Blog

12. November 2025

C++ Implementation Status Update

The C++ implementation of AIKA has reached 72% completion. Major milestones:

• ✅ Fields Module (100%): Fully production-ready with complete event-driven queue system
• ⚠️ Network Module (85%): Core functionality complete, latent linking in progress
• ⚠️ Transformer (60%): Type structure complete, attention mechanism under development

Key improvements include modern C++20 design patterns, builder API for type construction, and comprehensive Python bindings via pybind11. See the Specification Alignment Report for detailed status.

12. March 2025

Migration from Java to C++/Python

AIKA has undergone a fundamental architectural transformation. The original Java-based implementation has been migrated to a more modular and efficient C++/Python structure. This change introduces three core components:

• Fields Module (C++): Provides foundational type system and mathematical operations.
• Network Module (C++): Implements neuron, synapse, and activation mechanics.
• Python Bindings: Defines the neural network's computation graph using Python API.

This transition significantly enhances performance, flexibility, and integration with modern AI workflows. The new architecture follows PyTorch's model: define in Python, execute in C++.

1. January 2020

Can the dynamic linking of neural activations bring us closer to strong AI?

Exploring how event-driven activation linking might lead to stronger AI models.

23. September 2019

Using Information Gain for the Unsupervised Training of Excitatory Neurons

A biologically inspired approach to neural network training.

25. October 2017

AIKA: A Semantic Neural Network

This blog post introduces AIKA's early concepts in semantic text analysis (German language).