The Quest for AI Intelligence and Models, Software and Hardware, Policy and Regulations
The AI Intelligence as "the simulation or approximation of human intelligence in machines" is the bandwagon fallacy, Argumentum ad populum, with all the consequences for AI software and hardware, policy and frameworks, intended and unintended, anticipated and anticipated.
Another names for the informal fallacy include
Appeals to popularity "are common in commercial advertising that portrays products as desirable because they are used by many people or associated with popular sentiments instead of communicating the merits of the products themselves".
Introduction
AI's impact in the next years is completely uncertain and unpredictable. But what is certain, AI is transforming every part of human lives. It influences how we work and play, study and entertain. It promises to help solve all the global challenges like climate change and nuclear war.
Yet AI also brings real challenges and risks for governments, businesses and citizens alike.
As it permeates economies and societies, what sort of AI Intelligence and Model, hardware and software, policy and institutional frameworks should guide its design and use, deployment and distribution, and how can we ensure that it benefits society as a whole?
For examples, the EU AI Act's Risks are decided by the quality of AI Intelligence Modeling.
Microsoft, OpenAI; Google, DeepMind; Nvidia and Meta are ambitious to create artificial general intelligence, AGI, or Superintelligence, missing that the key point is the AI Intelligence Models to be developed and deployed.
In all, there is an evolutionary ladder of AI Intelligence models in the context of their reality and generality:
Then commercial AI models, such as Microsoft/Open AI’s GPT-4, that support General Purpose AI systems like ChatGPT and Bard are not real intelligent due to lacking the World Model Intelligence Engine.
Intelligence Models of AI – Analysis of the 10 cycles
The evolution of AI has been passing 10 cycles, eras, periods, or waves:
The Era of Ancient AI, since antiquity, in the form of myths, stories and rumors of artificial superbeings, gods, titans, angels, demons, mythical creatures, and synthetic beings endowed with intelligence or consciousness by master craftsmen.
The Era of Sci-Fi AI, Robots, Humanoids, Aliens, etc., in literature and cinema, from the Frankenstein novel to Terminator, the Matrix, the Ex Machina movies
The third wave – AI in program code, from 1950s to 1970s, when the term “thinking machines” and “artificial intelligence” was introduced by Alan Turing and John McCarthy in 1955. The Darthmouth workshop proposed to proceed the study “of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves “. Later in 1950s McCarthy introduced Lisp language (LISt Processor), which became the first tool to develop “real” AI applications.
The fourth wave – Expert Systems, from 1970s to 1980s. Expert system (ES) is a computer application that has “built-in” intelligence – knowledge in the form of a rule base. By definition, expert system is a computer system emulating the decision-making ability of a human expert. Instead of programming language the end-user is defining his problems to the system by using the structures of the problem specific user interface.
Hypertext (Hypermedia) and WWW and Semantic Web are technologies closely related to AI, computing and information / knowledge management in the form of linked content structures; in a way this represents built-in structural intelligence in documents and document structures.
The fifth wave – AI in Architectures, from 1980s to 1990s. Knowledge engineering and AI systems are based on implementing reasoning and inference processing, instead of algorithmic data processing, directly to the computer architecture to get processing in such tasks more effective to allow effective application-specific computing. The most famous activity in this area was the Japanese nationwide project called “New (Fifth) Generation Computer System” (FGCS) coordinated by the Institute for New Generation Computer Technology (ICOT).
The sixth wave – Learning-based Narrow and Weak AI, from 2000s and continuing. Intelligent systems are based on system’s ability to adapt (change the behavior, react in feedback) and to learn about the situation, in which it is used. Learning might be first taught and then self-learning during the use of the system. The current wave of AI is based on the effective use of learning algorithms: Neural Networks, Self-Organizing Maps, Deep Learning.
The seventh wave, Deep Learning and NLP-based Generative AI, multimodal producing various types of content, including code, text, imagery, audio, video and synthetic data.
The eighth wave of human-like AI, Learning-based General AI, AGI, by means of Generative AI and Large Language Foundation Models , as a human-like, human-level Strong AI
The ninth wave of AI, superintelligence, as a human-like superhuman AI via the superintelligence alignment
The Era of Real-World AI and Hyperintelligent Hyperautomation, from 2020 and continuing.
Discovering AI World Intelligence
The Real AI has the causative power to acquire, learn and apply knowledge to manipulate a broad range of environments. The World model learning and inference are taught or encoded manifesting a deep and broad understanding of reality and self-learning and self-knowing during the use of the system.
It is plain and clear that the human-mimicking AI will never cover Human Intelligence for its biological complexity of evolution, while completing it as more powerful technological intelligence working on its own principles and rules.
Automating Learning and Intelligence in term of the anthropomorphic paradigm that Computers like Humans is the reason of all the booming and busting waves, eras or periods or cycles.
In reality, intelligent systems are based on system’s ability to interact with the world, simulating and modeling, learning and self-knowing, inferencing and communicating, adjusting and adapting to its environments (change the behavior or the settings, react in feedback, etc.) and to learn about the situation, in which it is acting.
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Then any human-mimicking AI, as ESs, AI programming, AI architectures, ML statistic algorithms, ANNs, self-driving transportation, robots, large language models (LLMs), voice assistants, GPTs, or Artificial General Intelligence, is Non-Real AI but the hardware/software/data/algorithmic automation.
To be real intelligent, the World Model Engine is requested. It is semantically and ontologically aligned with World's Data/Information/Knowledge, Algorithms, Programs and Supercomputing forms the foundation of the hyperintelligent technology.
Or, there is no real AI hardware and software, technology and techniques, models and applications, systems and platform without the Integrated World Model Intelligence Engine :
Global AI Development (GAID) Formula =
Transdisciplinary/Transformative/Translational/Transcendental/Techno-Scientific AI = Real-World AI = Man-Machine Trans-Intelligence = AI4EE =
World Data/Information/Knowledge +
Global Ontology [Causal Interaction Graph Network+ Global Knowledge Graph] +
the Web Data + Web3.0 +
AI/ML/DL/ANNs Models +
GenAI [Multimodal GenAI, Intelligent Agents, tools, platforms, applications, Google's AI Platform, Amazon Web Services' SageMaker platform] +
LLMs [LFMs, LVMs, MLMs] + General-Purpose AI +
AI Hardware [AI chips/accelerators, CPU, GPU, TPU, FPGAs, NPU, Edge AI Hardware] +
the Internet + the Internet of Things + the Industrial Internet of Things +
Extended/Virtual/Augmented Reality + Multiverse + Digital Twins (virtual replicas of physical objects, processes, or systems) +
Robotics & Automation + Edge AI on devices [laptops, smartphones, cameras, drones, robots, and sensors] + Drones + Weaponized AI/ML +
6G + Quantum Communication + Blockchain + 4D +
Emerging Technologies (Robotics, Quantum, Genetic, Bio-, Neuro-, Nano-, Cognitive, Social, Ecological and Space Engineering)+ Green Climate Technology (Renewable energy, cleaner/greener transport, energy-efficient buildings, and sustainable water consumption, eco communities) +...
All in all, the AI World Intelligence?could be?the summit of human development with?human knowledge having a long evolutionary?history:
Homo Sapiens Sapiens > Natural Language > Mythology > Religion > Literature > Philosophy > Metaphysics > Mathematics > Physics/Natural Philosophy > Science & Engineering & Technology & Arts & Humanities > Cybernetics > Computing Machines > the Internet/WWW > Emerging Technologies (Robotics, Quantum, Genetic, Bio-, Neuro-, Nano-, Cognitive and Social Engineering) > AI/ML/DL > GenAI/LLMs > Real AI > Human Intelligence > BMI/MMI > Digital Reality/Cyberspace > Digital Superintelligence > Human-Machine Superintelligence >?Global AI Internet > Intelligent Communities > 5I-World
Trans-AI: How to Build True AI or Real Machine Intelligence and Learning
Abstract
We are at the edge of colossal changes. This is a critical moment of historical choice and opportunity. It could be the best 5 years ahead of us that we have ever had in human history or one of the worst, because we have all the power, technology and knowledge to create the most fundamental general-purpose technology (GPT), which could completely upend the whole human history.
The most important GPTs were fire, the wheel, language, writing, the printing press, the steam engine, electric power, information and telecommunications technology, all to be topped by real artificial intelligence technology.
Our study refers to Why and How the Real Machine Intelligence or True AI or Real Superintelligence (RSI) could be designed and developed, deployed and distributed in the next 5 years. The whole idea of RSI took about three decades in three phases. The first conceptual model of TransAI was published in 1989. It covered all possible physical phenomena, effects and processes. The more extended model of Real AI was developed in 1999. A complete theory of superintelligence, with its reality model, global knowledge base, NL programing language, and master algorithm, was presented in 2008.
The RSI project has been finally completed in 2020, with some key findings and discoveries being published on the EU AI Alliance/Futurium site in 20+ articles. The RSI features a unifying World Metamodel (Global Ontology), with a General Intelligence Framework (Master Algorithm), Standard Data Type Hierarchy, NL Programming Language, to effectively interact with the world by intelligent processing of its data, from the web data to the real-world data.
The basic results with technical specifications, classifications, formulas, algorithms, designs and patterns, were kept as a trade secret and documented as the Corporate Confidential Report: How to Engineer Man-Machine Superintelligence 2025.
As a member of EU AI Alliance, the author has proposed the Man-Machine RSI Platform as a key part of Transnational EU-Russia Project. To shape a smart and sustainable future, the world should invest into the RSI Science and Technology, for the Trans-AI paradigm is the way to an inclusive, instrumented, interconnected and intelligent world.
HR Operations | Implementation of HRIS systems & Employee Onboarding | HR Policies | Exit Interviews
5 个月Well summarized. The eighth characteristic of industrial revolutions includes focusing on the role of government, education, and private investment. Governments are advised to maintain a laissez-faire attitude while enforcing anti-monopolistic regulations, especially in AI-related industries. The delicate balance of regulating AI systems without hindering innovation should be emphasized. With the advent of AI potentially displacing workers, governments are urged to support transitions, provide assistance, unemployment insurance, and facilitate upskilling and reskilling. High school and college education are proposed to include essential AI and Data Science courses, with a call to make college education more affordable. The role of private investment is crucial, as is evidenced by a significant increase in global investments in AI firms, with a focus on sectors like autonomous vehicles, healthcare, and business processes. The analysis underscores the need for a comprehensive approach involving government, education, and private sectors to navigate the challenges and opportunities presented by the ongoing industrial revolution. More about this topic: https://lnkd.in/gPjFMgy7
Senior Managing Director
9 个月Azamat Abdoullaev Great post! You’ve raised some interesting points.