From silo mentality to man-machine superintelligence
Scientists like a silos mentality, digging deep in the same hole, unreachable to any light or public scrutiny. Besides, disciplinary-specific knowledge with like-minded researchers could be useful to solving special problems and getting all possible public rewards.
But, the big problems of today – machine intelligence, energy, health, the environment –?require multi-, inter- or trans-disciplinary science and teams.
Specialisation and compartmentalisation are critical to narrow expertise, but harmful for general knowledge and intelligence. The human body is organised into specialised organs to be controlled by the CNS, governing the whole organism, all?the body's organs, muscles, senses, thoughts and actions.
Ranking of the best silo scientists in the world has more social harm than any public good. Silo scientists should be categorized as technicians, "experts in the practical application of a science or persons skilled in the technique of an art or craft".
One could not be designated as a real scientist, without professional knowledge of three fundamental scientific realms: the world of reality, the world of intelligence, human or machine, and the world of data/information/knowledge.
"To be rationally bounded is human"
We like to say "to err is human", instead of "to be deluded is in human nature" or "to be ignorant is human", mostly coming from the division and specialization of labor, physical and mental.
From one side, humans have long been designated as the most intelligent species on the planet, with big brains, cognitive abilities and processing power outcompeting all other species.
From other side, there is no fundamental idea or life-critical practice which is without deep defects and gaps, mistakes and confusions, ending with all sort of sins and evils, from poverty to wars.
Regardless of our great intelligent deeds, humans are deeply ignorant of primary things, failing to recognize the depth of ignorance.
As a lack of knowledge and understanding, ignorance has two forms, primary ignorance when we do not know we are ignorant, and secondary, recognized ignorance, as factual ignorance, object ignorance, and technical ignorance.
The most fundamental ignorance is the primary ignorance when human beings are wrong about their knowledge, beliefs, expectations, or their understanding of the world, without knowing it.
We don't know that we don't know what reality, being, or the universe is and how the world works,
We don't know that we don't know what relationship is and how its forms, correlations and associations, causation and interactions, are interrelated,
We don't know that we don't know what intelligence is and how it all works, as animal, human, artificial or machine intelligence.
Many still think that man-machine superintelligence is just a theoretical concept, while the real superintelligence technology will soon become a reality.
To overcome the mentality silos and ignorance in all its forms, humanity needs powerful superintelligence partners, call it artificial intelligence or technological intelligence, computing intellect, cybernetic mind, general machine intelligence and learning, universal intelligence, trans-human intelligence, or Trans-AI [Trans-AI: How to Build True AI or Real Machine Intelligence and Learning].
Due to all the inherent biases and prejudices, hyper-specialization and in-group thinking of human intelligence, building general AI and ML as non-human machine intelligence is a matter of life or death for humanity.
Superintelligent machines will be able to conceptualize the real-world abstractions and interpretations that are inconceivable to humans.
The big tech projects, like as OpenAI’s DALL-E and DeepMind’s Gato and LaMDA, are promising the same human-like and human-level AI, modeling human brains/mind/intelligence/cognition/behavior.
Superintelligent machines are capable of conceptualizing concepts and interpretations that are not possible to comprehend for humans, and man-machine hyperintelligent systems can instantly comprehend, evaluate, and process the environment, and all the meaningful interactions of its causal variables.
The coming AI revolution could be a boon to mankind — if only it’s driven by modeling the world of reality, its entities, phenomena and interactions, causal patterns, laws and rules.
The fundamental knowledge of fundamental things is fundamental for real intelligence, natural, artificial or alien.
What is reality? Why we still don't understand how the world works
It’s the ultimate human quest – to understand everything that there is and how it is reflected by intelligence, human or machine, as the sensemaking and determination of reality.
It has started since the times of Parmenides, Reality is a single unchanging Being, and Heraclitus, Reality is all things that flow.
We humans have an existential problem with reality. We experience it all the time, but struggle to define it, let alone understand it. When we examine it closely, it disappeas like a mirage. We don’t know when it appeared, how big it is, where it came from and where it goes, and we have no idea why it exists and what we doing in it.
Nonetheless, the desire to understand reality as the meaning of everything or the true nature of the cosmos seems part of our nature, and we have come a long way since the ancient times.
For us, it is equally credible to claim that reality is entirely dependent on subjective experience, or entirely independent of it.?Reality has never felt so unreal.
It could be all what describes the wiki article?Reality:
In reality, reality is all what was, is and will be. It is marked with one truth, absolute truth, which is true at all times and in all places.
There is one absolute truth in the world,?universal, complete truth; unvarying and permanent truth, which must be true for all and everybody everywhere all the time, including humans and AI machines:
"ALL REALITY IS INTERACTION",
"EVERYHTING IS CAUSALLY INTERRELATED", at all its levels, scales and scopes.
The formula of Reality is as simple as:
Reality/World/Universe/Being (W) = Essence (hidden variables, noumena, interaction/interrelationship/causation) + Existence (observable variables, phenomena, substances, things. objects and events, changes, processes, causes/effects)
The real-world axioms are:
Reality vs. perception: Why computers can’t think as humans
AI formally started as a new science and engineering in 1950. In?Computing Machinery and Intelligence, Alan Turing argued that?"the Imitation Game", a thought experiment, is sufficient to determine a machine's thinking ability. It was renamed as the Turing's test...with prejudice against machine intelligence. It demands the machine's ability to?exhibit intelligent behavior?"equivalent to, or indistinguishable from, that of a human".
In August 1955, a group of scientists, J. McCarthy, Dartmouth College; M. L. Minsky, Harvard University; N. Rochester, I.B.M. Corporation; C.E. Shannon, Bell Telephone Laboratories, made a funding request for US$13,500, 2 month and 10 men summer workshop at Dartmouth College, New Hampshire, to study and build AI.
[A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE].
The historical promise was that: “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”
Since these beginnings, the whole life cycle story of human-like and human-level AI had begun, having its booming and busting, while movies and media have demonized or romanticised AI.
Still at the very beginning was the Turing article, if not to count the ancient myths and fantasies about artificial minds, as Talos,?a giant bronze man built by Hephaestus, the Greek god of invention and blacksmithing.
All depends on the human prejudices and how truthful models of machine intelligence. Who is building AI? Artificial intelligence will do great evil or great good depending on the beliefs of those who make it. And right now, it is primarily being built by tech leaders with little to no understanding of or respect for the morality, virtues, and wisdom... The greatest threat with AI isn’t all-powerful robots. It’s amoral people.
This deeply defective human-driven approach has led many to predict that AI will end life as we know it. Elon Musk has said that adopting AI is like “summoning the devil.” Stephen Hawking declared that it may “replace humans altogether.” And many other scientific and social leaders have spoken in the same vein, saying that AI is among “the new tools of oppression” and likening it to “children playing with a bomb.” Hardly a day goes by without another apocalyptic prediction.
The AI computing systems are all about quantifying, tokenizing and digitizing the world, with its variables, values and interactions, functions, maps and mappings, in terms of machine universe data ontology and quantitative sciences, physics, mathematics and statistics and probability, programming and computing.
That's it. No human-like subjective perceptions, concepts and ideas, knowledge and wisdom, intuition and and thoughts, feelings and experience, cognition, reasoning and understanding, intellect and intelligence, sentience and consciousness, decision-making, willing, action and interaction.
There is perception and reality. qualia and quantity, subjective experience and reality. Unlike machines, for humans perception is reality. The real reality is different and it’s above any perception. In this interaction between?perception vs reality, reality often has a leading role.?
Subjective character of human experience implies that the perception of all things, concepts, and "truths" in the universe differs between individuals. We all live in different worlds, because of our unique perspectives on our worlds. The only thing to which one can hold oneself is something one has experienced or perceived. Until someone has had an experience of something it is not "real."
Human perception is the mental representation or reflection of reality after being affected. moderated or influenced by many factors, as in:
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Human reality as a collection of everyone’s perceptions.?Perception is how humans see the world, Reality is how the world is, and seen by machines.
Computing machinery is after reality, not human perceptions.
Reality is the truth and facts, data and evidence.
Reality is the actual state of things and affairs.
Reality is the opposite of all things imaginary and subjective.
Reality is the opposite of all things non-existent, spurious and fictitious.
Reality is how things actually are before our minds and emotions and biased/preconceived notions corrupt them.
All our errors come from the?Deviation of Perception from Reality: Error (e) = Reality - Perception
Real/True AI with its technology is all about reality and its quantities, multitudes or magnitudes (matter, mass, energy, liquid, material), variables and values, data sets and datum, as classes and objects, sets and elements, instances and cases, individuals and events, numbers, datum, data sets, or statistics, tokens and collections of tokens, digital/binary numbers of zeroes and ones, with all its possible types and patterns and operations.
Quantitative AI is dumb to Quality and its qualitative research, focused on discovering underlying meanings and patterns of relationships, including classifications of types of phenomena and entities, in a manner that does not involve mathematical/numerical/statistical models.
It implies the scientific method through observation to empirically test hypotheses explaining and predicting what, where, why, how, and when phenomena occurred.
Scientific AI operates in the space of data points classified as statistical data types (from simple to complex, multivariate data, from random vectors to random fields) and levels of measurements, nominal, ordinal, interval, and ratio scales, with associated distributions, permissible operations, regression analysis, etc.
It discovers not spurious associations and data correlations, but causal relationships by manipulating some variables as causal factors to influence the phenomena of interest while controlling other variables relevant to the experimental outcomes. For example, as a healthcare application, it might measure and study the relationship between dietary intake and measurable physiological effects such as weight loss, controlling for other key variables such as exercise.?
All the AI-world of interactions is the data universe of numerous variables, an infinite collection of discrete?values?that convey?information and knowledge, describing?things and entities, substances and objects, events and changes, actions and processes, quantity,?quality,?fact,?statistics, etc.
In all, it tokenizes and digitizes and processes all quantitative data [as to ASCII characters], thus creating [non-human] intelligent computing systems, from self-driving cars to large natural language models and generative AI models.
Interaction vs. Causality: How to determine causalities across reality and its data universe
We are badly aware of the nature of the interrelationships [among variables], as Correlation with its measuring techniques, Association with its measuring techniques, and Causation with its measuring techniques.
Correlation and Regression analysis, Bayesian graphical models [Bayesian networks, Bayesian belief networks, Bayesian Net, causal probabilistic network, Influence diagrams], or the?Causal Markov Condition, this is what plays a central role in statistical processes or causal modeling and causal inference.
"Correlation does not imply causation" is a sample of human mass confusion. It is certain that correlation implies causation. For any interrelationship is interrelated, be it associations and correlations, causation and its interaction.
Most of us are not aware that "all the world is interaction". Interactions are all and everything.
Interactions are productive interrelationships, nonlinear cause- effect relationships. causal associations and causative correlations, where all causal variables interact with each other, thus governing the world, its matter and energy, objects and processes, structures and functions.
EVERYTHING IS A MATTER OF INTERACTION, NOT OF SUBSTANCE OR OF PROCESS. ANY ENITY IS AN INTERACTION EFFECT of interacting factors, forces, and causes.
Here is a historically entrenched misunderstanding of causality (causation cause and effect). which is supposed to govern the world' progression or regression.
A naive definition from the Britannica dictionary: "the relationship between something that happens or exists and the thing that causes it".
A messy definition from the Wikipedia article: "Causality is influence by which one event, process, state, or object contributes to the production of another event, process, state, or object where the cause is partly responsible for the effect, and the effect is partly dependent on the cause".
Another harmful confusion coming from the Book Why: the New Science of Cause and Effect, as the 'ladder of causation' - a diagram used to illustrate the three levels of causal reasoning: 'Association', which discusses associations between variables; 'Intervention', 'if I make the intervention X, how will this affect the probability of the outcome Y?'; 'Counterfactuals', involving answering questions which ask what might have been, had circumstances been different.
A dialectical definition: "Causality is?a genetic connection of phenomena through which one thing (the cause) under certain conditions gives rise to, causes something else (the effect). The essence of causality is the generation and determination of one phenomenon by another... Any effect is evoked by the interaction of at least two phenomena. Therefore the? interaction phenomenon ?is the true cause of the? effect phenomenon... The cause-effect connection can be conceived as a one-way, one-directional action only in the simplest and most limited cases. The idea of causality as the influence of one thing on another is applied in fields of knowledge where it is possible and necessary to ignore feedback and actually measure the quantitative effect achieved by the cause. Such a situation is mostly characteristic of mechanical causality... Therefore, in experiencing effect they in their turn act on their cause and the resulting action is not one-way but an interaction."
?It is a rare source which is close to the truth: "To sum up, all processes in the world are evoked not by a one-way or one-sided action but are based on the relationship of at least two interacting objects".
Causality is the study of how things interact or influence one other, how causes lead to effects via the interactions.
The natural truth is, causality is interaction, and vice versa, interaction is causation.
Everything interacts. Things mutually influence other things in reciprocal ways. We live in the interactive reality, in the world which is causally entangled at all its levels, from the quantum reality scale to the cosmological level. To be all interacted is a basic rule of our ever-changing, dynamic world where all things are causally entangled.
Again, the COMMON CAUSE PRINCIPLE is implying that No correlation without causation.
Reichenbach’s Common Cause Principle (RCCP) is a metaphysical claim about the causal structure of the world, and it has been debated extensively in the philosophical literature if it is a valid principle.
It says that when such a probabilistic correlation between?A?and?B?exists, this is due to one of the following causal relations:?A?is a cause of?B;?B?is a cause of?A; or?A?and?B?are both caused by a third factor,?C, like the heat of the sun is the common cause of all life.?
Or, if two events are correlated, then either there is a causal interaction between the correlated events responsible for the correlation or there is a third event, a common cause, which brings about the correlation.
Formally, if two events?A?and?B?are correlated, then either?A?and?B?stand in a causal interrelation,?Rcause(A, B), or, if?A?and?B?are causally independent,?Rind(A, B), then there is a third event?C, a common cause that brings about the correlation by being related to?A?and?B?as the following probabilistic descriptions, first given by Reichenbach (1956):
(1)??????p?(A?∧?B?)???p?(A?)p?(B?) > 0
if the following conditions hold:
(2)??????p?(A?∧B?|C?) =?p?(A?|C?)p?(B?|C?)
(3)??????p?(A?∧?B?|C?⊥) =?p?(A?|C?⊥)p?(B?|C?⊥)
(4)??????p?(A?|C?) >?p?(A?|C?⊥)
(5)??????p?(B?|C?) >?p?(B?|C?⊥)
Here?A, B, and?C?are assumed to be elements in a?Boolean algebra????and they are to be interpreted as representatives of random events.?p?(A?|C?) =?p?(A?∧?C?)/p?(C?) and so on denote the conditional probability of?A?on condition?C, C⊥?denotes the complement of?C, and it is assumed that none of the probabilities involved is equal to zero.
The principle posits a connection between causal structure and probabilistic correlations to infer causal relationships from empirically observable correlations. It is thus intended to be an empirical generalization about the relationship between causation and probability in the actual world
Integrated with the Interactive Causality, the Common Cause Principle involves entangled states in quantum mechanics, such as those found in Einstein-Podolski-Rosen (EPR) thought experiment, published in "Can Quantum-Mechanical Description of Physical Reality be Considered Complete?", where the EPR-type correlations can have an explanation in terms of the common causes.
The quantum entanglement has been demonstrated experimentally with?photons,?neutrinos, electrons,?and molecules,?The phenomenon of QE is studied in in?communication,?computation?and?quantum radar.
Conclusion
Due to all the inherent biases and prejudices, hyper-specialization and in-group thinking of human intelligence, building a machine superintelligence is a matter of life or death for humanity.
Resources
[Creating AI without Turing's prejudice: Reality vs. Perception, Quantity vs. Quality: why computers can’t think as humans]