primary interpretation of the nature of things? When it comes to the deepest questions about human life and existence, does anything surpass the final implications of the answers supplied by one's essential Weltanschauung?

D. K. Naugle, Worldview: The History of a Concept, Wm. B. Eerdmans Publishing, 2002, p.345.

?The term worldview (Weltanschauung in German) has a long and fascinating history going back to Kant2. It has been and is used not only in philosophy, but also among others in theology, anthropology, or in education. David K. Naugle wrote a history of this concept and the above quotation shows its central importance.

The term is unfortunately often used without any precise definition behind it. What is more precisely a worldview? How can we define it? Even inside philosophy, many different definitions have been provided (e.g. by Kant, Hegel, Kierkegaard, Dilthey, Husserl, Jaspers, Heidegger, etc.). Conducting a systematic historical comparison of the different worldview definitions is outside the scope of this paper. Instead, we restrict our analysis to a clear and fruitful definition proposed by Leo Apostel and Jan van der Veken that we will detail in our first section.

The rest of the article may be found by clicking here.

3) The Psychology of Worldviews

It is a commonplace observation that “everybody sees the world in his or her own way.” However trite, this truism conceals an ancient and profound insight, the implications of which have been but poorly grasped in contemporary psychology. Approximately 2,500 years ago, it is said, the person we know as Buddha noted:

We are what we think.

All that we are arises with our thoughts.

With our thoughts we make the world.

(Byrom, 1976/1993, p. 1)

In modern times, we have seen this insight phrased in notable ways by poets and artists. Ana?s Nin is said to have observed, “We don’t see things as they are, we see them as we are.” As the artist Marvin Hill expressed it in one of his wood block prints: “The eye forms the world / the world forms the eye.” Put more prosaically, the nature of this in-sight is that human cognition and behavior are powerfully influenced by sets of beliefs and assumptions about life and reality. Applied to the individual level, this insight has implications for theories of personality, cognition, education, and intervention. Applied to the collective level, this insight can provide a basis for psychological theories of culture and conflict, faith and coping, war and peace. Particularly as psychologists search for ways to reintegrate the discipline after a century of tumultuous and fractious growth, it would be worthwhile for psychology and its subdisciplines to focus on a construct that is central to this aforementioned insight, a construct with a long history and road applicability but a dearth of serious theoretical formulation. This is the construct of worldview (or “world view”).

Worldviews are sets of beliefs and assumptions that describe reality. A given worldview encompasses assumptions about a heterogeneous variety of topics, including human nature, the meaning and nature of life, and the composition of the universe itself, to name but a few issues. The term worldview comes from the German Weltanschauung, meaning a view or perspective on the world or the universe “used to describe one’s total outlook on life, society and its institutions (Wolman, 1973, p. 406).

“A set of interrelated assumptions about the nature of the world is called a worldview” (Overton,1991, p. 269).

In the largest sense, a worldview is the interpretive lens one uses to understand reality and one’s existence within it (M. E. Miller & West, 1993).

Specialists in various subdisciplines of psychology have indicated that worldview has a central role in such fields as developmental psychology (Overton, 1991), environmental psychology (Altman & Rogoff, 1987), sport psychology (Kontos& Breland-Noble, 2002), general counseling and psychotherapy (Ibrahim, 1991; A. P. Jackson & Meadows, 1991), and especially multicultural counseling and psychotherapy (Fischer, Jome, & Atkinson, 1998; Ibrahim, 1999; Ibrahim, Roysircar-Sodowsky, & Ohnishi, 2001; Trevino, 1996). Indeed, if we are willing to consider ways in which aspects of worldview may appear under other names (e.g.“ values” or “schemas”), we may find the worldview construct hidden in the central literature of a number of psychological subdisciplines, including cognitive, social, personality, and cultural psychology. All of this is so despite the construct’s neglect in the mainstream theoretical literature.

If one reads how some authors describe the value of the worldview construct to their sub discipline (e.g., “One of the most popular constructs in the multicultural counseling literature is that of ‘worldview’”; Grieger & Ponterotto, 1995, p. 358) and then contrasts such comments with the absence of the construct from standard texts, handbooks, encyclopedias, and so forth (e.g., Kazdin, 2000), one comes away with the impression that worldview is the most important construct that the typical psychologist has never heard of.

If the worldview construct is to contribute appropriately across disciplines in the social sciences, and across subdisciplines within psychology, it will be necessary to come to a common understanding about what sorts of things the worldview construct addresses and how it functions within individual psychology. The present article is meant to advance this effort inseveral ways. First, I briefly define worldview in formal terms and specify its relationship to other important constructs, such as beliefs and values. Second, I review the major conceptualizations of worldview that emerged during the 20th century, focusing on authors in psychology, anthropology, and philosophy. Third, I justify the status of worldview as a psychological construct. Fourth, on the basis of the earlier review, I propose a model of the different dimensions of worldview. Fifth, I outline a theory of how worldview functions within individual personality. Finally, I suggest items for a worldview-oriented research agenda within personality and social psychology.

Defining “Worldview”

Worldview has gone by many names in the literature: “philosophy of life” (Jung, 1942/1954), “world hypotheses” (Pepper, 1942/1970), “world outlook” (Maslow, 1970a, p. 39), “assumptive worlds” (Frank, 1973), “visions of reality” (Messer, 1992, 2000), “self-and-world construct system” (Kottler & Hazler, 2001, p.361), and many others. In anthropology alone, worldviews have been denoted as “cultural orientations” (Kluckhohn, 1950), “value orientations,” “unconscious systems of meaning,” “unconscious canons of choice,” “configurations,” “culture themes,” and “core culture” (Kluckhohn & Strodtbeck, 1961/1973, pp. 1

2). Beyond the confusion created by using many names for the same construct, the worldview concept, as shall be seen, has been defined in perhaps as many ways as it has been named. For present purposes, worldview may be defined conceptually as follows:

"A worldview is a way of describing the universe and life within it, both in terms of what is and what ought to be. A given worldview is a set of beliefs that includes limiting statements and assumptions regarding what exists and what does not (either in actuality, or in principle), what objects or experiences are good or bad, and what objectives, behaviors, and relationships are desirable or undesirable. A worldview defines what can be known or done in the world, and how it can be known or done. In addition to defining what goals can be sought in life, a worldview defines what goals should be pursued. Worldviews include assumptions that may be unproven, and even unprovable, but these assumptions are superordinate, in that they provide the epistemic and ontological foundations for other beliefs within a belief system." (adapted from Koltko-Rivera, 2000, p. 2)

The theorists reviewed in this article were chosen because they explicitly spoke to such beliefs, whether or not they used the term worldview.

https://psychodramaaustralia.edu.au/ontology-and-all

This article may be downloaded by clicking here.