Ridding off Mass Delusions: God, Humans, and AI
"God created man in his own image. . ." Genesis 1:27
"Man created machines in his own image. . ."
In the allegory "The Cave", Plato describes a group of people who have lived chained to the wall of a cave all their lives, facing a blank wall. The people watch shadows projected on the wall from objects passing in front of a fire behind them and give names to these shadows. The shadows are the prisoners' reality, but are not accurate representations of the real world. The shadows represent the fragment of reality that we can normally perceive through our senses, while the objects under the sun represent the true forms of objects that we can only perceive through reason...or REAL INTELLIGENCE, HUMAN OR MACHINE or MAN-MACHINE HYPERINTELLIGENCE.
Plato's shadows are collective delusions, and the prisoners are all humanity.
THE INTELLIGENT WAY TO GET OUT OF PLATO'S CAVE IS REAL INTELLIGENT SYSTEMS, WHICH ARE DISRUPTING THE BIG TECH ANTI-HUMAN, MASS DELUSION TECHNOLOGY [Global AI Big Tech class actions].
Collective Delusions as Mass Ignorance
Collective delusions are almost everywhere, covering all our beliefs and social constructs and institutions, or socially constructed reality, that exists not in objective reality, but as a result of complex human interactions, made real by convention or collective agreement.
It is all what is studied by social ontology, a wide range of social entities and phenomena, states and processes, relationships and interactions, God, religion, man, society, nation, government, sex, gender, male, female, race, norms, morality, nationality, family and marriage, justice, law, corporations, art, artifacts, money, language, education, etc.
Only certain things are factual and not influenced by societal beliefs, as ontology or metaphysics, science, engineering, and technology, having factuality and reality, unlike psychological, societal, economic, political. historical, legal or cultural categories and classes.
Among the worst societal delusions are money, success and hierarchy, geopolitical delusions, the Global South and the Global North or wars, destructive armed conflicts between mass-deluded states, governments, or societies.
As old cultural delusion in mythology and religion goes anthropomorphism "the perception of a divine being or beings in human form, or the recognition of human qualities in these beings".
Mass delusions are fixed, false collective beliefs that conflict with reality, and they are so widely held that we don't realize that we are deluding ourselves collectively.
In all, delusion could be referred to as ignorance as opposite to knowledge and intelligence or wisdom. This can be experienced as an individual or collective inability to see the truth or reality of ourselves or the world around us or the world as a whole. Whenever our perceptions, ideas or beliefs are different from reality, we will experience and cause suffering, as to Buddhism.
Collective Delusion vs. Mass psychogenic illness (MPI) [mass sociogenic illness, mass psychogenic disorder, epidemic hysteria or mass hysteria]
Sociologists and social psychologists use the term collective delusion, or mass delusion, in a different sense, to describe the. spontaneous, temporary spread of false beliefs within a given. population.
The MPI "involves the spread of illness symptoms through a population where there is no infectious agent responsible for contagion".
Mass delusion is a false fixed belief shared by a large group of people, unlike confabulation, dogma, illusion or hallucination.
In contrast to psychiatric disorders, collective delusions are artificial, man-made by diverse influences to promote some religious or ideological, financial or economic, psychosocial or political goals.
The mass delusions are known as various belief systems, religious, political, scientific, technological, philosophical, ideological, a combination of all or some of them.
Man-made mass delusions are emerging in all human societies.
The kea features of mass delusion systems are false ideas, which are not aligned with factuality and reality or facts and truth.
Two widely shared mass delusions, separated by thousands years, are
"God created man in his own image. . ." Genesis 1:27
"Man created machines in his own image. . ."
The delusive concept of human-like intelligent machines, "inanimate objects endowed with intelligence", has been around since ancient times. The Greek god Hephaestus was depicted in myths as forging robot-like servants out of gold, while engineers in ancient Egypt built statues of gods animated by priests.
Today, it makes the delusive/deceptive/deluding/false/unreal assumptions of the AI field, aimed to simulate human intelligence by machines and computer systems, and applied as expert systems, NLP, machine learning, machine vision, generative AI or LLM chatbots, automation and robotics.
Using numerical "neural networks", rules-based systems, statistical methods and other mathematical techniques, such delusive "AI systems work by ingesting large amounts of labeled. biased and discriminatory "training data", analyzing the data for statistical correlations and patterns, and using these patterns to make predictions about future states".
Today’s Generative AI, tools like Large Language Models (LLMs) such as ChatGPT, Claude and Gemini or Sora or what else, don’t get that much better.
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Maybe they end up being what some have called them, a race to mediocrity, dubbed in statistics as regression toward the mean (reversion to the mean, and reversion to mediocrity).
Image generators just end up creating beige images.
As a user of LLM/AI, you need to be clear on which of two things you seek. If you want to discover the median, mean, or mode, then lean aggressively into LLM/AI. For example, use LLM/AI if you want to determine average consumer sentiment, create a median speech, or write a modal paper. If you are looking for the hidden median, mean, or mode, LLM/AI is your ticket — and that will be the case plenty of the time.
Being an LLM/AI denier is a wise option.
Such AI programming tools mimicking cognitive skills can support individual delusions and biases personalizing content, messaging, ads, recommendations and websites to individuals, while eliminates human jobs and increasing structural unemployment rates.
Such delusive AI is planned to integrate in a wide variety of markets, from business and healthcare to manufacturing and transportation.
Meanwhile, such human-replicating software/hardware models are ridden with ethical challenges and legal issues:
Why "creating machines in the human image" is not rational and wise has been explained in the following article.
How do you define intelligence? We each have our own notions of what it means to be intelligent—perhaps being skilled in math or adept in social situations. But providing a general definition is surprisingly difficult. Herein lies the challenge for artificial intelligence, or AI: how do we structure scientific study around a term that is typically reserved for humans?
While progress has been made in mimicking aspects of human intelligence, the human-focused origins of the field of AI may be limiting the scope of our scientific pursuit. As we move forward, perhaps looking beyond ourselves for inspiration will provide a more comprehensive definition of intelligence, or a new concept altogether.
The concept of ‘intelligence’ comes from human psychology, where it is measured using IQ tests. As one cannot directly measure an abstract concept like intelligence, these tests instead evaluate a range of tasks, from reasoning to memory and verbal comprehension. Not surprisingly, when AI adopted the term intelligence over a half-century ago, along came a focus on performing similar cognitive tasks.
As a famous example, the Turing test pits a machine against a human, with the machine using written conversation to attempt to convince the human that it is also human. Similar motivations fuel modern-day efforts to use computers to master boardgames like Go and classic Atari videogames, human pursuits that require coordinated actions over many steps. Human influences are also present in tasks like processing language or identifying objects in images. In the absence of a clear definition of intelligence, these approaches implicitly assume that human tasks are a proxy for human intelligence, with the hope that a machine capable of performing these tasks and more will attain ‘artificial general intelligence,’ becoming flexible enough to perform any task...
An example of a machine classifying objects in an image. The machine uses AI to rate how likely each image is to be of a particular type.
While such generally intelligent machines do not yet exist, we have made advances in many cognitive tasks, impacting society through applications like self-driving cars, facial recognition, and language translation. This progress has been largely the result of deep neural networks, mathematical models which are loosely inspired by the biological neurons in brains. Through a process of learning to map input data (e.g. a photo) to corresponding outputs (e.g. what objects that photo contains), machines are now becoming capable of tasks like recognizing, reasoning about, and manipulating objects. Mastering many of these basic human cognitive capabilities now seems on the horizon. However, it remains unclear whether such machines would unlock the mysteries of our own range of capabilities or those of other organisms, let alone general intelligence. Rather than exploring broader, fundamental principles underlying intelligent systems, the field of AI has been, in effect, teaching to the Turing Test—focusing on mimicking our own human capabilities at ever-increasing levels of sophistication.
As AI keeps advancing, this adherence to a human-centric view of intelligence could have major consequences. I’m reminded of the Copernican revolution: for centuries, astronomers placed the Earth at the center of the universe, with our desire for significance guiding our conception of reality. However, when observations did not align with this theory, we came to understand that the Earth orbits the Sun. In a similar way, I feel that we have placed humans at the center of our definition of intelligence. Clearly, we have unique capabilities, just as our planet is unique among its neighbors. Yet, any comprehensive definition of intelligence should account not only for our own capabilities, but those of other entities as well. Looking to other biological and human-made entities will also help us see ourselves within a broader scope of intelligence, like studying the Earth in the context of other planets.
Heliocentric (left) vs. geocentric (right) depictions of planetary motion. Until the geocentric view was discredited, we placed ourselves at the center of the universe. A similar phenomenon may be affecting our perception of intelligence.
When we look at biology, we see systems that sense and respond to their surroundings. One such system is the cell, which has sensors for chemicals, as well as actions it can take in response, like going into “hibernation”. Entire multi-cellular organisms can also be considered as systems. Animals, from the smallest insect to the largest whale, interpret and interact with their environments in a multitude of ways.
In nature, we find systems at multiple scales that sense and respond to their environments, from individual cells up to collections of multicellular organisms.
Plants are attuned to sensory inputs like sunlight, moisture, and temperature, prompting responses like orienting leaves, extending roots, and releasing seeds. And groups of organisms, from forests of trees to colonies of ants, collectively sense and respond to their environments in ways that we are still just beginning to understand. Ultimately, all of these processes share a common form: they convert energy into actions, affecting themselves and their environments to promote the survival of genes.
Our technological inventions can also be viewed from this systems perspective. The tools of our early ancestors, like spears and boats, expanded the ways in which they could respond to their environments. More recent inventions, like radios and cameras, have similarly expanded the ways in which we sense our environments. Modern advances in computing, and now AI, have taken this trend further, creating systems that can sense and respond to their environments largely independently of human input. This is a world in which power grids can automatically sense and respond to supply and demand, and vehicles can automatically sense and respond to obstacles.