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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 a “30-year-old technology”
While LLMs and retrieval-augmented generation (RAG) systems can answer questions about existing knowledge, they are fundamentally retrospective. They retrieve and reason over information that is already there. For predictive business tasks, companies still rely on classic machine learning.
For example, to build a model that predicts customer churn, a business must hire a team of data scientists who spend a considerably long time doing “feature engineering,” the process of manually creating predictive signals from the data. This involves complex data wrangling to join information from different tables, such as a customer’s purchase history and website clicks, to create a single, massive training table.
“If you want to do machine learning (ML), sorry, you are stuck in the past,” Leskovec said. Expensive and time-consuming bottlenecks prevent most organizations from being truly agile with their data.
How Kumo is generalizing transformers for databases
Kumo’s approach, “relational deep learning,” sidesteps this manual process with two key insights. First, it automatically represents any relational database as a single, interconnected graph. For example, if the database has a “users” table to record customer information and an “orders” table to record customer purchases, every row in the users table becomes a user node, every row in an orders table becomes an order node, and so on. These nodes are then automatically connected using the database’s existing relationships, such as foreign keys, creating a rich map of the entire dataset with no manual effort.
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