Multimodal LLMs

A camera sees the scene. The model reads the question. The options look reasonable. One of them must be right. That last sentence is the problem. Many enterprise video-AI workflows are built around this quiet assumption. A model reviews a warehouse clip and chooses the most likely safety violation. It watches a customer interaction and classifies the complaint. It checks a manufacturing video and identifies the defect category. The system may be wrong, of course, but the menu is treated as complete. The correct answer is assumed to be hiding somewhere among the choices, waiting for the model to point at it with sufficient confidence. ...

Dashboard work has a familiar little ritual. Someone opens a chart, zooms into the last few points, notices a dip, a rebound, or a suspiciously clean trend line, and then says something that sounds analytical: “Looks like it will continue.” Sometimes that is wisdom. Sometimes it is just a human staring confidently at a squiggle. ...

A bus operator does not usually suffer from a shortage of charts. It suffers from the more irritating problem: charts that explain themselves only to the person who made them. The fuel-efficiency analyst has a histogram. The data scientist has a clustering plot. The operations manager has a timetable to defend, a fuel bill to reduce, and perhaps a driver-training programme to justify. Somewhere between those roles, insight quietly evaporates into a PDF appendix. ...