An AI video based on a famous religious artifact has revealed what Christ may have looked like. The Shroud of Turin is an ancient cloth which many Christians believe was used to wrap Jesus' mutilated body after he died on the cross. Photos of the cloth were fed into Midjourney, an AI image generator, which then produced a lifelike image and video of Christ blinking, smiling and praying as he may have once did before the crucifixion around 33AD. The clip was posted on X, where users have called being touted as'the true face of Jesus.' However, others have pointed out that the technology made Jesus appear white when he would have been Middle Eastern with a darker complexion.

North Korean leader Kim Jong Un supervises the test of suicide drones with artificial intelligence technology, according to local media, at an unknown location, in this photo released by North Korea's official Korean Central News Agency on March 27, 2025. KIM POWER PLAY: North Korean dictator Kim Jong Un oversaw tests of newly developed AI-powered suicide drones and called for their increased production, North Korean state media said Thursday. A photo taken on October 4, 2023 in Manta, near Turin, shows a smartphone and a laptop displaying the logos of the artificial intelligence OpenAI research company and ChatGPT chatbot. SUZANNE'S TWIN: Suzanne Somers passed away two years ago, but her memory lives on, not only through her Hollywood career and businesses, but artificial intelligence too. Her widower, Alan Hamel, worked with an AI company called Hollo to create a "twin" of his late wife.

Recent advances in generative AI have enabled increasingly sophisticated applications in various domains, from customer service chatbots to automated content generation. However, alongside these advancements, the vulnerability of large language models (LLMs) to adversarial inputs has emerged as a critical concern. Among these, prompt injection attacks pose a particularly insidious challenge, as they exploit the model's inherent instruction-following behavior to override intended constraints. While prompt injection is often discussed in theoretical contexts, its impact on deployed AI systems has been observed in practical settings. Research has demonstrated that even models with reinforced safety mechanisms--or with specific Knowledge based on RAG (Retrieval Augmented Generation)--can be manipulated into disclosing sensitive data, executing unauthorized instructions, or producing harmful content [4].

Intel emphasizes the efficiency advantage of its Granite Rapids Xeon 6 server chips compared to AMD Turin chips that use more processor cores. Intel on Monday revealed new versions of its Xeon 6 server processors, in a bid to proliferate AI processing throughout its data center product line as it fends off incursions on two fronts -- from AMD and ARM Holdings. The new processors, dubbed Xeon 6 6500 and 6700, extend the chip giant's product lineup first announced in September 2024. Code-named "Granite Rapids," the Xeon 6 chips feature what are called performance cores, dozens of individual computing elements designed to deliver the most powerful computing activity in the company's chip lineup. Intel's initial 6900 Xeon 6 chips, announced in September, offer 128 of the performance cores in each chip, whereas the 6500 and 6700 chips offer lower core counts, 16 to 86, at lower prices and lower power consumption.

We describe Evalita-LLM, a new benchmark designed to evaluate Large Language Models (LLMs) on Italian tasks. The distinguishing and innovative features of Evalita-LLM are the following: (i) all tasks are native Italian, avoiding issues of translating from Italian and potential cultural biases; (ii) in addition to well established multiple-choice tasks, the benchmark includes generative tasks, enabling more natural interaction with LLMs; (iii) all tasks are evaluated against multiple prompts, this way mitigating the model sensitivity to specific prompts and allowing a fairer and objective evaluation. We propose an iterative methodology, where candidate tasks and candidate prompts are validated against a set of LLMs used for development. We report experimental results from the benchmark's development phase, and provide performance statistics for several state-of-the-art LLMs.

Claire chatted to Isabella Fiorello from the University of Freiburg about plant-inspired robots made from living materials. Isabella Fiorello is a Junior Group Leader and Principal Investigator of the Bioinspired Plant-hybrid Materials group at the University of Freiburg in Germany. She has a Master's Degree in Industrial Biotechnology from the University of Turin in Italy and a PhD in Biorobotics from Scuola Superiore Sant'Anna in Italy. Her research focusses on the development of biologically-inspired microfabricated living materials able to precisely interact with complex unstructured surfaces for applications in precision agriculture, smart fabrics, space, and soft robotics.

The face on the Shroud of Turin could not have come from Jesus' head – and it's doubtful he ever touched it, an explosive new study suggests. Marked with a faint impression of a body and face, the artifact is believed by many to be the actual fabric used to wrap Christ's corpse after his crucifixion. But its documented history only starts in the mid-14th century, and it's been a source of scepticism for almost as long, with many dismissing it as a medieval forgery. Now a new study has found that the impression on the shroud could not have been made by a three-dimensional human body, but was perhaps from a bas-relief – a shallow carving. To reach this conclusion, Cicero Moraes, author of the new study, created a virtual simulation in which a fabric was placed over a body in a bid to replicate the famous shroud.

Scientists in Italy hit the headlines this week, after claiming the famous Shroud of Turin dates from Jesus' lifetime around 2,000 years ago. Now, AI has reimagined what the son of God might have actually looked like based on the treasured relic, which is said to feature an imprint of Jesus' face. MailOnline asked the AI tool Merlin: 'Can you generate a realistic image of Jesus Christ based on the face in the Shroud of Turin?' The AI-generated result suggests Christ was white with big blue eyes, a trim beard and thorn marks on his face. So, can you see the similarities with the famous holy imprint? The Shroud of Turin is a 14-foot-long linen cloth with a faint image of a crucified man.

In the effort to learn from extensive collections of distributed data, federated learning has emerged as a promising approach for preserving privacy by using a gradient-sharing mechanism instead of exchanging raw data. However, recent studies show that private training data can be leaked through many gradient attacks. While previous analytical-based attacks have successfully reconstructed input data from fully connected layers, their effectiveness diminishes when applied to convolutional layers. This paper introduces an advanced data leakage method to efficiently exploit convolutional layers' gradients. We present a surprising finding: even with non-fully invertible activation functions, such as ReLU, we can analytically reconstruct training samples from the gradients. To the best of our knowledge, this is the first analytical approach that successfully reconstructs convolutional layer inputs directly from the gradients, bypassing the need to reconstruct layers' outputs. Prior research has mainly concentrated on the weight constraints of convolution layers, overlooking the significance of gradient constraints. Our findings demonstrate that existing analytical methods used to estimate the risk of gradient attacks lack accuracy. In some layers, attacks can be launched with less than 5 % of the reported constraints.

Urban heat islands, defined as specific zones exhibiting substantially higher temperatures than their immediate environs, pose significant threats to environmental sustainability and public health. This study introduces a novel machine-learning model that amalgamates data from the Sentinel-3 satellite, meteorological predictions, and additional remote sensing inputs. The primary aim is to generate detailed spatiotemporal maps that forecast the peak temperatures within a 24-hour period in Turin. Experimental results validate the model's proficiency in predicting temperature patterns, achieving a Mean Absolute Error (MAE) of 2.09 degrees Celsius for the year 2023 at a resolution of 20 meters per pixel, thereby enriching our knowledge of urban climatic behavior. This investigation enhances the understanding of urban microclimates, emphasizing the importance of cross-disciplinary data integration, and laying the groundwork for informed policy-making aimed at alleviating the negative impacts of extreme urban temperatures.