True Real AI (TRAI): the Trans-AI Platform of AI/ML/DL/NNs/LLMs/ChatGPT
“On one hand, you have AI people complaining that the term has lost its meaning. On the other hand, you have reporters, startups, S&P 500 boards and every VC firm on the planet all claiming that anything slightly complex or slightly automated is AI.” – Ronald Ashri,?Hackernoon
Today, every tech company is touting their AI-driven hardware, software or applications or solutions. As a?piece?in?The Atlantic?put it, “deflationary examples of AI are everywhere.” If I had a dollar for every empty claim that someone “uses AI” in their platform,??I’d have enough to build a real AI vs. artificial AI… and that real costs a lot, multi-billions, if not trillions.
Most AAI claims are marketing ploys, capitalizing on the hottest ever trend in the market history. But “true real AI” is rara avis as a black swan event.?
We one critical step away from a true or real-world?AI (TRAI) —?the AI we have all read in the sci-fi literature and seen in the movies, one of humanity’s cherished ideas going back to classical Greece and the myth of Hephaestus and beyond.
Large language models (LLMs) like ChatGPT are an impressive advance in AI, but it is only one part in the formula/equation/model for a true real-world artificial general intelligence (TRAGI).
The TRAI has been a dream for centuries, but it is now becoming a reality because of enormous progress in transdisciplinary science and emerging technology, computing power and big data analysis.
The TRAI is the machine intelligence and learning (MIL) whose superintelligence transcendence/perfection largely depends on its causal power to detect, identify, process, compute, remember and manipulate any number of causal variables from any environment, physical, mental, digital, or virtual.
The real-world AI is rising combining and transcending all the special designed intelligent algorithms.
The True MIL is emerging as one of the greatest ever techno-science discoveries and engineering innovations and inventions, making the future of trillion-dollar big tech companies at stake.
By 2020, EIS Encyclopedic Intelligent Systems LTD, of which I am the only founder, has completed the TRAI Model as Causal Machine Intelligence and Learning, trademarked as the Trans-AI = Real Superintelligence (RSI) complementing human intelligence, collective and individual.
The company has spent zero public funding and private investment for its TRAI R&D, relying only on its own resources, intelligent and material.
EIS is aimed to build/engineer the TRAI Platform, to be open to the general public, developing the Proof-of-Concept/Mechanism/Principle Prototype to demonstrate the RSI feasibility for a full-scale global deployment.
Some original resources and general description follow:
EIS has Created the First Trans-AI Model for Narrow AI, ML, DL, and Human Intelligence
Trans-AI: meet the disruptive discovery, innovation, and technology of all time
Man-Machine Superintelligence Paradigm Shift: Trans-AI, Deep AI, or Real-World AI as a Real Trustworthy AI, or why ML/DL/ANNs/DNNs must be disrupted
Researchers have claimed that AI will reach the singularity within seven years, after attempting to quantify its progress, measuring TTE in machine translation over 2 billion MT suggestions by tens of thousands of professional translators worldwide. These translations span multiple subject domains, ranging from literature to technical translation.
Many AI researchers believe that solving the language translation problem is the closest thing to producing Artificial General Intelligence (AGI). This is because natural language is by far the most complex problem we have in AI. It requires accurate modeling of reality in order to work, more so than any other narrow AI.
Again, there is no reason to believe the Singularity AI would require AI to simulate a human brain due to its inhuman complexity. A human brain features about 100 billion neurons in total, connected via as many as 1,000 trillion synaptic connections. Meantime, the state of the art narrow AI model of GPT-3 had 175 billion parameters in total and GPT-4 reportedly has 1 trillion. It must be plain and clear that today's AAI is not True AI, as LLMs are not Real AI.
What is NOT a True and Real AI
Dear Reader, let me start with a naked truth as from a fable in which Truth and Falsehood went bathing, Falsehood then dressed in Truth's clothes, and Truth, refusing to take another's clothes, went naked. [ Late 1500s]
AI, as the True/Real/Genuine/Authentic/Actual Man-Machine Intelligence,?is NOT reduced to what we misled to massively believe:
1:?a branch of computer science dealing with the simulation of intelligent behavior in computers; the capability of a machine to imitate intelligent human behavior;
2: an area of computer science that deals with giving machines the ability to seem like they have human intelligence; ?
4: system that perceives its environment and takes actions that maximize its chance of achieving its goals;
5: machines that mimic cognitive functions that humans associate with the?human mind, such as learning and problem solving.
That's all wishful thinking. Today's AI is not copying human brains, mind, intelligence, cognition, or behavior. It is all advanced hardware, software and dataware, information processing technology, big data collection, big computing power. As it is rightly noted at the Financial Times Future Forum “The Impact of Artificial Intelligence on Business and Society”: “Machines will outperform us not by copying us but by harnessing the combination of colossal quantities of data, massive processing power and remarkable algorithms.”
True and Real AI embraces as its parts data-processing systems: weak or narrow AI applications, neural networks, machine learning, deep learning, multiple linear regression, RFM modeling, cognitive computing, predictive intelligence/analytics, language models, or knowledge graphs. Be it web searches or self-driving transportation, GPT-3-4-5 or BERT, Microsoft' KG, Google's KG or Diffbot, ?training their knowledge graph on the entire internet, encoding entities like people, places and objects into nodes, connected to other entities via edges.
Without the Real AI, its world/reality/data modeling and simulation (R&M&S), today's "AI is meaningless" and "often just a fancy name for a computer program", software patches, like bugfixes, to legacy software or big databases to improve their functionality,?security, usability, or?performance.
If Diffbot aims to "build the world's first complete map of human knowledge to enable intelligent systems, crawling the entire public web and providing the world's largest searchable knowledge graph, it first needs "a theory of reality" (dubbed as the general schema of things) encoded and programmed, like "the theory of mind", genetically encoded or socially cultivated, in human minds.
The Real-World AI means a new reality shift from a dumb and dull statistic data universe to a meaningful world of physical and digital realities, mixed reality, augmented reality, virtual reality, or simulated reality, all running by the Trans-AI entities, machines, robots, engines, codes, algorithms, and platforms.
The Trans-AI General-Purpose Technology is to change the world in all respects and aspects, the economy and industry, society and life, governments and international relations.
Today, even with a statistic AI/ML/DL, every day we are witnessing new kinds of developments in each part of life, from politics to economics. It is impacting our daily life with language machines, deep fake news, chatbots, IoT devices, travel navigation, fraud detection, mass surveillance, smart home devices, smartphones, voice assistants, drones, self-driving cars, cybersecurity, and LAWs.
As Stephen Hawking had noted: “Success in creating effective AI, could be the biggest event in the history of our civilisation. Or the worst. We just don’t know. So we cannot know if we will be infinitely helped by AI, or ignored by it and side-lined, or conceivably destroyed by it”.
With artificial intelligence poised to disrupt everything, it is time to know
What is True AI or Real Machine Intelligence and Learning
True AI or Real Machine Intelligence and Learning is Transdisciplinary AI (Trans-AI). The Trans-AI is about programming/encoding/mapping/representing/modeling/simulating reality, physical, mental, social and digital, in computing machinery and robots, to effectively, efficiently and sustainably interact with the world.
It is embracing the human-imitating AIs, narrow and weak, strong and general, superhuman or superintelligence, programming/encoding/mapping/representing/modeling/simulating human brains/cognition/mind/intelligence/behavior in computing machinery and robots.
The Trans-AI is innovated not as an alternative to the pre-determined mainstream paradigm, but as a unifier to all the human-like AIs, from neural networks to machine learning to symbolic AI to cognitive AI and beyond.
The Trans-AI is designed and developed as the Real AI for Everything and Everybody (RAI4EE). Below we are to show how human-centric AI/ML/DL projects, as the AI4EU platform, are integrated by the RealAI4EE Platform.
The Trans-AI paradigm integrates the natural, human, social, and?engineering?AI models in a?unifying?context, a whole that is greater than the sum of its parts?and transcends their traditional boundaries.
Transdisciplinary research integrates information, data, concepts, theories,?techniques, tools, technologies, people, organizations, policies, and environments,?as?all sides of the real-world problems.
“Addressing societal challenges, as embedded in SDGs, using transdisciplinary research” is considered a “mainstream modus operandi for research” by the OECD Global Science Forum (GSF).
The RAI4EE Platform: True and Real and Scientific AI vs. Artificial Human Intelligence
The mainstream (human-centric) narrow or weak AI has some fundamental problems looking for fundamental solutions.
First, it is about AI philosophy, or rather lack of any philosophy, and blindly relying on observations and empirical data or statistics, its processes, algorithms, and inductive inferences, needing a large volume of big data as the ”fuel” to train the model for the special tasks of the classifications and the predictions in very specific cases.
Second, extreme anthropomorphism in today's AI/ML/DL, "attributing distinctively human-like feelings, mental states, and behavioral characteristics to inanimate objects, animals, religious figures, the environment, and technological artifacts (from computational artifacts to robots)". Anthropomorphism permeates AI R & TD & D & D, making the very language of computer scientists, designers, and programmers, as "machine learning", which is not any human-like learning, "neural networks", which are not any biological neural networks, or "artificial intelligence", which is not any human-like intelligence. What entails the whole gamut of humanitarian issues, like AI ethics and morality, responsibility and trust, etc.
Third, today's AI is not a scientific AI agreed with the rules, principles, and method of science. Today’s AI is failing to deal with reality and its causality and mentality strictly following a scientific method of inquiry depending upon the reciprocal interaction of generalizations (hypothesis, laws, theories, and models) and observable/experimental data.
As a result, its trends are chaotic, sporadic and unsystematic, as the Gartner Hype Cycle for Artificial Intelligence 2021 demonstrate.
As a consequence, there is no common definition of AI, and each one sees AI in its own way, narrowly special or broadly general.
AI Generalists vs. Experts and Specialists
Generally, there are two groups of AI researchers, specialists and generalists.
Most of AI folks are narrow specialists, 99.999…%, involved with different aspects of the Artificial Human Intelligence (AHI), where AI is about programming human brains/mind/intelligence/behavior in computing machines or robots.
Artificial Human Intelligence (AHI) is sometimes defined as “the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity”.
The EC High-Level Expert Group on?Artificial Intelligence has formulated its own specific behaviorist definition.
“Artificial intelligence (AI) refers to systems that display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals
“Artificial intelligence (AI) refers to systems designed by humans that, given a complex goal, act in the physical or digital world by perceiving their environment, interpreting the collected structured or unstructured data, reasoning on the knowledge derived from this data and deciding the best action(s) to take (according to pre-defined parameters) to achieve the given goal. AI systems can also be designed to learn to adapt their behaviour by analysing how the environment is affected by their previous actions".
In all, the AHI is fragmented as in:
·????????Computer Vision, machine vision
·????????NLP, speech recognition, conversational AI
·????????Machine Learning, Deep Learning, Neural Networks
·????????Machine Reasoning, symbolic AI, expert systems
·????????Machine Action, robotics and autonomous vehicles
Very few of AI researchers (or generalists), 00.0001%, know that AI is about programming reality models and causal algorithms in computing machines or robots.
The first group lives on the anthropomorphic idea of AHI of ML, DL and NNs, dubbed as a narrow, weak, strong or general, superhuman or superintelligent AI, or Fake AI simply. Its machine learning models are built on the principle of statistical?induction: inferring patterns from specific observations, doing statistical generalization from observations or acquiring knowledge from experience.
“This inductive approach is useful for building tools for specific tasks on well-defined inputs; analyzing satellite imagery, recommending movies, and detecting cancerous cells, for example. But induction is incapable of the general-purpose knowledge creation exemplified by the human mind. Humans develop general theories about the world, often about things of which we’ve had no direct experience.
Whereas induction implies that you can only know what you observe, many of our best ideas don’t come from experience. Indeed, if they did, we could never solve novel problems, or create novel things. Instead, we explain the inside of stars, bacteria, and electric fields; we create computers, build cities, and change nature — feats of human creativity and explanation, not mere statistical correlation and prediction”.
The second advances a true and real AI, which is programming general theories about the world, instead of cognitive functions and human actions, dubbed as the real-world AI, or Transdisciplinary AI, the Trans-AI simply.
The first one has their fathers, leaders or champions, who since 1950 systematically had been confusing the general public and funding institutions, with empty promises, as in:
“Human level AI will be passed in 1976 (Shannon), 1980 (Simon), 2000 (Turing or in the mid 2020's.”
If to summarize the hardest ever problem, the philosophical and scientific definitions of AI are of two polar types, subjective, human-dependent, and anthropomorphic vs. objective, scientific and reality-related.
So, we have a critical distinction, AHI vs. Real AI, and should choose and follow the true way.
Today’s narrow AI advances are due to the computing brute force: the rise of big data combined with the emergence of powerful graphics processing units (GPUs) for complex computations and the re-emergence of a decades-old AI computation model—the compute-hungry machine deep learning. Its proponents are now looking for a new equation for future AI innovation, that includes the advent of small data, more efficient deep learning models, deep reasoning, new AI hardware, as neuromorphic chips or quantum computers, and progress toward unsupervised self-learning and transfer learning.
Ultimately, researchers hope to create future AI systems that do more than mimic human thought patterns like reasoning and perception—they see it performing an entirely new type of thinking. While this might not happen in the very next wave of AI innovation, it’s in the sights of AI thought leaders.
Considering an existential value of AI Science and Technology, we must be absolutely honest and perfectly fair here. Today’s AI is hardly any real and true AI, if you automate the statistical generalization from observations, with data pattern matching, statistical correlations, and interpolations (predictions), as the AI4EU is promoting.
“Today’s AI is narrow. Applying trained models to new challenges requires an immense amount of new data training, and time. We need AI that combines different forms of knowledge, unpacks causal relationships, and learns new things on its own”.
Such a defective AI can only compute what it observes being fed with its training data, for very special tasks on well-defined inputs: blindly text translating, analyzing satellite imagery, recommending movies, or detecting cancerous cells, for example. By the very design it is incapable of the general-purpose knowledge creation, where the beauty of intelligence is sitting.
Google, as Facebook, Microsoft, Amazon, etc., is addicted with a sort of human-like AI (AHI), rightly dubbed as a fake AI, marked with all sorts of human biases. We can’t classify them, no explain properly and they are a zillion. See the list of cognitive biases, Wiki.
Their machine learning models are built on the principle of?induction: inferring patterns from specific observations or acquiring knowledge from experience, focused on “big-data” — the more observations, the better the model. They have to feed their statistical algorithm millions of labelled pictures of cats, or millions of games of chess to reach the best prediction accuracy.
As the article,?The False Philosophy Plaguing AI,?wisely noted:
“In fact, most of science involves the search for theories which explain the observed by the unobserved. We explain apples falling with gravitational fields, mountains with continental drift, disease transmission with germs. Meanwhile, current AI systems are constrained by what they observe, entirely unable to theorize about the unknown”.
Again, no big data can lead you to a general principle, law, theory, or fundamental knowledge. That is the damnation of induction, be it mathematical or logical or experimental.
Due to lack of a deep conceptual foundation, today’s AI is closely associated with its logical consequences, “AI will automate entirety and remove people out of work”,?“AI is totally a science-fiction based technology”, or?“Robots will command the world”? It is misrepresented as the?top five myths about Artificial Intelligence:
That means we need the true real scientific AI, not AHI, but the Real-World Machine Intelligence and Learning, or the Trans-AI, simulating and modeling reality, physical. mental or virtual, not just mentality, as reflected in the real superintelligence (RSI).
The Trans-AI technology is what the Google’s founder is dreaming about “AI would be the ultimate version of Google. The ultimate search engine would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing.” —Larry Page
The Trans-AI Elements
All in all, the Real AI as the Trans-AI embraces as its interdependent modules or elements all its domain intelligences:
Philosophical AI
(Ontological AI, Epistemological AI, Semantic AI, Ethical AI; Artificial intelligence (AI) has closer scientific connections with philosophy than do other sciences, because AI shares many concepts with philosophy, e.g. action, consciousness, epistemology (what it is sensible to say about the world), and even free will. This article treats the philosophy of AI but also analyzes some concepts common to philosophy and AI from the standpoint of AI. The Philosophy of AI and the AI of Philosophy
Logical AI
(Symbolic AI, Knowledge Engineering, Expert Systems, Rules-based AI; logical AI representing human knowledge in a declarative form (i.e. axioms, facts and rules), as embedded into computer programs)
Mathematical AI (https://medium.com/swlh/ai-mathematics-699a9ea2a0d6)
Statistic AI
(Data Analytics systems, Predictive Modelling algorithms, ML, DL; model-free neural networks, connectionist, machine learning AI; Statistical AI, arising from machine learning, tends to be more concerned with "inductive" thought: given a set of patterns, induce the trend; the use of probabilistic graphical models has revolutionized AI by exploiting probabilistic independencies; Statistical Relational Artificial Intelligence (StarAI) combines logical (or relational) AI and probabilistic (or statistical) AI. https://www.frontiersin.org/research-topics/5640/statistical-relational-artificial-intelligence)
Digital AI
(Computing Intelligence, Virtual Intelligence, Software Intelligence; data-based and date-processing AI: Input (Perception in the Digital World) > Web Data > Feature extraction + Classification/Prediction/Decision/Recommendation > Output (Behavior in the Digital World). Digital AI models, as image classification and automatic speech recognition, are typically the approach of processing the signal and data from the sources of the image, the sound, the text and the temporal data. At best, it uses the Knowledge Graph to store the ontology from different data, to associate the semantic data. But KG considers all the data at the same hierarchical layer, what does not work very well in the real world.
Physical AI
(Encyclopedic AI Knowledge Base: the Physical AI Prototype Model, Moscow, 1989; SCIENCE AND TECHNOLOGY XXI: New Physica, Physics X.0 & Technology X.0)
Chemical AI
(AI is being used more and more by chemists to perform various tasks. Originally, research in AI applied to chemistry has largely been fueled by the need to accelerate drug discovery and reduce its huge costs and the time to market for new drugs. However, the applications of AI in chemistry are not limited to drug discovery, extending to designing new molecules and synthesize them, https://chemintelligence.com/ai-for-chemistry)
Human AI
(Weak and Narrow AI, Strong and General AI, Superhuman and Superintelligence AI)
Biological AI
(Sub-symbolic AI, Neural networks, ML, DL, supervised, semi-supervised, unsupervised, and reinforcement learning)
Cognitive AI
(Cognitive Computing focuses?on mimicking human behavior and reasoning to solve complex problems. Neurosymbolic AI. Cognitive technology is radically disruptive systems that?understand?unstructured data,?reason?to form hypotheses,?learn?from experience and?interact?with humans naturally. Success in the cognitive era will depend on the ability to derive intelligence from all forms of data with this technology. Common Sense AI, https://research.ibm.com/blog/icml-darpa-agent; https://www.ibm.com/watson/advantage-reports/getting-started-cognitive-technology.html; Teaching Machines Common Sense Reasoning. DARPA program seeks to articulate and encode humans’ basic background knowledge for intelligent systems); https://www.darpa.mil/news-events/2018-10-11; https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence.html)
Social AI
(Social Network Services, as Facebook, YouTube, Instagram, Twitter, LinkedIn, Reddit, Snapchat, Tumblr, Pinterest, and TikTok)
Economic AI
(Industrial AI, Financial AI, Industry 4.0; The macroeconomic impact of artificial intelligence, https://www.pwc.co.uk/economic-services/assets/macroeconomic-impact-of-ai-technical-report-feb-18.pdf; ?Economic impacts of artificial intelligence (AI) https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/637967/EPRS_BRI(2019)637967_EN.pdf)
Political AI
(Smart government platforms; Artificial Intelligence and Public Policy, https://www.mercatus.org/system/files/thierer-artificial-intelligence-policy-mr-mercatus-v1.pdf)
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Environmental AI
(The emergence of artificial intelligence can pave the way towards pursuing the United Nations’ Sustainable Development Goals (SDGs) for protecting our environment. AI technologies and algorithms are being developed to monitor pollution levels, reduce energy consumption, and better understand the effects of climate change. https://www.capgemini.com/service/perform-ai/do-good-with-data-ai/ai-for-the-environment/;?Artificial Intelligence finds application in a wide array of?environmental sectors, including natural resource conservation, wildlife protection,?energy management, clean energy, waste management, pollution control and agriculture. https://www.ecomena.org/artificial-intelligence-environmental-sustainability/?Microsoft’s AI for Earth, https://www.microsoft.com/en-us/ai/ai-for-earth ??Environmental.ai, data driven platform which help to simulate, analyse, and predict?Environmental?condition of the city, https://environmental.ai/; 5G PROMOTE CLIMATE CHANGE OR HARM OUR PLANET? 5G IS EMPOWERING AI, IOT, BLOCKCHAIN AND DECARBONIZATION, https://www.bbntimes.com/environment/will-5g-promote-climate-change-or-harm-our-planet-5g-is-empowering-ai-iot-blockchain-and-decarbonization?fbclid=IwAR037sMnWmB-kF5U3rBeSF-ZzMIi2pMWIlZ-mw5d6Vx3SVQuaCV7w2XfPHE) …
The World of Reality as the RAI’s Universe of Discourse: The Real AI Formula
Reality is studied and interacted by ontology and applied ontologies, broadly known as science, engineering and technology.
The majority believe that perfect top-level ontology of reality is the one that has exactly one node, and exactly one axiom.?That node called as Entity, and the axiom is:?For every x, Entity(x).?"Everything that exists is an entity."?Such fundamental ontological and logical assumption is a key obstacle to the real-world, transdisciplinary research and creating a real superintelligence.
Indeed, the perfect top-level ontology is the one that has exactly one node, and exactly one axiom. But the top node is the total whole of Everything, where the World or Reality is Everything. And the first axiom is "Everything exists".
Everything (or Every thing) is all that exists, regardless time or space. It is the opposite or complement of nothing. It is the totality of all things in the world as the largest universe of discourse or the totality of things relevant to some subject matter. Without limits, everything may refer to the universe, or the world.
The Universe [multiverse or all possible worlds] is everything that exists actually or theoretically, according to global ontology and theoretical cosmology predictions.?The whole universe is eternal and has always existed. Every entity is a part of everything, including all physical bodies and all abstract objects.
The Universe is commonly defined as everything that physically exists: the entirety of space-time, all forms of matter, energy and momentum, and the physical laws and constants that govern them. However, the term "universe" may be used to denote such concepts as the cosmos, the world or Nature.
World may refer to everything that constitutes reality and the Universe: see World (philosophy).
Here, in its broadest sense, the term "world" refers to the totality of entities, to the whole of reality or to everything that is.
Still its conceptualizations are as different as different fields. Some conceptions see the world as unique while others talk of a "plurality of worlds". Some treat the world as one simple object while others analyze the world as a complex made up of many parts. In scientific cosmology the world or universe is commonly defined as?"[t]he totality of all space and time; all that is, has been, and will be".
In various special contexts, the term "world" takes a more restricted meaning associated with the Earth and all life on it, with humanity as a whole or with an international or intercontinental scope. Then, world history refers to the history of humanity as a whole or world politics is the discipline of political science studying issues that transcend nations and continents. Other examples include "world religion", "world language", "world government", "world war", "world population", "world economy", etc.
Overall, reality or the world or everything is "the totality of all entities and relationships, all that is, has been, and will be. Its properties, attributes or traits are four fundamental classes of Thing, or categories of Entity,
Substance (Object/Entity/Thing/Matter), State (Quality and Quantity or Energy), Change (Action and Process or Cause and Effect and Information) and Relationship (Causality, Space-Time or Communication).
This makes the condensed version of Aristotle’s categories, leading philosophy, science and research and human practice for a couple of thousand years.
???(1) substance; (2) quantity; (3) quality;?(4)?relatives; (5) somewhere; (6) sometime; (7) being in a position; (8) having; (9) acting; and (10) being acted upon (Categories, 1b25–2a4)
Attributes as qualities of things are closely related to variables, operationalized for data processing human or machine, where datasets are represented as a matrix of entity variables (organized in columns) and entity items (individuals organized in rows). Values of each entity variable statistically vary or distributed across the variable’s domain. A domain is a set of individual/values that the world/entity variable is allowed to have, from two, binary variables, to non-dichotomous variables and higher level of measurement or scale of measure. All measurement in science and statistics and engineering is conducted using five different types of scale, categorical, ordinal, interval, ratio, and cardinal, unifying both qualitative and quantitative degrees.
Then, for example, substance is a fundamental attribute of reality could be operationalized in different ways. It can be dichotomized as categorical variables so that only two values, solid and liquid, or matter and anti-matter or substance and non-substance, are allowed for further processing. Or, it is represented as ordinal variables, as solid, liquid, gas and plasma. It can be made of interval, rational or numeric values as well.
The similar logic refers to the rest world attributes and entity variables of different kinds, types and sorts.?We have a hierarchy of variables, ontological, logical, mathematical, physical, chemical, biological, mental, social, or technical variables. As far as any variable is something that may or does vary or change, like a variable element, feature or factor, all of them by the very changing nature are causal variables about causal data.
Again, data as statistics, individual facts, or items of information, collected via experiments or observations, are not a set of values of quantitative and qualitative variables about some individuals and objects.
Real-world data is a universe of individual values of entity variables about substances, states, changes and relationships. Such data, as information and knowledge, is measured, collected, reported, analyzed, visualized, represented, or coded in some human or computer-friendly forms for meaningful usage or machine processing.
The series data (all digital data, web data, human data, machine data, scientific, social, economic, political, environmental data), information (all digital information systems, physical, biological, cognitive, social or technical information), knowledge (science and technology, arts and culture), intelligence (natural intelligence, human intelligence, social intelligence, machine intelligence, or techno-intelligence), wisdom (the sum of universal knowledge, philosophical knowledge) of increasingly generalized concepts is completed with superintelligence (the world’s knowledge as organized, digitized, processed and reified).???
All in all, the world is formalized as the ordered totality of world’s variables, as substantial variable?O, state variable?S, change variable?C, and relational variable?R,?W = <O, S, C, R>,?underlying all the rest causal variables with real-world causal data.
The world is not an infinite regress of entities “governed by a recursive principle that determines how each entity in the series depends on or is produced by its predecessor”, as it is advanced by?mono-disciplinary science and technology.
The world is rather a global?dynamic?causal?network (GCN) where?all?entity?variables?influence each other, and?formally presented as the infinite Cartesian products of universal world sets:
W x W x W…W…
It could be approximated as the?n-ary Cartesian product?over?n?sets?W1, ...,?Wn?as the set?of n-tuples.
The GCN?can?be mathematically modeled as?the Cartesian product of?n?sets, also known as an?n-fold Cartesian product, which can be represented by an?n-dimensional array, where each element is an?n-tuple.?A?a global data?table can be created by taking the Cartesian product of a set of rows and a set of columns. If the Cartesian product?rows?×?columns?is taken, the cells of the table contain ordered pairs of the form?(row value, column value). [See for more details “What Organizes the World: N-Relational Entities”:?https://www.igi-global.com/chapter/organizes-world-relational-entities/28313
It is generally understood that scientific laws and theories implicitly reflect or explicitly assert causal relationships fundamental to reality, which are discovered rather than invented.
Not mentioning statistics and its ML techniques, science and engineering are limited by special types of linear causal relationships between independent and dependent variables, investigated by experimental research to determine if changes in one variable result in changes in another variable.
In a sense, the world’s formula is making the real superintelligence’s worldview, its fundamental cognitive orientation encompassing the whole of the world’s knowledge, including philosophy, science and engineering, arts and culture, with all possible inter- and trans-disciplinary interconnections; fundamental, existential, and normative postulates, scientific theories and laws.
It is the master equation or world’s formula, acting as the ontological framework for scientific and technological knowledge and practice and the master algorithm for human-machine superintelligence, digital superminds.
R & M & S as the Trans-AI Science, Engineering and Technology
Reality modeling and simulation (R & M & S) –world conceptualization and implementation – everything design and development - are key stages in creating the real-world man-machine superintelligence
Modeling and simulation (M&S) is generally defined as "the use of models (e.g., physical, mathematical, or logical representation of a system, entity, phenomenon, or process) as a basis for simulations to develop data utilized for managerial or technical decision making".
Modeling is broadly understood as "the purposeful abstraction of reality, resulting in the formal specification of a conceptualization and underlying assumptions and constraints". M&S is in particular interested in models that are used to support the implementation of an executable version on a computer.
The execution of a model over time is understood as the simulation. While modeling targets the conceptualization, simulation challenges mainly focus on implementation, in other words, modeling resides on the abstraction level, whereas simulation resides on the implementation level".
M&S Science, Engineering, Technology (Applications), involving diverse computer science areas as well as Systems Science and Theory, Systems Engineering, Software Engineering, Artificial Intelligence, etc., contributes to the R & M & S Science, Engineering and Technology.
The Trans-AI Provenance: EIS Encyclopedic Intelligent Systems
Many “elite researchers in HAI”, not mentioning the general public, are still biased in “human level machine intelligence,” or HLMI, AGI, as if having a 50 percent chance of occurring within 45 years and a 10 percent chance of occurring within 9 years. And there comes out an intelligence explosion, summitting with a superintelligence
AGI, emulating or mimicking human brains/mind/intelligence, will never bring us to real superintelligence (RSI).
It is anthropomorphically naive to define it like this: “a superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds”.
And it is philosophically naive to believe in the induction principle, that more big data, more speed and more compute could generate something super-valuable, besides of the energy crises.
Computer components could greatly surpass human performance in speed, memory and computing power, still staying a dumb idiot, fast-calculating idiot.
"Biological neurons operate at a peak speed of about 200 Hz, a full seven orders of magnitude slower than a modern microprocessor (~2 GHz)." But the human mind outperforms the most advanced computing machinery due to its small smart data, real intelligence, generalizations, leaning transfer, and programmed ability to deep causal reason, finding from a billion of problem selection alternatives a few.
It is regardless that your “neurons transmit spike signals across axons at no greater than 120 m/s, "whereas existing electronic processing cores can communicate optically at the speed of light".
A human-like superintelligence emulating human mind running on much faster hardware than the brain does not make an essential difference.
A human-like reasoner that could think millions of times faster than current humans might have only a speed dominant advantage in reasoning tasks, performing the same biases and mental mistakes, but with the speed of light.
The only real prospect here is what emerging as the Trans-AI, man-machine general superintelligence platform, integrating all the multitude of human minds and machine intelligences, problem-solving systems, and data/information/knowledge systems, as the internet/Web:
Human intelligence, individual and collective,
Narrow and Weak AIs, experts systems, neural networks, ML and DL;
Strong and General AI;
Superhuman or superintelligent AI.
?We have to see the general trend: even narrow AI machines are already surpassing humans in many domains. It is that narrow and weak AI applications outsmarting human minds in more and more fields:
NLP (machine translation)
Strategic gaming, like chess, the board game Go, and some Atari video games
Autonomous driving
Agricultural industry
Manufacturing
Safety and security
Supercomputing
Communication, as technology platforms, social media networks, bots and digital assistants
Health care
Education
Defense
Space exploration
The existential question is when will AI be smart enough to outsmart people?
The Real and True AI as the optimal solution of existential risks
For all its 10+k civilized history, humanity has been existing for no noble goal, having only nature-motivated life purposes, to survive and preserve, reproducing and extending themselves, and at best, leaving some notable historical “foot printing”.
In the Age of AI, things become radically different. A new race of superintelligent machines is on the horizon, raising existential questions for the modern humanity
Where Do We Come From? What Are We? Where Are We Going?
If all human race is doomed to be disrupted by the Machina Sapiens or to emerge as a superhuman-machine cosmic race.
starting from narrow and weak AIs, one passes to AGI, general or strong AI, all ending with ASI, artificial superintelligence. It all resulted with raising existential concerns for the whole of humanity.
“The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year time frame. 10 years at most.”
—Elon Musk wrote in a comment on Edge.org
“I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish. I mean with artificial intelligence we’re summoning the demon.”
—Elon Musk warned at MIT’s AeroAstro Centennial Symposium
“The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.”
— Stephen Hawking told the BBC
“I visualise a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.”
—Claude Shannon
The Real-World AI is an alternative to the human-replacing AI. It is a radically new transdisciplinary AI paradigm. The Trans-AI is designed, developed, and deployed aiming to simulate reality as well as model mentality, reflecting all in digital reality, to understand, learn and interact with any environments, physical or mental, social, digital, or virtual. It is embracing the AAAI algorithms, techniques, and methods as well as human individual and collective intelligence, or superminds, in the up-down ways.?
As a result, we have the hybrid machine-human superintelligence (cyber-human superminds) as the Real-World AI or the Trans-AI GPT Human-Internet Platform.
It is emerging as the disruptive general-purpose technology platform, in the context?of the Internet, the web, narrow ML/DL/AI technology platforms, big data analytics applications, the internet of things and all advanced emerging and digital technologies.?
Resources
Global AI Academy: AI4EE
The World’s First Trans-AI Model for Narrow AI, ML, DL, and Human Intelligence: Innovating A Superintelligent Human-Machine General-Purpose Technology Platform
Real Superintelligence: Why and How We Create the Top Human-Machine Technology Platform
Machina Sapiens vs. Human Sapiens, or why Musk is after humanoid robots while being deeply scared of AI
The RSI Technology: Self-Aware Machines, Conscious AI, and Sentient Robotics
What's Wrong With the EU AI? Why the I-EUROPE PLATFORM Is the Cardinal Solution
The end of "normal science and technology" , or why humanity faces global risks and mass technological unemployment
Why the EU lags behind in AI, Science and Technology
SCIENCE AND TECHNOLOGY XXI: New Physica, Physics X.0 & Technology X.0
Addressing societal challenges using transdisciplinary research
This report looks at how transdisciplinary research, which combines knowledge from different scientific disciplines with that of public and private sector stakeholders and citizens, can be used to address complex societal challenges. This includes developing effective responses in acute crises, such as the COVID-19 pandemic, as well as longer-term solutions for sustainability development. In a series of 28 case studies, each of which is briefly summarised in the report, it identifies the key obstacles to effectively implementing transdisciplinary research. Many of these are embedded in the way that research systems are structured and managed and they are amenable to policy intervention. Examples of how various actors, including funding agencies and universities are adapting to better accommodate the requirements of transdisciplinary research are included in the report and related policy actions are ascribed for these actors.
‘Artificial Intelligence’ Has Become Meaningless
SUPPLEMENT. THE AI4EU PLATFORM: The European AI on Demand Platform
AI4EU is a one-stop-shop for anyone looking for AI knowledge, technology, tools, services and experts.
“The AI4EU Platform will bring the AI stakeholders and AI resources together in one dedicated place, overcoming fragmentation, so that AI-based innovations (research, products, solutions) will be accelerated. The AI4EU Platform will act as the one-stop-shop for anyone looking for AI knowledge, technology, services, software, and experts. AI4EU will function as European AI market driver, offering a critical mass of resources, community networking effects, and rapid development and growth.
?The European AI on Demand Platform brings together the AI community while promoting European values. The platform is a facilitator of knowledge transfer from research to business application.
?To fulfil the user needs and strategic objectives, the following design principles were considered:
In summary, the European AI on-Demand Platform aims to fulfil the needs of the European AI community at large. To that end, it will promote four main services:
THE AI4EU SCIENTIFIC VISION
AI systems are human-centred. Such systems would need to be: