Blind Spot

Blind spots, referring to areas of limited or no perception, are a significant challenge across various fields, from autonomous vehicle navigation to the evaluation of large language models. Current research focuses on identifying and mitigating these blind spots using diverse approaches, including advanced neural network architectures like transformers and convolutional neural networks, Bayesian reinforcement learning, and novel visual analysis techniques. This work aims to improve the reliability and safety of AI systems and robotic applications by enhancing perception and reducing errors stemming from incomplete or inaccurate information. The ultimate goal is to create more robust and trustworthy systems capable of operating effectively in complex and unpredictable environments.