Researchers at OpenAI have found a way to make neural networks more resistant to adversarial attacks by sacrificing some of their computational efficiency. This is achieved by adding noise to the input data, which helps to mislead attackers trying to manipulate the network's output. The trade-off is that this approach requires more computations during inference time, making it less efficient than traditional methods. --- Why it matters: This discovery matters because it highlights a key challenge in AI development: balancing accuracy and robustness. By understanding how to make neural networks more resilient to attacks, researchers can improve the security of applications relying on these models, such as image recognition systems or autonomous vehicles. Source: https://openai.com/index/trading-inference-time-compute-for-adversarial-robustness