AI Types: ANI, AGI, ASI

This repository explores the theoretical and mathematical foundations of Artificial Intelligence types:

• ANI — Artificial Narrow Intelligence
• AGI — Artificial General Intelligence
• ASI — Artificial Superintelligence

It serves as an educational framework for understanding the cognitive evolution from narrow tools to superintelligent systems through code, formulas, and conceptual models.

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1. Introduction

Artificial Intelligence (AI) is often grouped into three conceptual types based on its capabilities:

• ANI is task-specific, pattern-based intelligence.
• AGI generalizes learning across domains — closer to human cognition.
• ASI is a hypothetical entity that surpasses human-level reasoning, learning, and creativity.

This project aims to describe each level using mathematical notation and minimal code examples.

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2. Mathematical Foundations

ANI — Artificial Narrow Intelligence

A narrow AI is typically represented as a function trained for a specific domain:

$$f(x) = y,$$

Where:

• ( x ): input (image, text, etc.)
• ( y ): predicted output
• ( f ): model trained on domain-specific data

These models do not generalize to new contexts.

Example in Python:

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)

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AGI — Artificial General Intelligence

AGI requires meta-learning capabilities:

$$M(f_i) \Rightarrow f_j,$$

Where:

• ( f_i ): function for task i
• ( f_j ): function for task j
• ( M ): meta-model that transforms or generalizes across tasks

AGI should dynamically create or adapt models:

def meta_learn(task_data):
    model = initialize_model()
    model.train(task_data)
    return model

AGI agents must also exhibit:

• Context transfer
• Long-term planning
• Dynamic memory

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ASI — Artificial Superintelligence

ASI operates recursively and improves itself beyond human capabilities:

$$f_{t+1} = G(f_t),$$

Where:

• ( f_t ): current model at time t
• ( G ): generator or evolver function

This assumes:

• No fixed limits on compute or memory
• Ability to create new learning architectures

"The mind that redesigns itself faster than we can follow."

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3. Code Directory

File	Description
notebooks/ani_example.ipynb	Basic classifier (ANI) example
notebooks/agi_simulation.ipynb	Prototype of meta-learning loop
notebooks/asi_theory.md	Conceptual model of ASI architecture

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4. References

Life 3.0 – Max Tegmark

• Artificial Intelligence: A Modern Approach – Russell & Norvig
• OpenAI Blog: https://openai.com/blog
• DeepMind AGI Research: https://deepmind.com/research/highlighted-research/agi
• The Anatomy of an AI System: https://anatomyof.ai

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Author's Note

This project is an open educational resource. Contributions, ideas, and pull requests are welcome.

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"To understand AI is to reflect on ourselves: our limitations, our hopes, and our next evolution."