My last post explored a Genie use case built on a commercial data model in Databricks. This is the natural next step — moving from structured data into a full knowledge layer and using it to drive ...

Traditional machine learning in banking requires 3–4 months per use case: feature engineering, data labeling, model training, validation, deployment. Zero-shot inference on knowledge graphs eliminates ...

Integrating AI into chip workflows is pushing companies to overhaul their data management strategies, shifting from passive storage to active, structured, and machine-readable systems. As training and ...

Snowflake wants to reduce enterprises’ reliance on data engineers and data scientists for unstructured data analysis with its new SQL functions powered by generative AI. Snowflake is adding generative ...

Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultrasound reports include structured and free text and ...

In 2023, Idaho National Laboratory (INL) and Idaho State University unveiled the industry’s first near real-time digital twin of a nuclear reactor. The virtual replica of the 5-Wth AGN-201 research ...

The advent of RNA sequencing (RNA-Seq) has significantly advanced our understanding of the transcriptomic landscape, revealing intricate gene expression patterns across biological states and ...

Momentum is building for digital twins in semiconductor manufacturing, tying together the various processes and steps to improve efficiency and quality, and to enable more flexibility in the fab and ...

A comprehensive understanding of the spatial organization and differentiation of the mammalian brain requires interpreting 3D structural and molecular information in biologically plausible ways. At ...

ABSTRACT: With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with ...