Reproducing all of Schmidhuber's papers with Claude (cybertronai.github.io)

A comprehensive walkthrough has been published, detailing the reproduction of all 58 implementations of Jürgen Schmidhuber's seminal papers using the Claude AI framework. This work presents each implementation with animated GIFs and high-resolution static figures, showcasing various learning dynamics such as phase transitions and learning curves. The project adheres strictly to the original algorithms introduced in Schmidhuber's papers, including notable techniques like Backpropagation Through Time (BPTT) in Long Short-Term Memory (LSTM) networks and the Neural Bucket Brigade (NBB) for local credit assignment, ensuring algorithmic fidelity throughout. This endeavor is significant for the AI/ML community as it emphasizes algorithmic reproducibility, a crucial aspect of scientific validation in machine learning research. By documenting the deviations and experimental designs of each stub, it enhances transparency and allows researchers to understand the nuances of each implementation better. Furthermore, this detailed examination has implications for future research on learning dynamics, neural architectures, and their evolution over decades, showcasing how early concepts can inform modern AI developments.