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.

Quantum Machine Learning (QML) is considered one of the most promising applications of Quantum Computing in the Noisy Intermediate Scale Quantum (NISQ) era for the impact it is thought to have in the near future. Although promising theoretical assumptions, the exploration of how QML could foster new discoveries in Medicine and Biology fields is still in its infancy with few examples. In this study, we aimed to assess whether Quantum Kernels (QK) could effectively classify subtypes of Breast Cancer (BC) patients on the basis of molecular characteristics. We performed an heuristic exploration of encoding configurations with different entanglement levels to determine a trade-off between kernel expressivity and performances. Our results show that QKs yield comparable clustering results with classical methods while using fewer data points, and are able to fit the data with a higher number of clusters. Additionally, we conducted the experiments on the Quantum Processing Unit (QPU) to evaluate the effect of noise on the outcome. We found that less expressive encodings showed a higher resilience to noise, indicating that the computational pipeline can be reliably implemented on the NISQ devices. Our findings suggest that QK methods show promises for application in Precision Oncology, especially in scenarios where the dataset is limited in size and a granular non-trivial stratification of complex molecular data cannot be achieved classically.

The remarkable capabilities of Large Language Models (LLMs) in text comprehension and generation have revolutionized Information Extraction (IE). One such advancement is in Document-level Relation Triplet Extraction (DocRTE), a critical task in information systems that aims to extract entities and their semantic relationships from documents. However, existing methods are primarily designed for Sentence level Relation Triplet Extraction (SentRTE), which typically handles a limited set of relations and triplet facts within a single sentence. Additionally, some approaches treat relations as candidate choices integrated into prompt templates, resulting in inefficient processing and suboptimal performance when determining the relation elements in triplets. To address these limitations, we introduce a Discriminative and Voice Aware Paradigm DiVA. DiVA involves only two steps: performing document-level relation extraction (DocRE) and then identifying the subject object entities based on the relation. No additional processing is required simply input the document to directly obtain the triplets. This streamlined process more accurately reflects real-world scenarios for triplet extraction. Our innovation lies in transforming DocRE into a discriminative task, where the model pays attention to each relation and to the often overlooked issue of active vs. passive voice within the triplet. Our experiments on the Re-DocRED and DocRED datasets demonstrate state-of-the-art results for the DocRTE task.

Sign Language Processing (SLP) provides a foundation for a more inclusive future in language technology; however, the field faces several significant challenges that must be addressed to achieve practical, real-world applications. This work addresses multi-view isolated sign recognition (MV-ISR), and highlights the essential role of 3D awareness and geometry in SLP systems. We introduce the NGT200 dataset, a novel spatio-temporal multi-view benchmark, establishing MV-ISR as distinct from single-view ISR (SV-ISR). We demonstrate the benefits of synthetic data and propose conditioning sign representations on spatial symmetries inherent in sign language. Leveraging an SE(2) equivariant model improves MV-ISR performance by 8%-22% over the baseline.