Imagine an AI that could watch any video and instantly tell you how dangerous the situation is. Researchers are working on exactly that, developing systems to analyze footage and assess risk levels in everything from extreme sports to workplace hazards. In a newly published research paper, "ViDAS: Vision-based Danger Assessment and Scoring," scientists introduce a novel approach to measuring danger in video content. They've created a dataset of 100 YouTube videos depicting various events, each annotated by humans on a danger scale of 0 to 10. This dataset then gets used to see how closely Large Language Models (LLMs), like those powering ChatGPT, align with human perceptions of risk. The team uses video summaries generated by Large Vision Models and feeds these summaries into LLMs using several prompting techniques. By comparing the LLM-generated danger scores to the human annotations, the researchers found promising results, although inconsistencies remain. One interesting finding is that the size and type of LLM significantly impact performance, with larger models generally showing better alignment with human judgment. This suggests that as AI models grow more sophisticated, they become more capable of understanding complex real-world situations and nuances. The ViDAS research opens up exciting possibilities for a range of applications. Think about AI-powered safety systems providing real-time danger alerts or content moderation tools that automatically flag risky videos. While these early findings demonstrate the potential of AI for assessing risk, there's more work to do. The researchers highlight the need for a larger, more varied video dataset and more sophisticated computer vision techniques. Imagine AI that not only identifies danger but also explains its reasoning, just like a human expert. This kind of transparency and explainability will be crucial as we bring these technologies into our lives, shaping a future where AI helps us navigate and mitigate risks more effectively than ever before.