After digitalisation comes automation: the publisher's best next step

Recently, we’ve discussed why embracing content agility is key and explained that the road towards it is rather agile in itself. But what to do once you’ve gone down this road? What is the publisher’s next step after digitalisation? Automation tooling: what’s out there? Even though publishers use an increasing number of digital tools (such […]

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Securing the World Model: How Determinism Protects Enterprises from LLM Poisoning Attacks

In October 2025, a research collaboration between Anthropic and the Alan Turing Institute exposed one of the most uncomfortable truths in modern AI. Their study, “Poisoning Attacks on LLMs Require a Near-Constant Number of Poison Samples” revealed that large language models (LLMs), no matter how vast or sophisticated, can be compromised even when only a

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Honoring Labor, Embracing the Future: How AI and Human-in-the-Loop Intelligence Will Redefine Work

SHARE THIS: Twitter Facebook LinkedIn Labor Day is more than a three-day weekend. It’s a national pause button — a chance to recognize the hard-earned progress of workers and the value of labor itself. Born from the sweat of laborers demanding fair treatment and safe working conditions, the holiday honors the dignity of work and

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Understanding the Significance of Singapore’s Anti-Money Laundering and Other Matters Act 

In August of 2024, the Singapore Parliament passed into law the Anti-Money Laundering and Other Matters Act 2024. While many financial industry regulation experts have noted that the Act likely arose as a response to a two-billion dollar+ money-laundering operation uncovered by the Singapore Police Force in August 2023, there had been multiple instances of

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How Do LLMs Think? 5 Approaches Powering the Next Generation of AI Reasoning

Large Language Models (LLMs) have come a long way since their early days of mimicking autocomplete on steroids. But generating fluent text isn’t enough – true intelligence demands reasoning. That means solving math problems, debugging code, drawing logical conclusions, and even reflecting on errors. Yet modern LLMs are trained to predict the next word, not

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