The Paradigm Revolution: AI as a Trans-Technoscience Technology (TTT)
The success of Generative AI & LLMs & Foundational Models has generated much interest in creating real and true AI and machine intelligence and learning.
AI is defined and designed and developed as the transdisciplinary technoscience systems of Science, Technology, Engineering, Mathematics (STEM), the Humanities, Metaphysics, and Politics.
AI as Philosophical Technoscience Engineering Technology (TSET)
AI is a philosophical technoscientific engineering technology, where the integrated world data, information and knowledge is encoded and embodied and embedded in software/hardware systems or computing machinery networks.
World knowledge is a scientific modeling and simulation and manipulation (having an understanding and awareness) of the world at large and in detail, its different subjects and disciplines or knowledge domains.
Technoscience suggests that science and technology are relational, rather than two separate elements. Instead of being separate, science is always enabled by technology, and technology and science are co-produced.
TSET refers to the complex interactions of STEM (science, technology, engineering, mathematics) with the humanities and arts and philosophy, as "the study of the fundamental nature of knowledge, reality, and existence", "a quest for a comprehensive understanding of the world".
TSET is a trans-technoscience that encompasses all fundamental truths and scientific facts and principles, laws and theories, models and paradigms and associated mathematics that underlie engineering and technology.
It integrates engineering, scientific disciplines, non-scientific disciplines or non-disciplines, as metaphysical, physical, chemical, biological, mathematical, and technological sciences with the arts, humanities, social sciences, all to tackle the most demanding challenges and advance the global human-machine society.
TSET researchers research and develop, design and deploy and distribute intelligent technologies, software systems, hardware infrastructure and computing networks and digital platforms, AI machines, devices, and sensors, structures, systems and processes for a diverse range of applications.
What is Not AI: a separated Science, Technology, Engineering, Mathematics, the Arts, Philosophy, or Politics
AI is not a single science, technology, engineering, applied mathematics technology, or computer science and engineering, as listed below:
Human Intelligence Engineering Technology, human-like AI, rules-based, symbolic AI, expert systems, AGI, ASI
Human Brain Engineering Technology, brain-inspired technologies, artificial intelligence and neuromorphic computing, NNs
Information (Data/Knowledge) Engineering Technology, machine learning,?artificial intelligence,?control theory,?signal processing, and?information theory, and more applied fields such as?computer vision,?natural language processing,?bioinformatics,?medical image computing,?cheminformatics,?autonomous robotics,?mobile robotics, and telecommunications.
Biological Engineering, Computational intelligence frameworks as inspired by biological systems and evolutionary processes. Artificial neural networks, fuzzy systems, evolutionary computation, cognitive AI, swarm intelligence, developmental AI, and hybrid intelligent systems
NL Engineering Technology, LLMs, GPT-n, ChatGPT, Bard, etc.
AI/ML/DL/LLM's Paradigm Shift
STEM, Ontology and Computation, it is what we really need for creating a general-purpose intelligent technology as scientific and knowledge-based AI models, systems and applications:
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true discriminative and generative AI
causal and objective ML
interactive DL Neural Networks, as LLMs and Foundational Models
AI and ML field has experienced several hype cycles, followed by disappointment and criticism, funding cuts and disinterest for years or even decades later, as in 1966-69 for ANNs, 1974–1980 for experts systems and 1987–2000 for symbolic, logical AI (the US SCI or Japan's Fifth Generation Computer).
Since 2010-2012, interest in AI and ML from the research communities and big tech corporations caused a dramatic increase in funding and investment, leading to the current (as of 2023) AI boom, with the generative AI & LLMs & Foundation Models race being a key driver of this hype.
We conclude that the world knowledge encoded as the science at large, philosophical and formal, natural and social, technological and engineering, with its global data ontology could help avoid reversing the current AI spring into the last AI/ML "nuclear winter".
Conclusion
In its nature, real or true or genuine AI is overruling a single silos technology of computer science or information engineering or neuromorphic engineering or NLP engineering as a computerized world knowledge and intelligence developed as an Intelligent Trans-Technoscience Technology.
Resources
The Paradigm Revolution: AI = Automated STEM: The Nobel Turing Challenge
AI as implementing human intelligence in machines to create human-level systems that understand, think, learn, and behave like humans, as generative AI applications, is an obsolete paradigm.
Real AI as implementing the real-world world data and knowledge in machines to create intelligent systems that know, learn, explain, discover, predict, act and effectively interact with the world is the next paradigm.
Real AI is Philosophy, Science, Technology, Engineering and Mathematics (STEM) as the sum of universal knowledge, built in software systems and hardware infrastructure due to computer science and engineering.
Its AI models involve?scientific?models of reality to explain and predict the behavior of real-world phenomena or virtual objects and systems, such as?conceptual models?to understand,?computational models?to simulate, mathematical models?to quantify,?operational models to?operationalize, and?graphical models?to visualize the world.
In essence, the true potential of AI lies not in its ability to mimic human intelligence or behavior, but in its capacity to effectively and sustainably interact with the world.
In fact, AI is all science at large, as the world knowledge of data and facts, phenomena and laws, causes and effects, patterns and interactions, embodied in machines, as powerful scientific AI models, interactive ML algorithms or hyperintelligent hyperautomation.
Again, TruthAI is the automated STEM, not the automated human intelligence.