### Q1: What is the primary distinction between Artificial Intelligence (AI) and Machine Learning (ML)?  
### Q2: How does Machine Learning (ML) improve its performance over time without explicit programming, and what are the implications of this for product development?  
### Q3: In what scenarios would Deep Learning (DL) be necessary over Machine Learning (ML), and what specific data challenges might arise during implementation?  
### Q4: How can AI be effectively integrated into customer service operations in a fintech environment?  
### Q5: Which use case of Machine Learning (ML) can directly address the problem of credit scoring, and what impact could this have on loan approval processes?  
### Q6: What unique advantages does Deep Learning (DL) offer in algorithmic trading that traditional methods do not?  
### Q7: How can you present the benefits of AI, ML, and DL technologies to stakeholders in a concise manner?  
### Q8: What strategies can enhance communication of complex AI concepts to non-technical team members?  
### Q9: How would you effectively articulate the value of iterative improvements in AI systems to ensure stakeholder buy-in?