C++ PCN library nears backprop accuracy

The article introduces Deepity, a C++ library developed over the past month to explore Predictive Coding Networks (PCNs), which are biologically plausible and promising for continual learning. However, naive PCN implementations are slow, limiting their practical use. By incorporating recent research and algorithmic caching to skip redundant computations during inference, the developer achieved near-backprop performance on a CPU, a significant milestone for PCN viability.

This result positions PCNs as a credible alternative to backpropagation, especially for continual learning, where standard backpropagation is known to struggle. The accuracy gap on MNIST is small: 97.73% versus 98.27%, with Deepity even training faster (59.5s versus ~70s). While MNIST is a simple benchmark, the achievement suggests that with further optimization, such as CUDA porting, PCNs might scale to more complex tasks.

The developer's immediate focus is scaling up the architecture and testing continual learning, which could reveal whether PCNs offer real advantages in that domain. For business readers, this matters as it hints at more efficient, human-like learning algorithms that could eventually lead to AI systems that adapt without forgetting, though such applications remain speculative at this stage.