AI Access and Inclusivity as a Technical Challenge with Prem Natarajan - #658
Analysis
This article summarizes a podcast episode featuring Prem Natarajan, discussing AI access, inclusivity, and related technical challenges. The conversation covers bias, class imbalances, and the integration of research initiatives. Natarajan highlights his team's work on foundation models for financial data, emphasizing data quality, federated learning, and their impact on model performance, particularly in fraud detection. The article also touches upon Natarajan's approach to AI research within a banking enterprise, focusing on mission-driven research, investment in talent and infrastructure, and strategic partnerships.
Key Takeaways
- •AI access and inclusivity are key technical challenges.
- •Data quality and federated learning are crucial for model performance, especially in financial applications.
- •Mission-inspired research, diverse talent, and strategic partnerships are important for AI research in a banking context.
“Prem shares his overall approach to tackling AI research in the context of a banking enterprise, including prioritizing mission-inspired research aiming to deliver tangible benefits to customers and the broader community, investing in diverse talent and the best infrastructure, and forging strategic partnerships with a variety of academic labs.”