Q: Which type of neural network is commonly used in Generative AI?  

Recurrent Neural Networks (RNNs) are commonly used in Generative AI due to their ability to handle sequential data and capture temporal dependencies. RNNs are designed to process sequences of inputs by maintaining a state that captures information from previous steps in the sequence. This characteristic makes them well-suited for tasks such as generating text, music, and other forms of sequential content. Unlike other neural network architectures, RNNs can generate new sequences by learning patterns and structures from existing data, making them ideal for applications in natural language processing, speech synthesis, and creative content generation. RNNs' capacity to generate coherent and contextually relevant sequences is a key reason why they are widely used in Generative AI.