👉 Stable computing, also known as stable AI or robust machine learning, is an approach to artificial intelligence that focuses on creating models and systems resilient to various forms of adversarial attacks, data corruption, and environmental changes. Unlike traditional machine learning models that can be easily fooled by small perturbations in input data (adversarial examples), stable computing aims to develop algorithms and frameworks that maintain their performance and reliability even when faced with such challenges. This involves techniques like adversarial training, where models are exposed to manipulated data during training to improve their robustness, and the use of regularization methods that prevent overfitting to specific data patterns. The goal is to build AI systems that are not only highly accurate but also secure and dependable in real-world applications where data integrity and consistency are critical.
