What if it could learn and update its own weights as it's being used? (twitter.com)

A groundbreaking development in AI has emerged with the announcement of "Living Weights," an engineering innovation that allows models to learn and update their weights dynamically while in use. After three months of development, this feature enables an AI model to adapt uniquely to its environment without the need for key-value stashing or external files, ensuring a seamless experience when moving models across machines. Once unloaded, the model retains its learned information, signaling a potential leap towards recursive self-improvement in local AI systems. This release comes with the latest TensorFold update, featuring compatibility with NVIDIA's Nemotron Lightning. The significance of "Living Weights" lies in its ability to empower users by creating models that become personalized through real-time learning, potentially enhancing their utility in various applications. As the AI/ML community seeks more adaptive and resilient systems, this innovation could pave the way for future developments in intelligent self-evolving algorithms.