Fractal learning

Research
1

Fractal learning

Overview

FractalLearner is a comprehensive, experimental learning system designed to process and analyze information through recursive, fractal-inspired methods. It supports multiple learning modes, multimedia input (text, images, audio), persistent knowledge storage, and semantic search. This proof of concept demonstrates core functionalities for adaptive, multimodal learning.

Features

Recursive Learning: Processes information in layered, recursive depths with configurable max depth.

Multiple Learning Modes: Supports comparative, critical, creative, and standard analytical learning.

Multimedia Learning: Handles text, image (via CLIP), and audio (via speech recognition) inputs.

Knowledge Persistence: Stores knowledge base and vector database for persistent learning.

Semantic Search: Uses FAISS vector database for similarity-based knowledge retrieval.

Interactive Sessions: Facilitates guided learning with adaptive next-step suggestions.

Integration: Supports integration with external AI systems for knowledge sharing.

Project Structure

fractal_learner/

├── fractal_learner.py

└── my_knowledge_base/

├── knowledge_base.json

├── vector_db.index

└── learner_state\_\*.pkl

Prerequisites

Python: Version 3.8+ recommended.

Dependencies: Install required packages:

pip install torch transformers faiss-cpu numpy pillow soundfile speechrecognition

Directory Setup: Create storage directory:

mkdir -p my_knowledge_base

Installation

Clone or create the project directory:

mkdir fractal_learner

cd fractal_learner

Save the provided fractal_learner.py in the project directory.

Install dependencies:

pip install torch transformers faiss-cpu numpy pillow soundfile speechrecognition

Create the knowledge base directory:

mkdir -p my_knowledge_base

Usage

Run the FractalLearner script to start learning or interacting with the system.

Example Commands

Interactive Learning:

from fractal_learner import FractalLearner

learner = FractalLearner(max_depth=6, storage_path=“./my_knowledge_base”)

session_id = learner.start_interactive_session(“science_tutorial”)

response = learner.interactive_learn(

session_id,

"Explain quantum entanglement in simple terms",

learning_mode="comparative"

)

print(“Learning insights:”)

for insight in response[‘insights’]:

print(f"Depth {insight\['depth'\]}: {insight\['insight'\]}")

print(“\nSuggested next steps:”)

for step in response[‘next_steps’]:

print(f"- {step}")

Multimedia Learning:

learner.learn_from_image(“physics_diagram.jpg”, “Quantum mechanics illustration”)

learner.learn_from_audio(“lecture.wav”)

Semantic Search:

results = learner.semantic_search(“quantum physics”, k=3)

print(f"Semantic search results: {len(results)} found")

for result in results:

print(f"- {result\['text'\]} (Similarity: {result\['similarity'\]:.2f})")

Save State:

learner.save_state()

Testing Key Features

Recursive Learning: Observe layered insights generated for input text in different modes.

Multimedia Processing: Test image and audio inputs to see extracted knowledge.

Semantic Search: Query the knowledge base to retrieve relevant insights.

Interactive Sessions: Start a session and provide inputs to see adaptive learning paths.

Persistence: Save and load the learner state to verify knowledge retention.

Customization

Modify max_depth in FractalLearner initialization to adjust learning recursion.

Add new learning modes by extending learning_modes in fractal_learner.py.

Adjust embedding generation in _get_embedding for more sophisticated vectorization (e.g., use sentence transformers).

Customize storage paths or file formats in my_knowledge_base.

Dependencies

torch

transformers

faiss-cpu

numpy

pillow

soundfile

speechrecognition

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