Creating Intelligence

This project aims to build a computational theory of mind based on a new theory of hyperdimensional computing (HDC), entirely departing from neural-network-based connectionist models which rely on matrix calculations and continuous weights.

In this model, the mind performs computations on sparse binary data. The fundamental data structures are Sparse Distributed Representations (SDRs) for perceptual information and Sparse Holographic Representations (SHRs) for symbols.

The centerpiece of this project is Topological Associative Memory — a newly discovered associative memory for sparse data, serving as the computational kernel that models the brain's core algorithm. A software frontend for modeling and simulating neural circuits and pathways complements the memory backend.

The methods introduced here lend themselves to binary in-memory and neuromorphic computating, opening a new route toward synthetic intelligence that does not rely on GPU acceleration.

General Information

• Website: https://creatingintelligence.org
• Documentation: https://creatingintelligence.org/docs
• E-Book PDF: https://creatingintelligence.org/CreatingIntelligence.pdf
• Preprint: https://arxiv.org/abs/2606.31819
• Discussions: https://github.com/PeterOvermann/CreatingIntelligence/discussions
• Issue Tracker: https://github.com/PeterOvermann/CreatingIntelligence/issues

Python Installation

pip install creating-intelligence

Development Setup

On Unix, Linux, BSD, and macOS, run

src/python/setup.sh

On Windows, run

src/python/setup.bat

This will create a Python virtual environment and build the creating-intelligence package. The package includes a high-performance memory backend written in Standard C, and an alternative native Python backend.

Run the following command to build the standalone memory CLI and test program from the zero-dependency Standard C source:

make -C src/c/

Getting Started

Run this:

python3 experiments/hello-world/hello-world.py 

Testing

To test the circuit package (and indirectly the memory backend), run

python3 experiments/circuit-tests/run.py

Test the topological associative memory backend (storage capacity, retrieval accuracy, and I/O performance) with

python3 experiments/memory-tests/run.py

or as a stand-alone C binary with

src/c/build/memorytest

Mathematica

A Mathematica version, functionally equivalent to the Python package, is available here.

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Copyright and License Information

Copyright © 2026 Peter Overmann. All rights reserved.

This repository contains two distinct categories of content, which are licensed separately:

• The Software: Licensed under the MIT License.

• The Manuscript & Assets: All Rights Reserved. These materials may not be reproduced, distributed, modified, translated, or commercialized without explicit, prior written permission from the author.

See the full LICENSE file for details.