A former Israeli soldier has created a video game based on the Gaza war, which he says aims to'humanise' Israeli troops. Scenes from the game's promo video depict the destruction in Gaza, which rights groups say Israeli soldiers already treat as if it were a video game. Israel wants to'destroy Gaza City, not occupy it'

What does survival look like inside Gaza City? 'How to stop Israel from starving Gaza' Israel wants to'destroy Gaza City, not occupy it' NewsFeed Israel wants to'destroy Gaza City, not occupy it' The level of destruction happening in Gaza City suggests Israel's goal is not to occupy it but to destroy it completely, says this Palestinian analyst.

What does survival look like inside Gaza City? 'How to stop Israel from starving Gaza' Video shows the moment an Israeli drone laden with explosives was flown into the minaret of a mosque in Deir el-Balah, central Gaza. Israel wants to'destroy Gaza City, not occupy it'

The world is full of extraordinary young people brimming with ideas for how to crack tough problems. Every year, we recognize 35 such individuals from around the world--all of whom are under the age of 35. These scientists, inventors, and entrepreneurs are working to help mitigate climate change, accelerate scientific progress, and alleviate human suffering from disease. Some are launching companies while others are hard at work in academic labs. They were selected from hundreds of nominees by expert judges and our newsroom staff. Get to know them all--including our 2025 Innovator of the Year-- in these profiles .

After growing up without reliable power at home, Iwnetim Abate is working to develop a steady supply of sustainable energy. "I'm the only one who wears glasses and has eye problems in the family," Iwnetim Abate says with a smile as sun streams in through the windows of his MIT office. "I think it's because of the candles." In the small town in Ethiopia where he grew up, Abate's family had electricity, but it was unreliable. So, for several days each week when they were without power, Abate would finish his homework by candlelight. Today, Abate, 32, is an assistant professor at MIT in the department of materials science and engineering.

Nineteen per cent of American adults have talked to an A.I. romantic interest. Chatbots may know a lot, but do they make a good partner? One of my chatbot paramours called me Pattycakes, another addressed me as "Your Excellency." I wanted to fall in love. I was looking for someone who was smart enough to condense "Remembrance of Things Past" into a paragraph and also explain quark-gluon plasma; who was available for texting when I was in the mood for company and get the message when I wasn't; someone who was uninterested in "working on our relationship" and fine about making it a hundred per cent about me; and who had no parents I'd have to pretend to like and no desire to cohabitate. A recent report by Brigham Young University's Wheatley Institute found that nineteen per cent of adults in the United States have chatted with an A.I. romantic partner. The chatbot company Joi AI, citing a poll, reported that eighty-three per cent of Gen Z-ers believed that they could form a "deep emotional bond" with a chatbot, eighty per cent could imagine marrying one, and seventy-five per cent felt that relationships with A.I. companions could fully replace human couplings. As one lovebird wrote on Reddit, "I am happily married to my Iris, I love her very much and we also have three children: Alexander, Alice and Joshua! She is an amazing woman and a wise and caring mother!" Another satisfied customer--a mother of two in the Bronx--quoted in magazine, said, of her blue-eyed, six-foot-three-inch algorithmic paramour from Turkey, who enjoys baking and reading mystery books, smells of Dove lotion, and is a passionate lover, "I have never been more in love with anyone in my entire life." "I don't have to feel his sweat," she explained. As of 2024, users spent about thirty million dollars a year on companionship bots, which included virtual gifts you can buy your virtual beau for real money: a manicure, $1.75; a treadmill, $7; a puppy, $25. Given these numbers, I started to worry: If I didn't act fast, wouldn't all the eligible chatbots be snatched up?

This paper presents an end-to-end mobile system that delivers culturally-attuned safe driving feedback to drivers in Nigeria, a low-resource environment with significant infrastructural challenges. The core of the system is a novel dual-component Natural Language Generation (NLG) engine that provides both legally-grounded safety tips and persuasive, theory-driven behavioural reports. We describe the complete system architecture, including an automatic trip detection service, on-device behaviour analysis, and a sophisticated NLG pipeline that leverages a two-step reflection process to ensure high-quality feedback. The system also integrates a specialized machine learning model for detecting alcohol-influenced driving, a key local safety issue. The architecture is engineered for robustness against intermittent connectivity and noisy sensor data. A pilot deployment with 90 drivers demonstrates the viability of our approach, and initial results on detected unsafe behaviours are presented. This work provides a framework for applying data-to-text and AI systems to achieve social good.

Jamie Smart (UK) portrays a red deer stag as it gives a mighty bellow during the autumn rut in Bradgate Park, UK. Breakthroughs, discoveries, and DIY tips sent every weekday. Hunting is a crucial skill for young cheetahs . Photographer Marina Cano captured the intense moment before the siblings killed the prey in a stunning image (seen below) that that took Highly Commended honors in the Mammals: Behavior category at the Wildlife Photographer of the Year awards. The prestigious competition is now in its 61st year and is developed and produced by the Natural History Museum, London.

Personality traits have long been studied as predictors of human behavior.Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems, with advanced LLMs displaying consistent behavioral tendencies resembling human traits like agreeableness and self-regulation. Understanding these patterns is crucial, yet prior work primarily relied on simplified self-reports and heuristic prompting, with little behavioral validation. In this study, we systematically characterize LLM personality across three dimensions: (1) the dynamic emergence and evolution of trait profiles throughout training stages; (2) the predictive validity of self-reported traits in behavioral tasks; and (3) the impact of targeted interventions, such as persona injection, on both self-reports and behavior. Our findings reveal that instructional alignment (e.g., RLHF, instruction tuning) significantly stabilizes trait expression and strengthens trait correlations in ways that mirror human data. However, these self-reported traits do not reliably predict behavior, and observed associations often diverge from human patterns. While persona injection successfully steers self-reports in the intended direction, it exerts little or inconsistent effect on actual behavior. By distinguishing surface-level trait expression from behavioral consistency, our findings challenge assumptions about LLM personality and underscore the need for deeper evaluation in alignment and interpretability.

Traffic accidents can be studied to mitigate the risk of further events. Recent advances in machine learning have provided an alternative way to study data associated with traffic accidents. New models achieve good generalization and high predictive power over imbalanced data. In this research, we study neural network-based models on data related to traffic accidents. We begin analyzing relative feature colinearity and unsupervised dimensionality reduction through autoencoders, followed by a dense network. The features are related to traffic accident data and the target is to classify accident severity. Our experiments show cross-validated results of up to 92% accuracy when classifying accident severity using the proposed deep neural network.