Show HN: Stability First AI – Recovering memory without training data (github.com)

Stability First AI, led by Vitali Sialedchyk, has made significant strides in addressing the challenges of memory retention and catastrophic forgetting in neural networks through a series of innovative experiments. By implementing the Stability-First hypothesis, the project treats weight stability as a form of "System Time," allowing AI models to achieve reversible learning without relying on past training data. Notably, its experiments include the "Temporal LoRA" system, which enables dynamic context switching in language models like GPT-2, and has demonstrated 100% accuracy in distinguishing between different knowledge epochs. Additionally, memory recovery rates reached 94.65% using minimal examples, showcasing a powerful retrieval mechanism through latent memory rather than traditional data storage. The implications for the AI/ML community are profound, as these findings not only challenge existing paradigms of training and memory in neural networks but also introduce techniques that may enhance the modularity and resilience of AI systems. The promise of maintaining knowledge across varying contexts while mitigating forgetting is vital for developing increasingly sophisticated models capable of continuous learning. Furthermore, incorporating a metacognitive regulator that adapts learning rates based on prediction error mimics a biological learning approach, hinting at exciting avenues for future research and application in AI.