Fri 6 Nov 2026 13.51 ESTLast modified on Fri 6 Nov 2026 14.10 EST Activists have stormed the site of a Microsoft datacentre being built in Amsterdam, as the backlash to the social and environmental harms of the AI boom spreads across Europe. Protesters from Geef Tegengas, a Dutch direct action group, clashed with police while breaking into a construction site at Westpoort Amsterdam, where some members climbed a crane, lit flares and tied helium balloons to equipment. The action comes amid Europe-wide resistance to the rapid expansion of energy-hungry AI, with more than 100 groups involved in a two-day series of protests to demand moratoria on the construction of new datacentres. Geef Tegengas activists attach a'Genocide' banner to scaffolding at the Microsoft site. Photograph: Storm Groenendijk/Zuma Press Wire/ShutterstockThe break-in at the Dutch site, where protesters hung banners objecting to Microsoft's services to the Israeli military and US Immigration and Customs Enforcement (ICE), was separate from the pan-European "Stop Dirty Data Centres" campaign, which targeted the same site but whose affiliated members stayed outside the perimeter fence.

More than 100 activists broke into a Microsoft data centre construction site in Amsterdam, climbing cranes and hanging anti-AI banners. This marks the start of two days of coordinated protests across 16 countries against the rapid expansion of AI infrastructure. Who is Kenya's starvation cult leader? In Athens's pro-Palestinian heartland, Israeli buyers spark resentment

Katie Zacharia Was Linked to an AI Rare-Earths Startup. It's Even Weirder Than It Sounds The short-lived White House press secretary was listed as an adviser to a Kristi Noem-linked company that aims to use AI for mining. Both the company and she say she never worked there. Before being fired five days into her stint as White House press secretary, Katie Zacharia spent much of the day attempting to explain her past. Throughout her brief tenure, Zacharia's resume was the source of much intrigue.

Modeling a system'stemporal behaviour in reaction to external stimuli is a fundamentalprobleminmanyareas.

Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole. At one extreme, AI coding assistants have revolutionized the work of software developers. At the other extreme, most companies are seeing little if any benefit from their initial investments. That has provided fuel for the skeptics who maintain that--by its very nature as a probabilistic technology prone to hallucinating--generative AI will never have a deep impact on business. To students of tech history, though, the lack of immediate impact is normal.

Recent outages have revealed how vulnerable the internet is, but there seems to be no official plan in the event of a catastrophic failure. Vladimir Lenin is said to have warned that all societies are three square meals from chaos. But in the modern world, it is only a Wi-Fi signal that separates us from anarchy. Every aspect of our lives is reliant on computers and the internet, and when they fail, they do so with disorientating speed. This became abundantly clear during power cuts across Spain and Portugal earlier this year.

Plus: OpenAI has unveiled estimates of how many of ChatGPT's weekly users are experiencing severe mental health symptoms "We will never build a sex robot," says Mustafa Suleyman Mustafa Suleyman, CEO of Microsoft AI, is trying to walk a fine line. On the one hand, he thinks that the industry is taking AI in a dangerous direction by building chatbots that present as human: He worries that people will be tricked into seeing life instead of lifelike behavior. On the other hand, Suleyman runs a product shop that must compete with those peers. Last week, Microsoft announced a string of updates to its Copilot chatbot designed to make Copilot more expressive, engaging, and helpful. Will Douglas Heaven, our senior AI editor, talked to Suleyman about the tension at play when it comes to designing our interactions with chatbots and his ultimate vision for what this new technology should be. A few weeks ago, I set out on what I thought would be a straightforward reporting journey.

Northeastern University, Boston, US A Abstract: Large Language Models (LLMs) are capable of solving complex math problems or answer difficult questions on almost any topic, but can they generate random street addresses for European cities? Large Language Models (LLMs) have shown impressive performance across a wide range of task s, such as answering questions on virtually any topic. However, there remain areas in wh ich their performance falls short, for example, seemingly simple tasks like counting the letters in a word. In this column, we explore another such challenge: generatin g random street addresses for four major European cities. Our results reveal that LLMs exhibit strong biases, repeatedly selecting a limited set of streets and, for some models, even specific street numbers. Surprisingly, so me of the more prominent and ico nic streets are not selected by the models and the most frequent numbers in the responses lack any clear significance.

Productivity culture encourages us to live inside our tasks and projects. But nature offers its own organizational system. This summer, I reread the novel " Aurora," by Kim Stanley Robinson, a science-fiction writer whom I profiled a few years ago. Robinson has an ecological orientation, and "Aurora" is basically a book about how we fit into nature. It ends on a beach, with an extended description of swimming in big waves. It's early morning, and the waves, as they rise, "turn a deep translucent green."

Recommender systems for niche and dynamic communities face persistent challenges from data sparsity, cold start users and items, and privacy constraints. Traditional collaborative filtering and content-based approaches underperform in these settings, either requiring invasive user data or failing when preference histories are absent. We present DeepNaniNet, a deep neural recommendation framework that addresses these challenges through an inductive graph-based architecture combining user-item interactions, item-item relations, and rich textual review embeddings derived from BERT. Our design enables cold start recommendations without profile mining, using a novel "content basket" user representation and an autoencoder-based generalization strategy for unseen users. We introduce AnimeULike, a new dataset of 10,000 anime titles and 13,000 users, to evaluate performance in realistic scenarios with high proportions of guest or low-activity users. DeepNaniNet achieves state-of-the-art cold start results on the CiteULike benchmark, matches DropoutNet in user recall without performance degradation for out-of-matrix users, and outperforms Weighted Matrix Factorization (WMF) and DropoutNet on AnimeULike warm start by up to 7x and 1.5x in Recall@100, respectively. Our findings demonstrate that DeepNaniNet delivers high-quality, privacy-preserving recommendations in data-sparse, cold start-heavy environments while effectively integrating heterogeneous content sources.