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More than half of new articles on the internet are being written by AI

By Francesco Agnellini, Binghamton University, State University of New York The line between human and machine authorship is blurring, particularly as it’s become increasingly difficult to tell whether something was written by a person or AI. Now, in what may seem like a tipping point, the digital marketing firm Graphite recently published a study showing […]

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2025 digest of digests

2025 has seen another busy 12 months in the world of artificial intelligence. Throughout the year we’ve reported on some of the larger stories, and some of the lesser-covered happenings, in our regular monthly digests. We look back through the archives and pick out one or two stories from each of our digests. January 2025

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Panda and tiger reading

AIhub monthly digest: December 2025 – studying bias in AI-based recruitment tools, an image dataset for ethical AI benchmarking, and end of year compilations

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we look into bias in AI-based recruitment tools, find out about a new image dataset for ethical AI benchmarking, dig into human-robot interactions and social robotics,

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Half of UK novelists believe AI is likely to replace their work entirely

By Fred Lewsey A new report involving hundreds of literary creatives from across the UK fiction publishing industry reveals widespread fears over copyright violation, lost income, and the future of the art form, as generative AI tools and LLM-authored books flood the market. Just over half (51%) of published novelists in the UK say that

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RL without TD learning

By Seohong Park In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer. Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges), and scales well to long-horizon tasks. We can do Reinforcement Learning (RL) based on divide and conquer,

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Scientific Visualization: Python + Matplotlib

The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential. Some of these tools are community based while others are developed by companies. Some are made specifically for the web, others are for the desktop

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Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP

In this survey, we connect several lines of work from the pre-neural and neural era, by showing how hybrid approaches of words and characters as well as subword-based approaches based on learned segmentation have been proposed and evaluated. We conclude that there is and likely will never be a silver bullet singular solution for all

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Ethics-based auditing of automated decision-making systems: intervention points and policy implications

Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS.

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Introduction to Datascience: Learn Julia Programming, Math & Datascience from Scratch

I was emboldened to write this book after my video series called Data Science With Julia got some traction. That too after a tweet about Decision Tree was liked by Julia Language itself. So I thought why not give it more?

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Identifying patterns in insect scents using machine learning

Scents play a central role in nature, as olfactory interactions are the language of life. In a new research project of the UvA Molecular and Materials Design Technology hub, scientists will use machine learning to predict what types of olfactory molecules interact with insect olfactory receptors. This information is important to develop safe-by-design molecules that

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