High computational requirements can increase costs, but it also provides an opportunity to invest in more energy-efficient hardware.

Data privacy concerns limit the availability of training data, but this also drives the development of synthetic data generation.

Model interpretability is difficult, but it can also lead to advancements in explainable AI.

Bias in the training data can lead to unfair or harmful outputs, but it also presents an opportunity to develop more ethical AI.