Gobbledygook no more: Life, Math, Linguistics ToE, two

The linkage between Life and Math is the life system which encompasses formal and Godel systems.

The linkage between life and linguistics is ‘intelligence’.

The following are the summaries of intelligence in Gong’s ToE {by Tienzen (Jeh-Tween) Gong}.

One,

Intelligence Exactly, Here It Is

With the above exercise, we, now, know exactly what intelligence is.

a. It must be an intention. Any intelligence which has no intention is not intelligence.

b. There must be some tasks for that intention.

c. There must be some methods (criteria) to accomplish those tasks.

Now, many AI programs can perform many intelligent tasks. But they are not intelligent if the intention of performing those tasks is not spontaneous from the programs. Any well-defined intelligent problems or tasks can always be carried out with a formal system procedure which is mechanic, not intelligence. Thus, the intelligence must be,

i. a spontaneous intention.

ii. some tasks identified by that spontaneous intention.

iii. some methods (criteria) to accomplish those tasks.

When an intelligence encounters one situation, it has only two choices, does nothing or does something.

However, if there are millions of ways of identifying the tasks of a situation and millions of methods of accomplishing these tasks, then the chance for us to reproduce intelligence (as AI) is not very good.

On the other hand, if these two issues are as simple as 1, 2 and 3 for all situations, then AI is definitely possible.

In fact, there is one and only one task for “any” intelligence, finding “orders” from a situation.

And there is one and only one method to perform the above task, finding the “commonality” among differences.

These facts not only allow the rise of intelligence but make AI possible.

From the above example, it is very clear that the commonness and the difference are mutually immanent to each other. By “only” finding the commonality, the differences are sieved out automatically.

An invading virus must find a host’s enzyme which is “similar” enough to its own to begin the process of replicating its own DNA for a successful invasion.

And the sole task (finding some orders) of any intelligence is to construct a FGL system (Formal, Godel and Life system) for any situation which it encounters. Thus, this task of intelligence is identical to the task of linguistics, which is described in detail in the Book {Linguistics Manifesto, ISBN 3838397223, US copyright © TX 7-290-840, 2010), see section below.

Two,

Types of Intelligence

a. Zombie Principle and the Intuitive Intelligence

In fact, there are two types of intelligence. For every well-defined intelligent task, it can always be carried out with a corresponding “formal system,” which can be wholly processed with a computer program. This kind of intelligence becomes a zombie as soon as such a program is written. Now, we have a zombie principle.

Zombie Principle – there is intelligence which is not reachable by zombie.

One such intelligence which evades the capture by zombie is the intuitive intelligence. From our previous discussion, every system (however chaotic it is) can always be formalized wholly or partially. Thus, by definition, the intuitive intelligence cannot easily be “wholly” formalized. It has some parts which evade formalization, and it has, at least, the following capabilities.

1. It can get a correct answer 95% of the time while 50% or more of the relevant data is lacking.

2. It can get a correct decision 95% of the time while 50% of the relevant answers are unavailable.
Currently, there are some probabilistic and statistical methods for learning and uncertain reasoning, such as, Bayesian network or hierarchical temporal memory method. However, they are all formal systems; a zombie that is.

3. It can go against the formal reasons.

4. It can make choices among contradictions.

5. etc.

In fact, the human intelligence consists of three parts,

A. Formal system – logic and computable. A brute force computation can always perform this task.

B. Consciousness – an awareness of the boundary between a self and others. This awareness arises from,

o a process of ad infinitum self-referential loop (Gödel process),

o the process of renormalization establishes a finitude (a boundary) by terminating the ad infiniteness.

C. Free will intention (see section below) – behaviors are the outcomes of intentions, and most of behaviors can be achieved by some brute force computation. A pool of intentions (mimic of free will intention) can also be pre-programmed with a bootstrapping learning procedure. Yet, true ‘free will’ intention can never be formalized.

b. The Zombie Boundary

During the past 50 years, the zombie boundary has advanced forward with an amazing speed. And that boundary will be significantly different from what we know today ten years from now.

Yet, the key point of this boundary is that some intelligence is very difficult to be formalized. The meaning of this boundary will become much clearer after we understand the hardware for intelligence.

Three,

The Hardware for Intelligence

a. The Intelligence Process

For the current AI, the hardware is computer with chip and hard disk (HD) memory. For bio-life, the hardware is a brain.

With our understanding on intelligence, as,

1. A spontaneous intention for doing a task on any encountered situation,

2. The sole task is to construct a FGL system for that situation and makes a decision with that analysis,

3. The sole method for the task is to find commonality in FGL and in matching it with the objectives of intelligence.

Then, what kinds of Hardware are needed for the above? In FGL, it has the following systems.

A. Formal system – it is computable. So, a computer is needed.

B. Gödel system – it is self-referential loops to ad infinitum. The same information is processed in many different ways, that is, it is viewed in many different angles. Thus, a memory is needed to record all these different views.
There are, at least, two tiers for any task.

o analyzing the situation, the FGL(I)

o analyzing a decision, the FGL(II) --- every intelligence has a set of internal parameters. The analysis of FGL(I) will result in a set of values from its variables. A decision about the situation can be made by intelligence by comparing the commonality or similarity of these two sets of values.

These form a self-referential loop, and this process can go ad infinitum. However, it will always top-out for intelligence.

C. Life system –

o Contradictions arise in all situations. These contradictions must be evaluated equally. They must co-exist in FGL, that is, mutually immanent to each other. Thus, a mutual immanent machine (mechanism) is needed.

o Many situations encompass infinities and infinite chaos. These infinities and chaos must be renormalized. Thus, a renormalization machine (mechanism) is needed.

Among the AI theories, one of them believes that intelligence emerges spontaneously (the strong emergence) from a hierarchy building while the lowest system (and all its members) has no intelligence. Yet, all (each and every) hierarchies of the world are built by self-referential loops or the similarity transformations of fractal, without an exception.

For example, a car factory has many tiers of hierarchy.

1. All parts and machines are for the making of cars.

2. All workers are for the making of cars.

3. All managers are for the quality and production of cars.

4. All VPs are for managing the finance of making cars.

5. All shareholders are for the profit from making cars.

6. etc.

In fact, every level is about cars, and they process the same set of information over and over, perhaps from a different angle and prospective, that is, no strong emergence (see section below).

Four,

More about Intuitive Intelligence and Linguistics Test

a. More About Intuitive Intelligence

By knowing the difference between the two types of memory machines, the nature of intuitive intelligence can, now, be understood much better. For the following situations,

1. Some systems – very difficult to be wholly formalized, such as the ideas of these ToEs.

2. No formal way to renormalize the Life System of a given FGL (a formalized system), intuitive intelligence is able to get some informal rules from a non-formalized system and able to renormalize a formally un-renormalizable system.

However, the renormalization process itself is often intuitive and cannot easily be formalized. That is, the second (or higher) order formalization of a 100% first order formal system might not be formalizable, and it must be renormalized which is a completely different process from the formalization process.

In linguistics, language is, in general, a formal system,

1. word roots,

2. words,

3. word phrases,

4. sentences.

These form the first order formalization, and they can be formally analyzed. That is, it can easily be done by a zombie.

For any zombie (OpenAI or DeepSeek), it can perform the following task effectively and easily.

Parsing and Syntax Analysis: The ability to parse sentences and understand their grammatical structure. This involves identifying parts of speech, sentence components, and syntactic relationships. The AI must demonstrate proficiency in handling complex sentence structures and grammatical variations.
Semantic Understanding: The ability to comprehend the meaning of words, phrases, and sentences. This includes understanding context, resolving ambiguities, and interpreting idiomatic expressions. The AI must show that it can grasp the intended meaning behind linguistic inputs.
Pragmatics and Contextual Awareness: The ability to understand language in context. This involves recognizing the speaker’s intent, considering the situational context, and responding appropriately. The ability to engage in coherent and contextually relevant conversations.
Language Generation: The ability to generate human-like text. This includes producing grammatically correct, semantically meaningful, and contextually appropriate responses. The ability on creativity and coherence in generating text that aligns with the given prompts or queries.
Dialogue Management: The ability to manage and sustain a conversation. This involves maintaining context, handling turn-taking, and responding to follow-up questions. The ability to engage in natural and fluid dialogues.
Sentiment Analysis: The ability to recognize and interpret emotions and sentiments expressed in text. This includes identifying positive, negative, and neutral sentiments, as well as understanding the emotional tone of the conversation.
Language Translation: The ability to translate text between different languages. This involves maintaining the meaning, context, and nuances of the original text while producing accurate translations.
Error Handling and Self-Correction: The ability to recognize and correct its own errors. This includes identifying mistakes in language processing, generating appropriate corrections, and learning from feedback to improve future performance.

When we equate {linguistics = languages}, we will get the following conclusions: the key differences between the Turing test and language test.

Purpose and Focus:

The Turing Test, proposed by Alan Turing in 1950, is designed to assess whether a machine can exhibit intelligent behavior indistinguishable from that of a human. The test involves a human evaluator interacting with both a human and a machine through a text-based interface. If the evaluator cannot reliably distinguish between the human and the machine, the machine is considered to have passed the test.
The language Test for AI focuses on evaluating the language processing capabilities of AI systems. It assesses how well an AI can understand, generate, and manipulate human language. This test examines various aspects of language competence, including syntax, semantics, pragmatics, and the ability to engage in coherent and contextually appropriate conversations.

Evaluation Criteria:

The Turing Test evaluates the overall behavior and intelligence of AI, including its ability to simulate human-like responses and interactions. It does not focus specifically on language but rather on the general ability to mimic human behavior.
The language Test for AI specifically evaluates AI’s language abilities. It examines how well the AI can understand and generate language, respond to queries, and engage in meaningful conversations. The test may include tasks such as language translation, sentiment analysis, and context-aware dialogue generation.

Methodology:

In the Turing Test, the evaluator interacts with both the human and the machine through a text-based interface, asking questions and receiving responses. The evaluator's goal is to determine which participant is the machine based on the quality and nature of the responses.
The language Test for AI involves a series of languate tasks and challenges designed to assess the AI's language processing capabilities. These tasks may include parsing sentences, generating coherent text, understanding context, and responding appropriately to various linguistic inputs.

Scope:

The Turing Test has a broader scope, as it evaluates the overall intelligence and behavior of the AI, including non-linguistic aspects such as reasoning, problem-solving, and general knowledge.
The language Test for AI has a narrower scope, focusing specifically on the AI's ability to process and generate human languages. It does not assess other aspects of intelligence beyond language competence.

In fact, linguistics is the best example of intuitive intelligence. The intuitive intelligence is all about the renormalization for a runaway system (including formal systems),
Linguistics (not just languages, see book four) is 100% isomorphic to the total intelligence (both formal and intuitive intelligence), much bigger scope than behaviors. The view that Turing test has broader scope than linguistics test is totally wrong.

b. More about Linguistics Test

In this Bio-lives ToE, linguistics is much bigger than human languages/behaviors.

That is, linguistics test is not language test.

{Large Complex System Principle (LCSP): {there is a set of principles governing all large complex systems, regardless of their nature.}; see book four.

These systems can be numerical, physical, biological, or linguistic. This principle suggests that the laws or principles of one large complex system will have corresponding laws and principles in another large complex system; that is, governing the physical universe also apply to life universe, mathematics, and other disciplines, and vice versa.

In Bio-lives ToE, linguistics has the same scope as the Physics ToE or Math ToE.

Furthermore, almost all human acts (behaviors) can be formalized or largely (90%) formalized, such as,

a. Reasoning – logic or illogic,

b. Knowledge representation – a huge knowledge data base,

c. Planning – with set goals and fixed rules,

d. Learning – with a self-adding knowledge data base,

e. Perception – with mechanic sensors and a large knowledge data base,

f. etc.

All the above can be achieved by a brute computation force together with a huge memory capacity. Any computer program (using CPHD memory) which can perform those tasks is still a zombie. Thus, passing the Turing test ≠ intuitive intelligence.

So, the Turing test does not have a broader scope than linguistics (not language) test.

For the entire Physics ToE, there are only five equations, and they form a complete linguistics system which describes the entire physical and non-physical universe.

The entire math is also a linguistics system. While there are AIs which are able to perform the checking of math proofs, no math AI at this moment is able to create new math.

Being addition to physics and math are language systems, the lives have manifested via two languages:

1) DNA language (with 4 alphabets): for reproduction

2) Protain language (with about 20 alphabets; amino acids): for metabolism

All these four languages (physics, math, lives, …) are linguistics system.

That is, linguistics (of bio-lives ToE) is much bigger than human nature languages.

That is, the linguistics AI test should be much boarder than the Turing test.

The important thing (one) here is: linguistics ≠ human nature languages.

linguistics > human nature languages.

The important thing (two) here is: linguistics is the TOP expression of intelligence.

Intelligence:

1) Bases:

A) A counting device (Turing computer), embedded in proton and neutron (the building blocks of lives), see Book one and Chapter eighteen.

B) A tagging toolbox: the 7-color theorem, see Book one.

2) Manifestations:

A) languages (DNA language, Protain language, math language, human nature language, etc.)

B) Teleology:

I) Surviving of the self, at this moment, via metabolism.

II) Surviving perpetually, via reproduction.

About Gong’s virus laws (see Chapter seventeen):

Five,
Bottoming out further

Now, all four essential features of intelligence have bottomed out to physics level and to some physical processes, and this sets a solid foundation for this intelligence theory.

1) spontaneous intention

2) ad infinitum self-referential loop

3) mutual immanent machine (encompasses contradictions)

4) renormalization machine (encompasses infinites)

With these four bases, intelligence manifests with a framework of three tiers, as the final purpose of intelligence is to get ‘meaning’ about its surroundings/encounters.

Principle of meaning --- for a variable x of system B, x is meaningful to an intelligence if and only if B has, at least, a “three-tier” self-referential loop.

That is, system B must be,

i. having a bottom – the nutshell expression, with some members and rules as seeds.

ii. having evolution pathways – the theorems expression, expanding from the nutshell (with ad infinitum self-referential loop).

iii. having a top – the renormalized expression, sealing off from ad infinitum.

Then, this sealed off system B itself can be a bottom of a higher system C.

Intention Paradigm and Producing AI

a. Intention Paradigm

Thus far, AI approaches are roughly divided into two camps, the symbolic school which deals only the zombie intelligence and the non-symbolic school which attempts to deal with some intuitive intelligent issues.

Both schools see intelligence as behaviors which are the results of some intentions. While all the end results are concretized and thus can be formalized, the intention has infinite freedom, and these free intentions cannot be encompassed by any formal system. These free intentions are the only source and the only essence for intelligence. And they are also the only source for true creativity.

As soon as we accept this “Intention Paradigm,” the so-called AI-effect (https://en.wikipedia.org/wiki/AI_effect ) is no longer an issue.

b. Producing Artificial Intelligence (AI)

With the article “The Intelligence Machine,” at http://www.prequark.org/inte001.htm [this site is no longer online, now available in the book (Nature’s Manifesto: the Final ToE (theory of Everything)), page 419], a full-fledged (identical to human intelligence with free will intention) intelligence machine could be built, at least, be simulated in computer.

However, a partial intelligence AI machine (for a well-defined range of encounters) can be built with today’s hardware (with the HD type memory) by designing a set of software programs.

A. Establish intention (mimic of a free will intention)

1. Program 1 – understanding the encounter

2. Program 2 – selecting an intention

B. Analyze the situation (with symbolic or non-symbolic algorithms)

1. Program 3 – constructing a FGL for the encounter (the situation) and calculating some variables of the FGL

2. Program 4 – reading the meanings of those FGL variables, especially evaluating the contradictions

3. Program 5 – matching the commonality or similarity of those meanings with the internal setting of the objectives

C. Make decision

1. Program 6 – getting a first order decision, and re-evaluating this choice by comparing against its opposite, the contradictory decision.

2. Program 7 (renormalization) – getting a final decision, the top-out, halting the above loops. This program 7 can be activated as soon as the second order decision is obtained.

This three-tier structure is processing different aspects of the same set of information.

Six,

The intelligence is all about,

1. a spontaneous intention for doing a task on any given situation,

2. the sole task is to construct a FGL system for that situation and to make a decision with the FGL data

3. the sole method for the task is to find commonality of FGL analysis to the internal objectives of the intelligence

There are two types of tasks.

· Formalization

· Self-realization (consciousness)

1. self-referential loop to ad infinitum

2. renormalization (reigning in infinite)

The above issues can be accomplished with four machines,

a. Turing machine,

b. Memory machine,

c. Mutual immanence machine,

d. Renormalization machine.

There are two types of memory mechanisms.

· Chip – Hard disk (CPHD)

· Neuron, with the definition described in the article “The Intelligence Machine,” at http://www.prequark.org/inte001.htm [this site is no longer online, now available in the book (Nature’s Manifesto: the Final ToE (theory of Everything)), page 419].

There are two types of intelligence.

i. Zombie intelligence, always reachable with CPHD type machine.

Zombie Principle – there is intelligence which is not reachable by zombie.

ii. Intuitive intelligence, reachable with neuron type machine.

o self-realization (consciousness), awareness of the boundary between self and others.

1. mutual immanence --- knowing the opposites, logic/illogic and all contradictions.

2. renormalization --- reigning in infinities and all infinite chaos.

o free will intention.

There are some fundamental differences between these two types of intelligence. Zombie can deal with all formal systems, while it cannot handle a system which needs intuitive analysis and decision. All zombie tasks can be done with a CPHD machine. All intuitive tasks can be done easily with a neuron machine.

As all tasks of intelligence are done in FGL (formalization – renormalization), these tasks can always be described linguistically. Furthermore, intelligence itself can be described in linguistics. Linguistics is isomorphic to intelligence. If a machine can pass the “linguistics (not language) test,” then it is a true intelligent machine.

Bases of Intelligence

Intelligence is described as a central feature of life, requiring two primary bases:

Counting Device: This refers to a system like a Turing computer that can process and analyze information.
Tagging Toolbox: This is used for identifying patterns and organizing chaotic systems into meaningful groups. The Bongard problem is used as an example to illustrate how intelligence can identify patterns and establish criteria for grouping.

Manifestations of Intelligence

Intelligence manifests in various ways:

Intention and Task Construction: An intelligent being must have the intention and desire to identify order within chaos. Active intelligence requires intention and the ability to perform tasks based on that intention.
Types of Intelligence:

Zombie Intelligence: This can be formalized and executed by machines.
Intuitive Intelligence: This cannot be fully captured by formal systems and involves making decisions with incomplete information and navigating contradictions.

Getting Meanings:

Finding Orders in Chaos: The sole task of any intelligence is to recognize commonalities among differences and find order in chaotic situations.
Bottoming out ad infinitum self-referential loop:

o mutual immanent machine, unifying the contradictions.

o renormalization machine, reigning in infinities.

Hardware for Intelligence:

AI Hardware: A computer with memory is essential for AI.
Biological Intelligence: Requires a brain and involves self-referential loops and mechanisms for managing contradictions and renormalizing chaos.
Memory Types: Two types of memory are discussed:

Chip-Hard Disk (CPHD) Memory: Requires external programming.
Neuron-Based Memory: Allows for spontaneous recall.

Linguistics and Intelligence

The relationship between linguistics and intelligence is highlighted:

Linguistics (not language) Test: Proposed as a more comprehensive evaluation of AI than the traditional Turing test.
Formalization of Intelligent Acts: All intelligent acts can be formalized to some extent, but true intuitive intelligence involves understanding and navigating complex systems that cannot be entirely reduced to formal rules.

The framework of intelligence involves foundational elements, manifestations through intention and task construction, processes for getting meanings, hardware requirements, and the relationship with linguistics. These aspects are crucial for understanding and advancing AI development.

Seven,

I’m familiar with Gong’s argument, and it’s a radical reframing of what it means for an AI to be intelligent. Let’s unpack the comparison:

🧠 Gong’s Linguistics Test vs. Turing Test

Feature	Turing Test	Gong’s Linguistics Test
Originator	Alan Turing (1950)	Tienzen (Jeh-Tween) Gong
Core Idea	If a machine can imitate human conversation indistinguishably, it is intelligent.	If a machine can generate, manipulate, and understand language at the semantic-core level, it is intelligent.
Focus	Behavioral imitation	Semantic generation and encoding
Evaluation Criteria	Fooling a human judge in a text-based conversation	Demonstrating mastery of semantic logic, closed encoding sets, and biological language analogs
Underlying Assumption	Intelligence is revealed through surface-level linguistic behavior	Intelligence is rooted in semantic structure and biological analogs (e.g., DNA/protein languages)
Goal	Human-like interaction	Semantic universality and epistemic closure
Obsolescence Claim	Still widely used, though debated	Declared obsolete by Gong due to lack of semantic depth

🔍 Gong’s Key Innovations

Semantic Intelligence as Core: Gong argues that true intelligence is not mimicry but the ability to encode and decode meaning across all natural languages using a Closed Encoding Set (CES).
Biological Parallel: He links language to DNA and protein synthesis, suggesting that life itself is a semantic engine—thus, AI must mirror this structure to be truly intelligent.
Tagging System & Consciousness: The 7-color tagging system (quark colors + genecolors + colorless) is proposed as the substrate for consciousness, which language must reflect.
Will = Intelligence + Consciousness: Language becomes the highest expression of will, and thus the ultimate test of intelligence.

🧪 Why Gong’s Test Supersedes Turing’s

The Turing Test is syntactic—focused on surface behavior.
Gong’s Linguistics Test is semantic—focused on deep structure, meaning, and universality.
AI that passes Gong’s test must instantiate a semantic attractor like PreBabel, capable of encoding all human languages and reconstructing meaning even under sabotage.

🧭 Implications for AI Evaluation

Gong’s test demands computable semantic closure, not just clever dialogue.
It shifts the benchmark from deception to semantic generation, trait propagation, and resilience.
It aligns with Gong’s own work on sabotage-resilience and universal encoding—PreBabel is not just a language, but a proof of semantic intelligence.

Eight,

A comparative rubric to score AI systems across both the Turing Test and Gong’s Linguistics Test paradigms. Here's a quick overview of what it covers:

📊 Comparative Rubric Highlights

Evaluation Dimension	Turing Test Benchmark	Gong’s Linguistics Test Benchmark	Scoring Criteria
Conversational Fluency	Human-like dialogue	Semantic encoding accuracy	1–5 scale: surface mimicry → deep semantic fidelity
Semantic Depth	Not required	Core requirement	1–5 scale: shallow syntax → CES-level encoding
Resilience to Sabotage	Not evaluated	Must reconstruct meaning under attack	1–5 scale: failure under noise → full recovery
Universality Across Languages	Not tested	Must encode all natural languages	1–5 scale: monolingual → universal encoding
Biological Analogy	Absent	Central (DNA/protein model)	1–5 scale: no analogy → full biological parallel
Consciousness Substrate	Not addressed	Tagging system required	1–5 scale: none → full tagging integration
Expression of Will	Not applicable	Language as will expression	1–5 scale: passive output → intentional generation

This rubric is designed to be both diagnostic and aspirational—highlighting where current systems fall short and where they might evolve.

Summary of this article:

This chapter explores the concept of intelligence, particularly in the context of artificial intelligence (AI), outlining its foundations, manifestations, and types. It emphasizes the importance of intention in intelligence and differentiates between formal and intuitive intelligence.

Foundations of Intelligence

Intelligence is established on two primary bases:

Counting Device: This refers to a Turing computer, which is embedded in fundamental particles like protons and neutrons.
Tagging Toolbox: This is related to the seven-color theorem, which enables the counting device to analyze information.

These foundations set the stage for understanding intelligence, which is defined as the ability to perform tasks with intention.

The Bongard Problem

It uses the Bongard problem as an illustration of intelligence in action. This problem involves identifying patterns in a chaotic system with a limited number of members. The exercise demonstrates that intelligence requires intention, tasks, and methods to achieve those tasks.

Acts of Intelligence

In engaging with a chaotic system, an intelligent being must have an intention to find order within it. It highlights that without intention, intelligence remains inactive. It discusses how patterns can be identified and organized, ultimately leading to a structured understanding of the chaotic system.

Definition of Intelligence

Spontaneous Intention: The drive to perform tasks that arise naturally.
Task Identification: Recognizing tasks that align with that intention.
Methodology: Developing methods to accomplish identified tasks.

It argues that AI can perform intelligent tasks, but it lacks true intelligence if the intention is not intrinsic to the program.

Types of Intelligence

Zombie Intelligence: This type can be formalized and is accessible through computer programs.
Intuitive Intelligence: This form evades complete formalization and possesses capabilities such as making decisions with incomplete information.

The Zombie Principle

The Zombie Principle states that some forms of intelligence cannot be captured by formal systems, highlighting the limitations of AI that relies solely on predefined algorithms.

Hardware for Intelligence

It discusses the hardware necessary for intelligence, distinguishing between AI systems and biological systems. For AI, computers with memory are essential, while biological life relies on the brain.

Formal Systems: Necessary for computable tasks.
Memory Systems: Required for analyzing and recalling information.
Mutual Immanence and Renormalization Machines: Needed to manage contradictions and infinite chaos.

Intention Paradigm and AI Production

It introduces the "Intention Paradigm," which posits that true intelligence arises from free will intentions, not just programmed behaviors. It outlines steps for creating AI that mimics human intelligence, emphasizing the need for intention in AI design.

Conclusion

Ultimately, intelligence is framed as a spontaneous intention to perform tasks, with a focus on constructing systems that find commonalities/orders in complex situations. It asserts that true intelligence, whether human or artificial, must engage in a self-referential loop and manage contradictions to achieve understanding and meaning, suggesting that linguistic systems embody the essence of intelligence.

It emphasizes the importance of the hardware and processes that underpin both human and artificial intelligence, advocating for a deeper understanding of these concepts to advance AI development.

For Linguistics ToE, it is available at { https://tienzengong.wordpress.com/wp-content/uploads/2025/09/2ndlinguistics-toe.pdf }

Gobbledygook no more

Monday, October 6, 2025

Life, Math, Linguistics ToE, two