The Importance of a Global AI Academy

The Importance of a Global AI Academy

The Importance of a Global AI Academy

The COVID-19 crisis has accelerated the need for new knowledge, competences and workforce skills in artificial intelligence.

Modern society requires advanced cognitive, scientific, technological, social and emotional skills.

In the artificial intelligence (AI) and robotics era, there is a high demand of 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, 6G, cybersecurity and digital reality.

The European Round Table for Industry launched a pan-European training initiative to help unemployed and at-risk workers. The Reskilling 4 Employment aims to reskill one million workers by 2025, and up to five million by 2030. Initial pilot projects are planned in Portugal, Spain, and Sweden, and corporate supporters include AstraZeneca, Iberdrola, Nestlé, SAP, Sonae, and Volvo Group.

The European Network of AI Excellence Centres is driving up collaboration in research across Europe, bringing together world-class researchers and establishing a common approach, vision and identity for the European AI ecosystem. The initiative aims the following:

  • Support and make the most of the AI talent and excellence already available in Europe;
  • Foster exchange of knowledge and expertise, and attract and maintain these talents;
  • Further develop collaboration between the network and industry;
  • Foster diversity and inclusion;
  • Develop a unifying visual identity.

Another example is Cisco’s Networking Academy. The company partners with educators and instructors around the world to offer students IT training in a range of areas such as big data, cloud, cybersecurity, and machine learning. The effort connects students to jobs inside Cisco and with its external partners, while creating a much larger pool of skills the company prioritizes. Besides, Cisco Systems teamed up with universities to create centralized data science and AI training programs units for employees to transform them into experts.

Global AI Academy Platform: Human Learning of Machine Intelligence and Learning

The Global AI Academy (GAIA) is an innovative knowledge digital platform to share knowledge about the most advanced digital and emerging technologies. The GAIA is acting through a global AI platform to spread worldwide the state-of-the art knowledge of the leading edge innovations, inventions, applications, and possible effects in the most critical fields of socio-technological change: real artificial intelligence, causal machine learning and robotics, data science and big data, cloud and edge computing, the Internet of Things and 5-6G, cybersecurity, digital reality, smart cities, intelligent nations, smart sustainable world, and i-world.

The Global AI Academy is providing a real AI education, re-skilling and up-skilling narrow and weak AI mindsets. In a cutting-edge business perspective, it is advising how to set up competence centers (CC) or centers of excellence (COE) in real AI.

The idea of establishing a CC or COE in real AI is particularly radical. The most existing CoEs are largely about the weak and narrow AI of statistical ML and predictive data analytics, like the ones recently established by Deutsche Bank, J.P. Morgan Chase, Pfizer, Procter & Gamble, Anthem, and Farmers Insurance. Many tech strategists, visionaries and experts have poor understanding what kinds of AI CoEs the Real AI Economy is in need, AI Centers Of Excellence Accelerate AI Industry AdoptionHow to Set Up an AI Center of Excellence

The GAIA is assisting the creation of a smart data strategy with a vision for Real AI in companies, teaching executives to know what AI is, what it can do, and how it might enable new business models and strategies.

The current understanding of AI and ML is so poor that many believe, as ARK Invest, the language models such as the OpenAI’s GPT-3 “understand” language, and the statistical deep learning can create more economic value than the internet did

Why We All Need the Next Generation Machine Intelligence, Causal AI and Explainable ML

Today's Narrow and Weak AI of Machine Learning and Data Science is not a Real or True 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.

Most of the ML/DL algorithms and models are heavily relying on the statistical learning theory instead of causal learning, thus predicting spurious correlations instead of meaningful causation. This makes a critical difference for the whole enterprise, its applications, prospects, and impacts on every part of human life. 

We have to be intelligently critical and fully objective as modern science demands it, as far as it concerns all of us and our human future. 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 by revolutionizing chatbots and the search engine. And now the Beijing Academy of Artificial Intelligence (BAAI) conference presented Wu Dao 2.0.

Global Human-AI Platform as the Universal GPT (General-Purpose Technology)

The Real AI Science and Technology is emerging as one of the best ideas which will change the world:

  • THE COMPUTER. originated in philosophical speculations about calculating machines, notably those of 17th-century German philosopher GW Leibniz.
  • MACHINE INTELLIGENCE [Global AI = Human-AI Synergetic Intelligence]

A Global AI Platform implies a unifying model of reality/world in terms of causality/actuality, mentality/intelligence and computing/data /virtuality/cyberspace, where neural networks are brain-encoded causal networks.

Such background and internally encoded knowledge serves as an integrative world model, WorldNet, the universal “master algorithm” that would unlock a Real/Global AI, integrating all sorts of MI and ML with human intelligence (HI):

HMIL = HI + Machine Intelligence [ANI, CC, AGI, ASI] + Machine Learning (DNNs) = Global AI = Real Intelligence

HMIL integrates all valuable approaches and techniques, such as ML (of which deep learning and reinforcement learning are specific examples), machine reasoning (which includes planning, scheduling, knowledge representation and reasoning, search, and optimization), and robotics (which includes control, perception, sensors and actuators, as well as the integration of all other techniques into cyber-physical systems).

Human intelligence has to be embraced by the global AI together with other emerging technologies and various sorts of AIs:

The Global or Real AI platform = Human Intelligence, Big Data Analytics, Digital Reality, Robotics, Narrow AI, ML. DL, AGI, ASI, the Internet of Everything.


The Global AI Academy's mission is to transfer a critical learning worldwide that there are two broad types of Machine Intelligence, General [Scientific or Real] AI vs. Narrow {Statistic or Symbolic} AI, with all possible consequences for learning and education, cutting-edge technology development, business and policy and all sides of human life.

Real/general/causal AI is emerging as the integral human-machine digital GPT of AI, ML, DL Robotics, and other emerging technologies, being all about reality, mentality and virtuality, or digital reality, relying on the scientific world models and substantial causality instead of spurious correlations and statistic models.

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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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