Towards Data Science

Ten Is Not a Hundred

The article titled "Ten Is Not a Hundred" discusses how the number ten can mislead hallucination detectors. It highlights a specific instance where this numerical confusion caused errors in automated systems. The piece was originally published on Towards Data Science.

Towards Data Science
3d ago

How Many Stories Can Your Data Tell?

The article explores how different data representations can alter our interpretation of the same dataset, highlighting that the way data is visualized or structured can lead to varying narratives. It examines the implications of these storytelling choices for data analysis and communication. The piece emphasizes the importance of thoughtful data presentation in shaping conclusions.

By Sara A. Metwalli
Towards Data Science
2d ago

Your AI Bill Is a Toll Booth. Stop Paying Twice.

The article titled "Your AI Bill Is a Toll Booth. Stop Paying Twice." discusses how users are unexpectedly paying more for AI services than anticipated, likening the experience to a toll booth where one pays twice. It highlights the unseen costs that can arise when using AI tools and urges readers to be vigilant about their expenses. The piece was first published on Towards Data Science.

By Gursimar Singh
Towards Data Science
Sep 2

A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence

The article discusses the importance of a Retrieval-Augmented Generation (RAG) system providing clear evidence when it states that information is not present in a document. It outlines four distinct types of evidence that should accompany such a claim to avoid presenting a confident but incorrect answer or an unsupported “no answer.” The piece emphasizes that each evidence type serves as a safeguard against misinformation in enterprise document intelligence.

By Kezhan Shi
Towards Data Science
Sep 1

What We Miss About Missing Values

The article titled "What We Miss About Missing Values" explores the often overlooked assumptions embedded in the data we observe, particularly focusing on how missing values can influence analysis and interpretation. It delves into the hidden biases and methodological implications that arise when data is incomplete, urging readers to consider these factors when working with real-world datasets.

By David Conneely