Machine Intelligence and Knowledge = Universal AI Platform = World Knowledge Hypergraph + Categorization [Intelligence] Algorithms

"Scientia potentia est, mundi scientia mundi potentia est"

"Knowledge is Power, World Knowledge is World Power"

The essence of world knowledge is how humans and machines are to categorize and classify, group or typify, organize and identify, conceptualize and define the things in the world.

Categorization is the most fundamental intelligent power, being decisive in knowing and learning, inference and prediction, solving problem and decision-making, language and communication, and many forms of intelligences' interactions with any range of environments.

Classifying, statistical and empirical, taxonomic or systematic, typological or conceptual, causal or metaphysical, is a fundamental concept and a key part of almost all kinds of human activities, including all the sciences, from philosophy to statistics and mathematics, data science and computer science, and emerging technologies.

How we classify our big and small problems and issues, subjects and objects, topics and themes, such as matter and energy, animals and humans, life and aging, brains and intelligence, climate change or global risks , it all decides their principal solutions.

An example of world knowledge, it is how the WHO's International Classification of Diseases reclassified the term "old age" as “ageing associated decline in intrinsic capacity”, while a real classification could be as "Aging is a disease, a pathological process, not a natural process" (ICD-11) . The causal factor in all age-related diseases is aging . If you want to cure cancer, diabetes or neurodegenerative disorders, reverse the aging by controlling its pathophysiological mechanisms by anti-ageing AI technology.”

Or, how the mainstream defines the most disruptive emerging technology, artificial intelligence and machine learning for pattern classification:

"AI is the intelligence of machines or software, as opposed to the intelligence of humans or animals". Or, AI is the intelligence of machines or software, as mimicking or replicating the intelligence of humans.

Just to the point, statistical classification algorithms from objects' feature vectors/space of numerical or categorical variables as data input enable today's AI, as pattern recognition systems, machine learning models and deep structural learning algorithms, artificial neural networks, generative AI, large language foundation models and ChatGPT.

Considering all that, encoding or embedding in computing machines the world knowledge of the world’s entities and their interactions is a strategic goal of next-generation AI as Machine Intelligence and Knowledge and Learning (AI/MIKL).

The best application of the NextGen AI/MIKL is a Universal AI Platform, the techno-scientific summit of increasingly generalizing classifications of the world knowledge:

Philosophia Universalis >

Mathesis Universalis >

Scientia Universalis >

Computare Universalis >

Intelligentia Universalis >

Technologia Universalis >

Global AI Technology Platform

Big AI is Coming...

Artificial Intelligence (AI) and Machine Learning, Robotics and Hyperintelligent Automation have become the emerging technologies promising to upend our world, the way we live, study, work and interact with each other, with our technological, social, political, economic, cultural or natural environment.

Ideas and ideologies, money and assets, products and services, materials and energy, data and technology, information and people flow feely across national boundaries due to advances in scientific knowledge, digital and emerging technologies, as the mobile Internet, the web 3.0, 5-6G technologies, IoT and Automation, AI and ML, Blockchain, Self-driving Transportation, 3-4D Manufacturing, etc.

Innovating a universal AI technology platform could radically change the whole process, in all its forms and levels, by which ideas and innovations, data and knowledge, information and technologies, ?goods and services circulating around the world. Just a generative AI’s impact on productivity could add trillions of dollars in value to the global economy, adding the equivalent of $2.6 trillion to $4.4 trillion annually.

The World Knowledge Hypergraph is all we need for the Machine Intelligence and Knowledge and Learning Platform

As we argued in Universal Techno-Science (UTS) , the world knowledge + classification algorithms construct is isomorphic to the world knowledge hypergraph representing complex group relationships mapping the higher-order interactions among the vertices, as all the possible philosophical, mathematical, natural, cognitive, social, technical sciences and technological domains.

It is to be represented as a knowledge universe hypergraph (K) with the high-order interactions between and among its domain-hypervertices, as Philosophy, Science, Technology, Engineering and Mathematics

Formally, it is an undirected world knowledge hypergraph K = (d, h), where d is a set of techno-science knowledge disciplines, subjects or domains (units, nodes, elements, vertices, points) and h is a set of pairs of subsets of K. Its order is the number of vertices, and the size of K is the number of symmetrical hyperedges/hyperlinks/connectors, which as hyper-techno-sciences can join any number of vertices, or domain techno-sciences, having mono-, multi-, inter-, or trans-disciplines as their domains and codomains.

World knowledge is an understanding (background knowledge) of many different subjects and disciplines (domains) and how they interrelate to one another, in terms of world modeling.

World model is all the basic categories and classifications, patterns and structures, of reality, its entities, properties and interactions, in terms of scientific knowledge, natural or programming languages or data.

Word knowledge is knowing the meanings of words, the relationships between words (word schema), and having linguistic knowledge about words, all in terms of world modeling..

Data knowledge is knowing the meanings of data, the relationships between data (data schema), and having data science knowledge about data, all in terms of world modeling.

In fact, the Data Pyramid is a hierarchy from from specific data to universal data structures, where its levels, Information, Knowledge, or Wisdom, are defined in terms of Data. For all is caused by Data, as raw facts/observations (signals/stimuli) of the state of the world, being organized and structured and processed, Information; have meaning or value, context and interpretation, learning and understanding, Knowledge; universal learning and integrated knowledge, deep understanding, general intelligence, Wisdom.

For humans, Word Knowledge is World Knowledge. For machines, Data Knowledge is World Knowledge, making real and true AI as Machine Intelligence, Knowledge and Learning. Real knowledge is not implicit/tacit, some subjective phenomenon or a mental state or justified true belief, or mental representation, as insight and intuition, experience or wisdom, but an objective phenomenon, explicit, formal, and codified, as measurable data structures and patterns.

Helping humans and machines understand the world, the disciplines could be as different as in: Mathematical sciences, with their branches. Natural and applied sciences: Physics, chemistry, biology, computer science, engineering, geology, physics, medicine. Social sciences: Anthropology, education, geography, law, political science, psychology, sociology. Humanities: Art, history, languages, literature, music, religion, theater. Philosophical sciences, with their branches.

Generally, an order-n TSUH Venn diagram may be viewed as a subdivision drawing of a hypergraph with n hyperedges (the curves defining the diagram) and 2n???1 vertices (represented by the regions into which these curves subdivide the plane).

For the case of Unified STEM (Science, Technology, Engineering and Mathematics), we have an order 4 Venn diagram, with 4 hyperedges (the 4 ellipses) and 15 vertices (the 15 colored regions).

Real-world examples of hypergraphs are all social networks, from Facebook to LinkedIn, where individual or corporate data units, with their features, as personal, demographic, economic, political or cultural variables, hyperconnected with other data units.

The World Knowledge Graph Network as the Universal World Model AI Engine

The idea of universal AI platform to build and deploy AI everywhere has been pursued as by governments and big tech corporations .

AI and Deep ML frameworks "provide data scientists, AI developers, and researchers the building blocks to architect, train, validate, and deploy models through a high-level programming interface". Here are the business use-cases from cloud AI service providers like Azure, Google, and AWS as they are advertising for their ML cloud solutions like in the following image from Microsoft Azure ML:

As to Intel, "a universal AI platform has the flexibility to run every AI code, scope to empower every developer, and scale to enable AI everywhere", with the 3 components: General Purpose and AI-Specific Compute; Open, Standards-based Software to build and deploy AI everywhere; Ecosystem engagement:

The key levels of the whole universal AI Platform stack are not the compute and AI semiconductor chips but rather the world data modeling and their master algorithms, as in the generative AI development stack:

The world with its data in general and in detail is the main universe of interest of such a universal data, knowledge and intelligence technology platform.

Again to model and simulate reality at large, with all its entities, properties and interactions, making sense of the world, processing its physical patterns (signals and signs, symbols and tokens, digits or numbers), representations and data structures, is the necessary and sufficient condition to create a universal AI platform.

It overrules the physical symbol system hypothesis (PSSH ) that only "physical symbol system has the necessary and sufficient means for general intelligent action."

There are no technical studies systematically addressing the whole world, in its generality and detail, as the meta-mathematical modeling and simulating of reality.

We have to mention some attempts but of data/information/knowledge representation and reasoning, such as information science upper/top-level/foundation ontologies, knowledge graphs, statistical learning classifiers or large language foundation models.

For example, making use of domain ontologies, knowledge graphs focused on the connections between concepts and entities, as objects, events, situations or abstract concepts. It could be defined as "a digital structure that represents knowledge as concepts and the relationships between them (facts), including an ontology that allows both humans and machines to understand and reason about its contents".

Several large multinationals have advertised their knowledge graphs use, as Google, Facebook, LinkedIn, Airbnb, Microsoft, Amazon, Uber, or eBay. The IEEE International Conference on Knowledge Graph (ICKG) replacing "Big Knowledge" and "Data Mining and Intelligent Computing".

The World Hypergraph of Knowledge Graphs, LLMs and Upper Ontologies

We are studying the world as such and its representations, and how to categorize it, classify its key elements and components, structures and regularities, entities, properties and interactions.

The human's and machine's true and complete category system that encompass the classification of all things in the world must be discovered by Universal Formal Ontology (UFO), as it was argued in the book, Reality, Universal Ontology and Knowledge Systems .

Covering upper ontologies, statistical/probabilistic ML classifiers, knowledge graphs, or LLMs, our universal classifier is formalized as the Universal Computing Ontology of Fundamental Categorical Variables of the World.

It is encoded as the World's Hypergraph Network Structure (the World's Formula):

W = <E, S, C, I; D; K; F>, where

• World, W, where W tensor world variables stand for the totality of entities and interactions, or the world or reality at large, all possible worlds and realities, physical, biological, mental, social, information, digital, virtual, cybernetic or cyber-physical, as statistical populations or universe of discourse or knowledge domains or subject matter
• Entity, E, where E tensor entity variables stand for all entities, substances and objects, individuals and instances
• State, S, where S tensor state variables stand for all states, qualities and quantities, as number, time and space
• Change, C, where C tensor change variables stand for all sorts and kinds of phenomena, changes and actions, events and operations, activities and functions, as causes and effects, or interactive causality, C X C
• Interaction, I = W x W, where I tensor interaction variables stand for all interactions, qualitative and quantitative, causal relationships, connections and links, correlations and associations, communication, processes and forces, as the the fundamental interactions or fundamental forces, gravity, electromagnetism, weak interaction and strong interaction, ruling all the physical reality. Note the principal difference from the Reality Structure Diagram, Relation is replaced with Interaction; for it is hardly a prime ontological category, but rather a logical and epistemological and mathematical abstract relationship approximating interactions.
• Data/Information Universe, D, and Knowledge/Intelligence Universe, K, where D is the World Data Metric Space, and K is the World Knowledge Space, taking the form of a global interaction of reality and the Data Universe with its knowledge subworld in a self-dual homomorphic identity "structure-preserving" mapping, D: W <> K (I), with 2 Qualitative (Categorical) and 3 Quantitative (Numerical, Discrete or Continuous) scales or measures, variables or data.
• World's Data/Knowledge representation function, F: D: K < > {0,1}, as the encoding/decoding and embedding techniques converting the world's data into a digital form as a series of impulses, digital, machine data, a structured numerical format to be processed by computers, as World Embeddings; Entity Embeddings, State Embeddings; Change Embeddings, or Interaction Embeddings. The traditional examples are ASCII encodings, URL encodings or programming language codes. All traditional ML/AI methods work with input feature vectors requiring input features to be digitally numerical. It is as in a word embedding, when words or phrases or sentences are mapped to vectors of real numbers using probabilistic language modeling or feature/representation learning techniques.

Universal Knowledge and Intelligence Engine: World > Data > Knowledge > Digital Knowledge > Computing > Interaction

The DIKI Universe modeling embraces "the Cognitive-Theoretic Model of the Universe taking the form of a global coupling or superposition of mind and physical reality in a self-dual metaphysical identity M: <> U, which can be intrinsically developed into a logico-geometrically self-dual, ontologically self- contained language incorporating its own medium of existence and comprising its own model therein":

Machine Intelligence (MI) < M < K (W)

It has paradigmatic consequences shifting the mainstream approaches to Data and Intelligence, human or machine.

In general, AI refers to the intelligence demonstrated by machines, i.e. machine intelligence and learning (MIL).

As such, there is a human-based or anthropomorphic AI and a reality-based or real AI.

Or, we have two classes of AI/MIL, as truth and falsity, real and true, objective and scientific AI and irreal and false, subjective and nonscientific AI, as different as General Global AI Models vs. Narrow Specialized AI Models.

The Unreal AI models are all about making computers and machines learning, reasoning or make decisions like humans, replicating human body/brain/brains/behavior/business/tasks.

Machine learning and artificial intelligence, as statistical classifiers, generative or discriminative, with the classification algorithms and pattern recognition systems, are statistically correlative and non-causal, wanting the real-world (ontological, semantic and scientific) classification and inferencing algorithms.

The real and true AI NOT to "implement human intelligence in machines i.e., create systems that understand, think, learn, and behave like humans", involving human cognitive science, neuroscience, psychology, etc.

The reality-based AI Models is all about making computers and machines effectively and sustainably interact with the world, simulating and modelling directly reality itself, in all its complexity and dynamics, its entities, changes and interactions, laws, rules and patterns, to effectively and sustainably interact with the world.

So, the world's structural formula could be programmed or pre-trained, encoded and embedded as the universal world model engine of the universal AI platform with the universal learning and understanding of reality following the universal algorithm:

Reality causes Entity causes State causes Change causes Interaction causes Data causes Intelligence causes Real AI Technology causes Intelligent Reality.

AI's Universal Classifier/Master Algorithm/General Model

Bottomline

Again, why do we need the Global MIK Platform relying on the Universal Techno-Science world knowledge, data and intelligence?

Regardless its many applications and impressive feats, it is important to know that today's AI is not truly intelligent. Rather, it is well-trained to perform specific tasks within a predetermined set of parameters while being limited by its training data, lack of world knowledge and real intelligence.

"Current artificial intelligence systems like ChatGPT do not have human-level intelligence and they are not even as smart as a dog, They are not very intelligent because they are solely trained on language.

In the future, there will be machines that are more intelligent than humans, which should not be seen as a threat.

“Those systems are still very limited, they don’t have any understanding of the underlying reality of the real world, because they are purely ?trained on text, massive amount of text...Most of human knowledge has nothing to do with language … so that part of the human experience is not captured by AI.” Meta's AI chief Yann LeCunn about the limitations of generative AI trained on large language models.

Resource

Real AI Project Confidential Report: How to Engineer Man-Machine Superintelligence 2025: AI for Everything and Everyone (AI4EE); 179 pages, EIS LTD, EU, Russia, 2021

Content

The World of Reality, Causality and Real AI: Exposing the great unknown unknowns

Transforming a World of Data into a World of Intelligence

WorldNet: World Data Reference System: Global Data Platform

Universal Data Typology: the Standard Data Framework

The World-Data modeling: the Universe of Entity Variables

Global AI & ML disruptive investment projects

USECS, Universal Standard Entity Classification SYSTEM:

The WORLD.Schema, World Entities Global REFERENCE

GLOBAL ENTITY SEARCH SYSTEM: GESS

References

Supplement I: AI/ML/DL/CS/DS Knowledge Base

Supplement II: I-World

Supplement III: International and National AI Strategies

Trans-AI: How to Build True AI or Real Machine Intelligence and Learning

Techno-Scientia Universalis: Universal Mathematical Computing Metaphysics (UniMaCoM): Universal AI Platform

EIS HAS CREATED THE FIRST TRANS-AI MODEL FOR NARROW AI, ML, DL, AND HUMAN INTELLIGENCE

Universal Techno-Science (UTS): [A Global AI Platform for Global Interactions]