True AI vs. Fake AI: Between Technological Utopia and Dystopia
According to a poll conducted by Scientists for Global Responsibility, over 80 percent believe there’s a medium to high chance of things going badly wrong with Artificial Intelligence (AI) , while 96 percent say AI needs more regulation, and 82 percent thought that AI was most likely to create a dystopian rather than a utopian future.
We debunk the dystopian vision about "machines mimicking human intelligence", implementing human-like, human-level and superhuman intelligence, as humanoid artificial intelligence (HAI), creating a techno-social dystopia, arriving in a matter of years.
The dystopian HAI technology is substituted by the real, true, transdisciplinary AI technology, creating a socio-technological utopia disrupting the present governmental and corporate socio-technological dystopias.
A Disruptive Technology That Could Dominate the Future
There has been a lot of talks and hype around AI since ChatGPT was released in 2022. Nowadays, AI is everywhere; with all prospects to stay with us and impact our lives for the foreseeable future.
AI is emerging as a general-purpose, disruptive technology (innovative inventions) promising to disrupt the world, in all possible ways:
the way people interact with reality,
the way society behaves, thinks, or interacts,
the way businesses or industries operate,
the way we access and process information,
how humans do their jobs,
how we understand the nature of life and mind,
all to boost humanity into the future, techno-utopian and techno-dystopian.
Then sort and kind of AI to take over decides the sort and type of human future:
Tech-Utopia, where non-human AI machines completing humans, "in which laws, government, and social conditions are solely operating for the benefit and well-being of all its citizens", and AI technology allows mankind to make scientific, technological, social, economic, political, and cultural advancements
Tech-Dystopia, where humanoid, human-mimicking AI machines competing humans, taking over jobs and ultimately the human world.
So, how we define AI is a matter of life or death in all sense, what AI is, how it works, or how it must be defined, designed, developed, deployed and distributed are existentially important.
As it was posted in Trans-AI: a real and true AI (TruthAI):
We need to know what is the realistic conception of AI having the potential to transform the human world producing trillions of dollars of socio-economic value.
It is critical to differentiate real/causal machine intelligence and learning (MIL) from human-mimicking AI and statistical machine learning, as TruthAI vs. FalseAI, epitomized by ChatGPT-3 or ChatGPT-4.
What is True Artificial Intelligence?
AI is about endowing computing machinery with [non-human] intelligent powers and physical prowess.
AI is NOT about "simulating human thought processes" or "replicating certain types of human intelligence". It is is promoted by the SOTA multimodal large language models (MLLMs), as if merging the reasoning capabilities of Large Language Models (LLMs), for instance GPT-3/4/5] or LLaMA-3,
with the ability to receive, reason, and output with multimodal information.
[True. Real] AI is about "simulating and modeling and understanding reality and mentality and data, in all complexity", where
the world modeling of reality and machine intelligence involves philosophy, science, mathematics and computational science or scientific computing
the mentality model of reality and machine intelligence involves cognitive sciences and computational neuroscience
the data model of reality and machine intelligence involves mathematics, statistics, data science and computer science and ML models and algorithms
the computational model of reality and machine intelligence involves software and hardware AI tools, platforms and infrastructure
Complexity reflects the behavior of a reality-mentality-data-computation ecosystem "whose parts and components interact with each other in multiple ways and follow local rules, leading to non-linearity, randomness, collective dynamics, hierarchy, and emergence".
True AI looks for answers to the following fundamental questions:
Can machines autonomously interact with the world in non-human ways?
Can machines self-learn in non-human ways?
Can machines think in non-human ways?
Can machines decide in non-human ways?
Can machines act and react in non-human ways?
AI is commonly defined as "building machines that are intelligent".
True "AI holds the potential to address complex challenges from?enhancing education and improving health care, to driving scientific innovation and climate action". However, HAI systems pose risks to privacy, safety, security, and human autonomy.
Common AI as a human-like, humanoid intelligence
Common AI is on a wild goose chase of human-like, humanoid intelligence, misdefined as "the simulation of human intelligence processes by machines, especially computer systems".
For that “AI is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence” is not of any sense of value.
Turing's paper "Computing Machinery and Intelligence" (1950), and its subsequent Turing Test, established the fundamental goal and vision of artificial intelligence, as focused on only human intelligence or behavior.
Norvig and Russell go on to explore four different approaches of the field of AI:
Real and True AI is thinking and acting rationally...or CAUSALLY, involving deep understanding, innovation, real-world problem-solving, informed decision-making and effective interactions.
Its algorithm identifies CAUSATIVE patterns, relationships, or structures in the data it receives and then uses the CAUSAL UNDERSTANDING AND INFERENCE to discover, innovate, prescribe or predict outputs.
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Examples of Humanoid AI Technology
Humanoid Fake AI is "the development, deployment, and maintenance of computational systems that can replicate certain types of human intelligence", "the simulation of intelligent behavior in computers", etc.
Fake "AI applications typically use advanced?machine learning algorithms?and vast amounts of computational power to process, analyze, and learn from?data?in ways that mimic specific aspects of human cognition, like?pattern recognition?and?inductive reasoning".
Generative Fake AI "uses deep learning techniques to analyze huge datasets of text, code, or multimedia?content?– and then uses?predictive modeling?to create entirely original, yet stylistically consistent, outputs".
Deep learning stacks ML algorithms in a hierarchy of multi-layered neural networks of mathematical algorithms increasing complexity and abstraction.
WHAT ARE USE CASES OF FAKE ARTIFICIAL INTELLIGENCE?
AI's Typology
[Humanoid] AI is categorized as being either weak AI or strong AI,
All today's human-like, fake AI is weak AI, also known as?narrow AI, capable of performing a limited number of predetermined functions. It is a simulation of human cognitive function, appear to think, but which is never conscious in any sense of the word. Weak AI could look super intelligent on a super narrow task.
This includes?multimodal fake AI?chatbots?like?Google Gemini?and?ChatGPT. These two families of?large language models?(LLMs) had to be programmed how to respond to?user prompts, and they will require more programming if they are going to be used for new tasks.
Strong AI as a Human-like and Human-level or Beyond AI
Strong, human-like Fake AI doesn’t exist, and never will exist, but researchers and fake AI/AGI advocates have been involved in a wild goose chase in two distinct types of strong AI:
human-like and human-level?intelligence as artificial general intelligence?(AGI)
and?
human-like and superhuman intelligence as artificial superintelligence; it is often depicted in science fiction books and movies, as completely surpassing AGI capabilities or human beings.
A scientific alternative to the humanoid AI/AGI/ASI is a Real, True, Transdisciplinary AI, as it is described in Trans-AI: How to Build True AI or Real Machine Intelligence and Learning.
Trans-AI will be able to know, learn, understand, innovate, reason, interact, and solve real-world problems?in a mono-, inter-, multi-, trans-disciplinary manners across all domains and environments.
Trans-AI Technology (TAIT) lay the groundwork for the socio-technological deep utopia of man-machine hyperintelligence embracing techno-realism, techno-progressivism and post-humanism, while disrupting the currently prospering socio-technological humanoid AI dystopias (see the Supplement).
Conclusion
The journey of artificial intelligence oscillates between an idyllic tech-utopia and a dark tech-dystopia, the utopia-dystopia dichotomy of shadows and light.
In the techno-utopian world, real AI machines and humans complete each other, coexisting harmoniously, each augmenting the other’s abilities, transforming human lives, revolutionizing industries, and solving complex real-world problems.
In the techno-dystopian world, HAI systems are going rogue, outstripping humanity in intelligence and seizing control, manipulating human behavior, becoming autonomous, becoming devoid of human oversight, and existentially harmful. Hollywood has capitalized on these scenarios, producing techno-dystopian movies like The Terminator, The Matrix, I. Robot, Ex Machina, Blade Runner 2049.
Resources
SUPPLEMENT 1:
The Unbearable Costs of AI Bubble for AI Startups, Model Training and Operation
Most AI startups fail or sold to big tech due to lack of IPs, limited revenue streams and huge expenses:
Infrastructure: Expenses for server hosting, data management, and processing power to handle large-scale AI training, fine-tuning, and deployment.
Licensing and Data Annotation: Fees for acquiring or licensing datasets used to train models, as well as for characters’ IP.
R&D and Development: Salaries for AI researchers, data scientists, and engineers developing new models and algorithms.?Software Development: Costs related to building and maintaining the platform.
Operational Costs,?such as customer Support: Maintaining a team to handle user inquiries and issues.
Marketing and Sales (extremely expensive for B2C products like character.ai): Costs for customer acquisition, advertising, etc.
Compliance and Legal:?It is hard to say how much these AI companies invest in legal and compliance.
General Overhead,?eg., Office Space, administrative (Accounting, HR, Legal, and others)
Tech giants are projected to spend up to $1 trillion on data centers, real estate, chips, and other equipment to build AI models, tools, and products, while massively fail for real-world problem solving.
Big tech intends to over-profit on the current “picks and shovels” phase, while real AI’s “killer application” has yet to emerge.?
SUPPLEMENT 2: Socio-technological dystopias
Dystopias (cacotopias or anti-utopias)?are often characterized by?dehumanization,?fear and distress, propaganda, police state and total machine surveillance, authoritarian?governments, big tech megacorporations,?environmental disasters,?or other characteristics associated with a dramatic decline in society.
Many believe that dystopias are dystopian fictions, while existing in such societies.
Two types of social dystopia are domineering in our socio-techno-dystopian world:
The governmental socio-technological dystopias with total autocracy and technical information control and mass surveillance, led by China and Russia, "which has taken control of the lives of the masses and curved it to fit into the needs of their beliefs and practices".
the corporate dystopias with total digital control and mass surveillance, led by big tech, "which has taken control of the lives of the masses and curved it to fit into the needs of their beliefs and practices".
All modern warfare, physical, cyber or hybrid, is usually initiated by the socio-technological governmental dystopias, such as:
the U.S.-Iraq/Syria/Afghanistan wars
the Russia-Ukraine civil war
the Israel- Palestine/Gaza/Iran wars...
Everything we can imagine is real
2 个月We need more regulations on AI, what we must not do
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2 个月Good to know!