Kumo’s ‘relational foundation model’ predicts the future your LLM can’t see

The generative AI boom has given us powerful language models that can write, summarize and reason over vast amounts of text and other types of data. But when it comes to high-value predictive tasks like predicting customer churn or detecting fraud from structured, relational data, enterprises remain stuck in the world of traditional machine learning. 

Stanford professor and Kumo AI co-founder Jure Leskovec argues that this is the critical missing piece. His company’s tool, a relational foundation model (RFM), is a new kind of pre-trained AI that brings the “zero-shot” capabilities of large language models (LLMs) to structured databases.

“It’s about making a forecast about something you don’t know, something that has not happened yet,” Leskovec told VentureBeat. “And that’s a fundamentally new capability that is, I would argue, missing from the current purview of what we think of as gen AI.”

Why predictive ML is ...