AI Bible: True AI and Real ML for Decision-Makers and C-Level Executives

THOU SHALT NOT MAKE A MACHINE IN THE LIKENESS OF THE HUMAN MIND (Dune, F. Herbert)

THOU SHALT NOT MAKE A MACHINE IN THE LIKENESS OF HUMAN BODY

THOU SHALT NOT MAKE A MACHINE IN THE LIKENESS OF THE HUMAN BRAIN

THOU SHALT NOT MAKE A MACHINE IN THE LIKENESS OF THE HUMAN BRAINS

THOU SHALT NOT MAKE A MACHINE IN THE LIKENESS OF THE HUMAN BUSINESS

THOU SHALT NOT MAKE A MACHINE IN THE LIKENESS OF THE HUMAN

[AI BIBLE: the Old AI and The New AI]

Talking About Real AI: Machine Intelligence and Learning

We need powerful intellect, human or machine, to know truth from falsity, what is false and what is true in reality. The decisions we make today concerning machine intellect commercialized as artificial intelligence (AI), its definition and adoption, ethics and regulation will decide society, economy, and governance for all future.

AI is not merely a subject for scientists and technologists, data scientists or engineers, but for policymakers and C-level executives.

Understanding the multi-faceted impact of AI becomes not just advantageous but imperative for steering a city, country or corporation into the future. The strategic integration of AI into the government and public administration and international politics or organizational workflows, customer service, and product development will be the defining factor that separates leaders from those left behind.

AI will drive the agenda. AI has changed the game, taking center stage in political and economic life, emerging as

Global Intelligence Platform: world's intelligence = community's intelligence + human intelligence + AI [ML + LLMs] + KG + Web data + IoT +...

What is it all about?

With advancements in Generative AI and the rise of Language Models like GPT (Generative Pre-trained Transformer) and LLMs (Large Language Models), we’re paving the way for a new future of digital technologies, interfaces, applications, platform and networks, reactive and proactive, insightful and human-like.

OpenAI’s GPT-4 stands as an exemplary case of LLMs. With 175 billion machine modeling parameters, it has been trained to provide contextual and nuanced responses that rival human capability. Google and Microsoft are already incorporating the LLMs into their products, offering services like automated content generation, sentiment analysis, and coding assistance, improving efficiency and user experience.

Meanwhile, leading AI researchers and CEOs debated AGI’s likelihood in headlines, while policymakers started implementing the AI policy and regulation agenda.

The EU put forth the EU AI Act, the world’s first comprehensive AI law.

President Biden signed Executive Order 14110 on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (“the EO”)?detailing 150 requirements for federal agencies.

Each with the following purpose and definitions of AI and its derivatives.

The US Safe, Secure and Safe AI Order

Section 1 . Purpose. Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use has the potential to help solve urgent challenges while making our world more prosperous, productive, innovative, and secure. At the same time, irresponsible use could exacerbate societal harms such as fraud, discrimination, bias, and disinformation; displace and disempower workers; stifle competition; and pose risks to national security. Harnessing AI for good and realizing its myriad benefits requires mitigating its substantial risks. This endeavor demands a society-wide effort that includes government, the private sector, academia, and civil society.

(b) The term “artificial intelligence” or “AI” has the meaning set forth in 15 U.S.C. 9401(3): a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. Artificial intelligence systems use machine- and human-based inputs to perceive real and virtual environments; abstract such perceptions into models through analysis in an automated manner; and use model inference to formulate options for information or action.

(c) The term “AI model” means a component of an information system that implements AI technology and uses computational, statistical, or machine-learning techniques to produce outputs from a given set of inputs.

(e) The term “AI system” means any data system, software, hardware, application, tool, or utility that operates in whole or in part using AI.

(p) The term “generative AI” means the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content. This can include images, videos, audio, text, and other digital content.

(t) The term “machine learning” means a set of techniques that can be used to train AI algorithms to improve performance at a task based on data.

[Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence: A Presidential Document by the Executive Office of the President on 11/01/2023]

The EU AI Act

The EU AI Act considers systemic risks which could arise from general-purpose AI models, including large generative AI models, trained using a total computing power of more than 10^25 FLOPs.

It is introducing four levels of risk for AI systems, minimal, high-risk, unacceptable, specific transparency plus systemic risks.

The EU AI Act is the world's first comprehensive AI law. It aims to address risks to health, safety and fundamental rights. The regulation also protects democracy, rule of law and the environment. While most AI systems will pose low to no risk, certain AI systems create risks that need to be addressed to avoid undesirable outcomes.

For example, the opacity of many algorithms may create uncertainty and hamper the effective enforcement of the existing legislation on safety and fundamental rights. Responding to these challenges, legislative action was needed to ensure a well-functioning internal market for AI systems where both benefits and risks are adequately addressed. This includes applications such as biometric identification systems or AI decisions touching on important personal interests, such as in the areas of recruitment, education, healthcare, or law enforcement.

Recent advancements in AI gave rise to ever more powerful Generative AI. So-called “general-purpose AI models” that are being integrated in numerous AI systems are becoming too important for the economy and society not to be regulated. In light of potential systemic risks, the EU puts in place effective rules and oversight.

The Regulation sets out thresholds that need to be taken into account:

Up to €35m or 7% of the total worldwide annual turnover of the preceding financial year (whichever is higher) for infringements on prohibited practices or non-compliance related to requirements on data;

Up to €15m or 3% of the total worldwide annual turnover of the preceding financial year for non-compliance with any of the other requirements or obligations of the Regulation, including infringement of the rules on general-purpose AI models;

Up to €7.5m or 1.5% of the total worldwide annual turnover of the preceding financial year for the supply of incorrect, incomplete or misleading information to notified bodies and national competent authorities in reply to a request;

For each category of infringement, the threshold would be the lower of the two amounts for SMEs and the higher for other companies.

When will the AI Act be fully applicable?

Following its adoption by the European Parliament and the Council, the AI Act shall enter into force on the twentieth day following that of its publication in the official Journal.

It will be fully applicable 24 months after entry into force, with a graduated approach as follows:

6 months after entry into force, Member States shall phase out prohibited systems;

12 months: obligations for general purpose AI governance become applicable;

24 months: all rules of the AI Act become applicable including obligations for high-risk systems defined in Annex III (list of high-risk use cases);

36 months: obligations for high-risk systems defined in Annex II (list of Union harmonisation legislation) apply.

[EC Artificial Intelligence – Questions and Answers*]

Most of today's AI/ML/LLMs models fall under the risky risk category, from unacceptable to systemic.

How to make AI [GenAI/ML/DL/GPT/ChatGPT] Intelligent

Today, no AI, be it GenAI, LLMs or GPT or ChatGPT, is true or real intelligent; for a true and genuine intellect or general intelligence technology should have at least 5 encoded functionalities as inherent features, powers, abilities or capacities:

• World Model Engine. General schemata or reference framework to organize categories of data, information or knowledge and the relationships among them. This digital modeling/mapping/representation of the world, internal or external reality, which can be referred to as a machine “world knowledge, inference, interaction model”. Examples of schemata include?mental schemas or conceptual models,?social schemas,?stereotypes,?social roles,?scripts,?worldviews,?heuristics, archetypes, or database schema, describes how real-world entities are modelled in the database. All is to organize current data/information/knowledge and provide a framework for learning or inference, understanding or action.
• Learning and Knowing or Experience, rote learning, as in today's AI and ML, or meaningful learning, transfer of learning
• Abstraction/Generalization/Conceptualization/Classification/Perception — the ability to identify things/objects/changes/processes as part of a larger category or rule.
• Memory/attention/imagination — the ability to attach attributes to entities and relations between entities in the world model, and the ability to update them over time, the faculty by which data or information or knowledge is encoded, stored and retrieved upon necessity.
• Reasoning and inference, computation, evaluation and judgment — the ability to use the world model to draw conclusions on the behavior of entities in a new or imagined world state.
• Understanding, Self-Knowledge. Theory of Mind, Self-awareness and self-consciousness, knowledge of one's own states, processes, behavior and actions.
• Planning/foresight/forecasting/prediction/decision/choice/goal — the ability to use the world model to develop a set of actions to achieve a goal.

The True AI and Real ML paradigm covers the updated OECD’s definition of an AI system:

"a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment".

Labeled or unlabeled data sets that require human intervention for training, simple machine memorization and pattern matching should not be conflated with true intelligence.

AI as 'a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments" is hardly a true intelligence.

It is designed as bias-addicted stochastic systems, with unpredictable errors modelled as higher-order stochastic processes which could be catastrophic.

"Latent errors in AI systems, both unpredictable and undetectable, can pose significant risks to societies, especially as these systems become more advanced and complex. The complexity of such systems makes thorough testing increasingly challenging.

Furthermore, these latent errors are often polygenic, meaning they are small effect but high frequency, affecting many areas of an AI model. For instance, potential errors might exist in various components, such as input embeddings, positional encodings, attention matrices and weights, neural network embedding layers, activation mechanisms, and the training data corpus. Additional risks might arise during model fine-tuning, due to optimization imperfections, or through external integrations via APIs". [Unpredictable Latent Errors in AI can be Catastrophic — Mathematical Explanation]

To become a general-purpose AI technology, AI must feature all the powers itemized above.

Or, the foundational building blocks of generalized genAI tech stack pyramid should be founded on the World Model/Schema Engine.

Again, really REAL and TRUE "AI is about COMPLETING, not simulating, human intelligence, organizing and leveraging the world's data/information/knowledge/intelligence making it universally accessible and useful".

Its key property is hyperautomation — a holistic integral approach that combines AI, machine learning, LLMs, automation and robotics — enabling a transformative shift from rule-based or ML automation to intelligent, self-adjusting and autonomous systems. It’s automation with intellect, a general intelligence; a vertically and horizontally integrated techno-ecosystem designed for the agile and robust, adaptive and highly competitive business environment of the digital AI age.

Trans-AI = Real AI = Real Intelligence Machines = Hyperintelligent Hyper-Automation =

World [Learning, Inference and Interaction] Model

(Data Structures + Information Architecture + Knowledge Graphs) +

Narrow AI models + ML algorithms +

LLMs +

Robotics & Automation +

Software + Hardware +

the Internet/Web + the Internet of Things +

AI-phones +

Intelligent Communities (eco-communities, smart green cities, sustainable countries, intelligent nations and states...)

Prediction: Hyperintelligent Hyperautomation is predicted to augment human intelligence, paving the way for significant advances in productivity and creativity, speed and efficiency, predictive and decision-making capabilities, innovation and growth.

HH makes Artificial Intelligence the Cornerstone of the Next Technological Epoch.

AI/Data/Science Illiteracy: disrupting misconceptions and forging real intelligence and knowledge

From LLMs to Omniscient AI Machines: Navigating World's Complexity and Predicting the Unpredictable

Trans AI 2024-2025 for a more prosperous and sustainable, productive and innovative, inclusive and secure world

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

AI 2024-2025: Narrow AI > GenAI > LLMs > AGI > RealAI = Trans-AI

Is ChatGPT Intelligent? A Scientific Review: A layman’s review of the scientific debate on what the future holds for the current artificial intelligence paradigm

AI-pocalypse Now