Can AI truly understand the world's rich tapestry of cultures? Recent research delves into this question, exploring how Large Language Models (LLMs) grapple with cultural commonsense. It turns out, AI's understanding of cultural norms and beliefs isn't as uniform as you might think. The study reveals that LLMs exhibit a significant performance gap when tested on culture-specific knowledge. For example, they might excel at understanding American wedding traditions but struggle with those of other cultures, like traditional Iranian weddings. This discrepancy likely stems from the data these models are trained on, which often overrepresents certain cultures while neglecting others. Interestingly, the language used to query the LLMs also plays a crucial role. Even when asked about cultural practices in their native language, LLMs sometimes perform better when queried in English, highlighting the complex interplay between language and cultural knowledge within these models. This research also uncovers a fascinating bias: LLMs tend to associate general commonsense knowledge more strongly with dominant cultures. For instance, they might readily link a general statement about human behavior with American culture but less so with Kenyan culture. This suggests that LLMs, despite their vast knowledge, can still reflect the biases present in their training data. The implications of these findings are significant. As LLMs become increasingly integrated into our lives, it's crucial to address these cultural biases to ensure fair and equitable access to information and services for everyone, regardless of their cultural background. Future research could explore methods for creating more culturally aware LLMs, such as incorporating more diverse training data and developing techniques to mitigate language-based biases. This will pave the way for AI systems that truly understand and respect the world's cultural diversity.