Prem Natarajan, chief scientist at Capital One, discusses AI access and inclusivity as technical challenges. They explore issues of bias, class imbalances, and the integration of research initiatives. They discuss foundation models for financial data and the importance of data quality and federated learning. They also touch on the use of LLMs and deep learning in various applications and the importance of research and impact for a bank.
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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.
Achieving Inclusivity in AI
Prem emphasizes the importance of inclusivity and accessibility in AI. They discuss how they tackle the technical challenges of ensuring inclusivity by addressing biases and optimizing performance for different user groups. This involves improving data quality, normalizing scores, and considering different perspectives and equities in the research process. They also highlight the significance of partnerships with academia to broaden the diversity of thought and solve complex problems that go beyond the capacity of any single organization.
Data Curation and Federated Learning
Capital One's partnership with MIT focuses on data curation and privacy-preserving learning. They aim to curate high-quality training data and improve data sets' performance on entity resolution, fraud detection, and anomaly detection. The collaboration explores techniques like federated learning to train models directly on distributed data sets, reducing data movement. They also highlight the potential of leveraging LLMs and generative AI for data curation and synthesizing useful data sets.
Driving Impact and Research Rationale
Prem discusses how Capital One approaches research investments. They follow a mission-inspired research approach, focusing on solving specific problems that align with their goals in serving customers and advancing their business. By addressing these challenges, they aim to deliver impact and drive benefits to their customers. The solutions they develop often have broader applicability and contribute to advancements beyond the specific problem domain.
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.
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