Understanding and addressing the generative AI challenges is important for its successful implementation and trustworthiness. Some of them include:
Data Quality and Quantity. Models need vast amounts of high-quality data for effective training, which may not always be accessible.
Training Complexity. Generative models, especially deep learning ones like GANs, can be unstable during training.
Bias and Fairness. Like other AI models, generative models can perpetuate or amplify biases in their training data.
Overfitting. There's a risk of models becoming too specialized in the training data. They then become less effective with new, unseen data.
Interpretability. With deep generative models, it can be challenging to understand how they make decisions or derive outputs.
Computational Resources. Training models can be resource-intensive, necessitating powerful hardware.