The Need of A Real-World Artificial Intelligence in The Pandemic Era

The Need of A Real-World Artificial Intelligence in The Pandemic Era

The Need of A Real-World Artificial Intelligence in The Pandemic Era

The Covid-19 pandemic has accelerated the development of artificial intelligence across the globe. 

Organizations are using artificial intelligence to increase the productivity of remote workers, enhance the virtual shopping experience, drive the digital transformation process and speed up the development of important drugs to end this on-going pandemic. 

Real artificial intelligence is creating value by making humans more efficient, not redundant.

There are several levels of knowledge, research, education, theory, practice, and technology:

Specialization: Narrow AI, Specialists, Scientists, Learned Ignoramus, which divides, specializes, and thinks in special categories.

Disciplinarity: Analytical science and traditionally fragmented disciplines.

Interdisciplinarity: It “integrates” information, data, techniques, tools, concepts, and/or theories from within two or more disciplines.

Interdisciplinarity is about the interactions between specialised fields and cooperation among special disciplines to solve a specific problem. It concerns the transfer of methods and concepts from one discipline to another, allowing research to spill over disciplinary boundaries, still staying within the framework of disciplinary research. 

Transdisciplinarity: Synthetic science and technology and society, the ideas of a unified science and technology and human society, universal knowledge, synthesis and the integration of all knowledge, total convergence of knowledge, technology and people, Trans-AI = Narrow AI, ML, DL + Symbolic AI + Human Intelligence.

Transdisciplinarity is radically distinct from interdisciplinarity, multidisciplinarity and mono-disciplinarity. 

Transdisciplinarity analyzes, synthesizes and harmonizes links between disciplines into a coordinated and coherent whole, a global system where all interdisciplinary boundaries dissolve.

It is about addressing the world’s most pressing issues and seeing the world in a systemic, consistent, and holistic way at three levels:

(1) theoretical, (2) phenomenological, and (3) experimental (which is based on existing data in a diversity of fields, such as experimental science and technology, business, education, art, and literature).

Transdisciplinarity is a way of being radically distinct from interdisciplinarity, as well as multidisciplinarity and mono-disciplinarity.

Transdisciplinarity integrates the natural, social, and engineering sciences in a unifying context, a whole that is greater than the sum of its parts and transcends their traditional boundaries.

Transdisciplinarity connotes a research strategy that crosses many disciplinary boundaries to create a holistic approach.

Transdisciplinary research integrates information, data, concepts, theories, techniques, tools, technologies, people, organizations, policies, and environments, as all sides of the real-world problems.

Transdisciplinarity takes this integration of disciplines on the highest level. It is a holistic approach, placing these interactions in an integral system. It thus builds a total network of individual disciplines, with a view to understand the world in terms of integrity and unity and discovery.

Monodisciplinary: It involves a single academic discipline. It refers to a single discipline or body of specialized knowledge.

Multidisciplinarity: It draws on knowledge from different disciplines but stays within their boundaries. In multidisciplinarity, two or more disciplines work together on a common problem, but without altering their disciplinary approaches or developing a common conceptual framework. 

Why Transdisciplinarity AI?

In the context of the unprecedented worldwide pandemic-enhanced crises, the transdisciplinarity appears as an all-sustainable way of solving complex real-world problems pursuing a general search for a “unity of knowledge” or Real-World AI.

The Trans-AI paradigm means that the classic studies of Plato, Aristotle, Kant, Leibniz’s Logic as Calculation and Boole’s Logic as Algebra with modern ontological, scientific, mathematical and statistical research of reality/knowledge/intelligence/data formalization/computing/automation are a key to [Real] AI.

For example, the conception of AI was inherently implied in Aristotle’s Analytics, Prior and Posterior, Metaphysics/Ontology and Categories. 

Without the reality/category theory, as the mind theory for human minds, and prior data analytics, no deep AI/ML/DL classifiers with effective classification algorithms are possible, where classes are targets, labels, or categories. ML/DL predictive modeling is NOT just the task of approximating a mapping function (f) from input variables (X) to output variables (y). Therefore, it is widely recognized that the lack of reality with causality is the “black hole” of current machine learning systems. 

The Trans-AI is about the real-world data ontology, causality, real intelligence, science, computer model, semantics and syntax and pragmatics, universal knowledge/data synthesis vs. expert knowledge/data analytics, thus enabling a comprehensive machine understanding of data points, elements, sets, patterns, and relationships.

Without comprehensive causal world’s models integrating disciplinary, inter-, multi-, and trans-disciplinary knowledge, there is no real-world AI. A holistic research strategy integrating world’s knowledge into a meaningful whole is the systematic way of building the General Human-AI Platform as an Integrative General-Purpose Technology.

The current disciplinary approach to AI/ML/DL and Robotics is, at best or worst for humanity,  ending up with “superhuman” narrow human-mimicking AI applications, integrated in our smart networks, devices. processes and services.

Some, who limit AI as augmenting or substituting biological intelligence with machine intelligence, believe transdisciplinarity is a way to a human-level AI.

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What is Wrong with the Narrow AI of ML?

The mono-disciplinary narrow AI of machine deep learning is blooming today, bringing its stakeholders unprecedented profits. Five top-performing tech stocks in the market, namely, Facebook, Amazon, Apple, Microsoft, and Alphabet’s Google, FAAMG, represent the U.S.'s Narrow AI technology leaders whose products span machine learning and deep learning or data analytics cloud platforms, mobile and desktop systems, hosting services, online operations, and software products. The five FAAMG companies had a joint market capitalization of around $4.5 trillion a year ago, and now exceed $7.6 trillion, being all within the top 10 companies in the US. As to the modest Gartner's predictions, the total NAI-derived business value is forecast to reach $3.9 trillion in 2022.

The future superhuman narrow AI applications are here, within us, in our smart networks, devices. processes and services.

Special-designed automated intelligence outperforms humans in strategic games, chess/go playing, video gaming, self-driving mobility, stock trading, financial transactions, medical diagnosis, NLP, language translation, patterns/object/face recognition, manufacturing processes, etc.

And it is ONLY the narrow AI/ML/DL fragmented applications designed for narrow human-like tasks and jobs, as more efficient and effective than human labor, mental or menial.

The existential question is When Will Robots/Machines/Computers Emerge as a General-Purpose Real-World AI?

But most people are still blind to see the disruptive fundamental force of AI technology, its critical impact on our future.

Our company is proud to inform that EIS Encyclopedic Intelligent Systems LTD has completed studying, modeling, and designing the Real-World AI as a Causal Machine Intelligence and Learning, trademarked as Causal Artificial Superintelligence (CASI) GPT Platform complementing human intelligence, collective and individual.

The current disciplinary approach to AI/ML/DL and Robotics is ending up with “superhuman” narrow AI applications, integrated in our smart networks, devices. processes and services.

Special-designed automated intelligence outperforms humans in strategic games, chess/go playing, video gaming, self-driving mobility, stock trading, financial transactions, medical diagnosis, NLP, language translation, patterns/object/face recognition, manufacturing processes, etc.

It is still ONLY the narrow Anthropomorphic and Anthropocentric AI/ML/DL fragmented applications designed for narrow human-like tasks and jobs. Many scientists are trying to move the field of AI beyond data analytics, predictions and pattern-matching towards machines that could solve real-world problems. “Some people think it might be enough to take what we have and just grow the size of the dataset, the model sizes, computer speed—to just get a bigger brain” (Conference on Neural Information Processing Systems (NeurIPS 2019) Yoshua Bengio)

Still, the existential question is open: What If Robots/Machines/Computers were to Outsmart Humans in all special respects?

Meet a Trans-AI of Narrow AI, ML and DL

To address the moral and existential issues of disciplinary AI/ML/DL and robotics fragmentation, as Europe’s Responsible and Trustworthy AI, we have developed a Transdisciplinary Real AI model, as not competing with, but complementing human intelligence.

The Transdisciplinary AI Conferences are now emerging, but still considered as an interdisciplinary collection of academic research themes:

  • AI and computer science
  • AI and education
  • AI and humanities
  • AI and medicine
  • AI and agriculture
  • AI and sciences
  • AI and engineering
  • AI and law
  • AI and business

Transdisciplinary AI 2021 (TransAI 2021) is technically sponsored by the IEEE Computer Society.

Trans-AI aims to integrate disciplinary AIs, symbolic/logical or statistic/data, as ML Algorithms (DL, ANNs), which are designed to substitute biological intelligence with machine intelligence.

 Trans-AI is developed as a Man-Machine Global AI (GAI) Platform to integrate Human Intelligence with Narrow AI, ML, DL, Human-level AI, or Superhuman AI, all as Neural Information Processing Systems. It relies on fundamental scientific world’s knowledge, cybernetics, computer science, mathematics, statistics, data science, computing ontologies, robotics, psychology, linguistics, semantics, and philosophy.

The Trans AI model is mapped as an interdependent, mutually reinforcing, transdisciplinary quadrivium of the world’s knowledge depicted by the global knowledge graph (see the extended version).

The Trans-AI is a systematic, holistic and analytical means of obtaining knowledge about the world.

The Trans-AI is technologically designed as a Causal Machine Intelligence and Learning Platform, to be served as Artificial Intelligence for Everybody and Everything, AI4EE.

The Trans-AI technology could make the most disruptive general-purpose technology of the 21st Century, given an effective ecosystem of innovative business, government, policy-makers, NGOs, international organizations, civil society, academia, media and the arts.

The Trans-AI as Human-AI Global Platform is designed to extract knowledge from massive digital data for creating breakthroughs in all parts of human life, from government to industry to education to healthcare to global security.

It is aimed to process structured and unstructured digital data within unifying world-intelligence-data models and causal algorithms, shifting from supervised to self-supervised real learning. Making breakthroughs in these areas will be the matter of life or death for the future of humanity.

Why Trans-AI could be the disruptive discovery, innovation and unifying general-purpose technology…and the best smart investment

The Trans-AI could be the most disruptive research and breakthrough discovery, innovation and technology meeting the founding fathers of AI dreams to “make machines use language, form abstractions and concepts”, Google mission to “organize the world’s information and make it universally accessible and useful,” and best human ambitions for a unified knowledge of the world. 

Among other disruptive changes, the Trans-AI enriches, updates and scales up the disciplinary AIs, as proposed by the EC's  HIGH-LEVEL EXPERT GROUP ON ARTIFICIAL INTELLIGENCE:

“Artificial intelligence (AI) refers to systems that display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals. AI-based systems can be purely software-based, acting in the virtual world (e.g. voice assistants, image analysis software, search engines, speech and face recognition systems) or AI can be embedded in hardware devices (e.g. advanced robots, autonomous cars, drones or Internet of Things applications).”

The most concern of humanity must be the current accelerated growth of Big Tech’s Narrow and Weak AI of Machine Learning, ANNs and Deep Learning, as a Non-Real AI vs. Real World AI. It is fast emerging as narrow-minded automated “super intelligences” outperforming humans in any narrow cognitive tasks, and implemented as LAWs or military AI, ML/DL drones, killer robots, humanoid robots, self-driving transportation, smart manufacturing machines, RPAs, cyborgs, trading algorithms, smart government decision makers, recommendation engines, medical AI system, etc.

The whole idea of Anthropomorphic and Anthropocentric AI (AAAI) as the narrow or general ones, aimed at simulating human intelligence, cognitive skills, capacities, capabilities, and functions, as well as intelligent behavior and actions in computing machines is raising a number of undecidable social, moral, ethical and legal dilemmas.

The narrow and weak Deep-Learning AI programs classify tremendous amounts of data without any understanding of the world and meaning of their inputs or outputs (e.g., the recommendation to treat or a risk score or behaviour changes).

These consequences could be much worse than human cloning, which is prohibited in most countries, and massive technological unemployment without any compensation effects is just the beginning of the end.

This is what good minds forewarned humanity about the possibilities and possible perils of AAAI, mimicking human learning and reasoning by machines and humanoid robots:

“The development of full artificial intelligence could spell the end of the human race… It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.” — Stephen Hawking told the BBC

“I visualise a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” —Claude Shannon

“I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish. I mean with artificial intelligence we’re summoning the demon.” —Elon Musk warned at MIT’s AeroAstro Centennial Symposium

All that we need, is a radically new kind of AI, Real and True MI, Real World AI, the Trans-AI, which is to simulate and understand or compute reality, causality, and mentality in digital reality machines.

This is becoming clear even for profit-seeking industrialists, as E. Musk, who understands that without the Real-World AI no really intelligent machine is possible. “Self-driving requires solving a major part of real-world AI, so it’s an insanely hard problem, but Tesla is getting it done. AI Day will be great”. “Nothing has more degrees of freedom than reality.”

Conclusion

The rise of real artificial intelligence will create and destroy new jobs, improve healthcare, disrupt smart cities, and minimize the impact of the next pandemic. Despite the concerns about the dark side of artificial intelligence, we are still far away from super artificial intelligence.

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Azamat Abdoullaev

Tech Expert

Azamat Abdoullaev is a leading ontologist and theoretical physicist who introduced a universal world model as a standard ontology/semantics for human beings and computing machines. He holds a Ph.D. in mathematics and theoretical physics. 

   

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