We are all brainwashed by the big fake media that Artificial Intelligence (AI) is everywhere: from your pocket phones to your web search. It is in big tech platforms, small tech services, Hollywood movies, politics, elections, financial markets, sports, the cloud, the edge, your pocket, education, car, farming, pharmaceuticals, bio-engineering, geological exploration, physical modeling, and data analytics.
And if you aren’t already studying, developing or using AI, you are a relict or something’s wrong with you.
I have both good and bad news for you. The good one is nothing to worry about. You are on the safe way.
The bad one is, all the mainstream AI, be it narrow and weak AI or machine learning or deep learning or artificial neural networks or cognitive computing or AGI and ASI, is mere nonsense, with 70+ years history.
And the big tech AI/ML/DL cloud platforms are just a big fraud.
To be fair, it all started with the classic paper COMPUTING MACHINERY AND INTELLIGENCE (1950), opened as follows. ―I PROPOSE to consider the question, „Can machines think?‟ This should begin with definitions of the meaning of the terms „machine‟ and „think‖. Instead, it was suggested the ‗imitation game‘, played with three people, a man (A), a woman (B), and an interrogator (C), known as the Turing Test. Today Turing-like “AI has become meaningless” and “often just a fancy name for a computer program”, software patches, like bug fixes, to legacy software or big databases to improve their functionality, security, usability, or performance. With AI poised to disrupt everything, it is time to know what is machine intelligence and learning.
Machine Intelligence vs. Artificial Intelligence
Many our definitions and assumptions are plagued by three big prejudices, the confirmation, selection and bandwagon biases.
AI is not any exclusion here. The construct has been sold and resold to the general public under various persona, while being an anthropomorphic construction mimicking, replicating or simulating human brain/intelligence/brains/cognition/behavior.
Such a human-like AI (HAI) is like the human intelligence of varying complexity, dumb and dull, narrow and weak, strong and general, encyclopedic or superhuman:
- Task-specific and statistics-driven and biased AI, as ML, DL, DNNs;
- Weak, narrow-minded AI, or dumb. dull and deficient AI;
- Composite AI, combining “connectionist” AI approaches like ML/DL, with “symbolic” AI approaches like rule-based reasoning, graph analysis, agent-based modeling or optimization techniques;
- Full, strong, human-level AI, AGI;
- Superintelligent AI, Artificial Superintelligence, ASI, superintelligent boogeyman which is to take everything over making all humans obsolete.
So, AI = AHI. It is about artificial human intelligence, as mimicking human intelligence, brains, cognition, mind, or behavior. That means the very idea of AI is anthropomorphic, subjective and nonscientific.
Now, one of the top technology trends for 2022 is a so-called Generative Artificial Intelligence (AI), as announced by Gartner.
“One of the most visible and powerful AI techniques coming to market is generative AI – machine learning methods that learn about content or objects from their data, and use it to generate brand-new, completely original, realistic artifacts.
Generative AI can be used for a range of activities such as creating software code, facilitating drug development and targeted marketing, but also misused for scams, fraud, political disinformation, forged identities and more”.
True General AI, or Machine Intelligence, has nothing to do with the Turing’s imitation game, an anthropomorphic human-like AI mimicking, replicating or simulating or emulating human brain/intelligence/cognition/behavior.
We need to follow the scientific objective conception of man-machine intelligence (MMI), instead of the anthropomorphic subjective one, domineering today’s AI, as ANI, AGI or ASI.
The MMI is about modeling, simulating, and interacting with the world, its reality and causality, mentality and humanity, in terms of world’s data/information/knowledge and causal models, real intelligence and supercomputing.
I stand for building Man-Machine Metaintelligence (MMMI) as composed of Machine Intelligence and Human Intelligence, added up with some techniques, algorithms and models of anthropomorphic AI imitating human brains, cognition, intelligence or behavior:
Man-Machine Superintelligence = Machine Intelligence + Human Intelligence + Cognitive Computing + Machine Learning + Deep Learning + Artificial Intelligence [narrow and weak AI (ANI), strong and general AI (AGI) or ASI]
AI as a poor copy of human intelligence/brain/brains/cognition/behavior
There are some functional similarities with some special kind of narrow AI, ANNs. And both are black boxes, systems viewed in terms of its inputs and outputs (or a transfer/system/network function), without any knowledge of its internal workings and which decision processes are not completely understandable or predictable.
The human brain is the command center for the human nervous system, receiving signals from the body’s sensory organs and outputs information to the muscles.
Artificial neural networks (ANNs), neural networks (NNs), are computing systems as if inspired by the bio-NNs being not any strict copies of their biological counterparts. NNs are a collection of connected units/nodes called artificial neurons loosely modelling the neurons in a biological brain and transmitting a signal to each other via connections/edges.
The “signal” is coded as a real number, and the output of each neuron is computed by a non-linear transfer function of the sum of its inputs. Neurons and edges typically have an adjustable weight as drilling/rote learning proceeds. The weight increases or decreases the strength of the signal at a edge. Aggregated into layers, different neuronal layers may perform different transformations on their inputs. Signals/Numbers travel from the first layer (the input layer), to the last layer (the output layer), traversing the layers multiple times.
NNs learn (trained) by processing samples/examples of a known “input” and “result,” forming probability-weighted associations between the two, which are stored within the data structure of the net itself. They have no apriori knowledge, but only automatically generating identifying characteristics from the examples that they process.
In all, AI, as intelligent agents, is defined as any system perceiving its environment and takes actions that maximize its chance of achieving its goals.
As for the substance, structure, and architecture, there are no commonalities.
The human brain is a fat wetware, containing about 86 billion nerve cells (neurons) and non-neuronal cells, with 100 trillions of connections.
Again the brain has 200+ cognitive biases and can’t multitask, switching between tasks, which increases errors and makes decisions longer.
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 general-purpose 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.
Azamat Abdoullaev, EIS Encyclopedic Intelligent Systems Ltd, EU, Cyprus-Russia
Trans-Science and Technology, Scientific ToE, Trans-AI and I-World
TRANSDISCIPLINARY ARTIFICIAL INTELLIGENCE AS FUTURE INTELLIGENCE: The Trans-AI Platform of AI/ML/DL/NNs