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SE Radio 724: Jure Leskovec on Relational Graph and Foundational Models

SE Radio 724: Jure Leskovec on Relational Graph and Foundational Models

Software Engineering Radio - The Podcast for Professional Software Developers vor 4 Monaten

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1:02:12

Jure Leskovec, Professor of Computer Science at
Stanford University and Chief Scientist at Kumo.ai, speaks with
host Sriram Panyam about relational and graph language models and
their transformative impact on enterprise decision-making and
predictive modeling.

Jure begins by establishing the critical importance of predictive
modeling across industries - from fraud detection in financial
institutions to customer churn prediction, lifetime value
estimation, product recommendations, and healthcare risk
assessment. He notes that while AI has made remarkable advances
in natural language understanding and computer vision, predictive
modeling over enterprise operational data stored in relational
databases has been largely left behind, still relying on
30-year-old machine learning approaches that are expensive,
time-consuming, and require manual feature engineering.

His proposed solution to the fundamental problem with current
approaches is relational deep learning and relational
transformers. The discussion explores how this approach differs
from traditional graph neural networks (GNNs), which Jure
pioneered and deployed successfully at Pinterest. Jure concludes
with practical guidance for software engineers and data
scientists interested in exploring this technology.