What is the human understanding about artificial intelligence?

The greatest innovation of science and technology is an emerging digital man-machine superintelligence operationalized by the internet and world wide web, and all sorts and types of emerging digital technologies, techniques, algorithms, and applications, as narrow/weak AI systems of ML models and DL algorithms.

Today's AI, as mathematical algorithms and statistical rules and predictive analytics, excels humans in many specific areas, such as judging, strategic games, algorithmic trading, self-driving, diagnosing, computing, measuring, pattern/matching objects, characters, faces, human speech, or translating languages.

Such “narrow” AI have superhuman capabilities, in their specific areas of dominance, much outsmarting humans in doing specific tasks, jobs and works.

Hyperintelligent Hyperautomation as superintelligent machines are emerging as the greatest ever general-purpose technology combining a wide range of intelligences and skills in one entity, as a single integrated system/network/platform of man-machine superintelligence.

The real-world AI is to feature a unifying world model (global ontology + science + engineering + mathematics + statistics + data science and engineering), with a unifying computing and learning framework (master algorithm), to intelligently process the world's data, from the internet/web data to the real-world data.

Any intelligence, be it ANIMALS, HUMANS, MACHINES, ROBOTS, SUPERINTELLIGENCE, interacts with its environment.

It is governed by the World (the environment, humans, information processing systems, intelligent agents, etc.). Agent (humans, information processing systems, intelligent agents, humans, etc.) Interaction Algorithmic Causal Rules, as in:

The World = the Universe of Possible Realities (physical, biological, mental, social, informational, digital, virtual,...) -

Causal Input (Matter, Energy, Data/Information/Signals; causal variables) -

Transformation/Translation Mechanism -

Causal Output (Transformed Matter, Energy, Data/Information; causal variables) -

Causal Feedback - Reinforcing or Balancing Interactive Loops, with all the transformation and translation, explanation and transparency, inferences and predictions, functions, goals and solutions, optimal control and error corrections, learning and adaptation, emergence and complexity, actions and reactions, behaviors and interactions -

The Transformed World (total causal networks of causal systems of subsystems things and entities, objects and agents, states and variables, actions and interactions, at various scales and levels, from the elementary particles and fundamental interactions/forces to the human world to the total universe).

It is also true for agent/individual-based models, distributed/decentralized AI, or multi-agent systems, as multiple interacting intelligent agents, from simple reflex agents to autonomous learning systems.

Any real and true AI/ML/DL/ANNs/LLMs models are prompted by the causal loop graph networks, with ''the Human-in-the-Loop (HITL) enabling human verification and corrections.

Real and True AI [Machine Intelligence and Machine Learning] driven by its Machine World Model Learning, Inference, Interaction Engine is processing, analyzing, classifying and interpreting ML training web data sets and big data sets of sorts and types to extract information and knowledge, data insights or intelligence, as causal patterns and predictions, decisions and solutions, rules and laws.

As the causal data input, it could be any type or sort of big data: Customer Data, E-commerce; Transactions; Financial Transactions; Government and Public Data; Health and Medical Records; Internet of Things Devices Data; Research and Scientific Data from research experiments, academic studies, scientific observations, digital twin simulations, and genomic sequencing; Sensor Networks Data gathered from environmental sensors, industrial machinery, traffic monitoring systems; Social Media Platforms Data generated from social media platforms like Quora, Facebook, Twitter, Instagram, and LinkedIn, posts, comments, likes, shares, and user profiles.

The machine world model learning, inference and interaction (WMLII) is powerful and effective ways to create artificial intelligent machines and to understand intelligence in the context of how an intelligence (an AI, robot, or human) interacts with the world at large and their worlds (environments).

The way to transform today's AI, with its ML applications, DL algorithms and data analytics methods, is its integration with comprehensive scientific AI world models:

Trans-AI = Hyperintelligent Man-Machine Hyperautomation = Real/Generalized/Global/Transdisciplinary/Translational/Transformative Intelligence =

World/Reality/Causality/Science Knowledge Model Engine +

ANI +

ML +

DL +

ANNs +

LLMs +

AGI +

ASI.

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