Summer 2025

Features

Guarding Against Bias When Training Language Models

Machine learning models return biased results when the datasets used to train them contain bias. Instances of social bias, skewed model results, and outputs that don't represent the full scope of a business problem for a specific domain are some of the caveats when employing this technology.

Industry Voices

From Silver to Gold: Why the Semantic Layer Is the Future of Data Product Engineering

Modern data environments are becoming more complex. Data lives in cloud data warehouses and across lakes, SaaS platforms, real-time streams, and operational systems. The standard way to build an enterprise data lakehouse is to use the medallion architecture, where data is taken from the source system and transformed in stages.

AI Reflections

Devices Big and Small Can Learn What We Need Them to Learn

A few months ago, I noticed a non-trivial flaw in the setup of my home garage in Colorado. My driveway is tilted downward at approximately 10%. It is a lovely driveway despite settling ground underneath cracking the concrete about a dozen times, but nonetheless, it is a beautiful addition to my home.

Crossing the AI Chasm

From Market Adoption to Meaningful Integration

When Geoffrey Moore wrote Crossing the Chasm in 1991, he illuminated the gap between early adopters and mainstream markets, becoming a cornerstone of enterprise technology thinking. But AI doesn't play by those rules.

Legal Intelligence

Building Collaborative Intelligence in Legal Practice

The legal function's relationship with AI has evolved beyond cautious implementation into a fundamental reexamination of purpose. As business leaders scrutinize the very foundations of legal services, collaborative intelligence emerges not merely as a technological enhancement but as a pathway to reinvention.