AI as Machine Superintelligence: the secret sauce behind AI and ML technology
Keywords: Reality, Data, Intelligence, Superintelligence, Artificial Intelligence, Machine Intelligence, Artificial Narrow Intelligence, Machine Learning, Deep Learning, Neural Networks, Artificial General Intelligence, Artificial Superintelligence,?Real AI, Trans-AI, Meta-AI, Classical/Quantum Computing, Emerging Technology, Digital Technology, General-Purpose Technology, Future Technology
The greatest ever scientific discovery and invention
We live in the epoch of post-truth of fake news, deepfake bots and mis- and disinformation, where nobody trusts nobody. If somebody comes out with a fundamental, human-fate changing discovery but without heavy big media hypes and ads, a few could believe you even in the prejudice of ourselves.
Not mentioning fire, sailing, wheel, writing,?and agriculture, the 5 greatest ever scientific discoveries and inventions could be as following:
This all is to be topped by the most enabling, general-purpose technology, Artificial Intelligence, or Machine Intelligence and Learning.
In 2020, I was fortunate to complete the 3-decades independent research of machine superintelligence (MSI), as Real and True AI, designed as Trans-AI, Meta-AI, or Causal Machine Intelligence and Learning. The MSI discovery was properly documented, copyrighted and trademarked.
Still, some key points have been published in the article Trans-AI: How to Build True AI or Real Machine Intelligence and Learning.
To advance the MSI discovery, I made the following actions:
opened a Quora account giving most viewed answers about AI, AGI, and ASI, with 3.2K?Answers, 2.3K?Posts, 2.9K?Followers, 2.5m views
set up a Quora space, Global AI Platform: Real Artificial Intelligence, Machine Learning, Deep Learning, Data, the World
Posted a series of LinkedIn articles (200+),
Published a number of BBN Times articles,
Moderated a FB/Meta AI, ML, DL Public Group (200+k)
Published a dozen of books.
I bring it in for all who might doubt the credentials. ?Another big point is the greatest scientific discoveries and inventions are still out there, waiting for you.
Many science fiction movies, TV series and novels have featured an omniscient, omnipresent and omnipotent AI that transcending all human intelligence. What many don’t know is that this concept does have a core place in the fields of science and engineering, computer science, data science, artificial intelligence and machine learning, both as a fundamental theory and an integrative genera-purpose technology.?
It was first theorized by us in the book Artificial Superintelligence (1999) as a foundational theory in the field of Artificial Intelligence.
The idea was extended in the book Reality, Universal Ontology and Knowledge Systems: Toward the Intelligent World.
Later, the concept of Artificial Superintelligence has been hotly debated and discussed, by researchers, journalists, businessmen and politicians. It was widely promoted in the book The Superintelligence: Paths, Dangers, Strategies (2014), where superintelligence was anthropomorphized as "an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills". Such an AI superintelligence is supposed to performs human-like cognitive processes at exponentially higher speeds and efficiencies when compared to the human mind.?
The machine superintelligence and learning can perform infinitely beyond the limits of the human biological mind, augmenting it instead of replacing human intelligence. Our transdisciplinary approach relies of science, its technoscientific and philosophical knowledge, as the sum of universal knowledge.
The MSI discovery was properly documented, copyrighted and trademarked. Still, some key points have been published in the article Trans-AI: How to Build True AI or Real Machine Intelligence and Learning.
It is essentially different from the unscientific methods of achieving computing superintelligence by creating a human-like AI known as artificial general intelligence (AGI). Then superhuman artificial intelligence is hardly reachable, even within the first third of the next century.
Artificial Narrow Intelligence of Machine Deep Learning
With all respect, this fundamental mess-up was initiated by Alan Turing’s proposal in his paper “Computing Machinery and Intelligence“, in which the question "Can machines think?" was wrongly replaced with the question "Can machines do what we (as thinking entities) can do?".
As a result, we got two polar types of AI, Real and True AI and False, Human-Like AI.
Real AI is NOT ML, and vice versa. ML/DL/ANNs are not any real AI.
ML/DL/ANNs are a fake and false AI.
ML allows software applications to become more accurate at predicting outcomes using historical data as input to estimate new output values.
It is a computational application to classify data based on models which have been developed, to make estimations/predictions for future outcomes based on these models.
In reality, there are not such things as a Learning Machine or Machine Learning, as if learning from data or even experience, if only as a big commercial big tech fraud on the largest ever commercial scales.
Still, many AI/ML/DL/BD researchers and developers believe that the secret sauce behind machine intelligence and learning is a functional relationship (a machine or black box) from an input dataset X to an output dataset Y assigning to each element of X exactly one element of Y.
Then AI/ML/DL algorithms consist of a target / class/ outcome variable (or dependent variable as a "response variable", "regressand", "criterion", "predicted variable", "measured variable", "explained variable", "experimental variable", "responding variable", "outcome variable", "output variable", "target" or "label"), which is to be predicted from a given set of predictors (independent variables as a "predictor variable", "regressor", "covariate", "manipulated variable", "explanatory variable", “exposure variable”, "risk factor", "feature", "control variable", or "input variable").
Using these set of variables, you generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy or precision on the training data.
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That’s it. No learning or intelligence, but just mathematical manipulations and statistical tricks.
Real AI, or Causal Machine Intelligence and Learning
The real AI’s secret sauce is neither Data/Variables, nor their Model, but what transcends them both. It is the World’s Data Model organizing “elements of data and standardizing how they relate to one another and to the properties of real-world entities”.
The data model involves entity types, attributes, relationships, integrity rules, and the definitions of those things, making the start point for interface or database design or AI/ML technology
Data modelling, or Data structures for programming languages, is based on five Ds: Domain, Data, Data relationship, Data semantic and Data constraint.
As such, there are 5 deep levels of world’s data modelling, which are supposed to be in the deep neural networks:
Ontological/Conceptual Data Model, consisting of entity classes, representing kinds of things of significance in the domain, and all possible relationships between pairs of entity classes.
Logical Schema of data structures such as relational tables and columns, object-oriented classes, or XML tags.
Semantic Data Model
Statistical Data Model
Physical Data Model, concerned with computing memory, partitions, CPUs, GPUs, etc.
The standard Data Modelling Architecture is missing the Statistical Schema of Data:
RAI/ML Data Model describes the structure, manipulation and integrity aspects of the data stored in data management systems or AI applications, including structured and unstructured data, such as word processing documents, email messages, pictures, digital audio, and video.
Focus on three key things: Reality (Ontology & Science), Data (Computing & Mathematics & Statistics), and Digital Machine Intelligence (Computational science and computer science)
Keep in the mind some simple basic truths.
AI is a complex of hardware and software to effectively and sustainably interact with the world by intelligent modeling and simulating reality in all its complexity.
Real AI is not about replicating/imitating/simulating human intelligence/mind/brains/cognition/…
Real AI is not ML, not DL, not ANNs, not Predictive Analytics, not Programming Languages, not Applied Mathematics, not Computer Science, not Cognitive Science, but transcends all of them as special tools, theories, techniques, models, algorithms or technologies..
The whole idea of Artificial Human Intelligence, be it AI, ML, DL, AGI, or ASI, is the summit of human prejudices to anthropomorphize everything around us, including machine/computer intelligence.
Follow reality and causality with its best representations, real ontology and transdisciplinary science and engineering, as scientific computing or computational science and computer science.
Study the next gen AI, Causal Machine Intelligence and Learning Systems, with Artificial Neural Networks to be reified as Causal Probabilistic Networks (All-Directed Cyclic Graphs).
Real generalized machine intelligence will require something completely fundamental: a vertical intelligence integration of world’s data models, generalization or abstraction algorithms, mental model and simulation engine, hierarchical parallel processing (ANNs + AI Processor), and effective interaction with the world or other agents.
MSI Platform = Reality/World/Environment + Real Intelligence (Science, Abstractions, Mental Models/Schema, Reasoning/Inferences) + Big Data (Web data) + AI/ML models + Deep Neural Nets (Internet) + AGI Processors (AI Supercomputers) + Interaction Interface (Robotics, the Internet of Things)
Azamat Abdoullaev EIS Encyclopedic Intelligent Systems Ltd, EU, Cyprus-Russia
Abstract
We are at the edge of colossal changes. This is a critical moment of historical choice and opportunity. It could be the best 5 years ahead of us that we have ever had in human history or one of the worst, because we have all the power, technology and knowledge to create the most fundamental generalpurpose technology (GPT), which could completely upend the whole human history. The most important GPTs were fire, the wheel, language, writing, the printing press, the steam engine, electric power, information and telecommunications technology, all to be topped by real artificial intelligence technology. Our study refers to Why and How the Real Machine Intelligence or True AI or Real Superintelligence (RSI) could be designed and developed, deployed and distributed in the next 5 years. The whole idea of RSI took about three decades in three phases. The first conceptual model of TransAI was published in 1989. It covered all possible physical phenomena, effects and processes. The more extended model of Real AI was developed in 1999. A complete theory of superintelligence, with its reality model, global knowledge base, NL programing language, and master algorithm, was presented in 2008.
The RSI project has been finally completed in 2020, with some key findings and discoveries being published on the EU AI Alliance/Futurium site in 20+ articles. The RSI features a unifying World Metamodel (Global Ontology), with a General Intelligence Framework (Master Algorithm), Standard Data Type Hierarchy, NL Programming Language, to effectively interact with the world by intelligent processing of its data, from the web data to the real-world data.
The basic results with technical specifications, classifications, formulas, algorithms, designs and patterns, were kept as a trade secret and documented as the Corporate Confidential Report: How to Engineer Man-Machine Superintelligence 2025.
As a member of EU AI Alliance, the author has proposed the Man-Machine RSI Platform as a key part of Transnational EU-Russia Project. To shape a smart and sustainable future, the world should invest into the RSI Science and Technology, for the Trans-AI paradigm is the way to an inclusive, instrumented, interconnected and intelligent world. Key words: Artificial Intelligence, Machine Intelligence, Machine Learning, Trans-AI.
Citation: Azamat Abdoullaev. Trans-AI: How to Build True AI or Real Machine Intelligence and Learning. Ontology of Designing. 2021; 11(4): 402-421. DOI: 10.18287/2223-9537-2021-11-4-402- 421.
Enterprise Architect Consultant
2 年Azamat Excellent and long term engagement that led to the above. Very comprehensive. Technology Innovations such as Internet are consequences of science and it would be nice to attempt this. However ever merging activities in domains make it difficult. One more area would be understanding more about how mind, and knowledge processes work and relationships to "Reality" mentioned by you and cognition that includes NLP, visual information processing and nature of common scientific models when not purely computational and thus difficult to provide reproducible outcomes, etc. What role have you finally found in this work about ontologies, a consequence of AI or needed for AI? Excellent work and best wishes. Ravi (Dr. Ravi Sharma, Ph.D. USA) Chair, Ontology Summit 2022 Senior Enterprise Architect Particle and Space Physicist Elk Grove CA US