Reality > Causality = Interaction > Real Science and Technology > Causal/Interactive AI = Real Machine Intelligence and Learning > Trans-AI
Truth is Reality, Reality is Causality, Causality is Learning and Intelligence, Science and Technology
Causality predicts the future, explains the past, and changes the present
Power Intelligence [Artificial Intelligence (AI) and Human Intelligence (HI)] with Reality and Causality, Science and Technology to create Man-Machine Superintelligence
Power Reality and Causality, Science and Technology with Intelligence [AI and HI] to create an Intelligent World.
The science of reality with causality/causation/interaction enables creating the best ever human invention, "the first ultraintelligent machine, the last invention that man need ever make", as True Real AI or Causal Machine Intelligence and Learning, Causal AI, or Trans-AI.
Real/Causal AI Science and Technology disproves the potentially apocalyptic perspective of AI, pictured, narrated and described by many techno-dystopian movies, sci-fi literature and books as Our Final Invention: Artificial Intelligence and the End of the Human Era.?
Introduction
The only real thing in the world is the world of reality, its universe of entities and their interactions, all the rest is a big fiction and our inventions. The real-world knowledge is then all and everything.
It is acquired, explored, investigated, extracted and applied by Philosophy and Science, Engineering and Technology.
Its most advanced applications are leading emerging technologies playing a fundamental role in our life and practice:
This all should be taught at all education, business and government levels.
As for now, illiteracy of all sorts and types is intolerable. Here is the shocking case of AI literacy in America (16%).
The way to enrich our deep science and technology life, mentality, creativity, study, work, business and government is to learn the fundamentals about the world of reality. Namely, what is structured and codified by human and machine or computer ontology:
First of all, reading the fundamental sources about reality and causality, like as:
I learnt a lot from Bunge's Systems Ontology, who was largely ignored by the Western philosophers, ontologists, scientists and engineers to the prejudice of their competence.
The Real-World Science and Technology: Global Ontology, Metaphysics/Ontology and Applied Ontologies
My biggest intellectual concern is how the mother of all sciences and technologies, the real-world science of reality, ontology or metaphysics, created by the biggest minds ever, so misinterpreted?or downgraded, as?"abstract theory with no basis in reality".
For thousands years, ontology/metaphysics/philosophy has been after the first causes of things and the nature of being, dealing with the first principles of things, including abstract concepts such as being, knowing, identity, time, and space.
Today, instead of being "the study of the fundamental nature of reality, the first principles of being, identity and change, space and time", or "the philosophical study of being, as well as related concepts such as existence, becoming, and reality", we have an ontology, "a set of concepts and categories that represent the subject".
Nowadays, each academic discipline, knowledge field and business enterprise creates its own individual ontology silos promising "to limit complexity and organize data into information and knowledge; each uses its ad hoc ontological assumptions to frame explicit theories, research and applications".
Its most popular definition of an information science ontology is "An ontology is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity."
An ontology maps concepts or models domain-specific definitions of terms which belong to a realm of the world, such as physics or chemistry, biology or politics.
Such ontologies, domain-specific or upper, have as common components logical concepts, lacking the content or reality:
Individuals, Instances or objects (the basic or "ground level" objects)
Classes, sets, collections, concepts,?classes in programming,?types of objects?or kinds of things
Attributes, Aspects, properties, features, characteristics or parameters that objects (and classes) can have
Relations, ways in which classes and individuals can be related to one another
Function terms, complex structures formed from certain relations that can be used in place of an individual term in a statement
Restrictions, formally stated descriptions of what must be true in order for some assertion to be accepted as input
Rules, statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form
Axioms, assertions (including rules) in a?logical form?that together comprise the overall theory that the ontology describes in its domain of application. As used here, "axioms" also include the theory derived from axiomatic statements
Events, the changing of attributes or relations
Ontologies are commonly encoded using?ontology languages as formal logical languages, first-order logic or description logics, dubbed as formal knowledge representation languages.
Here is an example Biomedical Domain Ontologies
Usually domain ontologies are written by different designers, representing concepts in very subjective, specific and unique ways, incompatible within the same knowledge field or project. Due to different knowledge and languages, different intended usage and different perceptions of the domain (based on cultural background, education, ideology, etc.), we have very different ontologies in the same domain.
Because of lack of common machine ontology, there are no generalized techniques for merging ontologies, both domain and so-called upper ontologies, which are supposed to merge a wide range of domain ontologies. manually or automatically.
What is Machine or Computer Ontology?
In a Human-Like AI, a computer ontology is?transferred without any principal changes, again, a specification of the meanings of the symbols in an information system. It is all the same, "a specification of a conceptualization", a specification of what individuals and relationships are assumed to exist and what terminology is used for them.
In fact, machine/computer ontology to codify the concepts, characteristics and relationships of entities within a specific domain as well as to identify the domain itself.
It makes a conceptual information model for defining the domain that consists of a set of concepts, characteristics and relationships or a data framework or data sets, structures and patterns.
The meaning of certain information is generally expressed based on conceptual information models, which are used for modeling applications and structuring data. The primary ontological concepts used for building such models include entity, state, action, activity, relationship, cause and effect, purpose, time, space, etc.
The machine ontology renders the semantics and mechanisms for organizing the data/information/knowledge by making a set of assumptions about the actual applications to be modeled. For example, if an AI application is assumed to include interrelated entities, its world model operationalizes terms such as entity, property and cause-effect relationship.
In the context of real-world ontology, the machine ontology acts as a structural framework to organize data, information and knowledge in fields such computer science, artificial intelligence, machine learning, data analytics, systems science, semantics, biomedical and information architecture, etc.
What is the Real-World Ontology?
As a matter of fact, the real-world ontology is not some highly metaphysical "theory of everything", some top-abstract study of what exists, not mentioning applied ontologies and the idiosyncratic domain ontologies as above.
The real-life ontology is about what was/is/will be real, and it operates with the real-world categories, classes and kinds. Instead of individuals (instances), classes (concepts), attributes and logical relations, we have Reality [entity, substance, state, change and relationships, cause and effect interaction], as it is compressed on the figure below:
As the real-world, causal metaphysics/ontology, its is about the real-world things and relationships modelled in terms of the real-world categories formalized by the real-world variables and measured as the real-world data.?
All life-significant quantities and qualities or data are formalized as the real-life variable, causal variables, be it physical variables, chemical variables, biological variables, medical variables, economic variables, social variables, political variables, or environmental variables.??
Reality is the sum total of real-world entities, processes and relations,?causes and effects. Or, the world is essentially ontological and causal or scientific, neither mathematical, nor logical nor statistical...
Again, the systems ontology is about?the world of ontological systems and causative networks, neither formal systems, nor logical constructions from sets, types and relations, like in the core ontology construction project.
This is the reality of ontology, which could be avoided only at the prejudice of the truth.
Any intelligent entities, be it?human researchers or AI machines, sense?and affect real world things and processes to realize?real-world systems, obeying constraints/rules/laws inherited from the real world.?
Typically, important characteristics/features of real-world entities and?relationships?and their human-mind or machine-world representations are badly specified explicitly in thoughts?or code, and important opportunities for detecting errors due to mismatches are lost.?
Thus we have as many views, prospects, guesses and conjectures, hypotheses and theories as many as its holders,?being lost in the chaos of academic?noise and fake?articles,?thus creating antiscience in mass scale.????
Real-World Ontology (RWO) embraces the real-world knowledge, as global ontology, systems ontology and ontologies, and sciences, systems science, formal, natural, cognitive, social sciences and engineering, in all possible combinations and integrations, mono-, multi-, inter-, and trans-disciplinary ways.
Crucially, it innovates the generalized ontological/causal reasoning which is crucial to how humans or machines understand, explain, and make decisions and interact with the world.?
From Systems Science to Systems Ontology
Systems science, systems research, or, systems,?is an?interdisciplinary?field concerned with understanding?systems, from simple to complex, in?nature,?society,?cognition,?engineering,?technology?and?science?itself. The field is spanning the formal, natural, social, and applied sciences.
As such, it covers formal sciences, natural science, cognitive sciences, social sciences and engineering, as interdisciplinary foundations psychology, biology, medicine, communication, business management, technology, computer science, engineering, or social sciences.
If Systems Science is?an interdisciplinary field that studies the complexity of systems in nature, social or any other scientific field, then the Systems Ontology is a transdisciplinary R & D of the complexity of causal systems in all fields of science, engineering and practice.?
Then to systems ontologists and scientists, the world can be understood as the universal system of real-world systems or the global network of causal networks.
The real-world approach to reality is the theory of the world of systems (systems ontology), where a system is a network of interacting?or interrelated or interdependent entities that act according to causal/logical rules to form a unified whole, a complex emerging entity.
Our notion of system is specifying the Aristotelian idea that "the whole is greater than the sum of its parts", or a whole is made?up of its constituent parts/elements,?and involving Input/Cause, Transformation/Process/Mechanism/Causation, Output/Effects, Feedback/Circular Causality/Reversibility, Control, Boundaries, and Environment.
Systems Ontology is an unknown?unknown, as the transdisciplinary?study of real-world systems. Still it is the only systematic way to find answers to the most critical questions, It needs to uncover what is wholeness and totality and systemic properties and?emergence?(a product of particular patterns of interaction marked with a?top-down feedback in all systems with emergent properties), from the atom to the universe.
In all, there are two prospects to the systems ontology, systematic/scientific/theoretical and analytic/inductive/empirical.
With the bottom-up empirical approach, we might?start abstracting all the existing systems theories to the next levels,?be it chaos theory,?dynamics theory, information and game theory, cybernetics?of first, second, or third order,?or thermodynamics. mixing together relevant?principles and concepts of all sciences,?from philosophy and physics to engineering and sociology.??
The issue here is we never reach wholeness, totality, or real integrity,?reflected with a single and consolidated systems ontology.
This is exactly what happens with my life commitment, true AI or real machine intelligence and learning (MIL), Causal AI, Trans-AI, Man-Machine Superintelligence, Meta-AI, Ultraintelligent Machines.?
Rather than defining it as a single and consolidated discipline, ground to the real-world, the mainstream AI is today downgraded as a?set of different statistic learning technologies which are easier to?define individually.?
This set can include whatever you like and dislike: big data, data analytics, statistics, formal logic, data mining,?question answering, self-aware systems, pattern?recognition,?knowledge representation,?automatic?reasoning,?deep?learning,?expert?systems, information extraction, text mining, natural language processing, problem solving, intelligent agents, logic programming, machine learning, artificial neural networks, artificial vision, computational discovery, computational creativity.?
And its proponents naively expect that artificial “self-aware” or “conscious” systems could be the products of one of these technologies.
My message is rather simple, be really systemic and systematic or true scientific. If you claim, like Gruber, "Toward Principles for the Design of Ontologies Used for Knowledge Sharing", that "an ontology is a formal specification of shared conceptualization", go further to "a formal conceptualization of a specification", completing with full ontology, which is the interaction of conceptualizations and specifications. What is wisely reflected in the scientific method of inquiry, as the reciprocal interplay of observations and generalizations.??
The only real language is the language of cause and effect?
Whatever?you study, inquiry, investigate, or do, you need??causal conjunctions, transitions, prepositions:?
because, owing to the fact that, due to the fact that, for this reason, on the grounds that, since, as, in view of, because of, owing to, seeing that; as a result,?therefore, so, consequently, as a consequence.
The basic cause and effect, in all its?forms, types and modalities serves as paradigms or patterns of thinking or creating?or doing or constructing or destroying,?be it systems ontology or free talks?or student essay or literature text or political speech.
Here is the meme example: "I think, therefore I am". If we strictly follow causal logic, the cause is "I?am", while "to think" is the effect.?
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"I am, therefore I think".?
Or, in the closed form: "I am, I think; I think, I am"
Descartes fell?into reasoning errors SINCE he ignored Causality.??
Where I am directing.?
Most academic papers, including the mentioned ones, are not...friendly?with the language of reality, inventing their own??legalese, which is notoriously difficult for the public to understand.
If one likes to generate some formal and technical language, it must be expressed in terms of cause and effect, to be?explainable, true and rational.
After all, even medical or juridical legalese?are ultimately based on causal relationships.?
The Real-World Life and Causation
All our real life is basically about causal relationships, processes, as
Causes, Causation, Mechanisms, Processes, and Effects
getting born,?
having relationships
getting married
getting a job
losing?a job
making peace
making war
getting a disease
getting dying.
Causes and effects, as actions and interactions, influences?and affects, impact and forces, mechanisms and processes, agents and instruments...
All our infrastructure, be it grid networks, transportations systems, industrial systems, water/energy/ systems, communication systems, as well as ecosystems,?agroecosystem, aquatic ecosystem, coral reef, desert, forest, human ecosystem, littoral zone, marine ecosystem, prairie, rainforest, savanna, steppe, taiga, tundra, urban ecosystem. all causal systems.?
All our Earth is the planetary networks of causal networks, while the world is the universal systems of causal systems of different scales and levels.
The Real-World Life and Causation
All our real life is basically about causal relationships, processes, as
Causes, Causation, Mechanisms, Processes, and Effects
getting born,?
having relationships
getting married
getting a job
losing?a job
making peace
making war
getting a disease
getting dying.
Causes and effects, as actions and interactions, influences?and affects, impact and forces, mechanisms and processes, agents and instruments...
All our infrastructure, be it grid networks, transportations systems, industrial systems, water/energy/ systems, communication systems, as well as ecosystems,?agroecosystem, aquatic ecosystem, coral reef, desert, forest, human ecosystem, littoral zone, marine ecosystem, prairie, rainforest, savanna, steppe, taiga, tundra, urban ecosystem. all causal systems.?
All our Earth is the planetary networks of causal networks, while the world is the universal systems of causal systems of different scales and levels.
Ontological Machines: The Scientific Ways to a True General AI
McKinsey has even issued "An executive primer on artificial general intelligence".
As such, there are three types of human-like artificial intelligence:
Modern AI systems fall into the class of ANIs, handling many problems previously thought out of reach of computers:
However, despite the successes, many of these systems can be thought of as?technological parrots. Parrots can mimic their owners, but do not have a true awareness of what they are saying, nor why they are saying it.
The ANI/ML mechanics is based on correlational spurious pattern recognition what is insufficient for reliable predictions and reliable decision-making.
As a consequence, such statistics-driven AI systems suffer from the following three B’s:
Causal Statistical AI is an alternative to the modern ANI technology, provided that?causal laws with “what-if” scenarios are core to scientific experimentation, understanding, and decision-making. Beside of other many advantages, as adapting to the changing conditions in the real world, manipulating and simulating any range of situations, it promises to overcome the blind, bias, and brittle nature of modern AI/ML/DL algorithms.
The idea attracts intense attentions from researchers, developers and businesses, with the following free interpretations.
Causal AI is the only technology that can reason and make choices like humans do. It utilizes causality to go beyond narrow machine learning predictions and can be directly integrated into human decision-making. It is the only AI system organizations can trust with their biggest challenges – a revolution in enterprise AI.
Causal AI is?an artificial intelligence system that can explain the cause and the effect. You can use casual AI to interpret the solution given the AI Machine learning model and the algorithm. In different verticals, casual AI can help explain the decision making and the causes for a decision.
Causal Machine Learning (CausalML) is?an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM).
At Microsoft Research, our causality research spans a broad array of topics, including: using causal insights to improve machine learning methods; adapting and scaling causal methods to leverage large-scale and high-dimensional datasets; and applying all these methods for data-driven decision making in real-world contexts.
?In this book, What is Causal ML and Why You Should Care?, you will learn how to write algorithms that capture causal reasoning in the context of machine learning and automated data science.
Causal AI means both improving machine learning with causal reasoning, and automating causal reasoning with machine learning.?Today’s learning machines have superhuman prediction ability but aren’t particularly good at causal reasoning, even when we train them on obscenely large amounts of data.
The difficulty of answering causal questions has motivated the work of millennia of philosophers, centuries of scientists, and decades of statisticians. But now, a convergence of statistical and computational advances has shifted the focus from discourse to algorithms that we can train on data and deploy to software.?It is now a fascinating time to learn how to build causal AI.
It is not a real path to true, real AI, causal intelligence, or ontological machines, to be run by its machine ontology, the world model engine for general intelligence, learning and knowing, inference and interactions with the world. Its ontological/causal reasoning mechanism is a crucial element to how humans or machines understand, explain, and make decisions and interact with the world.?
In all, Ontological/Causal AI Science and Technology is an extension of the Real-World Science and Technology.
Building the General-Purpose Technology of Causal Learning AI Machines (CLAIMs)
Causal AI is highlighted by the Gartner Hype Cycle of Emerging Technology 2022 highlighting technologies that will significantly affect business, society and people over the next 2 to 10 years.
The CLAIMs top functions is to intelligently identify causal patterns in the data universe of data classes and sets and elements.
Its design, development and deployment pipeline is the causal chain from Reality to Computational Modeling/Representation to Real-World Applications:
World Learning and Inference Model Machine: Reality/Causality/Science > Statistics/Data Science > Mathematics/Set Theory/Optimization/Calculus/Linear Algebra/Probability > Programming/Algorithms/ANI/ML/DL/Neural Networks > Causal Regression > Software/Hardware > Real-World Applications
The CLAIMs act as primary decision makers in government and public bodies, businesses and others,
in ICT industries, to transform the internet into the Intelligent Internet of Everything, the WWW into the Web x.0, a web guided by real AI that makes use of machine ontology and causal intelligence and common sense to provide intelligent responses and interactions
in economical/financial industries, to govern economies and finances, automate trading decisions and detect prospective directions and investment opportunities
in legal sectors, to provide legal advice to individuals and small businesses. allow for informed decision-making by creating new insights from legal data
in criminal justice systems, to determine bail and prison sentences, crime pattern detection, and predictive policing
in the education sector, to enhance learning efficiency by selecting assessments and other learning resources for each student individually
in healthcare, to best the accuracy of diagnostics through causal pattern detection, to predict responses to particular treatment pathways, enabling causal decision-making around tailored treatment options
government, to provide autonomous government and algorithmic decision-making without biases or discrimination, informing decisions on existing services – such as health, social care, emergency services or public policy.