I turned myself into an AI-generated deathbot - here's what I found If a loved-one died tomorrow, would you want to keep talking to them? Not through memories or saved messages, but through artificial intelligence - a chatbot that uses their texts, emails and voice notes, to reply in their tone and style. A growing number of technology companies now offer such services as part of the digital afterlife industry, which is worth more than £100bn, with some people using it as a way to deal with their grief. Cardiff University's Dr Jenny Kidd has led research on so-called deathbots, published in the Cambridge University Press journal Memory, Mind and Media, and described the results as both fascinating and unsettling. Attempts to communicate with the dead are not new.

Sexual deepfakes continue to get more sophisticated, capable, easy to access, and perilous for millions of women who are abused with the technology. Open the website of one explicit deepfake generator and you'll be presented with a menu of horrors. With just a couple of clicks, it offers you the ability to convert a single photo into an eight-second explicit videoclip, inserting women into realistic-looking graphic sexual situations. "Transform any photo into a nude version with our advanced AI technology," text on the website says. The options for potential abuse are extensive.

Media watchdog Ofcom said it was seeking urgent answers from X, to announce action within'days not weeks'. Media watchdog Ofcom said it was seeking urgent answers from X, to announce action within'days not weeks'. Elon Musk's X threatened with UK ban over wave of indecent AI images Fri 9 Jan 2026 17.49 ESTFirst published on Fri 9 Jan 2026 15.00 EST Elon Musk's X has been ordered by the UK government to tackle a wave of indecent AI images or face a de facto ban, as an expert said the platform was no longer a "safe space" for women. The media watchdog, Ofcom, confirmed it would accelerate an investigation into X as a backlash grew against the site, which has hosted a deluge of images depicting partially stripped women and children. X announced a restriction on creating images via the Grok AI tool on Friday morning in response to the global outcry.

Predictions of fatalities from violent conflict on the PRIO-GRID-month (pgm) level are characterized by high levels of uncertainty, limiting their usefulness in practical applications. We discuss the two main sources of uncertainty for this prediction task, the nature of violent conflict and data limitations, embedding this in the wider literature on uncertainty quantification in machine learning. We develop a strategy to quantify uncertainty in conflict forecasting, shifting from traditional point predictions to full predictive distributions. Our approach compares and combines multiple tree-based classifiers and distributional regressors in a custom auto-ML setup, estimating distributions for each pgm individually. We also test the integration of regional models in spatial ensembles as a potential avenue to reduce uncertainty. The models are able to consistently outperform a suite of benchmarks derived from conflict history in predictions up to one year in advance, with performance driven by regions where conflict was observed. With our evaluation, we emphasize the need to understand how a metric behaves for a given prediction problem, in our case characterized by extremely high zero-inflatedness. While not resulting in better predictions, the integration of smaller models does not decrease performance for this prediction task, opening avenues to integrate data sources with less spatial coverage in the future.

Sam Kirchner wants to save the world from artificial superintelligence. He's been missing for two weeks. B efore Sam Kirchner vanished, before the San Francisco Police Department began to warn that he could be armed and dangerous, before OpenAI locked down its offices over the potential threat, those who encountered him saw him as an ordinary, if ardent, activist. Phoebe Thomas Sorgen met Kirchner a few months ago at Travis Air Force Base, northeast of San Francisco, at a protest against immigration policy and U.S. military aid to Israel. Sorgen, a longtime activist whose first protests were against the Vietnam War, was going to block an entrance to the base with six other older women. Kirchner, 27 years old, was there with a couple of other members of a new group called Stop AI, and they all agreed to go along to record video on their phones in case of a confrontation with the police.

'It reflects and reinforces a culture where consent and respect for personal boundaries are undermined.' 'It reflects and reinforces a culture where consent and respect for personal boundaries are undermined.' The rise of deepfake pornography in schools: 'One girl was so horrified she vomited' The use of'nudify' apps is becoming more and more prevalent, with hundreds of teachers having seen images created by pupils, often of their peers. He didn't feel this was something he shouldn't be doing. It was in the open and people saw it.

Abstract--Traditional efforts to measure historical structural oppression struggle with cross-national validity due to the unique, locally specified histories of exclusion, colonization, and social status in each country, and often have relied on structured indices that privilege material resources while overlooking lived, identity-based exclusion. We introduce a novel framework for oppression measurement that leverages Large Language Models (LLMs) to generate context-sensitive scores of lived historical disadvantage across diverse geopolitical settings. Using unstructured self-identified ethnicity utterances from a multilingual COVID-19 global study, we design rule-guided prompting strategies that encourage models to produce interpretable, theoretically grounded estimations of oppression. We systematically evaluate these strategies across multiple state-of-the-art LLMs. Our results demonstrate that LLMs, when guided by explicit rules, can capture nuanced forms of identity-based historical oppression within nations. This approach provides a complementary measurement tool that highlights dimensions of systemic exclusion, offering a scalable, cross-cultural lens for understanding how oppression manifests in data-driven research and public health contexts. The study of racial and ethnic inequality remains central to sociological research, with extensive research documenting how structural oppression is reproduced in historical and contemporary contexts [1]-[3]. Oppression can be understood as a social hierarchy in which some groups subject other groups to lower status and to systemic exclusion, dehumanization, and disadvantage. In public health and sociology, this oppression is closely aligned with definitions of systemic and structural racism, which describe racism as deeply embedded in laws, policies, institutional practices, and social norms that sustain widespread inequities, violence, and disadvantage over time [1]. Foundational works have demonstrated how ethnic and national hierarchies shape access to power, life opportunities, autonomy, and sovereignty, for example, primarily through institutionalized mechanisms such as legal structures, educational systems, and healthcare access, among others [2].

The report found 7% of respondents had been depicted in a sexual or intimate deepfake. The report found 7% of respondents had been depicted in a sexual or intimate deepfake. One in four people think there is nothing wrong with creating and sharing sexual deepfakes, or they feel neutral about it, even when the person depicted has not consented, according to a police-commissioned survey. The findings prompted a senior police officer to warn that the use of AI is accelerating an epidemic in violence against women and girls (VAWG), and that technology companies are complicit in this abuse. The survey of 1,700 people commissioned by the office of the police chief scientific adviser found 13% felt there was nothing wrong with creating and sharing sexual or intimate deepfakes - digitally altered content made using AI without consent.

Please refer to table 1. The harmful keywords are with red and the unharmful keywords are withgreen background color.

CAUTION: This paper includes model-generated content that may contain offensive or distressing material.