AA/AI Rule for Autonomous Machine Intelligence: "There is no True AI without Understanding the Cause and Effect of Interactions within the World"

Basic Principles and Assumptions about the World of Reality

I advance the Man-Machine Ontology/Science/Engineering which is after the consistent and complete, systemic and systematic world model featuring the fundamental [onto-scientific-engineering] principle:

All Reality is Interaction, or "Everything is Interaction and Reciprocal", and "Reality appears as a dynamically interdependent process. All factors, mental and physical, subsist in a web of mutual causal interaction, with no element or essence held to be immutable or autonomous".

It is designated as "the AA Interaction Principle of the Universe", implying three major facts and propositions:

The World, Reality, Being, Existence, or Universe is the Sum Total of All Interactions.

All of reality is interaction; interactions create all the substances, states, changes and relationships, all the networks and systems, all the phenomena and processes, forces and emerging properties,?including all the intelligence, natural or artificial.

Everything interacts with everything else: something (A/X) causes something else (B/Y) if and only if the something else (B/Y) causes the something (A/X).

Corollaries:

All interacts; nothing exists in isolation without interactions.

Something exists and changes, if it interacts with something else, having an effect on each other.

Anything is a node of interactions, being a net of interactions with the world around it.

Any intelligence consists in causal learning, inference and understanding to effectively interact with the world.

Any real intelligence, human or machine, deals with reality in terms of the world models and data/information/knowledge representations for cognition and reasoning, understanding and learning, problem-solving, predictions and decision-making, and interacting with the environment.

Real AI Machine Intelligence must have the world model learning, intelligence and inference engine to meaningfully and effectively or causally interact with the world, including nature, machines, humans, the internet, and other real-world networks.

AA/AI Iron Rule for Autonomous Machine Intelligence

"Without understanding the cause and effect of interactions within the world, no AI model, algorithm, technique, application, or technology is real and true", be it:

• Natural language generation converting structured data into the native language
• Speech recognition converting human speech into a useful and understandable format by computers
• Virtual agents, computer applications that interact with humans to answer their queries, from Google Assistant to the IMB Watson
• Biometrics, ?to identify individuals based on their biological characteristics or behaviors, with fingerprints and faces, hand veins, irises, or voices biometric modalities
• Decision management systems for data conversion and interpretation into predictive models
• Machine learning empowering machine to make sense from data sets without being actually programmed, to make informed decisions with data analytics and statistical models
• Robotic process automation configuring a robot (software application) to interpret, communicate and analyze data
• Peer-to-peer network connecting between different systems and computers for data sharing without the data transmitting via server
• Deep learning platforms based on ANNs teaching computers and machines to learn by example just the way humans do
• Generative AI (GANs, Transformers, Autoencoders) referring to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing content like text, audio and video files, images, or code to create new possible content as completely original artifacts. It leverages AI and ML algorithms to generate artificial content such as text, images, audio and video content based on its training data to trick the user into believing the content is real, facing legal challenges concerning data privacy and use in fraudulent or criminal acts.
• Generative AI models with image generation algorithms generating photographs of human faces, objects and scenes, image-to-image conversion, text-to-image translation, film restoration, semantic-image-to-photo translation, face frontal view generation, photos to emojis, face aging, media and entertainment: deep fake technology
• AI optimized hardware support artificial intelligence models, as neural networks, deep learning, and computer vision, including CPUs, GPUs, TPUs, OPUs to handle scalable workloads, special purpose built-in silicon for neural networks, neuromorphic chips, etc.

Real AI is NOT about representing computational models of intelligence, described as structures, models, and operational functions that can be programmed for problem-solving, inferences, language processing, etc.

Real AI is about the computational models of reality and mentality, described as causal structures, models, and operational functions that can be programmed for problem-solving and inferences for a wide range of goals in a wide range of environments.

What Is Real World AI Modeling?

The purpose of Real World AI models is to apply the world model engine to discover new patterns, predict outcomes or make decisions by understanding the interrelationships between multiple inputs of varying type to effectively interact with the world and its environment.

The creation of intelligent machine deep learning and inference models is the creation of Causal AI modeling that follows three basic steps:

• Modeling:?The first step is to create a Causal AI world learning and inference model machine (Reality AI Engine), which uses a complex algorithm or layers of algorithms to interpret real-world data and make decisions based on that data.
• AI model training:?The second step could be to train the Real AI Engine for special tasks or knowledge domains, processing large amounts of data through the Causal AI model machine in iterative test loops and checking the results to ensure accuracy, and that the model is behaving as expected and desired as it learns.
• Inference:?The third step is machine discovery/reasoning/inference, from causal to categorical to analogical to realistic. This step refers to the deployment of the Causal AI world model into its real-world use case, where the RAI engine routinely infers real-world conclusions based on the data, information and knowledge.

Causal AI/ML/DL is a complex process with high computational, storage,?data security, and networking requirements. What could be supported by by AI hardware and software resources, like as?Intel? Xeon? Scalable processors, Intel? storage and networking solutions, and Intel? AI toolkits and software optimizations, to design and deploy RAI/ML solutions with ease and cost efficiency.

Conclusion

The iron rule of real AI: without understanding the cause and effect of interactions within the world, there is no True, Autonomous Machine Intelligence

The Man-Machine Interaction Ontology: The Reality AI Engine: Causal Machine Intelligence and Learning