The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

AI Access and Inclusivity as a Technical Challenge with Prem Natarajan - #658

Dec 4, 2023

In this discussion, Prem Natarajan, Chief Scientist and Head of Enterprise AI at Capital One, tackles AI access and inclusivity as critical challenges in banking. He highlights the importance of diversity in data sets to combat biases and improve fraud detection. Prem shares insights on the use of foundation models and federated learning, emphasizing data quality and privacy preservation. He also stresses the need for collaboration between academia and industry to enhance AI impact, ultimately advocating for mission-inspired research that benefits customers and the broader community.

41:46

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Prem Natarajan

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Episode notes

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Quick takeaways

Addressing bias and optimizing performance for different user groups is crucial for inclusivity in AI.

Partnerships with academia can provide diverse perspectives and help solve complex problems in AI research.

Deep dives

Building an AI Research Program at Capital One

Prem Nauterajan, Chief Scientist and Head of Enterprise AI at Capital One, discusses how they have crafted an AI research program in collaboration with academic partners. The program focuses on addressing specific research priorities in areas such as deep learning and transformer-based learning. They are also exploring techniques to combine structured data with new AI techniques. The goal is to bring together different methodologies to collectively improve performance.

Intro

4min

Advancements and Inclusivity in AI Research

23min

Bridging Academia and Industry in AI Benchmarking

2min

Enhancing AI with Quality Data

6min

The Future of AI: Building Expertise and Infrastructure for Diverse Applications

3min

The Importance of Mission-Inspired Research in Banking

4min

Today we’re joined by Prem Natarajan, chief scientist and head of enterprise AI at Capital One. In our conversation, we discuss AI access and inclusivity as technical challenges and explore some of Prem and his team’s multidisciplinary approaches to tackling these complexities. We dive into the issues of bias, dealing with class imbalances, and the integration of various research initiatives to achieve additive results. Prem also shares his team’s work on foundation models for financial data curation, highlighting the importance of data quality and the use of federated learning, and emphasizing the impact these factors have on the model performance and reliability in critical applications like fraud detection. Lastly, 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.

The complete show notes for this episode can be found at twimlai.com/go/658.