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Hyperintelligent [Hyperautomation] Technology: World Knowledge and Intelligence Platform (WKIP) for Global AI Infrastructure
In the age of AI, automation and robotics, hyperintelligent hyperautomation is the next paradigm in the IT evolution and human development, and the key driver of emerging technologies: autonomous things, hyperautomation, intelligent robotics, human augmentation technology, the future internet of everything, the fourth industrial revolution, Industry 4.0, real artificial intelligence, or man-machine superintelligence, the most significant scientific and techno-engineering disruption since the existence of humanity.
$100 trillion is held by pension funds, sovereign wealth funds, mutual funds, and other institutional investors. The Hyperintelligent Infrastructure is worth the multi-trillion investment. Hyperintelligent Infrastructure connects nations and people to smart services, maintains quality of life, and boosts economic productivity, accelerating economic recovery, creating jobs, reducing poverty, stimulating a new AI economy, Industry 4.0 and smart green cities and communities, like a $1 trillion intelligent city in Saudi Arabia.
Hyperintelligent AI technology is like Russian dolls:
It is like Russian dolls: DL < NNs < ML (Supervised, Unsupervised, Self-Supervised, Reinforcement,...) < Narrow AI (NLP/NLG, Chatbots, VAs, VAs, Expert Systems, RPA) < AGI < ASI < Causal AI < Interactive Machine Intelligence and Learning = Real/True AI = Trans-AI =Man-Machine Hyperintelligence
The Global AI Platform smart investment mega project is founded on "Trans-AI: How to Build True AI or Real Machine Intelligence and Learning".
The Project Mission: THE FIRST TRANS-AI MODEL FOR NARROW AI, ML, DL, AGI, ASI, AND HUMAN INTELLIGENCE
Real Artificial Intelligence (RAI) Science and Technology is set to change how our world works and humans live, study, work, and play.
RAI is the main engine of the new total digital revolution, scientific and technological, social and economic, cultural and religious.
The COVID-19 crisis has accelerated the need for human-machine digital intelligent platforms facilitating new knowledge, competences and workforce skills, advanced cognitive, scientific, technological, and engineering, social, and emotional skills.
In the AI and Robotics era, there is a high demand for the scientific knowledge, digital competence, and high-technology training in a range of innovative areas of exponential technologies, such as artificial intelligence, machine learning and robotics, data science and big data, cloud and edge computing, the Internet of Thing, 5G, cybersecurity and digital reality.
The combined value – to society and industry – of digital transformation across industries could be greater than $100 trillion over the next 10 years.
“Combinatorial” effects of AI, ML, DL, Robotics with mobile, cloud, sensors, and analytics among others – are accelerating progress exponentially, but the full potential will not be achieved without the collaboration between humans and machines.
The Trans-AI is embracing the major AI innovations, such as specified in the 2022 Gartner Hype Cycle for AI:
Data-centric AI:
synthetic data,
knowledge graphs,
data labeling and annotation
Model-centric AI:
physics-informed AI,
composite AI,
causal AI,
generative AI,
foundation models and deep learning
Applications-centric AI:
AI engineering,
decision intelligence,
edge AI,
operational AI systems,
ModelOps,
AI cloud services,
smart robots,
natural language processing (NLP),
autonomous vehicles,
intelligent applications,
computer vision
Human-centric AI:
AI trust, risk and security management (TRiSM),
responsible AI,
digital ethics,
and AI maker and teaching kits.
Causal AI includes different techniques, like causal graphs and simulation, that help uncover causal relationships to improve decision making.
Trans-AI: How to Build True AI or Real Machine Intelligence and Learning
When Real AI/AGI/ASI is to be achieved
Real/Nonhuman AI/AGI/ASI is to be achieved by 2025 due to its scientific reality. Fake/Human AI/AGI/ASI is hardly ever be achieved due to the scientific unreality to simulate the unknown knowns, human brain/mind/intelligence, the dark brain matter and dark mind energy in one.
There are two radically different types of AI, ANI, AGI, ASI: Real AI vs. Fake AI.
Real/True/Genuine AI, AGI, ASI, implying reality and causality, data and knowledge, algorithms and computation, software and hardware, and all possible interactions with the world, as effective, efficient and sustainable.
Fake/False/Counterfeit AI, ANI, AGI, ASI, which is human-like, human-level or superhuman AI
Today's AI is the Fake AI/ML/DL simply mimicking, replicating, or counterfeiting all what could be human, in isolated, fragmented ways:
Body. Humanoid robots
Brain. ANNs/DNNs
Perception. Machine perception
Language. LLMs/NLP/NLG
Cognition. Cognitive computing
Behavior. Behavior-based robotics
Human tasks, from writing to driving.
What is missing is real intelligence, implying reality and causality, data and knowledge, with all possible interactions with the world.
That’s why all the current attempts towards a Human-Level and Human-Like AGI, as large language models power applications like LLaMA, ChatGPT, Bing AI, and Bard, GPT-4 and Dalle-E, Bert, AlphaFold and AlphaStar, are NOT examples of breakthrough AI systems, but rather samples of counterfeit human AI/AGI/ASIs.
Smart/Sustainable Investing in the Smart/Sustainable World Future
As artificial intelligence (AI) continues to advance, the world is experiencing an unprecedented era of transformation.
AI is coming into the human world, revolutionizing all parts of human life, from private life to social, political, economic, and cultural life.
AI is impacting nearly every industry, from healthcare and finance to retail and transportation, and is poised to revolutionize the way we live, work, and interact with one another.
As Bill Gates has noted in his notes, the Age of AI has begun:
"The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it".
"Developing AI and AGI has been the great dream of the computing industry".
"Superintelligent AIs are in our future. Once developers can generalize a learning algorithm and run it at the speed of a computer—an accomplishment that could be a decade away or a century away—we’ll have an incredibly powerful AGI. It will be able to do everything that a human brain can, but without any practical limits on the size of its memory or the speed at which it operates. This will be a profound change".
Given the scale and scope of AI revolution, it is existentially important what types of machine intelligence we are all after, Imitative AI or Real AI:
Human AI, as a way of making software or hardware act rationally or think intelligently, in a similar way the intelligent humans think, studying how the human brain thinks, and how humans learn, decide and work, while trying to solve a specific problem or provide a particular service.
Real Hyperintelligent AI, as a machine/robotic/computing/automated/electronic/artificial/technological intelligence depending on its power, ability and capacity to detect, identify, interpret, process, register, compute and manipulate all the key variables from any complex environments to effectively, efficiently and sustainably interact with the world.
The iron law of intelligence is that "an interactive [common sense causal] world [knowledge and intelligence] model is the essence of any real intelligent systems, natural or artificial".
If your AI/ML/DL/NLP models have no encoded or pre-trained interactive commonsense world knowledge and intelligence models, then your applications are non-intelligent by the very design.
That's why Microsoft's/OpenAI/ChatGPT, Google/LaMDA/Bard, Meta/LLaMA and other modern generative or conversational AI models based on a transformer language model [finding and storing surface data patterns within sequences in the neural network architecture] are getting things wrong.
Failing to deliver the promise, "Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence and creativity of our large language models", has ended with $100 billion swiped from Alphabet's market value.
The same fate had before visited on Meta's Galactica AI, "a large language model for science, trained on 48 million examples of scientific articles, websites, textbooks, lecture notes, and encyclopedias", for mindlessly spewing out biased and incorrect nonsense, reproducing prejudice and falsehoods as facts.
Machine learning models have been welcomed as a game-changing technology that has the potential to transform the way humans live and businesses operate.
Despite its potential benefits, many organisations are still hesitant to implement it. One of the most difficult challenges is integration, as machine learning often requires the integration of multiple data sources and formats, technologies and systems, such as cloud computing platforms, data storage, and data processing tools.
First and foremost, it is necessary to build the World Knowledge and Intelligence Platform (WKIP) for HHIT, as an "automated [human-robotic] hyperintelligence platform", which might cost a substantial amount of investment.
To estimate the value of the project, I refer to IBM, which estimates that poor quality Web Data Integration (WDI) is costing companies over $3 trillion in revenue each year.
Who are prospective investors in Real AI Technology?
Its scale and scope are too challenging for private investors or for government subsidies.
Among the prospective investors of the Hyper-Intelligent AI systems could be the following soft and hard superpowers and tech giants:
It is necessary to substantially invest in the hyperintelligent hyperautomation technologies, to lead in the global race of emerging technologies:
The World Knowledge and Intelligence Platform (WKIP) Project
The WKIP Project is the driving engine of global interactive Human-AI (IAI) Program to enable Global Data/Information/Knowledge Fusion
It combines the internet content and web data, information and knowledge with world data models and causal networks and inference rules providing a universal intelligent interface for machines and people to make decisions and predictions or discover new data, information and knowledge.
It will involve automated extracting and structuring structured data (knowledge/intelligence) from low-structured sources, using the world knowledge models and causal rules.
The WKIP Platform is based on the world knowledge network graph organizing human knowledge, its sciences, engineering and technology, in comprehensive, systematic, consistent and formal ways.
The WKIP is to integrate into an interactive Human-AI Hyperintelligence data/information/knowledge silos digital technologies, services and products, processes and platforms:
The assumption of the WKIP project is an objective, scientific, non-human or realistic interpretation of AI.
[Real/Scientific/Interactive] AI is innovated as a machine/robotic/computing/technological intelligence depending on its power, ability and capacity to detect, identify, interpret, process, register, compute and manipulate all the key variables from any complex environments to effectively interact with the world.
Embracing model-based and symbolic AI, probabilistic and statistical machine learning, real/true AI is to model and simulate reality and causality, rather than mimicking human intelligence thereby threatening worth of human intelligence, seeking to replace the human mind/brain as such.
Its basic mechanism is the world (learning and inference) model engine, following the development architecture.
Hyperintelligent AI = Real/Scientific/Interactive AI: WKIP/World Model Engine + Symbolic AI (Wolfram/Alpha: Computational Intelligence) + ML/DL/ANNs + NLP/NLU (LLMs + ChatGPT)
All the possible methods, models and techniques of AI, as pictured on the Gartner Hype Cycle for AI 2022, with data-driven logical and statistical learning AI/ML/DL/NLU models, are integrated with the RAI’s model of the world.
If your AI/ML/DL/NLP models have no encoded or pre-trained world models, then your applications are non-intelligent by the very design.
This is "the iron law of intelligence" that an interactive [common sense causal] world model is the essence of any real intelligent systems, natural or artificial.
Interactive world models in machines and humans
The power of superintelligence, natural or machine, consists in its integral power to make sense of the world, modeling and simulating the most general categories of reality and how they are interrelated, to interact effectively with any environments of any scope, scale and complexity.
The real-world intelligent systems should be pre-programmed or trained to learn the categories of being/existence/reality as the highest genera or kinds of entities, the most fundamental and the broadest classes of entities, computed in terms of digital data, qualitative and quantitative, categorical and non-categorical.
Various systems of world's categories have been proposed, as global and local or domain-specific, all sorts of classifications, metaphysics, ontologies, typologies, taxonomies, metaphysic and scientific, semantic and lexical, industrial and computing.
Whatever, they often include primary categories for Entity or Thing, Substance, State, Chang and Relationship. Secondary categories are Object, Situation, Condition, Quantity and Quality, Event, Action, Process, Place, Time.
It is traditionally described by metaphysics or ontology and ontologies, the science of reality/existence/being with domain theories within the science of the world as a whole.
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On the most fundamental level there exists only one thing: the world as a whole, which is driven by Interaction, The universal set of interacting or interdependent entities is forming an integrated dynamic world of reality, as the universal causal network of all dynamic systems, modeled as the world hypergraph.
The fundamental interactions/forces rule the physical world at all its scopes and levels. There are four fundamental interactions or forces at work in the universe: the strong force, the weak force, the electromagnetic force, and the gravitational force, with a speculative fifth force, dark energy.
There is no reality but for interaction, "sine qua non causation", "but-for causation." Entities, universal or particular, substantial or non-substantial, interact with reality, being the result of there interactions. Reality is built up through the interplay of entities: particular entities instantiate universal entities, and non-substantial entities characterize substantial entities.
Such a interactive world model (IWM) is ordered by the universal ladder of realities, the framework for learning a hierarchical representation of the world:
The IWM is reflected by the Data Universe of various data units, items, observations and sets, providing real-world interpretations for AI/ML/DL/NL models and algorithms.
There is no real intelligence without the comprehensive and consistent world modelling, or universal computing ontology, which is coding the division of all reality and the classification of all its entities in terms of world's data modeling and processing.
The interactive world modeling is the Intelligence Engine of all general-purpose knowledge and language understanding models, reflecting, mirroring and mapping, encoding and decoding designated pieces of reality:
Causal Models, from material, formal, efficient and final causes to mathematical models representing causal relationships within an individual system or population, facilitating inferences about causal relationships from statistical data
Mathematical models, a mathematical representation of reality, as logical models, dynamical systems, statistical models, differential equations, or game theoretic models
Scientific Modelling, reflections of reality, representing empirical objects, phenomena, and physical processes in a logical and objective way "conceptual models to better understand, operational models to operationalize, mathematical models to quantify, computational models to simulate, and graphical models to visualize the subject"
Probabilistic models, using probability theory , involving probability space or triple, discrete and continuous random variables, probability distributions, and stochastic processes. Examples: statistics or quantum mechanics
Stochastic/Random/Statistical Models, descriptive statistics and inference statistics
Language models, a probability distribution over words or word sequences, generating probabilities by training on text corpora, using statistical and probabilistic techniques, Google's BERT, Microsoft's Transformer, OpenAI's GPT-3, ChatGPT
Language language models (LLMs) may be categorized as probabilistic methods and neural network language models. The problems of LLMs stem from the following: 1) large amounts of text to build the models, mostly from the Web. If to use Wikipedia as a proxy, of 6,900 languages alive, only 291 have Wikipedia. 2) LLMs learn many social biases such as gender, race, religion, demographics, etc. 3) LLMs do not understand the semantics of the text they learn from or their generated text. 4) The context problem, texts have deep context influencing the choice of the next word. 5) As the size increases (n), the number of possible permutations rises uncontrollably.
Faulty, inadequately trained, poorly understood algorithms, data poisoning and incorrect statistical approximation producing erroneous results could be disastrous for people’s lives.
Besides, today's AI works on limited models and cannot mimic general human understanding, cognition and intelligence.
Meantime, lacking the meaning-, truth- and intelligence bearing world models, LLMs have taken the world by storm having many applications—from part-of-speech tagging to automatic text generation, machine translation, QAC, OCR to Speech Recognition, sentiment analysis, or chatbots, as ChatGPT "interacting in a conversational way using Reinforcement Learning from Human Feedback (RLHF).
From Narrow AIs to Trans-Intelligence
There are 5 AI models of increasing quality and generality, as simulating human mind/intelligence, statistic, synthetic and real world:
In essence, any real intelligence involves real world models as the framework for knowing, thought and planning to provide the deep learning and understanding of things, both qualitatively and quantitatively.
Hyperintelligent AI could be described as the power/faculty/ability, state, act, process or product/object of
In all, there are three generations of the future AI to be developed at the same time, ANI > AGI > ASI, and Real AI, or Causal/Scientific/True AI, driving the HHIT:
ANI, Narrow, human-like AI systems (ANI), imitating parts of human intelligence (all today’s AI/ML/DL are narrow and weak AI, as LLMs, ChatGPT), all subordinated as below.
AGI, General, human-like and human-level AI systems (HLAI, Full AI, Strong AI), imitating all human intelligence (Multi-modal and multi-task AI, OpenAI Project, DeepMind AI project, etc).
Trans-AI, Really intelligent, autonomous machines, augmenting and complementing humans (Causal Machine Intelligence and Learning, Man-Machine Hyperintelligence, Real Superintelligence).
In reality, there is nothing common between real machine intelligence and learning (MIL) and human intelligence and learning (HIL), with a human-like AI/ML/DL, if only both of them are black box data/information/knowledge systems.
Machines operate in terms of world models and causal patterns, computing power and algorithms, quantities and data, numbers and statistics, figures and digits, tokens and syntax, mathematics and probabilities, precision and accuracy.
It is a stimulus-response black box model, having its inputs and outputs (or transfer characteristics, a transfer function, system function, or network function) producing useful conclusions/information without showing any information about its internal workings, which mechanisms/explanations remain opaque/“black.”
Humans think in terms of world models but qualities, senses and meanings, concepts and ideas, thoughts and images, semantics and pragmatics, biases and prejudices.
In all, it is two different worlds, the world of quantitative/physical/cybernetic/causal machines vs. the world of qualitative/emotional/feeling/reasoning/live humans.
Machines are machines, with their virtually unlimited world simulations, humans are humans, with our naturally limited mental worlds, they can only complement each other, in the best way.
Machine World Models and/or Human World Models = AI World Models
There are a few research and a lot of misunderstanding of the world modeling and understanding, which is the most critical and decisive subject of all science and human practice, of all human intelligence and learning and machine intelligence and learning.
World models are commonly reduced to the mental models of the world, as if "the mind constructs "small-scale models" of reality to predict/anticipate events. Following J. Forrester, general mental models defined as:
“The image of the world around us, which we carry in our head, is just a model. Nobody in his head imagines all the world, government or country. He has only selected concepts, and relationships between them, and uses those to represent the real system.” J.W. Forrester. Counterintuitive behavior of social systems, Technology Review. 1971.
A world model is generally viewed as "an abstract representation of the spatial or temporal dimensions of our world".
"Humans develop a mental model of the world based on what they are able to perceive with their limited senses. The decisions and actions we make are based on this internal model and what we perceive at any given moment is governed by our brain’s prediction of the future based on our internal model". This guides model-based ML NNs models aimed to learn condensed/compressed spatial and temporal representations of data for real-life interactions with the environment, like Vision (V), Memory (M), and Controller (C) VAE (V) agent model. The role of the V model is to learn an abstract, compressed representation of each observed input frame, the role of the M model is to predict the future. The Controller (C) model is responsible for determining the course of actions to take in order to maximize the expected cumulative reward of the agent during a rollout of the environment. [World Models: Can agents learn inside of their own dreams?]
In the similar vein, Y. LeCun, in his proposal/position/proposal paper, Yann LeCun: A Path Towards Autonomous AI, modeling mental models to find answers on "How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons?"
I will propose a possible avenue to construct autonomous intelligent agents, based on a modular cognitive architecture and a new self-supervised training paradigm. The centerpiece of the proposed architecture is a configurable predictive world model that allows the system to plan. An intrinsic cost function drives behavior and learning. The world model uses a new type of energy-based model architecture called H-JEPA (Hierarchical Joint Embedding Predictive Architecture) trained to extract and predict relevant information using a self-supervised learning criterion called VICReg (Variance, Invariance, Covariance Regularization).
There are two uses for self-supervised learning. The first one will be learning hierarchical representations of the world. The learned representations by self-supervised pre-training can be used in supervised learning or RL afterward. The second one is learning predictive (forward) models of the world. The learned predictive forward models can be used for model-predictive control or model-based RL. The essence of intelligence is the ability to predict and the big technical problem we are going to face is how to represent uncertainty/multi-modality in the prediction. For this, Energy-Based Model was proposed (LeCun, Chopra, Hadsell, Marc’Aurelio, & Huang, 2006).
World Models vs. Mental Models
Real AI is not after human mental models referred to mental representation or mental simulation of external reality for cognition, reasoning and decision-making.
For generalized machine intelligence and learning, the term "world" refers to the totality of entities, to the whole world of reality or to everything that is, was and will be.
In various specific contexts and settings, it could take special senses and meaning associated, for example, with the Earth and all life on it, with humanity as a whole or with an international or intercontinental scope, the surrounding environment or the universe as a whole.
In terms of scales and scopes, levels, complexity and extension, there are few models of world models, to be modeled, embedded and coded my general MIL systems:
Real AI Model as an Integral General-Purpose Technology
Today's AI is too BRAIN-minded, anthropomorphic, futile and dangerous construct, in any its forms, sorts and varieties:
narrow, general or super AI;
embedded or trustworthy AI;
cloud AI or edge AI;
cognitive computing or AI chips;
machine learning, deep learning or artificial neural networks;
AI platforms, tools, services, applications;
AI industry, FBSI, Healthcare, Telecommunications, Transport, Education, Government.
They all are somehow mindlessly engaged in copycatting some parts of human intelligence, cognition or behavior, showing zero mind, intellect or understanding.
Why We Need to Kill the Anthropomorphic Constructs “Artificial Intelligence”, "Machine Learning" , and "Deep Neural Network"
And all the History of AI Research looks as a History of Fake AAI Research.
A true goal of Machine Intelligence and Learning is not to be equal or exceed human intelligence, but to become the last and the best of “general purpose technologies” (GPTs).
GPTs are technologies that can affect an entire economy at a global level revolutionizing societies through their impact on pre-existing economic and social structures.
GPTs' examples: the steam engine, railroad, interchangeable parts and mass production, electricity, electronics, material handling, mechanization, nuclear energy control theory (automation), the automobile, the computer, the Internet, medicine, space industries, robotics, software automation and artificial intelligence.
The four most important GPTs of the last two centuries were the steam engine, electric power, information technology (IT), and general artificial intelligence (gAI).
And the time between invention and implementation has been shrinking, cutting in half with each GPT wave. The time between invention and widespread use for the steam engine was about 80 years; 40 years for electricity, and about 20 years for IT. Source: Comin and Mestieri (2017)
Now the implementation lag for the MIL-GPT technologies will be about 5 years.
Conclusion from Musk
“My assessment about why A.I. is overlooked by very smart people is that very smart people do not think a computer can ever be as smart as they are. And this is hubris and obviously false. Working with A.I. at Tesla lets me say with confidence “that we’re headed toward a situation where A.I. is vastly smarter than humans and I think that time frame is less than five years from now. But that doesn’t mean that everything goes to hell in five years. It just means that things get unstable or weird.” Elon Musk
Trans-AI: How to Build True AI or Real Machine Intelligence and Learning
Abstract: We are at the edge of colossal changes. This is a critical moment of historical choice and opportunity. It could be the best 5 years ahead of us that we have ever had in human history or one of the worst, because we have all the power, technology and knowledge to create the most fundamental general-purpose technology (GPT), which could completely upend the whole human history.
The most important GPTs were fire, the wheel, language, writing, the printing press, the steam engine, electric power, information and telecommunications technology, all to be topped by real artificial intelligence technology.
Our study refers to Why and How the Real Machine Intelligence or True AI or Real Superintelligence (RSI) could be designed and developed, deployed and distributed in the next 5 years. The whole idea of RSI took about three decades in three phases.
The first conceptual model of TransAI was published in 1989. It covered all possible physical phenomena, effects and processes. The more extended model of Real AI was developed in 1999. A complete theory of superintelligence, with its reality model, global knowledge base, NL programing language, and master algorithm, was presented in 2008. The RSI project has been finally completed in 2020, with some key findings and discoveries being published on the EU AI Alliance/Futurium site in 20+ articles.
The RSI features a unifying World Metamodel (Global Computing Data Ontology), with a General Intelligence Framework (Master Algorithm), Standard Data Type Hierarchy, NL Programming Language, to effectively interact with the world by intelligent processing of its data, from the web data to the real-world data.
The basic results with technical specifications, classifications, formulas, algorithms, designs and patterns, were kept as a trade secret and documented as the Corporate Confidential Report: How to Engineer Man-Machine Superintelligence 2025.
As a member of EU AI Alliance, the author has proposed the Man-Machine RSI Platform as a key part of Transnational EU-Russia Project. To shape a smart and sustainable future, the world should invest into the RSI Science and Technology, for the Trans-AI paradigm is the way to an inclusive, instrumented, interconnected and intelligent world.
Resources
2021 Special Issue on AI and Brain Science: Perspective
Abstract
Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings. In this review, we summarize talks and discussions in the “Deep Learning and Reinforcement Learning” session of the symposium, International Symposium on Artificial Intelligence and Brain Science. In this session, we discussed whether we can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms. Speakers contributed to provide talks about their recent studies that can be key technologies to achieve human-level intelligence.
Since the rapid development of deep learning starting in the late 2010s, research on learning data-based models of the external world and their use for cognition and behavioral tasks has greatly spread. Given this, this special issue deals with topics related to artificial intelligence research (especially machine learning) in which systems demonstrate their intelligence capability by using acquired models of the world.
Topics of interest include, but are not limited to:
LEXICON: New Concepts and Definitions
"Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms, including: artificial intelligence (AI), machine learning, event-driven software architecture, robotic process automation (RPA), business process management (BPM) and intelligent business process management suites (iBPMS), integration platform as a service (iPaaS), low-code/no-code tools, packaged software, and other types of decision, process and task automation tools".
"Hyperautomation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyperautomation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.)"
"Hyperautomation is a framework and set of advanced technologies for scaling automation in the enterprise to develop a process for automating enterprise automation".
Hyperautomation is not focused on a single technology solution or vendor, but involves the orchestrated use of multiple technologies, such as artificial intelligence (AI), machine learning (ML), chatbots/conversational platforms, and robotic process automation (RPA).
Hyperintelligent automation is a combination of next-gen technologies like artificial intelligence (AI), robotic process automation (RPA), business process automation (BPA), intelligent document processing (IDP), machine learning (ML) and process mining.
Hyperintelligent AI is a machine/robotic/computing/automated/electronic/artificial/technological hyperintelligence depending on its power, ability and capacity to model, detect, identify, interpret, process, register, compute and manipulate all the key variables from any complex environments to effectively interact with the world.
NCR Chair & Prof; Founding Director, AI Institute at University of South Carolina
1yIt may be instructive to look at the practical use of the term "World Model" in this 2001 semantic search patent and corresponding commercially deployed system: http://bit.ly/15yrSemS