In my previous role, I spearheaded a project to develop an AI-driven recommendation system for an e-commerce platform. I initiated the project by defining the scope and objectives, followed by selecting the appropriate machine learning models. I coordinated with the data science team to gather and preprocess data, and then trained and fine-tuned the models. Throughout the deployment phase, I ensured seamless integration with the existing infrastructure and conducted rigorous testing to optimize performance. The system successfully increased user engagement and sales by 30%.

I maintain a proactive approach to staying current with AI advancements by regularly reading academic papers, attending webinars, and participating in online forums and conferences. I also engage with professional networks on LinkedIn and follow key influencers in the field. Additionally, I dedicate time each week to hands-on experimentation with new tools and frameworks, ensuring that my skills remain cutting-edge and relevant to industry needs.

Ensuring ethical AI use is paramount. I start by establishing clear guidelines and principles at the project's inception, focusing on transparency, fairness, and accountability. I implement rigorous testing for biases in data and algorithms, and I involve diverse stakeholders in the decision-making process to mitigate risks. Regular audits and updates to the AI systems help maintain ethical standards as the technology evolves.

Supervised learning involves training models on labeled data to predict outcomes, making it suitable for classification and regression tasks. Unsupervised learning, on the other hand, works with unlabeled data to find patterns or structures, ideal for clustering and association tasks. I would use supervised learning when the goal is to make predictions based on historical data, such as customer churn prediction. Conversely, unsupervised learning is appropriate for exploratory data analysis, like grouping customers by purchasing behavior.

Debugging AI models requires a systematic approach. I begin by isolating the issue through detailed logging and monitoring of model performance. I then review the data preprocessing steps and model architecture for any errors or inefficiencies. Using visualization tools helps identify patterns or anomalies in the data. For complex issues, I may employ techniques like unit testing for individual components or ablation studies to understand the impact of different model parts. Collaboration with peers often provides additional insights and accelerates the resolution process.