Human-Machine Ontology: World/Data Ontology/Engineering: Data Science and Engineering, AI, ML, DL, KG, and Smart Web Search

The World/Data Machine Ontology is All We Need

The World/Data Ontology as a unified man-machine model of the world of all possible realities 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.

It underpins ontologies and semantic models, typologies and taxonomies, knowledge graphs and data bases, scientific methodologies and intelligent algorithms, information models and data schemas, data types and structures and data sets, natural and programming languages, etc.

As such, the World/Data Ontology is rethinking data science and engineering, with its inferential or descriptive statistics, data analytics and big data, data acquisition, data correlation, data migration, data classification, data governance, data integration, AI and ML models and DL algorithms.

I innovate several ontological ideas and general scientific concepts such as World Data Ontology, World Ontology, Data Ontology, Global Ontology, Global Scientific Ontology, Systems Ontology, Human-Machine Ontology (HMO) or Man-Machine Ontology (MMO), as transcending and underlying science and engineering, data science and engineering, trans-disciplinaries and systems science, artificial intelligence and machine intelligence and learning, as computer intelligence and technological intelligence.

The World Data Ontology serves as the Single Source/Point of Truth (SSOT/SPOT) Architecture or Framework or Reference System for Data and Information, Knowledge and Intelligence, Human and Machine.

The MMO works as the world model engine for learning, inference, decision-making and complex problem solving for real/true AI, as man-machine hyperintelligence, innovated as Trans-AI or Meta-AI (Transdisciplinary, Transformative and Translational):

Real AI = Transdisciplinary AI = Machine Intelligence/Learning/Inference/Interaction +

World/Reality Man-Machine Ontology +

World Data/Information/Knowledge +

the Internet/Web Data + LLMs +

Causal Interaction Graph Network+ Global Knowledge Graph + AI/ML/DL/ANNs Models + GenAI +

Intelligent Agents + Robotics & Automation +

Internet of [Every]Things + Industrial IoT ...

+ Human Intelligence, Individual and Collective

The brute force compute and big data approach to AI, ML, GenAI and LLMs is not more sufficient. Infinitely bigger model and dataset sizes are not enough to make AI emerge by the scaling power laws of intelligence, stating that infinite compute, data and model size make the intelligence capacity of the system keep increasing indefinitely eventually surpassing humans.

It’s time to move past this obsession with scaling hardware/software/data and training models with their GIGO rules and start focusing on what actually matters: real-world ontology, structured knowledge, the world modeling mechanism or domain ontologies with meaningful context.

The answer to machine intelligence scaling isn’t centralized in some mega data center. It’s rather using the world models and ontologies and causal hypergraphs networks. It’s about grounding AI systems with REALITY, general computing ontology with rich, domain-specific ontologies that actually make sense of the world, and generate intelligent decisions, content, actions.

The world ontology capacity determines the working of scaling laws in AI models with all three key variables, computing power, measured by the number of calculations performed over a certain time and more powerful processors, model size, measured in the number of parameters, adjusted weights, and dataset size, measured in tokens, pixels, or other fundamental units.

OpenAI and others seek new path to smarter AI as current methods hit limitations

Data Universe Pyramid

Data is a basic source of information and knowledge, learning and intelligence, computing and information and communication technology.

It is sold as the new oil and gas, currency and intelligence in the digital world.

Being a general concept, Data can range from abstract ideas to concrete measurements and statistics and beyond.?

The Latin word?data?is the plural of 'datum', "(thing) given.", i.e., a piece of actuality, a fact, a state of affairs. Again, a?datum?is an individual value in a collection of data.?

Data acts as a triple-faced thing, being itself, its reflection or representation and its coding, in the form of text, observations, figures, images, numbers, graphs, characters, symbols, software or applications.

There is a data unit (experimental units,?sampling units?or?units of observation vs. unit of analysis) as one entity in the population (a single person, animal, plant, manufactured item, country or groups, organizations, and institutions); ?a data item as a characteristic (or attribute or variable, quantity ir number ) of a data unit which is measured or counted; datum (data point), an observation as an occurrence of a specific data item that is recorded about a data unit; a dataset as a complete collection of all observations or measurements; and the data universe as a total population/collection of all datasets.

As such, data is defined in three ways:

1. things known, as an entity or object or given as facts, an aspect of things, a state of affairs (quantity, quality, fact, relationship),??

2. a collection of values, variables, information, facts, measurements, observations and statistics (the smallest units of factual information that can be used as a basis for calculation, reasoning, or discussion, analysis, presentation, or visualization); information in the binary digital form to be organized in data hierarchy, a character (bit and byte), field, record, file, database, databank, cloud data, the internet/web data,?

3. the quantities, numbers, characters, symbols, coded information/knowledge, transformed and operated, stored and transmitted through various storing and communication media

First of all, data is an ontological category, as the state of affairs or facts, while ranging from abstract ideas to concrete measurements and statistics

We might say "the world is the totality of data/facts,?not things".

As such, Data is orthogonal, or independent of, not only to theories, but also to all human mentality/epistemology: knowledge, values, opinion, beliefs or theories.

We have the data universe of the whole world partitioned into the finite number of datasets (categories and classes, kinds or types or variables) of an innumerable?number of data items/elements/points, as instances, individuals, cases, or values (tokens)?

Broadly speaking, all data falls into one or more of five categories:?nominal,?ordinal,?interval,?ratio, and number, going as the levels or scales of measurements.?

• Nominal data/numbers, the simplest data type, classifying (or naming) data without suggesting any implied relationship between those data,?as basic ontological categories, countries or species of animals.
• Ordinal data/numbers,?classifying data but it introduces the concept of ranking, as all hierarchical?ontological categories or the Likert scale or ‘slow’, ‘medium’, ‘fast’
• Interval data/numbers,?both classifying and ranking data (like ordinal data) but introduces continuous measurements, as the time of day or temperature measured on either the Celsius and Fahrenheit scale.?
• Ratio data/numbers,?it classifies and ranks data, and uses measured, continuous intervals, just like interval data. But, unlike interval data, ratio data has a true zero,?an absolute, below which there are no meaningful values. All physical quantities, as mass, speed, age, or weight are examples?of the?RD
• Numeral data/Numbers (Digital, text or binary, data and?analog real-valued data), they?count,?measure, order, and?label, number sets or number systems, N-natural numbers, Z-integers, Q-rationals, R-reals and C-complex numbers (number theory, set theory, arithmetic, statistics, algebra, probability theory) ??

The first four classes were introduced in 1946 by the psychologist Stanley Smith Stevens,?as a level of measurement?or?scale of measure,?widely used in sciences and engineering, statistics, data analytics, data science and business marketing. What is missing is the base, ground, or foundation of any real metrics, the base of reference and of?measurement?units for counting or measuring,?labeling and ordering, numbers.

They are used for counting and measuring, for labels (as with?numbering schemes for assigning nominal numbers to entities, names, ID numbers, routing numbers, telephone numbers, IP addresses), for ordering (as with?serial numbers), and for codes (as with?ISBNs, bank codes, postal codes)".

In all, it is a hierarchical scale, each level builds on the one that comes before it, as nominal numbers > ordinal numbers > interval numbers > ratio numbers > numbers.

It is crucial that for intelligent machines, the Data Universe Pyramid is replacing the?DIKW pyramid, the?DIKW hierarchy,?wisdom hierarchy,?knowledge hierarchy,?information hierarchy, information pyramid, or data hierarchy,?the Data, Information, Knowledge, Wisdom.

For Data to represent the things in the world, the World Data Ontology is necessary to complete the data science and statistics and engineering,?transforming the statistical AI and ML/DL into the real AI and ML/DL.

Why World Data Ontology?

Data is about parts and pieces of the world, from its smallest units, as elementary particles, to the whole universe.

Global Ontology (GO) is about the world of entities and relations. The topmost Thing or Entity or Being is Reality itself, the ultimate metaphysical and scientific grounding. As the most fundamental level there exists only one thing: the world as a whole, reality, everything, the world of everything.?

As a matter of fact, we have two massively wrong ontologies,?flat?ontologies,?where there is no difference in fundamentality between the different objects: they are all on the same level, and sorted ontologies classifying entities into different exclusive ontological categories, and?one true,?ordered?ontology with?hierarchical relations between the entities of the different categories, all topped by the world itself.?

Abdoullaev A. Reality, universal ontology, and knowledge systems: toward the intelligent world. - Hershey; New York: IGI Publishing, 2008

Global Data Ontology (GDO) is a specification (definitions and axioms and models) of reality, as the universe of data beings and information entities, including physical, mental, biological, social, digital, virtual or cybernetic realities, with all possible combinations.

GDO is about substances/matter and states/energy, information, computation, communication, and intelligence, in terms of ontological categories.

GDO is about a universally standard data type hierarchy and programming languages in terms of ontological categories.

GDO embraces a data fabric, an architecture that facilitates the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems. Data fabrics utilize semantic?knowledge graphs, metadata management, and?machine learning?to unify data across various data types and endpoints.

Data Science is a "concept to unify?statistics,?data analysis, informatics and their related methods" in order to "understand and analyze actual phenomena" with data.

GDO is to unify real-world ontology, data science and engineering, mathematics, pure and applied, probability theory and statistics, science, theoretical and empirical, and computer science to extract information, knowledge, intelligence and wisdom from world's data.

It integrates all 4 paradigms of science, empirical, theoretical, computational and data-driven.

Its key element is also mathematics, which is dealing with ontological entities but as mathematical objects, as quantities, changes, and relationships (numbers, magnitudes, multitudes, spaces, manifolds, etc.) and their functional relationships.

What is an Ontology and Real Global Ontology

"An ontology is a specification (axioms and definitions) of every kind of entity that may exist in the domain of discourse." John F. Sowa

It is a more sensible definition than a widely cited: "ontology?is a ”formal, explicit?specification of a shared conceptualization”.?Although, the term is borrowed from philosophy, where "Ontology is a systematic account of Existence” (Tom Gruber and the Knowledge Systems Laboratory at Stanford University).

In computer science, AN?ontology is a formal representation of the knowledge by a set of concepts within a domain and the relationships between those concepts.?

AN ontology provides a shared vocabulary, which can be used to model a domain, the type of entities and/or concepts that exist, and their properties and relations. So ontologies are to be used for knowledge sharing and interoperation among computing programs based on a shared conceptualization.

Ontologies are widely practiced in Science and Technology, implicitly, Artificial Intelligence, the Semantic Web, Systems Engineering, Software Engineering, Biomedical Informatics, Library Science, Information Architecture, explicitly, as a form of knowledge representation about some part of?the world.?

We need to tell apart domain ontologies and Ontology, as a general theory of reality. Ontology vs ontologies is like Generalized AI vs Applied AI.

Here is a simple version.

Real, True, Scientific or Global Ontology is a specification (axioms and definitions) of reality/the world of entities and relationships.

So, Computer Science Ontology is to model the world that consists of a set of types, properties, and relationship types.

Global Data Ontology and Mathematics

Mathematics is key to GDO. It is dealing with ontological entities but as mathematical objects, as quantities, changes, and relationships (numbers, magnitudes, multitudes, spaces, manifolds, etc.) and their functional relationships, as listed below:

• Number theory: numbers,?operations,
• Combinatorics: permutations,?derangements,?combinations
• Set theory: sets,?set partitions; functions, and?relations
• Geometry: points,?lines,?line segments, polygons?(triangles,?squares,?pentagons,?hexagons,...),?circles,?ellipses,?parabolas,?hyperbolas,polyhedra?(tetrahedrons,?cubes,?octahedrons,?dodecahedrons,?icosahedrons),?spheres,ellipsoids,?paraboloids,?hyperboloids,?cylinders,?cones.
• Graph theory: graphs,?trees,?nodes,?edges
• Topology: topological spaces?and?manifolds.
• Linear algebra: scalars,?vectors,?matrices,?tensors.
• Abstract algebra: groups, rings,?modules, fields,?vector spaces, group-theoretic lattices, and?order-theoretic lattices.
• Category Theory (a general theory of functions): categories, objects, edges.

There appear studies "using machine learning to discover potential patterns and relations between mathematical objects, understanding them with attribution techniques and using these observations to guide intuition and propose conjectures".

Advancing mathematics by guiding human intuition with AI

Data as a new Wealth: What is Data, Data Science and Data Intelligence

In the new Digital World, data is the new wealth, treasure, riches and assets, oil, gas, electricity, currency, gold and diamond in one face.

Let's see what makes data so valuable, starting from its definitions. "Data", "information" "knowledge" and "intelligence" are often used interchangeably, while each one has a distinct meaning, having data as its basis, a fundamental reality. Information is stimuli having contextual meaning for its receiver. When such information is entered into and stored in a computer, it is generally referred to as data. After processing, output data can again become information. When information is ordered, packaged and used for understanding, decision or action, it is known as knowledge.

Global Data Ontology and Knowledge Graph

The topic of KG is so complicated that there is no any certain definition, theoretical or operational, intensional and extensional.

Meantime, if you are lacking a clear definition, it is not clear what you are talking about at all.

The number of definitions is just numberless:

"a knowledge graph is a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between these entities"

“[a graph] that understands real-world entities and their relationships to one another”

"a graph where nodes represent entities, and edges represent relationships between those entities"

“A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge”

“A knowledge graph is a semi-structured data model characterized by three components: (i) a ground extensional component, that is, a set of relational constructs for schema and data (which can be effectively modeled as graphs or generalizations thereof); (ii) an intensional component, that is, a set of inference rules over the constructs of the ground extensional component; (iii) a derived extensional component that can be produced as the result of the application of the inference rules over the ground extensional component (with the so-called “reasoning” process)”

"A digital structure that represents knowledge as concepts and the relationships between them (facts)"

"A network of entities, their semantic types, properties, and relationships"...

Knowledge graphs are characterised by examples such as Wordnet, DBpedia, Google’s Knowledge Graph, Freebase, YAGO, Web search KGs (e.g., Microsoft's Satori/Bing, Google), commerce KGs (e.g., Airbnb, Amazon, eBay, Uber), social networks KGs (e.g., Facebook's entity graphs, LinkedIn), finance KGs (e.g., Accenture], Banca d’Italia, Bloomberg, Capital One, Wells Fargo).

Knowledge graphs aim to serve as an ever-evolving shared substrate of knowledge within an organisation or community, as open knowledge graphs and enterprise knowledge graphs. Open knowledge graphs are published online, making their content accessible for the public good... Knowledge Graphs,?https://arxiv.org/pdf/2003. https://arxiv.org/pdf/2003.02320.pdf

A fundamental question is, [comparing with obsolete constructions of SN/SW, KB, or ontologies], what does the KG tell new about the world, science and technology?

As for its definition, "a knowledge graph is a body of knowledge represented as nodes and edges in terms of graph theory and network science, computing and ontology".

Global Data Ontology and Data/Information/Knowledge/Intelligence Fusion/Integration/Synergy

Data fusion?is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.

Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place.?Low-level data fusion combines several sources of raw data to produce new raw data.

Information integration?(II) is the merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations. Information fusion, which is a related term, involves the combination of information into a new set of information towards reducing redundancy and uncertainty.

Data from multiple sources are characterized by multiple types of heterogeneity, syntactic and semantic, schematic and system heterogeneity. Data integration?involves combining?data?residing in different sources and providing users with a unified view of them.

Data integration systems are formally defined as a?tuple?{G, S, M}?where?G is the global (or mediated) schema,?S?is the heterogeneous set of source schemas, and?M is the mapping that maps queries between the source and the global schemas.

Ontology-based data integration?involves the use of one or more?ontologies?to effectively combine data or information from multiple heterogeneous sources.?It is one of the multiple?data integration?approaches and may be classified as Global-As-View (GAV).

Ontologies enable the unambiguous identification of entities in heterogeneous information systems and assertion of applicable named relationships that connect these entities together.

There are three main architectures that are implemented in ontology?based data integration applications,?namely,

Single ontology approach, a single ontology is used as a global reference model in the system.

Multiple ontologies, each modeling an individual data source, are used in combination for integration.

Hybrid approaches, it involves the use of multiple ontologies that subscribe to a common, top-level vocabulary.

DATA

• facts and statistics collected together for reference or analysis.
• quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.
• things known or assumed as facts, making the basis of reasoning or calculation.
• individual units of information; a datum describes a single quality or quantity of a thing, some object or phenomenon.?
• distinct pieces of information, usually formatted in a special way. All software is divided into two general categories: data and?programs. Programs are collections of instructions for manipulating data.

Data can exist in a variety of forms — as facts stored in a person's mind, numbers or text on pieces of paper, as bits and bytes stored in electronic memory, or computer information that is transmitted or stored.

Data processing systems, as computers, represent data, including video, images, sounds and text, as binary values using patterns of just two numbers: 1 and 0. A bit is the smallest unit of data, and represents just a single value. A byte is eight binary digits long. Storage and memory is measured in megabytes and gigabytes or brontobytes.

In data science and statistics and all analytical processes, data are represented by variables. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs, images or other analysis tools. Media are used as the communication outlets or tools to store and deliver information or data.

10 data-related definitions from Webopedia:

• Big Data: A massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques.
• Big Data Analytics: The process of collecting, organizing and analyzing large sets of data to discover patterns and other useful information.
• Data Center: Physical or virtual infrastructure used by enterprises to house computer, server and networking systems and components for the company's information technology (IT) needs.
• Data Integrity: Refers to the validity of data. Data integrity can be compromised in a number of ways, such as human data entry errors or errors that occur during data transmission.
• Data Miner: A software application that monitors and/or analyzes the activities of a computer, and subsequently its user, of the purpose of collecting information.
• Data Mining: A class of database applications that look for hidden patterns in a group of data that can be used to predict future behavior.
• Database: A database is basically a collection of information organized in such a way that a computer program can quickly select desired pieces of data.
• Raw Data: Information that has been collected but not formatted or analyzed.
• Structured Data: Structured data refers to any data that resides in a fixed field within a record or file. This includes data contained in relational databases and spreadsheets.
• Unstructured Data: Information that doesn't reside in a traditional row-column database. As you might expect, it's the opposite of structured data.

https://www.webopedia.com/TERM/D/data.html

As a general concept, Data refers to information or knowledge as represented or coded for usage or processing.

So, Data is an information entity producing knowledge and intelligence.

Datalogy, as Data Science and Technology

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract information and knowledge and insights from structured and unstructured data.

Data science involves mathematics, statistics, computer science, and information science; data analytics, data mining and big data, machine learning and their related methods to represent and understand and analyze and measure the world with data. Data science is viewed as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven).

Data Intelligence

Data intelligence covers predictive, decisive, descriptive, prescriptive, and?diagnostic data.

Data intelligence may sometimes be mistakenly referred to as business intelligence. Data intelligence focuses on data used for future endeavors like investments. Business intelligence, on the other hand, is the process of understanding a business process and the data associated with that process. Business intelligence involves organizing, rather than just gathering, data to make it useful and applicable to the business's practices.?https://www.datapine.com/blog/data-intelligence-and-information-intelligence-tools/

SAP is to deliver data intelligence with AI and information management

• Discover and connect multiple data types regardless of where the data resides
• Refine and reuse audio, image, and video streams and data from devices based on the Internet of Things
• Optimize governance and minimize compliance risk with robust metadata management rules
• Orchestrate and execute modular data pipelines across distributed infrastructures

https://www.sap.com/products/data-intelligence.html

To handle data from many sources: mobile apps, mobile devices, consumer electronics, marketing, logistics, manufacture, customer relations, enterprise resource planning, and human resources, Samsung Research aims to develop advanced data intelligence technologies, turning massive data into actionable insights to provide more meaningful user values. https://research.samsung.com/data-intelligence

How Real World Ontology can help us in the Data Science World of AI Technology

Ontology is the study of what exists or there is.?Ontology encompasses problems about the most general properties and relations of the entities which do exist.

Ontology is the way we can connect entities and understand their relationships, their types and tokens. With ontology one can enable such a description, but first we need to formally specify components such as individuals (tokens, instances of objects), classes (types), attributes (properties) and relations as well as limitations and restrictions, rules and axioms.

Formal ontology gives precise mathematical formulations of the properties and relations of certain entities.?Such theories usually propose axioms about these entities in question, represented as mathematical models or in some formal language based on some system of formal logic.

Data represented in a particular formal ontology can be more easily accessible to automated information processing, and how best to do this is an active area of research in computer science and data science.

There are 5 top classes?the most of us agree on to recognize:?

'Entity', 'Property', 'Relation', 'Type', 'Token'.

I'd add up here as a general class, 'Data', to?'entity'. 'relation',?'property', 'type', 'token'.?

Data is defined as?"a representation of facts, concepts, or instructions in a manner suitable for communication, interpretation, or processing by humans or by automatic means".?

Information ontology is failing to occupy its central place in Computing and Data Science, Big Data and IoT, AI and ML, etc., because of lack of World Data Ontology, representing reality,?real world entities and their interrelations,?as the fundamental data models.

Sporadically, some big institutions, as NASA, define ontologies in terms of data, its models and relationships. But no more.

Have to mention M. West's great work on data models, as "consisting of entity types, attributes, relationships, integrity rules, and the definitions of those objects". Developing High Quality Data Models.?

Another good sample, why Knowledge Graph is a really Real World Data Graph is KBpedia. It is designed as "a comprehensive knowledge structure for promoting data interoperability and knowledge-based artificial intelligence, or?KBAI,?by supporting the (nearly) automatic creation of training corpuses and positive and negative training sets and feature sets for deep, unsupervised and supervised machine learning".?

The KBpedia knowledge structure combines seven 'core' public knowledge bases —?Wikipedia,?Wikidata,?schema.org,?DBpedia,?GeoNames,?OpenCyc, and?UMBEL, their?concepts, entity types, attributes and relations?— into an integrated whole.?https://kbpedia.org/

The upper structure of the KBpedia Knowledge Ontology (KKO) is informed by the triadic logic and?universal categories:?possibilities?or?potentials;?entities, instances or individuals;?classes, kinds or types, or?laws, habits, regularities?and?continuities?

The only reason of such cognitive biases and distortions is the misunderstanding of the real nature of computing ontology.

Here is a fair attempt from Wiki's article?Ontology (information science)

"In?computer science?and?information science, an?ontology?encompasses a?representation, formal naming, and?definition?of the?categories,?properties, and?relations?between the?concepts,?data, and?entities?that substantiate one, many, or all?domains.?Every?field?creates ontologies to limit?complexity?and organize?information?into?data?and?knowledge".?

It is rather info and knowledge, facts and concepts, entities and relationships, represented as data, data elements, data points, data sets, data structures, data models, etc. what is the subject of Data Ontology.

It states that Google Knowledge Graph is another name for ontology; for?"knowledge graph represents a collection of interlinked descriptions of entities – real-world objects, events, situations or abstract concepts".

A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.

What sounds good but confusing, it is rather a Global Data Graph?representing entities, their types and tokens, relations and properties.??

Smart Data and Artificial Intelligence hardware: New horizons for semiconductor companies

The AI and DL revolution gives the semiconductor industry the greatest opportunity to generate value that it has had in decades.

Semiconductor leaders can create a new road map for winning in AI. This article begins by reviewing the opportunities that they will find across the technology stack, focusing on the impact of AI on hardware demand at data centers and the edge (computing that occurs with devices, such as self-driving cars).?

There are specific opportunities within AI hardware, compute, memory, storage, and networking. The new strategies can help semiconductor companies gain an advantage in the AI market, as well as issues they should consider as they plan their next steps.

AI should not be reduced to a narrow-field ML technology, marked with different compute requirements, when the optimal ML & DL hardware architecture varies.?

For instance, route-planning applications have different needs for processing speed, hardware interfaces, and other performance features than applications for autonomous driving or financial risk stratification

But the story for semiconductor companies could be different with the growth of AI as the ability of a machine to perform cognitive functions associated with human minds, such as perceiving, reasoning, and learning.

The AI Technology stack, from Training and Inference to Architecture and Interface to Hardware/Compute, is determined by Data, its ontology and nature, structure and meaning.?

Garbage Data in, Garbage Intelligence out. Smart Data in, Real Intelligence out.

Global Scientific Ontology: Modeling the World: Complex Systems and Complex Networks

Global Data Ontology (GDO) is the prime Single Source/Point of Truth?(SSOT/SPOT).

The single source of truth, knowledge and intelligence is the world and its data universe,?with its causal entities, forces, relationships, principles,?mechanisms, laws and regularities. This is the only rational, scientific way to build machine/computing/technological intelligence.

The SSOT or SPOT architecture is the working method in information science and information technology. For the information?systems, it is the practice of structuring information models and data schemas to master every data element?in only one place,?providing data?normalization to a canonical/standard/normal form.

The SSOT is the ontological condition that no more than a single truth (about any particular fact or idea or subject) exists.

Global Data Ontology (GDO) is key part of Global Scientific Ontology (GSO),?a single referential repository of philosophy, mathematics, science, engineering and technology as the transdisciplinary science and technology, engineering and mathematics:

GSO = Trans-Science and Technology = Universal Ontology (Metaphysics)?+ GDO + Ontologies + Mathematics?+ Statistics + Science?+ Engineering?+ Technology

GSO is a cohesive and integrating framework for learning/knowing, inference/reasoning, decision-making and acting for both for human and machine intelligence and learning.?

GSO models the world as the global system of systems and the universal network of networks, all governed by causality, causation and interaction.

GSO includes the Systems Ontology as transcending the systems science, "an interdisciplinary field concerned with understanding systems—from simple to complex—in nature, society, cognition, engineering, technology and science itself".

It is spanning the basic and applied sciences:

formal sciences, described by?formal systems, such as?logic,?mathematics,?statistics,?probability theory, theoretical computer science,?artificial intelligence,?information theory,?game theory,?systems theory,?decision theory,?theoretical linguistics,?complex systems,?cybernetics,?dynamical systems theory.

natural sciences (physical science and life science),

social sciences, anthropology,?archaeology,?economics,?human geography,?linguistics,?management science,?communication science,?political science?and?psychology,

applied sciences, including applied systems sciences, such as?control theory,?systems design,?operations research, social systems theory,?systems biology,?system dynamics,?human factors,?systems ecology,?computer science,?systems engineering?and?systems psychology.

To the general systems ontology, the world is a system of systems or a network of networks. A?system?is a group of?interacting?or interrelated elements forming a unified whole.?A system is described by its boundaries,?structure?and purpose and its functioning, surrounded and influenced by its?environment.

Systems characteristics: structure, function(s), behavior and interconnectivity.

There are open, closed and isolated systems, as the universe; ?conceptual,?concrete, and abstract systems; natural and artificial and conceptual.

Natural systems: subatomic systems,?living systems, the?Solar System,?galaxies, the?Universe or the world.

Artificial systems include man-made physical structures, hybrids of natural and artificial systems, and conceptual knowledge, as science.

Systems are the subjects of study of?systems theory?and other?systems sciences:?complex systems,?cybernetics,?dynamical systems theory,?information theory,?linguistics?or?systems theory;?control theory,?systems design,?operations research, social systems theory,?systems biology,?system dynamics,?human factors,?systems ecology,?computer science,?systems engineering?and?systems psychology.

A?complex system?is a?system?composed of many components which interact?with each other.

Properties:?nonlinearity,?emergence,?spontaneous order,?self-organization, chaos, adaptation, hierarchy, and?feedback loops, dependencies, competitions, relationships, or other types of interactions between the parts or between a given system and its environment.?

Complex systems: the entire?universe, galaxies, solar systems, Earth's global?climate,?organisms, the?human brain, infrastructure, power grid, transportation or communication systems, complex software and electronic systems, social and economic organizations (like?cities), an?ecosystem, a living?cell, etc.

Complex systems?studied in many diverse disciplines, including?statistical physics,?information theory,?nonlinear dynamics,?anthropology,?computer science,?meteorology,?sociology,?economics,?psychology, and?biology.

The interacting components of a complex system form a?network, which is a collection of discrete objects and relationships between them, usually depicted as a?graph?of vertices connected by edges.

Networks:?logistical?networks, the?World Wide Web,?Internet,?gene regulatory networks, metabolic networks,?epistemological?networks, or social networks.

Social network analysis?examines the structure of relationships between social entities, persons, but may also be?groups,?organizations,?nation states,?web sites, or?scholarly publications.

Network theory has applications in many disciplines including?statistical physics,?particle physics, computer science,?electrical engineering,?biology,?archaeology,?economics,?finance,?operations research,?climatology,?ecology,?public health,?sociology, and?neuroscience.

Bottom line:

The World Data Ontology could serve as the Single Source/Point of Truth (SSOT/SPOT), Knowledge and Intelligence, Human and Machine. It could be applied as the world model engine for intelligence, learning, inference, decision-making, complex problem-solving and interaction of man-machine superintelligence, innovated as Trans-AI or Meta-AI.

As long as (Information) Ontology ignores Data as a key category, it will be largely ignored by Big Science and Computing Technology, Artificial Intelligence and Robotics.?

Artificial General Intelligence Global Data Base: (AI + Big Data) Smart Investment Projects

The AGI GDB Platform facilitates the World Data Integration from many different sources as below to generate actionable knowledge, new intelligence and comprehensive profiles of entities, persons or groups, objects or substances, events or processes, links or relations or structure and patterns in behavior, communication, movement and relationships.

https://www.dhirubhai.net/pulse/artificial-general-intelligence-global-data-base-ai-big-abdoullaev/?published=t

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

https://www.slideshare.net/ashabook/encyclopedic-intelligence-deep-ai

Resources

https://www.dhirubhai.net/pulse/smart-data-global-platform-investment-artificial-big-abdoullaev/

https://www.dhirubhai.net/pulse/world-data-ontology-science-ai-ml-deep-learning-graph-abdoullaev/

God is Dead, Philosophy is Dead, and Science is Dead: The Rise of Real AI

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