What's up with AI?
First of all, Technological Mind or Machine Intellect or Computing Intelligence, widely known as Artificial intelligence (AI), as it is, symbolic AI, neural AI or neuro-symbolic AI, is largely a fictional construct of scientific imagination, being as real as the sci-fi AI, as from Hollywood studios.
This is well-supported by the irreality of so-called symbolic AI as well as subsymbolic ML, as large NLP ML models, Google's MUM or OpenAI's GPT-3, being numb and dumb by the very design.
AI has been sold for the last 70+ years as an umbrella term representing a wide range of techniques, programs, algorithms and models that would allow computing machinery to mimic, simulate, replicate or fake human body/brains/mind/intelligence/behavior.
As any big fictive construction, it has its hype cycles, boom and bust dynamics, winter and spring seasons, with its believers and unbelievers.
For example, the last four trends on the Gartner Hype Cycle for Artificial Intelligence, 2021 are: responsible AI; small and wide data approaches; operationalisation of AI platforms; and efficient use of data, model and compute resources.
In reality, we need a real cycle of real AI.
It is a truly intelligent AI powerful to model, understand and interact with any reality, physical, social or virtual, to reason about the world, while discovering and learning new things, [continually] expanding its knowledge and intelligence.
The best candidate for such technology is [Causal] Machine Intelligence and Learning, innovated as Meta-AI or Trans-AI.
ML DNNs could become the real start of the next best Meta-AI technology, as automated autonomous artificial man-machine meta-intelligence.
All the same. The dot.com-like fakery, scam, fraud and mystification, only in $multi-trillion scales. The tech industry today is full of snake oil, selling fake products and false promises. And the crown jewel of tech snake oil is Narrow/Weak/Fake AI, instead of Real AI, Trans-AI or RSI (Real Man-Machine Superintelligence).
It is what fed to us as a "junk food" of "machine learning", "artificial neural networks" and "deep learning" algorithms.
Today's pseudo-AI is the snake oil of the 21st century, led by the Big Tech ML/DL big data analytics platforms, dubbed as G-MAFIA and BAT-triada, with their startups off-shootings or acquisition....
As a result, Amazon, Microsoft, Alphabet, Apple, and Facebook are as important today as Standard Oil, Royal Dutch Shell and British Petroleum were a century ago.
The Economist?in 2017 painted a?dystopian picture?of our future — one where the tech giants remain unregulated, concluding antitrust regulators must step in to control the flow of data, as it happened with the recent hearing, just as they did with oil companies in the early 1900s.
Big Tech as Apple, Amazon, Facebook, Google and Microsoft are eliminated potential rivals and concentrated brain power in the quasi-AI field, IGNORING that
What is Real and True AI?
Real AI or true MI must involve its 5 interrelated universes/components/elements/parts/levels:
It is all represented, mapped, coded and processed by computing machinery of any complexity, from smart phones to the internet of everything and beyond.
Real AI/MI is thus the science and engineering of mind, intelligence or intellect, its nature, models, theories, algorithms, architectures and applications.
Real AI/MI as a symbiotic cyber-human superintelligence overruling the extant statistic narrow AI with its branches, as machine learning, deep learning, machine vision, NLP, cognitive computing, etc.
Again, today's big-tech ML/AI is a fake AI mimicking human brains/intelligence/mind/cognition/behavior with all its biological, cognitive and social biases and partialities, complexities and limitations.
A really intelligent AI is about modeling, mapping and simulating reality and its causality and mentality in the context of world’s data/information/knowledge/learning, causal algorithms and intelligent programming, computation and communication.
All What You Need to Know About AI
As to an urban legend, the term AI, as human-centered, was first coined by John McCarthy in 1956 when he held the first academic conference on the subject.
But the journey to understand if machines can truly think began much before, as sampled below.
1. Aristotle's Categories and Analytics, Leibniz's work on universal symbolism and a calculus of reasoning, who refined the binary number system, the foundation of digital (electronic, solid-state, discrete logic) computers, including Boolean algebra and "the Von Neumann machine", the standard design paradigm, or "computer architecture".
2. Wiener's Cybernetics: Or Control and Communication in the Animal and the Machine, 1948
3. Alan Turing published a paper entitled “Computing Machinery and Intelligence” in 1950, which opened the doors to the new field of AI.
During AI winter, AI research program had to disguise themselves under different names in order to continue receiving funding.
“Machine Learning”, “Informatics”, “Knowledge-based system”, “Pattern recognition”, ANN, etc.
Overall, the study of AI winter highlighted a few lessons:
Small-scale success in AI was deceptive. The complexity of AI implies that many issues will only be encountered and solved on large-scale problems in real life.
AI is a subject with broad intellectual challenges of its own. It is not limited to specific applications or certain biological structures. It requires combined basic research in philosophy, cognition, mathematics, statistics, computer science, algorithms, linguistics, neurosciences and much more.
AI again attracts all attention, especially after a number of sensational techno-political statements, as of Russian President: "Artificial intelligence is the future, ... for all humankind... Whoever becomes the leader in this sphere will become the ruler of the world".
But critical evaluations of AI, its nature, state-of-affairs, approaches, applications, techniques, algorithms and advances are a tall order.
We could outline some big points and problems.
For wanting a unified paradigm, people lost in the number of approaches, from cybernetic models and sub-symbolic and embodied models to symbolic and statistical learning models.
AI today is a combination of big data, mathematics and statistics, computer science, machine learning, cloud computing and internet of things.
We still in the beginning of the quest to build machines that can reason, learn, and act effectively and rationally or intelligently.
Most advances are in machine learning, neural networks, and robots, as social bots and chatbots, and military AI.
What matters is computer vision, face recognition; machine learning; robots; voice assistants; and weaponized AI and Robotization of the Armed Forces.
A military AI arms race is a competition to have the military forces equipped with the best AI, incorporating it into uninhabited aerial, naval, and undersea vehicles.
For example, China pursues a strategic policy of 'military-civil fusion' on AI for global technological supremacy. AI and the next revolution in military defense
Neural networks and deep learning is also driving the AI industry’s progress.
In all, there is a lot of confusion in 3 fundamental problems,
It is critical to see the difference between
To see differences among real AI, fake AI of machine learning and deep learning, one has to take a hierarchical model of intelligence.
It is all the matter of grade, ranking or levels of intelligence, like primary, secondary and post-secondary or tertiary education, graduate, post-graduate and polymath. Or, it is like a deep learning utilizing a hierarchical level of artificial neural networks to perform machine learning.
The equivalent classes of AI are graded as follows:
ML << Fake AI << Real AI + Human Intelligence = Trans-AI or Meta-AI
Machine Learning is about computational statistics and statistical learning theory, promoted as a computer program that can automatically adapt to new data without human interference, and which computer algorithm adjusts its parameters automatically to create a new pattern. Its programming code creates a model that identifies the data and builds predictions around the data it identifies. The model uses parameters built in the algorithm to form patterns for its decision-making process. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change, if any, while the model shouldn’t change. Machine learning is used in different sectors for various reasons.
DNNs = DL << ML << Fake AI << Real AI + Human Intelligence = Trans-AI or Meta-AI
Deep Learning, known as deep neural learning or deep neural network (DNN), coming in different architectures and topologies. The DNN is an?artificial neural network (ANN) with multiple layers between the input and output layers.?There are different types of neural networks but they always consist of the same components: neurons, synapses, weights, biases, and functions.
It is self-adaptive algorithms bettering its analysis and patterns with experience or with new data. It imitates the workings of the human brain in processing data and creating patterns in decision making.
Deep learning learns from vast amounts of unstructured data that would normally take humans ages to understand and process. Such big data is drawn from sources like social media, internet search engines, e-commerce or e-government platforms, online cinemas, and fintech applications like cloud computing.
领英推荐
DNNs = DL << ML << Fake AI = Narrow AI << Weak AI << Strong AI = AGI << Artificial Superintelligence << Real AI + Human Intelligence = Trans-AI or Meta-AI
Weak AI (narrow AI) – non-sentient automated intelligence, focused on a narrow task in specific domains (narrow AI).
Strong AI / artificial general intelligence (AGI) – real AI to be applied to any world problem.
Superintelligence – global AI far surpassing all human intelligence due to recursive self-improvement.
The AI world has the following evolutionary taxonomy, evolutionary systematics or Darwinian-like hierarchical classification, as Ladder of AI Being:
Machine Learning and Deep Learning
Artificial Narrow Intelligence (ANI), involving Machine Learning models and Deep Neural Networks,
Artificial General Intelligence (AGI), Strong AI, Human Intelligence
Global AI, Encyclopedic Intelligence, the Global Brain, I-Internet
Hybrid Super Intelligence (HSI), integrating Digital Superintelligence with Human Intelligence.
If Any Threat from the RSI, as Meta-AI or Trans-AI
There is no any threat or risk from a real true AI as Human-Machine Global Superintelligence:
neither global catastrophic risks which could damage human well-being on a global scale or destroying modern civilization,
nor existential risks which could cause human extinction, the hypothetical end of the human species.
Below the listing of potential risks decided by ASI:
Anthropogenic
Narrow Artificial intelligence, as Automated Machine Learning and Deep ANNs
Biotechnology
Cyberattack
Environmental disaster
Experimental technology accident
Global warming
Mineral resource exhaustion
Nanotechnology
Warfare and mass destruction
World population and agricultural crisis
Non-anthropogenic
Asteroid impact
Cosmic threats
Global pandemic
Natural climate change
Volcanism
Extraterrestrial invasion
There might be existential risk from Human-Level Artificial General Intelligence.
There is ZERO risk from Superhuman Artificial Superintelligence.
The ASI is the universal solution to human global nuclear annihilation, biological warfare, pandemic-causing agents, overpopulation, ecological collapse, and climate change.
ASI existential risk, AI control problem, technological singularity, the rise of the robots, and AI takeover for the world, its resources, eradicating the human race in the process, all is just thrilling themes in techno-dystopian sci-fi having nothing to do with real world.
All such fake fears come from a poor understanding, softly speaking, of the nature of superintelligence, from the paperclips philosophers, as below:
“When we create the first superintelligent entity, we might make a mistake and give it goals that lead it to annihilate humankind, assuming its enormous intellectual advantage gives it the power to do so. For example, we could mistakenly elevate a subgoal to the status of a supergoal. We tell it to solve a mathematical problem, and it complies by turning all the matter in the solar system into a giant calculating device, in the process killing the person who asked the question”. Existential Risks
Or, “as the fate of the mountain gorilla depends on human goodwill, so might the fate of humanity depend on the actions of a future machine superintelligence”.
Superintelligence: Paths, Dangers, Strategies
Today's big-tech AI/ML/DL presents an existential danger to humanity if it progresses as it is, as specialized superhuman automated machine learning systems, from task-specific cognitive robots to professional bots to self-driving autonomous transport.
Conclusion
Any real intelligence has the causal power to effectively interact with the world integrating its information, analyzing data, and using the resulting insights for learning, reasoning, understanding and optimal decision making and highly rational actions and reactions.
There are several levels of scale of intelligence:
Animal Intelligence
Human Intelligence
Artificial Intelligence/Machine Intelligence
Alien Intelligence
Trans AI or Meta-Intelligence or Hyperintelligence or Real Superintelligence, rsi (don't mux up with Artificial Superintelligence, ASI)
Resources
Universal Artificial Intelligence, and how much might cost Real AI Model
I have developed the first real model of Universal AI, as Trans-AI or Meta-AI, transgressing and integrating all AI generations: DNNs = DL << ML << Fake AI = Narrow AI << Weak AI << Strong AI = AGI << Artificial Superintelligence.
The Trans-AI includes the following structural elements:
Man-Machine World Model;
Machine Intelligence and Learning Base World.Network.Graph;
Data Framework World.Data;
Global Knowledge Base World.Net
Domain Knowledge Base Domain.Net
Trans-AI: How to Build True AI or Real Machine Intelligence and Learning
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.
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.
Meta-AI or Trans-AI: ANI + ML + DL + AGI + ASI: How to Build True AI or Real Machine Intelligence and Learning