The Birth of a Causal Artificial Superintelligence (CASI) 2025: A Human-Machine General Purpose Technology

The Birth of a Causal Artificial Superintelligence (CASI) 2025: A Human-Machine General Purpose Technology

The Birth of a Causal Artificial Superintelligence (CASI) 2025: A Human-Machine General Purpose Technology

EIS Encyclopedic Intelligent Systems LTD (EIS) has completed studying, modeling, and designing artificial superintelligence as a causal machine intelligence and Learning GPT Platform complementing human intelligence, collectively and individually.

EIS is looking for big superintelligence investors in the EU, USA, China, Russia, and the UK and/or socially responsible big tech companies, to develop a proof-of-concept/mechanism/principle prototype to demonstrate the CASI feasibility to turn its concept into a reality for a full-scale global deployment.

To estimate a prospective CASI investment, Microsoft is investing $1 billion in OpenAI to support in building artificial general intelligence (AGI). 

The design, development, and deployment of human-machine superintelligent digital platforms could theoretically be worth trillions of US dollars.

But it has tremendous potential to benefit the world and its peoples, with immense value in growing a connected, intelligent and inclusive world (I-World) with a smart society and green economy, enhancing global sustainability and securities and opening new frontiers in science, technology, education, medicine, communication and deep space exploration.

The Causal Artificial Superintelligence (CASI) as the Summit of all Human Knowledge

Human knowledge is set to become all digital, more and more fitting with 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, a computer structure, AI software loaded, causal master algorithms, world’s knowledge, global data) > (output) > (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 set 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 expected 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 economic 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: Everything You Know about Machine Intelligence is Wrong

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.  

The 10 Commandments of the Causal Artificial Superintelligence (CASI)

1. Causation is the master principle and prime force of the universe.

2. There are no uncaused things or changes in the world.

3. Causality gives structure or order to everything in the world, from the microworld to the macroworld.

4. Causation determines the hierarchical structure of the world, its entities, processes and relationships, as well as its data, information and knowledge.

5. Causation is reverting, reversing, and going backwards, creating dynamic backward loops and causal circuits, complex control systems and nonlinear processes.

6. Causation flows in bottom-up ways, from micro to macro scales, as causal emergency, and vice versa, in top-down ways, from macro to micro scales, as causal control.

7. The interrelationships of microscale and macroscale are determined by the top-down and bottom-up causation. 

8. Causality is a symmetric, correlative, productive interrelation, X causes Y if and only if Y causes X.

9. Causation gives deep structures and ordering to mind, intelligence, learning, inference, cognition. reasoning, understanding, and action, human or machine.

10. Causal models, rules and relationships are the master models and algorithms for artificial intelligence, machine learning, artificial neural networks, and deep learning.

Original Resources

Why AI is not AI: Everything You Know about Machine Intelligence is Wrong:

Causal Learning vs. "Deep Learning"​: on a fatal flaw in machine learning:


If any real artificial intelligence in reality and who over-profits from the AI mythology:

Share this article

Leave your comments

Post comment as a guest

terms and condition.
  • No comments found

Share this article

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. 


Latest Articles

View all
  • Science
  • Technology
  • Companies
  • Environment
  • Global Economy
  • Finance
  • Politics
  • Society
Cookies user prefences
We use cookies to ensure you to get the best experience on our website. If you decline the use of cookies, this website may not function as expected.
Accept all
Decline all
Read more
Tools used to analyze the data to measure the effectiveness of a website and to understand how it works.
Google Analytics