Artificial Intelligence: Antihuman Intelligence of Anti-Intellectual Technology: MIL vs. Big Tech AI
Indeed, humans are the problem or natural mistakes. It’s not just that we’ve done the best or worst to destroy our singular world, making wars, building factories and polluting the oceans and creating gigatons of waste, launching tons of garbage even into space. It’s that there’s something about us — our mind, biology, psychology, our genetics or society — that makes it impossible for us to know the truth and make things better.
We repeat it again and again, it is NOT the climate or nuclear apocalypses are the next biggest threat, but the fast emerging anti-human AI technology, which is replicating, imitating and simulating humans, our bodies, brain, brains, behavior, business, our ideas, thoughts, jobs, tasks, or works.
We need to build real, true, ethical, intelligent machines, not competing, but completing humans, enhancing our bodies, brain, brains, behavior, business, our ideas, thoughts, jobs, tasks, or works.
REAL AI as Machine Intelligence and Learning is to model and understand reality and causality, instead of simulating human intelligence.
Or, it is a fake, antihuman intelligence and make-up learning statistical software of anti-intellectual technologies.
AI as "Antihuman Intelligence of Anti-Intellectual Technology" is devised, overhyped and owned by Big Tech AI oligopolies, as Apple, Microsoft, Nvidia, Alphabet/Google, Meta/Facebook, Amazon, and Apple, the biggest tech companies in the world by market cap in 2024. Since the launch of ChatGPT in November 2022, investors have added a speculative $8.2 trillion to the market valuations of tech’s Big Six firms: Alphabet Inc., Amazon.com Inc., Apple Inc., Meta Platforms Inc., Microsoft Corp. and Nvidia Corp.
[Real AGI Bible: Big Tech AI as an Anti-Human Intelligence: Overwriting AI, ML, LLMs, GPT-x, AGI...]
Intelligence as the World Modeling and Interaction Power
The most important challenge in AI, ML, DL or LLMs is not big data quantity and quality, powerful statistical models and algorithms or infinite computing power and specialized GPUs used for model training and inference to store model parameters, gradients, states, memory, make predictions, classify or generate similar data, like as the NVIDIA AI Inference Platform.
It is rather designing, devising, developing and deploying computing paradigms and architectures that would allow machines to program the world models, machine models of how the world works, to know, learn, predict, reason, and plan, decide, solve, act and interact with the world.
The world model or reality simulation and universe understanding computing engine constitutes the most critical element of intelligent computing machinery.
It is argued that the mainstream AI modeling, simulating, replicating or replacing human intelligence is classified as an "Antihuman Intelligence for Anti-Intellectual Technology".
We proceed with our examples of how conceptually defective the so-called Big Tech AI models are, all in line with Global AI Big Tech Class Actions: Machine Intelligence and Learning (MIL) vs. Antihuman Intelligence (AI): the Statement of Claims.
"Machine Intelligence and Learning (MIL) vs. Antihuman Intelligence (AI) (aka big tech artificial intelligence)"
"MIL as a real, human-friendly, protagonistic. moral transdisciplinary AI featuring the universe modeling, reality simulating, and world understanding and interaction capabilities vs. AI as a human-unfriendly, antagonistic, amoral technology mimicking, imitating, simulating or faking the human body, brain, brains, behavior, business"
[Azamat Abdoullaev, Trans-AI: How to Build True AI or Real Machine Intelligence and Learning]
Again, Real/True AI as MIL involves the universe modeling, reality simulating, or world understanding and interaction capabilities
vs.
AI as an anti-human intelligence of anti-intellectual technology mimicking, imitating, simulating or faking the human body, brain, brains, behavior, business.
AI as Antihuman Intelligence of Anti-Intellectual Technology
Antihuman Intelligence is artificial intelligence mimicking human intelligence or simulating human body, brain, brains, behavior, business, actions and tasks and jobs, in machines, or computer systems.
Anti-intellectual technology involves the concept of anti-intellectualism, as "hostility to and mistrust of
intellect, "the?ability?of the human mind to reach correct conclusions about what is?true?and what is?false?in?reality", arranged in a hierarchy ranging from the general to the specific, e.g. the I.Q. test, assessing intelligence, human or machine;?
an intellectual, an agent (a person or machine) engaging in?critical thinking,?research, and?reflection?about the?reality?of reality, universe, society, and who proposes solutions for the real-world problems or?socio-political-economic issues
intellectualism, the use, development, and exercise of the intellect, "critically thinking about the character of the world: (i)?rationalism, which is knowledge derived solely from?reason; and (ii) empiricism, which is knowledge derived solely from sense experience",?
all for comprehensive, coherent and consistent, truthful representations of the world.
Again, intelligence relates to the creation of new categories of understanding, based upon similarities and differences, while intellect relates to understanding existing?categories. Intellect?concerns the?logical?and the?rational?functions of the human mind, and commonly focused on?truths, facts?and?knowledge.
In philosophy, science, psychology,?systems neuroscience, or cognitive science, intelligence and?intellect?(mind) is to describe how people?understand?the world and?reality.
The idea that humans, animals, and intelligent systems use world models has been exploited in fields of engineering such as automata theory, control and robotics, cognitive science and engineering, reinforcement learning, and symbolic AI.
Real AI is not about replicating some intelligence, general or specific, but after modeling, simulating and understanding reality, like as human intellect and intelligence.
Otherwise, it is an antihuman intelligence of anti-intellectual technology, replicating, copying, imitating or faking humans, our bodies, brain, brains (intelligence), behavior and business, as our jobs, tasks and products, as plagiarizing human-generated thoughts, ideas, concepts, language, content, text, video, images, audio, code, arts, etc.
For example, generative AI is designed to mimic human-created content "pirating copyright data from all over the internet, from social media posts, newspapers, books and even YouTube videos.. and any lawsuit against the genAI companies could actually end the AI industry as we know it".
The Poverty of Big Tech AI World Models
Only some rare minds from the Big Tech R & D labs are getting some awareness of critical necessity for big/large world models, but as "anti-human intelligence of anti-intellectual technology", human-mimicking machines "to learn world models in a self-supervised fashion and then use those models to predict, reason, and plan".
One of them is Yann LeCun, VP and Chief AI scientists at Meta. LeCun proposes that the ability to learn “world models” — internal models of how the world works — may be the key. [Yann LeCun on a vision to make AI systems learn and reason like animals and humans].
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The idea that humans, animals, and intelligent systems use world models goes back many decades in psychology and in fields of engineering such as control and robotics. LeCun proposes that one of the most important challenges in AI today is devising learning paradigms and architectures that would allow machines to learn world models in a self-supervised fashion and then use those models to predict, reason, and plan.
The world model module constitutes the most complex piece of the architecture. Its role is twofold: (1) to estimate missing information about the state of the world not provided by perception, and (2) to predict plausible future states of the world. The world model may predict natural evolutions of the world or predict future world states resulting from a sequence of actions proposed by the actor module. The world model is a kind of simulator of the part of the world relevant to the task at hand. Since the world is full of uncertainty, the model must be able to represent multiple possible predictions. A driver approaching an intersection may slow down in case another car approaching the intersection doesn’t stop at the stop sign.
Lots of confusion about what a world model is. Here is my definition:
Given:
- an observation x(t)- a previous estimate of the state of the world s(t)- an action proposal a(t)- a latent variable proposal z(t)
A world model computes:- representation: h(t) = Enc(x(t))- prediction: s(t+1) = Pred( h(t), s(t), z(t), a(t) )
Where- Enc() is an encoder (a trainable deterministic function, e.g. a neural net)- Pred() is a hidden state predictor (also a trainable deterministic function).- the latent variable z(t) represents the unknown information that would allow us to predict exactly what happens. It must be sampled from a distribution or or varied over a set.
It parameterizes the set (or distribution) of plausible predictions.
The trick is to train the entire thing from observation triplets (x(t),a(t),x(t+1)) while preventing the Encoder from collapsing to a trivial solution on which it ignores the input.
Auto-regressive generative models (such as LLMs) are a simplified special case in which
1. the Encoder is the identity function: h(t) = x(t),
2. the state is a window of past inputs
3. there is no action variable a(t)
4. x(t) is discrete
5. the Predictor computes a distribution over outcomes for x(t+1) and uses the latent z(t) to select one value from that distribution.
The equations reduce to:
s(t) = [x(t),x(t-1),...x(t-k)]x(t+1) = Pred( s(t), z(t) )
There is no collapse issue in that case. As simple as that.
Real AI as Integrative World Models
The creation of powerful computing intelligence is associated with the innovation of AI/ML/DL capabilities in the following technologies:
Machine Intelligence and Learning implies the World Modeling and Reality Simulating and Universe Understanding Computing Framework integrating all valuable AI/ML/DL models.
MIL = Real/True AI = the World Models [Reality Simulation/Universe Understanding Engine, Universal Formal Ontology] + Artificial Intelligence + Machine Learning + Generative AI + Large Language Models +the Internet/Web + IoT + Robotics +....
The world model underpins and integrates all general or special models, such as mental models, conceptual models, scientific models, mathematical models or machine models, as AI/ML/DL models, with various special algorithms defining problem-solving operations, from automated planning to optimization algorithms to statistical and ML algorithms to quantum algorithms.
The best world models, as representations of reality or world representations or world simulation generally, enabled us to build our civilization, with its sciences and technologies, industries, infrastructure, cities and societies.
They help us understand our life and the world around us, guiding our perception, cognition, thinking, decision-making, or behavior.
The reality model theory of intelligence makes the fundamental assumption that intelligence, learning or reasoning, natural or artificial, depend, not on data or logical form, but on world models.
The world models are critical to think critically, rationally and effectively. As there is no complex human intelligence with its reality representations in the mind, there is no machine intelligence without its world simulations in machines.
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We need to remake society toward human ends rather than the end of humans