Scientific AI vs. Pseudoscientific AI: Big Tech AI, ML, DL as a pseudoscience and fake technology and mass market fraud

"If you’ve created a conscious machine, it’s not the history of man. It’s the history of gods". Ex Machina, 2014

The demarcation between science and pseudoscience, technology and pseudo-technology. has all sorts of critical implications, scientific and technological, economic and political, social and cultural.

I argue for how to distinguish scientific AI from pseudoscientific AI, true AI vs. false AI, real AI vs. fake AI, genuine ML vs. counterfeit ML. Prototyping the AI with humans, and vice versa, the human with AI, making machines mimic human behavior, and vice versa, humans mimicking machines, humans do bots work, and vice versa, bots do human tasks, that is termed as "pseudo-AI", or simply a double fake AI.

It must be stressed from the very beginning that the historical pathway to Scientific/Real/General/Autonomous Machine Intelligence had started not some 70+ years ago, but rather some thousands years ago:

Metaphysics > Logic > Mathematics > Science > Computing > Cybernetics > ANNs > Symbolic/Logical/General AI > Machine Learning > Deep Learning : Weak/Narrow AI > Neuro-symbolic General/Human-Level AI > ASI > Transdisciplinary AI = Trans AI > Man-Machine Hyperintelligence

Let’s start from the whole point, what must be the Real/Scientific AI Formula/Model/Architecture. Find out below "the secret of all secrets", "the knowledge of all knowledge", how to research, design, develop, deploy and distribute a universal man-machine intelligence platform:

Universal/General/Scientific/Real Intelligence [Natural or Artificial] =

• Intelligence Processes [Causal Rules and Intelligent Algorithms] +
• Real World Data Engine [facts, symbols, signs, signals and stimuli, Information and Knowledge] +
• World Model (Plan/Design/Schema) Intelligence, Learning, Inference and Interaction +
• Narrow AI Models (NLP/G, Symbolic AI, ML, DL, ANNs) +
• Computing (Supercomputing or Quantum Computing) +
• The Internet of Things (from i-phones to autonomous transportation and robotic spaceships)

Systematically, any real AI researchers/designers/developers should follow the DIKWDAD (decision-action-data) cycle embracing the famous the DIKW pyramid (chain, framework, graph, continuum, model), or the DIKW hierarchy, wisdom hierarchy, knowledge hierarchy, information hierarchy, information pyramid, and the data pyramid, representing structural and/or functional relationships between data, information, knowledge, and wisdom, to be completed with Decision-making, Action and New Data.

Science vs. Pseudoscience: Real AI vs. Fake AI

To remind the difference of science vs. pseudoscience and nonscience.

Science is a systematic enterprise that builds and organizes data, information and knowledge in the form of causal [testable] explanations and predictions about the world.

"Pseudoscience consists of statements, beliefs, or practices that claim to be both scientific and factual but are incompatible with the scientific method" (Wiki). Pseudoscience can also be the result of research that is based on faulty premises, a flawed experimental design or bad data.

There are four criteria how to mark it:

     (a) the 'pseudoscientific' group asserts that its beliefs, practices, theories, etc., are 'scientific';

     (b) the 'pseudoscientific' group claims that its allegedly established facts are justified true beliefs;

     (c) the 'pseudoscientific' group asserts that its 'established facts' have been justified by genuine, rigorous, scientific method; and

     (d) this assertion is false or deceptive: "it is not simply that subsequent evidence overturns established conclusions, but rather that the conclusions were never warranted in the first place"

“When I read about AI, 80% of the time it’s just flat out wrong information,” says Prof. Stewart Russell of the University of California at Berkeley. In reality, “When I hear or read about AI, it is largely confusing or wrong”. This misunderstanding of what AI really is has largely enabled the emergence of fake AI, a subjective, non-scientific, human-like, and human-level AI.

Fake AI is an aggregation of statistical techniques, mathematical methods, unethical marketing strategies and immature tech solutions. Fake AI does not offer a true competitive advantage, and the market is becoming increasingly aware of what real AI can achieve, fake AI products and novelties are unlikely to stand the test of time.

We stuck in an anthropomorphic intelligence

In all, there are huge and long misconceptions about AI as machine intelligence, computing mind, cybernetic intellect, or technological intelligence, what it is, why it is, and how it works. What is widely promoted by academic articles, encyclopedic articles, as the Wiki AI article and Britannica AI article, textbooks, websites, blogs, journals, sci-fi literature, movies and videogames and big corporations.

The field was founded on the fundamental misassumption that human intelligence can be simulated by computing machines, with all its harmful consequences, empty promises, booming and bustin: "Every aspect of learning or any other feature of intelligence can 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." [A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE: J. McCarthy, Dartmouth College; M. L. Minsky, Harvard University; N. Rochester, I.B.M. Corporation; C.E. Shannon, Bell Telephone Laboratories]

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings, such as the ability to reason, discover meaning, generalize, or learn from past experience. 

Artificial intelligence ... describes machines that mimic and display "human" cognitive skills that are associated with the human mind, such as "learning" and "problem-solving".

 Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

AI is the ability of a machine to display human-like capabilities such as reasoning, learning, planning and creativity...enabling technical systems to perceive their environment, deal with what they perceive, solve problems and act to achieve a specific goal.

A pseudoscientific, subjective, human-like AI is spreading like a wildfire, regardless that John McCarthy offered a more objective definition of AI in the 2004 paper: "It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

Due to the enormous misconceptions, existing 70+ years and entrenched in our minds and academic institutions and techno-enterprises, 80+% of AI projects in the real-world circumstances and real-life business settings, with all the applications, fail out of reliable and responsible or intelligent applications and services.

What is artificial intelligence and how is it used?

Such a narrow and weak, human-like, pseudoscientific AI is adopted by many countries as trans-national or national development strategies and policies or AI Acts:

Some good minds intuit that something deeply wrong with such a human-centric AI paradigm, asking themselves: Is all this worth it? If not, a logical response might be to stop everything, stop further development and deployment of AI, put the curses back in Pandora’s box.

Truth vs. Falsity: Science vs. Alchemy

Broadly, there are two polar types of AI, False and True:

• Fake, False and Counterfeit AI, or Pseudoscientific AI, Symbolic/Formal AI vs. Statistical Machine Learning and Big Data Deep Learning, when intelligence is associated with mathematical/statistical or logical/formal modelling of the human brain, mind, cognition, intelligence or behavior, 99.9999% are after such a narrow or weak or general and human-level and human-like AI. Such AI is relying on learning on training data/set/learning sets, and if a dataset didn’t exist, hire cheap labor spending hundreds or thousands of hours finding and labelling appropriate speech, video, images, text, or graphs for the dataset for a specific task, recognizing language, generating a new molecule or for drug discovery. Its machine learning and neural network programs is believed to provide models of how our human thought processes work. 99.9999% fake AI startups are dreaming to become parts of the big tech fake AI oligopolists, as Alphabet, Microsoft, Facebook/Meta, IBM, Apple or Amazon, massively using cheap labor to do bots' work.

• Real, True and Genuine AI, or Scientific AI (SAI), when intelligence is associated with scientific modelling of reality, causality, and mentality, simulation and computation, a few if nobody studies Real AI. Close to it foundation models representing a paradigm shift for the world of narrow artificial intelligence offering general purpose-based models that can be adopted to can be adapted to multiple use cases. As science itself, the SAI  builds and organizes world data/information/knowledge in the form of causal explanations and predictions about the world. It involves an automated and autonomous pursuit of "knowledge covering general truths or the operations of fundamental laws". Early examples of foundational models are GPT-3-4-5-X, BERT, or DALL-E 2, still lacking self-supervised learning and transfer learning due to missing the AI world model engine encoded scientific modeling and simulation, learning, inference and understanding of reality

The path to real/scientific AI

Fake AI and Real AI are different as a white box and black box.

Scientific AI is about Data, Intelligence, and Reality, or how the World is to be represented in the Data/Information/Knowledge Processing Systems to demonstrate learning and reasoning and intelligent behavior.

Fake AI/ML/DL is about Data Representation, or how the Data is to be represented in the Data Processing Systems as ANNs to demonstrate automated data learning/feature representation...

Fake AI/ML/DL is about the study and construction of algorithms that can learn from and make decisions or predictions on data, through building a mathematical model from input data, as training, test, and validation sets.

Machine learning algorithms are designed to optimize for a cost/loss function, having no intelligence, understanding or reasoning, while promising HL/HL AMI.

A system architecture for autonomous intelligence. All modules in this model are assumed to be “differentiable”, in that a module feeding into another one (through an arrow connecting them) can get gradient estimates of the cost’s scalar output with respect to its own output. 

"Devising learning paradigms and architectures that would allow machines to learn world models in an unsupervised (or self-supervised) fashion, and to use those models to predict, to reason, and to plan is one of the main challenges of AI and ML today. One major technical hurdle is how to devise trainable world models that can deal with complex uncertainty in the predictions". (Autonomous Machine Intelligence).

Again, it's advanced representative, ML/DL/AI. is easily hacked, duped and fooled due to lack of any world model intelligence, inference or understanding [Why deep-learning AIs are so easy to fool].

Who Needs a Fake/False AI?

Today’s AI, overhyped by the big tech, is the fake one; for fake/false AI refers to intelligent machines that can simulate. mimic, or learn, improvise and evolve like human beings.

To be fair, its fundamental assumption/misassumption was formulated in the famous Turing’s article “ Computing Machinery and Intelligence”, who failed to see three simple truths:

• Computing power is not reasoning power.
• Machine intelligence is not human intelligence.
• Human intelligence is not artificial intelligence.

In public culture, modern media depictions of AI technology are inaccurate at best, or dangerous at worst. They either lead to wild overestimations of AI capability, or create a false sense of security about the AI risks.

Who is Leading Fake AI Global Race?

It must be China, who is leading the Narrow/Weak AI/Robotics global race.

And it is plagued with all negative Fake AI disruptions:

• Total job automation, all predictable and repetitive works and knowledge workers positions, from black collar to gold collar, are susceptible to NWAI takeover
• Privacy, security, and ‘deep fakes’, as “Orwellian” usage of facial recognition technology in open spaces, workplaces and educational institutions
• AI bias and widening socioeconomic inequality
• Existential threats causing humanity’s displacement or extinction.
• According to Elon Musk, CEO of Tesla and SpaceX, AI is more lethal than nuclear weapons: "Mark my words; AI is far more dangerous than nukes,” he stated. Similar to this, Stephen Hawking warned us all that the impact of AI might be catastrophic if its rapid progress is not tightly and morally regulated. This is all about a human-like and human-level AI, as ANI, AGI or ASI.
• As for the General/Real/True AI/Robotics/Automation, the leader is that country which is the first to embrace the sustainable and smart development idea of Real AI/Robotics/Automation/Economy/Industry/Society.

Pseudo-AI in the Pop Culture

Starting with Frankenstein, all sci-fi art industry (literature, movies, TV series, videogames, virtual realties, and what not) is promoting a human-like and human-level AI, be it robotic humanoids or alien intelligence.

As evidenced by all sorts of space-wars films, from Dune to Start Wars and Start Trek and Space Odyssey, to urban dystopian sci-fi, Metropolis, the Terminator, the Matrix, Blade Runner, I, Robot, or Ex Machina, such cosmic lies thrill and attract, terrify and fascinate the billions of people, bringing a lot of attention and money to its stakeholders, however dumb, dull and defective the whole idea might be.

SUPPLEMENT

Find out below all the major stages of Scientific AI and any valuable SAI projects, to decide which part of AI is good for you for study, R & D & D.

Stage 1 - Study AI Fundamentals, as Rule-Based systems and Data-Based systems, ANI, AGI, ASI and Transdisciplinary AI or Real AI

Stage 2 – Project planning and data collection

Stage 3 - AI World Model Engine Creation

Stage 4 – Designing, Developing and Learning of the Real AI/Machine Intelligence and Learning (MIL) models: POC > Prototypes

Stage 5- Deployment, Distribution and Maintenance

Go for specific SAI Projects

• Step 1: Select a domain-specific problem with commercial potential, like intelligent matching algorithms on popular Dating applications, as Tinder (Android; iOS), Bumble (Android; iOS), OkCupid (Android; iOS), Hinge (Android; iOS), Match (Android; iOS), Grindr (Android; iOS), Her (Android; iOS)
• Step 2: Build the model architecture foundation. Aggregate and prepare your data and applications to migrate to the multicloud platform and data architecture environments.
• Step 3: Test/Improve/Modernize the data architecture
• Step 4: Build intelligent applications
• Step 5: Deploy your intelligent algorithms as open mobile applications

Resources

Real AI Project Confidential Report: How to Engineer Man-Machine Superintelligence 2025: AI for Everything and Everyone (AI4EE); 179 pages, EIS LTD, EU, Russia, 2021

Content

The World of Reality, Causality and Real AI: Exposing the great unknown unknowns

Transforming a World of Data into a World of Intelligence

WorldNet: World Data Reference System: Global Data Platform

Universal Data Typology: the Standard Data Framework

The World-Data modeling: the Universe of Entity Variables

Global AI & ML disruptive investment projects

USECS, Universal Standard Entity Classification SYSTEM:

The WORLD.Schema, World Entities Global REFERENCE

GLOBAL ENTITY SEARCH SYSTEM: GESS

References

Supplement I: AI/ML/DL/CS/DS Knowledge Base

Supplement II: I-World

Supplement III: International and National AI Strategies

Trans-AI: How to Build True AI or Real Machine Intelligence and Learning

Why and How to Build Digital Superintelligence: Real AI, Superhuman Intelligent Machines, Superintelligent Machines, or Superintelligent AI

A Global AI Infrastructure: RAI vs. BRI as the Most Valuable Project on the Earth

DISTINGUISHING SCIENTIFIC AI FROM PSEUDOSCIENTIFIC AI