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

Universal AI Platform [Evolutionary History]:

Philosophia Universalis >

Mathesis Universalis >

Scientia Universalis >

Computare Universalis >

Intelligentia Universalis >

Technologia Universalis >

Global AI Technology Platform

Artificial Intelligence (AI) and Machine Learning, Robotics and Hyperintelligent Automation have become buzzwords in recent years, promising to change the way we live, work and interact with each other, our environment and technology.

Specifically, it refers to Globalization changing the way nations, businesses and people interact, how people and ideas, knowledge and data, information and technology,  goods and services spread around the world.

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 science and technology. The next-gen emerging technologies, as the future internet, 5-6G technologies, IoT and Automation, AI and ML, Blockchain, Self-driving Transportation, 3D Manufacturing, have enabled and accelerated all flows and the resulting international interactions and interdependencies.

Innovating a global 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.

A Global Machine Intelligence and Knowledge Platform for Real Globalization

Encoding or embedding in computing machines a comprehensive scientific knowledge of the world’s entities and their interactions has been a strategic goal of AI as Machine Intelligence and Learning.

I explain why a Global AI Platform as a General Machine Intelligence and Learning (MIK) Network of philosophies and sciences, engineering and technologies, societies and economies, policies and ideologies, cultures and languages is the intelligent backbone infrastructure for all the meaningful globalization processes.

MIK is a reality-simulating-science-based-not-human-intelligence-imitating machine intelligence and knowledge possessing a causal understanding of the underlying reality of the real world.

MIK enables the intelligent processing of world data from multiple digital sources (scientific, climate, consumer, social media, economic) by unifying Scientific World Knowledge, as Data Science and Engineering (DSE), Ontology and Knowledge Graph Engineering, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) models, algorithms and techniques, all to obtain the optimal problem solutions in any range of environments.

Globalizing STEM (Science, Technology, Engineering, and Mathematics)

Our late modernity has been marked with an ICT-driven globalization, "the process of interaction and integration among people, companies, and governments worldwide", with the unlimited movements and exchanges (of human beings, goods, and services, capital, technologies or cultural practices, data and knowledge) all over the planet.

Globalization is a globally interacting network of economic globalization, social globalization, cultural globalization, political globalization, financial globalization, geographical globalization, environmental globalization and technological globalization. These types influence one another, interacting with each other and all together, locally and globally, with the complex interaction effects at level of individuals, communities, local or regional organizations, businesses and economies, institutions, multinational corporations, national governments and other organizations, nations and countries, trans-nations and the world at large.

So, globalization in all its forms is missing its backbone, a globally integrated techno-science technology platform, providing world knowledge and data unification and integration.

Globalization is driven by the convergence of cultural and economic systems, necessitating increased interaction, integration and interdependence among people and their ideas and cultures, economies and societies, and especially, sciences and technologies.

The more countries and regions of the world become intertwined politically, culturally and economically, and, especially, scientifically and technologically, the more truly globalized the world becomes. 

Say, an economic globalization involves people migrations, goods, services, data, technology, the economic resources of capital, and cheap labor outsourcing, instead of effectively and sustainably integrating all economic ideas and values, models and processes, economies and industries, technology and information.

To globalize and integrate sciences and technologies, data and knowledge, we have to introduce the meta-transdisciplinary construct for unity and cohesion, to avoid the negative impacts of globalization, global inequality, corruption, regional conflicts and wars, structural unemployment, environmental degradation and unlimited consumption.

Universal Techno-Science (UTS), Techno-Scientia Universalis, is to critically help overcome the destructive modernity divide and division of science and technology, engineering and industry, work and labor.

The UTS is emerging as the natural development of the generalization of human knowledge resulting with the universal man-machine technology platform: Philosophia Universalis > Mathesis Universalis > Scientia Universalis > Computare Universalis > Intelligentia Universalis > Technologia Universalis > Techno-Scientia Universalis > Universal AI Platform > Universal Man-Machine AI Platform

The Global Man-Machine AI Platform relies on the synthesis and synergy of philosophy and science, technology and engineering and mathematics, integrated as the universal techno-science.

Such a Universal MIK Platform is designed as as 3Trans-AI (transdisciplinary, transformative (or transformational), and translational) in terms of data and information, knowledge and intelligence, learning and inference, models and paradigms, techniques and methodologies, sciences and technologies, engineering and practices.

The World Knowledge and Intelligence Hypergraph (WKIH)

The UTS is isomorphic to the WKIH which can represent 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 language or computing programming languages and 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 Timeline of Universal Technological Platform

Philosophia Universalis (Metaphysics and Ontology, Cosmology and Theology, Platonic Realism and Aristotle' Metaphysica) >

Mathesis Universalis (Mathematics, Mathematical Logic, Universal Characteristics, Algebra, Pythagoras, Descartes, Leibniz) >

Scientia Universalis (Scientia Generalis, Philosophy, Unified Sciences, the Unity of Science, Trans-disciplinarity) >

Computare Universalis (Universal Computing, universal Turing machine (UTM), the computable universal function to calculate any computable function, artificial neural networks, quantum AI computing, Boole, Legendre, Gauss, Frege, Cantor, Hilbert, Gödel, and Turing) >

Intelligentia Universalis (human intelligence + machine intelligence, deep neural networks, large language models, generative AI, generalized AI systems, man-machine hyperintelligence >

Technologia Universalis (digital and emerging and sustainable and space technologies, universal AI platform, Trans/Meta-AI Technology)

Techno-Scientia Universalis (Philosophy & Science & Mathematics & Engineering & Technology & Arts) diagrammed as the radiating world knowledge hypergraph networks.

Techno-Scientia Universalis and its Universal Mathematical Computing Metaphysics (UniMaCoM)

Techno-Scientia Universalis has as its core, Universal Mathematical Computing Metaphysics (UniMaCoM), which has been proposed as the Universal Model and Language of Generalized AI and Machine Learning.

The rationales are evident.

Metaphysics is the material science of reality, knowledge and intelligence.

Mathematics is the formal science of reality, its entities and interactions, as quantities and numbers, objects and variables, structures and functions, forms and patterns, orders and relationships

Computer science is about the modeling and simulation of reality, as its pieces or as its domains or the whole of reality

We are after designing and developing, deploying and distributing man-machine hyperintelligence as a universal AI platform, as involving the universal causal cycle:

Reality/World/Universe/Being/Existence/All (Metaphysics and Ontology, Cosmology, Epistemology, Mathematics, Science, Engineering, Technology) >

Representations (Characteristica universalis, the universal symbolic language of mathematics and metaphysics and science, as Data, Information, or Knowledge) >

Computation (Universal Turing Machine, from the classical computing machines and traditional programming to quantum computing, ML algorithms, AI models and Trans-AI platforms) >

Interaction (Relationships, Causality, Transformation Mechanisms, Feedback Interactive Loops, Interactivities, Interfaces, Communication, Interaction Networks) >

Environment (all reality encompassing the interaction of all its elements, components, as the world or the the natural environment, the earth, the settings, conditions, surroundings, milieu, circumstances, resources, context, stimuli, the aggregate of surrounding things, conditions, or influences, all with which UAI platforms interacts) >

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

The idea of UDAIP 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

In computing science, data science and machine learning, "a classifier is an algorithm that automatically orders or categorizes data into one or more of a set of classes.” So, a classifier is the algorithm itself – the rules or mathematical functions used by machines to classify data. In its turn, a classification model is the result of your classifier’s machine learning, which is trained using the classifier, thus it is the model, ultimately, classifies the data using training data sets.

There are various statistical algorithms, sold as ML/AI algorithms, depending on the sorts of training data sets, labeled or unlabeled, structured or unstructured:

• linear regression calculating how the X input (meaning words and phrases) relates to the Y output (opinion polarity – positive, negative, neutral)
• logistic regression, estimating the probability of dependent categorical variable Y, given independent categoric or numeric variable X, predicting binary outcome, Yes/No, Existence/Non-existence, Pass/Fail
• naive Bayes classifier, a family of probabilistic algorithms that use Bayes’ Theorem to calculate the possibility of words or phrases falling into a set of predetermined “tags” (categories) or not. This can be used on text analysis, news articles, customer reviews, emails, general documents, etc.
• supervised learning algorithms
• unsupervised learning algorithms
• semi-supervised learning algorithms
• reinforcement, "trial and error" learning algorithms
• artificial neural networks as an ordered set of algorithms
• self-supervised learning algorithms...

In logic, mathematics and computer science, metalogic and computability theory, an algorithm is an effective method or effective procedure a mechanical method or procedure or process for solving a problem by "any effective means from a specific class".

Or, an algorithm is a finite sequence of rigorous instructions to solve a class of specific problems or to perform a computation.

Algorithms are used as specifications for performing calculations and data processing.

"Advanced algorithms can use conditionals to divert the code execution through various routes (automated decision-making) and deduce valid inferences (automated reasoning)", thus achieving automation.

Algorithms can be expressed within space and time and in a well-defined formal language for calculating a function.

The UFO master algorithm is generalizing the concept of algorithm, from quantum algorithms to computer science algorithms, informing all the possible types and sorts of algorithms, as in:

• Logico-philosophical algorithms: Induction > Abduction > Deduction > Analogy
• Mathematical algorithms: functions, maps, mappings, rules, assigning from a set X to a set Y each element of X exactly one element of Y, where X is called the domain of the function and Y is called the codomain of the function; Arithmetical Operations, Equations, Algebraic/Differential/Integral/Transcendental equations, Euclidean algorithm, or Euclid's algorithm, Binary Exponentiation, Modulo Arithmetic, the Calculus algorithms, Differentiation, Chain Rule, Integration, Analytic Geometry...

• Scientific algorithms: Observation > Induction > Hypothesis > Testing/Experiment > Evaluation > Theory > Technology > Observation
• Physical algorithms, physical laws, effects and equations
• Chemical algorithms, chemical formulas and reactions
• Biological algorithms, genetic algorithms
• Technological algorithms, engineering design algorithms
• Computing Algorithms

Traditional programming algorithms following a set of instructions to transform data into a desired output

ML algorithms enabling machines to solve problems based on past observations without being explicitly programmed: compare data, find patterns, or learn by trial and error to accurately predict with no human intervention

Pattern recognition algorithms are about "the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories".

Pattern-matching algorithms are checking a sequence of tokens or data structures for the presence of the constituents of some pattern (regularities in the world).

• Social/Political/Economic algorithms, constitutional codes, policies and norms, laws and regulations... As samples of regulatory compliance algorithms could be used the digital technology regulations and directives, legislations and regulatory frameworks: the the European Union Digital Services Act (DSA) to protect the rights of Internet users by holding ISPs, search engine, social media platforms, online marketplaces, content delivery networks and other online services legally accountable, as being transparent about their algorithm; the EU's General Data Protection Regulation (GDPR) for the protection of data in the European Union; the European Union Artificial Intelligence Act (EU AI Act) for the development, marketing, and use of AI to legally define AI and impose documentation, auditing, and process requirements for AI providers.

Crucial, the intelligent core of the the Universal World Model AI Engine is the World's Hypergraph Interaction Networks, acting as the Master Algorithm for all the traditional advanced, task-specific algorithms and programs, data-driven machine learning models, or AI technologies, from ANNs and DNNs to NLG/NLU to LLMs and generative AI.

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.

Conclusion

We have introduced Techno-Scientia Universalis as the Universal Model and Language of Global AI Platform for the Global Networks of Globalization Processes.

Such a global modeling of the world intersects Philosophy, Science, Engineering, Technology, Mathematics.

It has all the conceptual potentials for building the man-machine hyperintelligent hyper-automation platform.

The world's meta-mathematical 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 World-Intelligence Interaction Algorithm:

Reality > Representation > Computation > Interaction > Environment >...

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

Why and How to Build Digital Superintelligence: Real AI, Superhuman Intelligent Machines, Superintelligent Machines, or Superintelligent AI

A Global AI Infrastructure: RAI vs. BRI as the Most Valuable Project on the Earth

DISTINGUISHING SCIENTIFIC AI FROM PSEUDOSCIENTIFIC AI

Universal Ontology for Artificial Intelligence: building machine metaphysics for machine intelligence and learning

Scientific AI vs. Pseudoscientific AI: Big Tech AI, ML, DL as a pseudoscience and fake technology and mass market fraud

The rise of Real AI Industry: Causal Interactive Learning vs. Deep Statistical Learning

Real AI vs. Unreal AI = Causal Intelligence Interactive Machines (CIIM): Converging Symbolic, Predictive, Generative, Industrial, and Causal AI

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

Space at large> Hyperspace > Hypercomputing > Hyperintelligence

Real AI vs. Human AI: the Best Ideas vs. the Worst Ideas

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

NextGen AI as Hyperintelligent Hyperautomation: Universal Formal Ontology (UFO): World Model Computing Engine

FEDERATED LEARNING: A PRIVACY-PRESERVING PARADIGM TRANSFORMING AI

AI Foundation Model: a paradigm for Real AI Technology

AI Foundation Models. Part II: Generative AI + Universal World Model Engine

The Paradigm Shifts in Artificial Intelligence

SUPPLEMENT: Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science

After the many ups and downs over the past 70 years of AI and 50 years of data science (DS), AI/DS have migrated into their new age. This new-generation AI/DS build on the consilience and universology of science, technology and engineering. In particular, it synergizes AI and data science, inspiring Trans-AI/DS (i.e., Trans-AI, Trans-DS and their hybridization) thinking, vision, paradigms, approaches and practices. Trans-AI/DS feature their transformative (or transformational), transdisciplinary, and translational AI/DS in terms of thinking, paradigms, methodologies, technologies, engineering, and practices. Here, we discuss these important paradigm shifts and directions. Trans-AI/DS encourage big and outside-the-box thinking beyond the classic AI, data-driven, model-based, statistical, shallow and deep learning hypotheses, methodologies and developments. They pursue foundational and original AI/DS thinking, theories and practices from the essence of intelligences and complexities inherent in humans, nature, society, and their creations.

Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science