Barriers and Limitations in Artificial Intelligence for Decoding Non-Human Signals

The study of non-human signals is growing across many fields. These signals come from animals, plants, and microbes. Researchers use Artificial Intelligence to analyse these patterns. The method works well when data is clear and consistent. The work becomes difficult when the data is limited or noisy. Context also affects how signals behave. This reduces the accuracy of many models. Many systems work like a closed box. This makes their decisions hard to explain. It also creates problems when we try to understand biological meaning. Species differ in their signal patterns. This creates more confusion for researchers. Standard datasets are still missing in many areas. Human views can also influence how the signals are read. This adds more uncertainty to the results. Ethical concerns appear when these tools guide conservation or communication efforts. These issues need careful attention. Future progress depends on clear and explainable methods. It also needs combined data from different sources. Strong teamwork between computing and life sciences is also important.