SKA Single Digit Explorer

Explore the 4D entropy state trajectory for a single digit across different architectures.

Select Digit
8 512
8 512
8 256
2 64
1 200
0.1 0.75
1 100
0 99

Reference Paper

Abstract

We introduce the Structured Knowledge Accumulation (SKA) framework, which reinterprets entropy as a dynamic, layer-wise measure of knowledge alignment in neural networks. Instead of relying on traditional gradient-based optimization, SKA defines entropy in terms of knowledge vectors and their influence on decision probabilities across multiple layers. This formulation naturally leads to the emergence of activation functions such as the sigmoid as a consequence of entropy minimization. Unlike conventional backpropagation, SKA allows each layer to optimize independently by aligning its knowledge representation with changes in decision probabilities. As a result, total network entropy decreases in a hierarchical manner, allowing knowledge structures to evolve progressively. This approach provides a scalable, biologically plausible alternative to gradient-based learning, bridging information theory and artificial intelligence while offering promising applications in resource-constrained and parallel computing environments.

SKA Explorer Suite

About this App

Select a digit and explore how its 4D entropy state trajectory changes with architecture. Each digit has a unique geometric fingerprint — compare architectures for the same digit to probe the entropy manifold.