👉 Defeat computing, also known as "computing against defeat," is a theoretical approach in artificial intelligence and machine learning that focuses on developing algorithms and systems capable of outperforming their opponents, particularly in adversarial settings. This concept is rooted in game theory and involves creating models that not only excel at their primary tasks but also anticipate and counteract the strategies employed by adversaries. For instance, in cybersecurity, defeat computing could involve developing intrusion detection systems that are so adept at identifying and neutralizing attacks that they can predict and thwart sophisticated adversarial tactics. The goal is to achieve a level of robustness and adaptability where the system's performance improves not just in isolation but also in response to deliberate attempts to undermine it, making it a critical area of research for enhancing the security and reliability of AI systems.
