The Myth of AI And Machine Learning Debunked: Equating AI/ML with Human Intelligence
“AI is the science and engineering of making intelligent machines.” — John McCarthy
"Artificial intelligence is not, by definition, simulation of human intelligence". — John McCarthy
[A misconception is a viewpoint, factoid, stereotype, superstition, fallacy or misunderstanding that is accepted as true but which is actually false]
Today's AI/ML is full of anthropocentric and anthropomorphic misconceptions, while the nature (ontology)?of machine/artificial intelligence and learning is plain and clear.
It is all real-world data, world models and algorithms, software and hardware, or machine data ontology and science, with mathematics and statistics and probability, programming and computing. That's it.
No human-like perceptions, concepts, ideas, and thoughts, cognition, reasoning and understanding, intellect, intelligence, sentience and consciousness, decision-making, willing, action and interaction.
It is all numbers, variables and values, quantities and data or statistics, digital/binary numbers of zeroes and ones, with all its possible types and patterns and operations.
It tokenizes and digitizes and processes all data [as to ASCII characters], thus creating [non-human] intelligent computing systems, from self-driving cars to large natural language models.
The faster we comprehend that artificial/machine intelligence and learning is completely different and complementary to human intelligence, the better for all of us.
WHY MACHINE INTELLIGENCE SHOULD SIMULATE REALITY INSTEAD OF HUMAN INTELLIGENCE AND/OR BEHAVIOR
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.
Sill we don't know what is reality, being, or the universe is and how the world works,
Still we don't know what relationships are and how correlations and associations, causation and interaction are interrelated,
Still we don't know what is brain/mind/intelligence and how human or artificial intelligence works.
To survive and prosper, humanity needs powerful superintelligent 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.
We are totally biased creatures preferring or disliking someone or something more than someone or something else, in the most harmful ways.
We are born, then educated and then misinformed to be biased against everything and everybody else.
Our political systems are biased against true policy
Our social systems are biased against true society
Our economic systems are biased against true economy
Our justice systems are biased against true justice
Our scientific systems are biased against nature
Our technological systems are biased against humans
And all citizens are biased against each other, and so on and on.
Today's human-like AI of ML models and DL algorithms is a scientific misconception as alchemy, “the medieval forerunner of chemistry, based on the supposed transformation of matter; it was concerned particularly with attempts to convert base metals into gold or to find a universal elixir.” Its proponents were obsessed with the Philosopher’s Stone, the Elixir of Life or turning metal into gold, as much as the AI folks are obsessed with the Elixir of Mind, turning data into algorithms, models and intelligence.
There are Mass Layoffs in Tech . Tech giants like Amazon, Meta, and Twitter cut thousands of jobs. What does that mean for the future of AI?
Meta sank billions into the Metaverse — losing nearly 10 billion on the project this year alone — with no break-even point in sight yet. Twitter is currently losing $4M a day.
Amazon recently became the first company in history to lose one trillion (!) in market value, with Microsoft trailing not much behind.
Google continues to experience shrinking profits, partially due to an oversaturated ad market and partially due to failed innovations.
Facing economic headwind, tech giants like Amazon, Meta, and Twitter cut thousands of jobs. What does that mean for the future of AI?
LinkedIn was suddenly flooded with experienced data scientists looking for another job. Within a matter of days, Twitter fired half of its workforce, Amazon and Meta both cut over 10,000 jobs in mass layoffs, and many more companies either installed hiring freezes or substantially shrunk their work force.
Globally, an estimated 200,000 tech workers have lost their job already, and this number will likely rise in the months to come.
All of a sudden, it appears the bottom fell out from under the data science community.
Are we headed for last AI Winter? This time, a "AI Nuclear Winter" with Big Tech is going extinct. Why so? Let's read to understand what is really going on.
Machine Intelligence and Learning (& ML & DL & ANNs) vs. Artificial Human Intelligence (AI & AGI & ASI)
We run into a lot of myths and common sense (a flat earth), scientific, social and medical misconceptions, as the common life misconceptions about history, humanity, food, technology, animals, astronomy, religion, crime, and math.
There are plenty of harmless ones that are easy to correct, some could be harmful but for the personal mental health, like as "there is an evil global government" or "oil/gas company don't want solar energy technologies" or "drug companies don't want to find a cure for COVID-19 or cancer because it would put them out of business" line, etc.
But the end of the scale is occupied by the worst and craziest misconceptions which are potentially lethal for all human existence. It is one thing to have common misconceptions as superstitions and stereotypes or urban legends, the other thing is a deep misunderstanding of science, engineering, technology and business and public policy, which are the most critical factors for all human life.
The worst and dangerous misconception ever is what is known since 1950 as a human-like artificial intelligence. As thinking machines, it was first systematically discussed in Turing's Computing Machine and Intelligence and latter designated by the terms "artificial intelligence" at a workshop in Dartmouth college in 1956, ignoring cybernetics, stating:
"We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College... The study is to proceed on the basis of the conjecture 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 find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves".
It was dubbed as Artificial [Human] Intelligence (AI), "the simulation of human intelligence processes by machines, especially computer systems", "the capacity of a machine to carry out cognitive tasks similar to those carried out by humans, such as perception, learning, reasoning, and problem-solving', etc.
This misconception has been widely popularized by many textbooks, including "the most popular artificial intelligence textbook in the world" [Artificial Intelligence: A Modern Approach ?(AIMA )]
As a consequence, the basic understanding about AI is harmfully anthropomorphized, widely adopting and practicing one of the worst misconceptions ever.
I give reasons and cite evidence that Machine Intelligence (MI) is NOT Artificial [Human] Intelligence, AI is NOT Machine Learning, ML is NOT AHI, and Artificial NNs are NOT Human NNs.??
If you ask yourself ‘what is the greatest scientific discovery of all time?’
It is not the Copernicum System, gravity, electricity, quantum theory, evolution, relativity, DNA, Periodic Table, the Internet, or human-like AI.
It is machine intelligence, which is?the greatest scientific breakthroughs of all time, because it enables all advanced technology and machines to effectively and efficiently interact with the world processing the world's data with the speed of light.?
Crucially, our scientific, objective, reality-centered methodology and paradigm shift allows to model MI as Transdisciplinary AI, as True AI or Real Machine Intelligence and Learning .
AI/AI Metarules for Autonomous Machine Intelligence
I am asked on Quora on many occasions: why so far, nobody has developed any version of artificial intelligence that has reached the level of a cat, dog, or parrot...
Good news.
AI will never reach the level of any animal intelligence, not mentioning human intelligence.
Bad news. It will outperform all collective human intelligence, as its specialized applications easily outsmart humans in specialized tasks, from chess playing to art painting.
How so?
AI is a hype/buzz word of commercial lingo, if you wish, a commercial fraud on a large scale.
To make things just and fair, for the European Commission, I have suggested to launch a multibillion collective action case vs. the biggest tech corporations exploiting the AI fraud schema, like as known G-MAFIA and BAD-Triada, for a misrepresentation or deception which is (1) intentional and (2) designed for a tangible gain.
The total market caps of the largest tech companies (851) approach to $17.822 T and most of them, from Apple to eBay, misrepresent themselves as dealing with a human-like AI technology.
I label it as a fake and false AI, because machine learning and deep learning has nothing to do with the simulating/mimicking/emulating of the human brain/intelligence/cognition/behavior.
It is all about the world and its interactions (full causality) in terms of machine ontology and empirical science, mathematics and statistics and probability, programming and computing.
That's it. No human-like [epistemic] perceptions, experience, knowledge, concepts and theories, ideas and thoughts, cognition, reasoning and understanding, intellect and intelligence, sentience and consciousness, decision-making and willing, action and interaction.
It is all about the world of tokens, entities and things, items and instances, objects and units and cases, individuals and events, measurements and numbers, variables and values, quantities, multitudes and magnitudes, and data or statistics, tokens and collections of tokens, digital/binary numbers of zeroes and ones, with all its possible data types and patterns and operations.
It is about measurements of different levels, scales and types , within the values assigned to?variables, with specific properties, mathematical operations, central tendency and variability:
nominal scales, ?numbers could represent the variables while have no numerical value, specific meaning or relationship, with the classifications like gender, nationality, ethnicity, language, genre, style, biological species, and form
ordinal scales, measuring ranking of items and placing events in order, examples include?dichotomous?data with dichotomous (or dichotomized) values such as 'sick' vs. 'healthy' when measuring health, 'guilty' vs. 'not-guilty' when making judgments in courts, 'wrong/false' vs. 'right/true' when measuring?truth value, and?non-dichotomous?data consisting of a spectrum of values, such as 'completely agree', 'mostly agree', 'mostly disagree', 'completely disagree' when measuring?opinion.
interval scales, ?allows for the?degree of difference?between items, but not the ratio between them. Examples include?some temperature scales, which has two defined points (the freezing and boiling point of water at specific conditions) and then separated into 100 intervals,?date?when measured from an arbitrary epoch (such as AD),?location?in Cartesian coordinates, and?direction?measured in degrees from true or magnetic north.
ratio[nal] scales, the estimation of the ratio between a magnitude of a continuous quantity and a?unit of measurement?of the same kind (Michell, 1997, 1999). Most measurement in the physical sciences and engineering is done on ratio scales.
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and cardinal scales. it reflects cardinal numbers, or?cardinals, a generalization of the natural numbers?used to measure the?cardinality?(size) of?sets.?
It is for the MIL, not for the abstraction-ridden humans, To Be is to be a Value of a Variable (or to be Some Values of Some Variables). What reflects the Quine's ontological commitments to the FOL theories, "to be is to be the value of a bound variable".
It tokenizes and digitizes and processes all data [to ASCII characters], thus creating [non-human] intelligent computing systems, from self-driving cars to large natural language models.
The idea of machine intelligence and learning (MIL) is that all the world's data/information and knowledge with its causative regularities and patterns, principles, laws and rules could be formalized, encoded or programmed or trained as computing algorithms working via input and output and error-backpropagation algorithms. When data is entered, the system analyses the information given and executes the correct commands to produce the desired result, thus solving a problem of any complexity.
But it is an intelligence, a new type of intelligence which is to be recognized, like it or not.
It is a digital intelligence, data intelligence, electronic intelligence, statistical learning intelligence or simply machine intelligence which is orthogonal to human intelligence and its poor imitation, dubbed as AI.
The faster we comprehend that machine intelligence is completely different and complementary to human intelligence, the better for all of us.
What we know and what we thing we know is two different worlds. About AI you might know what you read in the textbook, social media networks, special blogs, etc.
Other thing is what you MUST know about AI, ML, DL, etc. First of all, you need to know its real definition, what it is about:
“AI is the science and engineering of making intelligent machines.” — John McCarthy
"Artificial intelligence is not, by definition, simulation of human intelligence". — John McCarthy
Next its basic 5 rules. I tried my best to formulate this all as 5 simple rules somehow to clear the whole messy situation, extending the AA/AI iron rule for autonomous machine intelligence :
“Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.”, as to Arthur Samuel. It is hardly ever that humans apply the statistical models and ML algorithms as below in their cognitive activities.
The common classification schema below is wrong: AHI and ML & DL are orthogonal to each other
Deep learning is a form of ML, which is NOT AHI.
Deep learning is a specialized form of ML, using ANNs to deliver statistical solutions. Able to determine accuracy on its own, deep learning classifies information NOT like a human brain, but relying on statistical models and data analytics algorithms.
What is real machine learning?
The science of training machines to analyze and learn from data NOT in the way humans do, but relying on mathematical modeling and statistical methods, techniques and algorithms to identify patterns within data to create a data model that can make predictions.?
AA/AI rules 5: Artificial Neural Networks are NOT Human NNs, containing an estimated 100 billion neurons connected by innumerable pathways and networks
ANNs are statistical models that are just inspired but not modeled after biological neural networks. They are advanced statistical techniques and mathematical concepts with the ability to model non-linear functional relationships between inputs and outputs in parallel.
The regression formula composed of input data, weights, a bias (or threshold), and an output is not any close formula how the human NNs work:
This is badly wrong or mere a commercial lingo to state that simulated "neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning".
ML has nothing to do with AHI, consisting of 3 steps to be implemented:
Step 1
Machine learning models are created by studying correlational patterns in the statistical data.
Step 2
Data scientists optimize the machine learning models based on patterns in the data.
Step 3
The process repeats and is refined until the models’ accuracy is high enough for the tasks that need to be done.
Most of ML applications, as GPT and Large Language Models (LLMs), apply the stochastic optimization algorithms, like as stochastic gradient descent, for optimizing an objective function as a key optimization method in ML/DL/ANNs.
The big problem is an unrealistic assumption abbreviated as?i.i.d.,?iid, or?IID. It is when random variables are modelled, solved, numerically or analytically, and analyzed to extract insight for decision-making by simplifying a random sample?as?“a sequence of independent, identically distributed (IID) random variables”.
The idea of machine intelligence is that all the world's data/information and knowledge with its regularities and patterns, principles, laws and rules could be formalized, encoded or programmed or trained as the reality model engine of computing algorithms working via input and output and error-backpropagation algorithms.
We witness an epoch of big data NLP machines, as LLMs, operating with huge amounts of internet data, structured, semi-structured or unstructured, led by WuDao with 1.75 trillion parameters, making it the world's LLM, 10x larger than GPT-3.
The moment when the LLMs integrates the reality model engines , giving them real intelligence, the human world will irreversibly change, with all our hopes and fantasies.
MI vs. AI: Real/Causal AI: Machine/computing/technology intelligence?
It must be clear that the whole idea of AHI/AI/AGI/ASI, with its forms or modalities as below, is mere a modern myth, which is not as harmless as the Greek myths:
The only real intelligence is the machine/computing/technology intelligence mapping/replicating/simulating/modeling reality, with its content and context, entities and relationships, categories and principles, laws and patterns, data and knowledge.
Such reality-centered MI enables predicting/forecasting laws, regularities, discoveries, trends and behavioral patterns by discovering cause-and-effect relationships in the data universe of data sets, points and elements, organized as world's knowledge of common sense, science, technology, business and human life as well as information systems of various complexities.
Repeat it again and again, to mimic human intelligence by super powerful computers is the way to human extinction as the result of the ASI technological singularity.
Requirements for Understanding the Fundamentals of Real MI
Instead of Conclusion
I was referred to a non-orthodox AI book in line with our views, which might be interesting to read and/or discuss:
Here is a passage: "Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. This is a profound mistake. AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don’t correlate data sets: we make conjectures informed by context and experience. Human intelligence is a web of best guesses, given what we know about the world. We haven’t a clue how to program this kind of intuitive reasoning, known as abduction. Yet it is the heart of common sense".
My philosophy and technology of MIL vs. AI is plain and clear.
It is all about the world in terms of mathematics and statistics and probability, programming and computing. That's it. No human-like perceptions, concepts, ideas, and thoughts, cognition, reasoning and understanding, intellect, intelligence, sentience and consciousness, decision-making, willing, action and interaction.
It is all numbers, variables and values, quantities and data or statistics, digital/binary numbers of zeroes and ones, with all its possible types and patterns and operations.
It tokenizes and digitizes and processes all data [as to ASCII characters], thus creating [non-human] intelligent computing systems, from self-driving cars to large natural language models.
The faster we comprehend that machine intelligence is completely different and complementary to human intelligence, the better for all of us.
AI is AHI, which has nothing to do with statistical ML and DL and ANNs, be it Azure AI, Google AI, Meta AI, etc., if only as a fake/false/simulated AI/ANI/AGI/ASI, but with real existential threats for humanity.
Gesch?ftsführung bei Kulturwerbung Lenkeit
1 年The key-science to AGI is psychology.It is typical of AI research to ignore this core science in matters of human intelligence.