Hallucinations, Watermarks, Removers, and a Squeezed Balloon

That Is Embarrassing: Why Frontier AI Still Makes Things Up, and What to Do About It

The best AI models still hallucinate. These hallucinations are sometimes funny, and sometimes cause actual damage.

Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent

Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.

Text Watermarking in Python: Catch Whoever Copies Your Writing

The article explains how AI companies embed watermarks into billions of words daily and shows how readers can apply three main watermarking techniques to their own writing. It details practical steps for implementing these methods in Python and discusses experimental results that test which watermarks endure copy‑paste, editing, and paraphrasing. The post provides actionable guidance for writers seeking to protect their content from unauthorized duplication.

Most RAG Hallucinations Are Retrieval Failures: Fix Retrieval, Not the Prompt

Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.

Everyone Is Selling AI at You — Here’s How to Keep Your Judgement

The article titled "Everyone Is Selling AI at You — Here’s How to Keep Your Judgement" discusses three strategies for navigating the current AI hype. It offers practical moves to help readers maintain critical thinking and discern genuine value from marketing claims. The piece was originally published on Towards Data Science.

The AI That Learned to Understand Long After It Stopped Trying

The article titled "The AI That Learned to Understand Long After It Stopped Trying" discusses a small, strange discovery in machine learning known as grokking. It highlights how this phenomenon involves an AI developing understanding after ceasing to actively try. The piece was originally published on Towards Data Science.

Is This Slop? Detecting AI-Generated Content Without a Model

Research-backed cues to detect LLM-generated text along with the mathematical intuition as to 'why' The post Is This Slop? Detecting AI-Generated Content Without a Model appeared first on Towards Data Science .

Model Hypnosis: Strong control of AI via additive subliminal effects

arXiv:2608. 16834v1 Announce Type: cross Abstract: We demonstrate that AI models are broadly susceptible to a phenomenon we call model hypnosis, in which individually weak and seemingly irrelevant cues in the prompt can be systematically combined to strongly control model behavior.

I Hid Four Traps in a Forecasting Task. Here Is What Four AI Assistants Did.

The article describes a controlled experiment in which four AI assistants—Gemini, DeepSeek, ChatGPT, and Claude—were tested on a forecasting task that included four hidden traps: leakage, reporting delays, promotion effects, and structural breaks. The study examines how each assistant handled these challenges and compares their performance. The post was published on Towards Data Science.

Our AI Has an Invisible Footprint — Let's see How to Measure It

The article discusses the challenge of measuring the impact of AI systems, noting that obtaining data is now easier than deciding how to interpret it. It highlights the difficulty of turning raw numbers into actionable insights. The piece was originally published on Towards Data Science.

AI Harms and Their Technical Causes

The article "AI Harms and Their Technical Causes" discusses how specific technical decisions in AI development can result in safety failures. It examines the underlying causes of these harms and offers guidance on alternative approaches to mitigate risks. The piece highlights the importance of thoughtful design choices in preventing unintended negative outcomes from AI systems.

AI Watermarking 101: Tools and Techniques