EIS Has Created the First Trans-AI Model for Narrow AI, ML, DL, and Human Intelligence

EIS Has Created the First Trans-AI Model for Narrow AI, ML, DL, and Human Intelligence

EIS Has Created the First Trans-AI Model for Narrow AI, ML, DL, and Human Intelligence

Artificial Intelligence (AI) is set to change how the world works.

It's the main engine of the digital revolution. 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. 

Given that, the conception of Transdisciplinary AI is proposed to integrate disciplinary AIs, symbolic/logical or statistic/data, as Machine Learning (ML) Algorithms (Deep Learning (DL), Artificial Neural Networks (ANNs)), aiming to augment or substitute biological intelligence or intelligent actions with machine intelligence.

The TransAI will be developed as a Man-Machine Global AI (GAI) Platform to integrate Human Intelligence with Narrow AI, ML, DL, Human-level AI, and Superhuman AI. It relies on fundamental scientific world’s knowledge, cybernetics, computer science, mathematics, statistics, data science, computing ontologies, robotics, psychology, linguistics, semantics, and philosophy.

Since it is widely recognized that the lack of reality with causality is the “root cause” of development problems of current machine learning systems, the Trans-AI is 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 Knowledge Graph covers ERC’s fields of research: Physical Sciences and Engineering (PE), Life Sciences (LS), and Social Sciences and Humanities (SH). 

Innovating Man-Machine Superintelligence 2025: AI for Everything and Everyone (AI4EE)

In 2020, EIS Encyclopedic Intelligent Systems LTD has successfully completed its unprecedented R&D of the Transdisciplinary AI Model as a Real-World AI or a Causal Machine Intelligence and Learning, trademarked as Causal Artificial Superintelligence (CASI) GPT Platform complementing human intelligence, collective and individual. 

The company has spent zero public funding and private investment for the outstanding discovery, which is set up to change the ways the world works, relying only on its own resources, intelligence and material.

EIS is set to build/engineer the Real AI GPT Platform. We welcome large public and private investors from the EU, the USA, China, Russia, and the UK and/or socially responsible big tech companies, to develop the Proof-of-Concept/Mechanism/Principle Prototype to demonstrate the CASI feasibility for a full-scale global deployment.

To estimate a prospective CASI investment, we could mention the business case of Microsoft, which is investing $1 billion in OpenAI to support it in building artificial general intelligence (AGI).

As the POC/POM/POP demonstrating that a CASI design concept is feasible could serve USECS, the Catalogue of the World, classifying all the possible things, states, processes and relationships in reality, physical, mental, social, digital or virtual (1717 pages). 

The CASI's Global Knowledge Base presents the universal schema of the world as the hierarchical order of all things, creating the World Reference Framework (WRF) and Universal Standard Entity Classification System, USECS. 

The universe of world entities is categorized as the Quadrivium of Substance, State, Change and Relationship. The USECS enables the United Global Encyclopedia, Dictionary and Thesaurus and the Unified Wisdom Science, as well as the Internet of Everything and Wise World Wide Web (WWWW). The New Wisdom Book provides the Total Catalog of the World, the Universal Directory of Entities, serving as the base for the classification systems in science, technology, and engineering, industry, commerce, and trade, as used by Amazon and Alibaba.

The CASI as the Summit of Human Knowledge

The CASI could be the summit of human development with human knowledge enjoying a revolutionary history:

Homo Sapiens Sapiens > Mythology > Religion > Philosophy > Science & Technology > Computing Machines > the Internet/WWW > Emerging Technologies (Robotics, Quantum, Genetic, Bio-, Neuro-, Nano-, Cognitive and Social Engineering) > NAI/ML/DL > Causal AL/ML/DL > Human Intelligence > BMI/MMI > Digital Reality/Cyberspace > Digital Superintelligence > Human-Machine Superintelligence > Global Human-AI Internet > I-World

Human knowledge is to become all digital, more and more fitting computing machines.

Digital knowledge/information is today stored on computers and in other digital media by a series of ones and zeros. Replacing books and libraries, digital knowledge is the commonly used method of storing and reading data, easily copied, edited, and moved without losing any quantity or quality.

And as a real Intelligent machinery emerges, it is to become the master of all digital knowledge.

Complex superintelligent algorithms as source programming codes are built into powerful supercomputers or quantum computers that allow them to identify real world data and make decisions, predictions, prescriptions, etc. around the cause and effect that they identify.

Superintelligent models apply causal world models to explain, discover, infer, decide, predict, and interact with any environment, physical, social, or digital, given the global datasets.

Superintelligent Machine = Digital Superintelligence Architecture: (Causality, the laws of physics, AI Technology stack, computer structure, AI software loaded, causal master algorithms, world’s knowledge, global data) > (output) > (causal feedback) > (control input)

Deploying a Superintelligence Innovation

It might take about 5 years to develop, deploy and diffuse a Global Man-AI Platform due to the accelerated growth of smart digital technologies, 5G, ML/DL social media platforms, narrow AI applications, intelligent robotics, self-driving transportation, automated software, codes, programs, algorithms, quantum computing, and digital reality.

There is a revolutionary socio-technological transition from the data-driven AI to the Human-Machine Superintelligence.

The HMSI is the science and engineering of real techno-mind, cybernetic intelligence or human intellect, their nature, models, theories, algorithms, architectures, and applications.

The paradigm shift brought on by the HMSI technology is to be more disruptive than the Internet, one of the most revolutionary and disruptive technologies in history.

Modern humanity lives in exponential times, 30 years is like 5 years.

The HMSI, as [Human-Machine] Digital SuperIntelligence, is to take over by 2025, as Musk wisely anticipated in his visionary forecasting.

The alternative scenario of the current big tech AI/ML/DL is a road to a human disaster/dystopia.

Today 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 standard machine learning and deep learning or data analytics cloud platforms, with mobile and desktop systems, hosting services, online operations, and software products. The 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 FAI-derived business value is forecast to reach $3.9 trillion in 2022.

A human-like AI/ML/DL technology might rapidly make entire industries obsolete, in either case triggering widespread mass unemployment, while over-enriching the FAI Big Tech.

You don’t need to be a great economist to foresee that such speculative, circular and leveraged mega bubbles lead the global COVID-19 plagued economy to its deep recession and real economy collapse.

A real solution here is not a fake and false, narrow and weak, acausal AI of ML and DL, relying on blind statistics and mathematics to imitate some specific parts of human cognition or intelligent behavior.

What the pandemic-stricken world needs is the Real Human-AI Technology which must be developed as a digital general purpose technology, like a Synergetic Cyber-Human Intelligence.

Human minds as collective intelligence and world knowledge will be integrated with the Human-Machine Intelligence and Learning (HMIL) Global Platform, or Global AI:

GAI = HMIL = AI + ML + DL + NLU + 6G+ Bio-, Nano-, Cognitive engineering + Robotics + SC, QC + the Internet of Everything + Human Minds + MME, BCE + Digital Superintelligence = Encyclopedic Intelligence = Real AI = Global AI = Global Cyber-Human Supermind

Again, the 4th Industrial Revolution (4IR) as a fusion of advances in artificial intelligence (AI), robotics, the Internet of Things (IoT), genetic engineering, quantum computing, and other digital technologies transforms human economy into the human-machine AI economy.

The Mainstream AI is an Acausal Non-Real AI

The AI world has been flooded with a series of gigantic language model projects promoted as the last word in AI. First, OpenAI shocked the world a year ago with GPT-3. In turn, Google presented LaMDA and MUM, two narrow AIs as revolutionizing chatbots and the search engine. And now the Beijing Academy of Artificial Intelligence (BAAI) conference presented Wu Dao 2.0.

The mainstream AI/MLANN/DL is not a Real Causal AI, be it large-scale language models as 17bn Turing-NLG, 175bn GPT-3, 1.75T Wu Dao 2.0, big tech ML platforms, recommending engines, digital assistants, self-driving transportation, or lethal autonomous weapon systems (LAWS), autonomous weapon systems (AWS), robotic weapons, killer robots operating in the air, on land, on water, under water, or in space.

It is the lost cause to reach an artificial superintelligence as an extension of artificial general intelligence, in the context of the current paradigm of anthropomorphic and anthropocentric AI (AAAI), as “the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions”.

The truly scientific paradigm is a Causal Machine Intelligence and Learning, the simulation of reality and mentality and causality in digital machines that are programmed to effectively interact with any complex environments, physical, mental, social, digital, or virtual.  

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  • Adrian Walker

    Depending on the reader's level of experience, this comes across either as technobabble, or as a potentially useful attempt at providing a meta level to talk about AI. Tighter links between non-profit AI research and the dot-coms' proprietary AI achievements might help to stabilize the situation.

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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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