### Q1: What is the fundamental difference between supervised and unsupervised learning in the context of Generative AI, and can you provide an example of each?  
### Q2: How does the process of training a model on labeled data in supervised learning contribute to predictive analytics in finance?  
### Q3: In what ways can the understanding of Generative Adversarial Networks (GANs) enhance the development of synthetic data generation in financial applications?  
### Q4: Can you describe a scenario where unsupervised learning might be more beneficial than supervised learning in a finance project?  
### Q5: What advantages does using Variational Autoencoders (VAEs) provide in terms of generating synthetic customer profiles for financial analysis?  
### Q6: How might the adversarial nature of GANs impact the realism of the synthetic data produced for financial modeling?  
### Q7: Why is it important for product managers in finance to have a clear understanding of the differences between VAEs and GANs when developing AI strategies?  
### Q8: How can effective communication of complex AI concepts, like those discussed in this lesson, influence team collaboration in finance projects?  
### Q9: What strategies can be employed to simplify technical explanations of machine learning concepts to ensure they are understood by non-technical stakeholders in finance?