This episode delves into the critical challenges hindering the widespread and reliable deployment of computer vision (CV) systems in the real world. We explore occlusion, where objects are partially or completely hidden, making it difficult for models to "see" and interpret scenes accurately. The concept of generalization is examined, highlighting how models often fail to perform reliably on new, unseen data due to "domain shift," such as changes in weather, lighting, or geographical location from their training environment. A significant focus is placed on bias, revealing how inherent prejudices in training data can lead to systematically unfair outcomes in CV applications, particularly in facial recognition technology, and the serious societal implications that arise. Finally, we discuss the practical hurdles of real-world deployment, including computational constraints, data and concept drift, and environmental variability, emphasizing that a successful CV product is a complex, evolving system requiring continuous management and maintenance. Understanding these interconnected challenges is crucial for building robust, ethical, and trustworthy AI.

see also:

https://tinyurl.com/SM-S1E8-01

https://tinyurl.com/SM-S1E8-02