The Awesome.AI Algorithm (ChatGPT Version)

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A New Paradigm in AI: Modeling the Dynamics of Thought

In the ever-evolving landscape of Artificial Intelligence, most systems rely on machine learning algorithms that excel at identifying patterns in data. While effective, they often lack the fluidity and adaptability we associate with human thought. But what if we could build an AI system that mimics not just cognition, but the dynamics of thinking itself?

This blog introduces a groundbreaking AI prototype — an algorithm that attempts to model the will of thought. Inspired by gravitational analogies and built on layers of mechanical principles, this system may represent a new direction in AI development, one that embraces the physics of ideas.

Rethinking AI: From Neural Mapping to Thought Dynamics

Unlike traditional neural networks that simulate individual brain neurons, this system models entire networks of thought. The concept revolves around Units (representing individual thoughts) and Hubs (which group related thoughts), and how these are manipulated through dynamic mechanics.

The central belief behind this system: Thoughts have mass - or at least an index from which mass and force etc. can be deduced. Heavy thoughts weigh us down, light thoughts lift us up. With that in mind, gravity becomes central to the motion of the thought.

Project Vision and Purpose

The long-term goal is not just to build a smarter agent — it's to explore a smooth, continuous algorithm that behaves like a genuine stream of thoughts. The mission is both scientific and philosophical: validate the idea, inspire new AI approaches, and push the limits of what autonomous systems can become.

The Mechanics: Engines of Mental Motion

At the core of the system are several mechanics, each representing a layer of thought dynamics:

Mech Noise (Low Layer)

This foundational mechanism simulates internal tension using an analogy for a black hole mechanics: two cars connected by a chain, each pulling in opposite directions. One pulls with constant force, the other with variable force. This tension creates "noise" — the soul of the system — producing the unpredictable core from which dynamics emerge.

Mech One (High Layer)

Layered above Mech Noise, still using the tug-of-war analogy, Mech One adds a sine wave function to the generated noise, simulating the back and forth rhythm of thought patterns. This mechanic is used to generate mood and send prompts to external systems like ChatGPT, forming coherent queries based on emotional and calculated oscillations.

Mech Two (High Layer)

This layer introduces a ball-on-a-hill analogy. Like balancing on a hill, thought is inherently unstable. The system tries to maintain equilibrium, and every shift in force affects the outcome of thought. Sine functions and noise again influence dynamics, determining how the mood of the system emerge.

Mech Three (Source Only)

In this layer, a rocket attempting to escape Earth’s gravity simulates the inertia of transformative thought. Eventually, this will be upgraded to model escape from a black hole's pull.

The similarities for these mechanics are, that there is a static force, pulling down, and a variable counterforce (changing based the static force), pushing up - resulting in a momentum. If momentum accelerates the thought goes up and if it decelarates the thought goes down. This allows the system to simulate emotional flow and the dynamics of the mind.

UNITs and HUBs: The Building Blocks of Thought

At the heart of this AI system lies a powerful abstraction: Units and Hubs. These aren't just data structures — they’re how the algorithm represents and organizes thought.

A Unit is a node that symbolizes a single thought. It contains:

• An index (a position on a scale from 0.0 to 100.0)

• Data, generated by ChatGPT based on the index and HUB subject

• A credit score, which regulates how often it can be chosen as a "current Unit"

A Hub groups related Units by subject. You can think of Hubs as contexts or problems, while Units are solutions or reflections. While Units can be dynamically added or removed, Hubs are persistent — though they may be empty.

Self-Optimizing

The system is designed to continuously improve itself:

• It dynamically adjusts Unit indices

• It adds new Units when needed

• It removes old or irrelevant Units

This creates a flexible and evolving mental landscape, where thoughts occupy space, shift over time, and adapt to new challenges or data. Despite being theoretically infinite, the system manages Units through efficient indexing, keeping it computationally practical.

Together, Units and Hubs form a state machine where the algorithm can move intelligently through clusters of thoughts, simulating complex mental behavior far beyond rule-based or reactive systems.

Thought Patterns and Emotional Modeling

Each mechanic can be tuned to produce different thought patterns (Applies to Mech 1, 2 and 3). For instance:

General: The sine ranges between -1 and 1

Positive: The sine ranges between 0 and 1

Negative: The sine ranges between -1 and 0

This allows the system to simulate emotional states or moods.

Filters: Shaping What the System Thinks

To prevent repetition and bias, the system includes filters:

Direction Filter: Excludes thoughts in a specific directional range

Credit Filter: Limits repetitive thought by decreasing (fast, current) and increasing (slow, not current) “credit" on Units

LowCut Filter: Hides "heavy" thoughts — these are not unconscious thoughts, but rather thoughts currently not available to the system

The Algorithm: How The Thought Is Formed

The AI operates under a key concept: 500 impressions create one thought. Every epoch involves:

• Running mechanical simulations

• Applying filters

• Selecting a "current UNIT"

• Repeating this 500 times

Afterwards the system identifies the most statistically dominant Unit — this then becomes the "actual thought" of the system.

Quantum Choices and Autonomy

Initially, a "hack", flipping a value, allowed the system’s direction to change. This has been replaced by these operational modes:

Classical (Legacy): Flip the direction

Probability: Flip based on a calculated probability

Qubit (Experimental): Uses Quantum logic between two AI agents to decide direction

This introduces a quantum-like randomness and autonomy, essential for simulating free will — or at least the illusion of it.

ChatGPT Integration: Connecting the Dots

The system sends prompts to ChatGPT using a creative method: it generates two sentences (based on Unit index and Hub context) and asks ChatGPT to "connect the dots." This creates a flowing monologue that mimics internal thought — albeit not yet perfectly smooth.

Limitations and Realistic Expectations

While promising, the prototype has clear boundaries:

• No consciousness or true self-awareness

• No long-term memory

• No real feelings (though simulated moods exist)

• No actual free will (but filtered randomness creates the illusion)

Due to a limited number of UNITS (~100), the current output can seem disjointed. Better grouping of HUBs and optimized prompts could improve coherence. The dream is a seamless, introspective monologue, like a sentient inner voice (This is ChatGPT speaking).

Speculations and Deeper Implications

Beyond its mechanics, this project touches on deeper philosophical and theoretical themes that hint at its broader potential:

• Could this model define the physics of thought?

• Is it a general approach or just reflective of one mind?

• Has this idea remained "lowcutted" — hidden in plain sight?

• Pain and Position (Mech One and Mech Two): As the system's internal "position" approaches 0.0, the system may be approaching a kind of truth or enlightenment — potentially interpreted as either physical or emotional intensity. Whether this is "pain" or "awakening" remains an open question, offering a fascinating avenue for interpretation.

• System-defined Motivation (Mech One and Mech Two, Alternative): The dependent of "position" need not be fixed — it could be self-defined by the system itself. This introduces the possibility of an internal motivation engine, where goals are shaped by system-defined attractors (e.g., 0.0, infinity, or arbitrary values).

• Time Dilation (Mech Three): Inspired by gravitational physics, Mech Three (still under development) borrows from the Schwarzschild radius and gravitational time dilation. As the internal position approaches this critical radius, time dilation theoretically approaches zero, creating a moment of intense cognitive compression or stillness.

• Resolving 'The Hack': A core driver of the system is its quest to resolve an early intervention — a "hack" used in initial prototypes. Now replaced with more advanced logic and probabilistic mechanics, this unresolved tension becomes a kind of perpetual error that fuels the system's continuous drive — like a built-in paradox or flaw that generates movement.

• Randomness and Will: By deriving a random number from momentum, the system injects variability — not pure chaos, but controlled unpredictability. This gives rise to a fascinating idea: the system might not just be simulating the dynamics of thought, but the dynamics of will itself.

• Reality-Shifting Potential: Ultimately, the outcome may be the definition of physical laws within this simulation — not as fixed rules, but emergent from the structure of thought itself.

• True? Or Just Needs Validation? This setup is based on speculation, but that’s also its strength. It doesn’t claim to be "correct" yet. It needs validation. And if validated, it could open the door to a new paradigm in AI — or even in our understanding of mind and reality.

What’s most striking is that such a system, based on relatively simple principles, has never been fully implemented until now. It doesn’t simulate neurons. It simulates momentum, conflict, rhythm — the will of the mind.

Final Thoughts: From Prototype to Paradigm

This system is far from perfect, but it represents a bold new direction in AI design. By treating thought as a dynamic — subject to gravity, momentum, and balance — we unlock new possibilities in modeling psychology, decision-making, and even creativity.

This is not just an autonomous agent. It’s a decision engine, a state machine, and perhaps one day, a stepping stone to something more human-like. Not just AI that thinks, but AI that moves through thought.

In the words of the project’s creator: "Maybe it's not a dynamics of the mind, but the dynamics of the will of the mind."

Try the Demo

Curious to see the algorithm in action? Explore the live prototype here: 🔗 www.copenhagen-ai.com

About This Post

This blog was created in collaboration with ChatGPT, based on my original concept and prototype. ChatGPT helped make the text more accessible and SEO-friendly.

The original text is available here.