Police believe live facial recognition cameras may become "commonplace" in England and Wales, according to internal documents, with the number of faces scanned having doubled to nearly 5m in the last year. A joint investigation by the Guardian and Liberty Investigates highlights the speed at which the technology is becoming a staple of British policing. Major funding is being allocated and hardware bought, while the British state is also looking to enable police forces to more easily access the full spread of its image stores, including passport and immigration databases, for retrospective facial recognition searches. Live facial recognition involves the matching of faces caught on surveillance camera footage against a police watchlist in real time, in what campaigners liken to the continual finger printing of members of the public as they go about their daily lives. Retrospective facial recognition software is used by the police to match images on databases with those caught on CCTV and other systems.

In less than a year-and-a-half, Frankie Johnson, a man incarcerated at the William E Donaldson prison outside Birmingham, Alabama, says he was stabbed around 20 times. In December of 2019, Johnson says, he was stabbed "at least nine times" in his housing unit. In March of 2020, an officer handcuffed him to a desk following a group therapy meeting, and left the unit, after which another prisoner came in and stabbed him five times. In November of the same year, Johnson says, he was handcuffed by an officer and brought to the prison yard, where another prisoner attacked him with an ice pick, stabbing him "five to six times", as two correctional officers looked on. According to Johnson, one of the officers had actually encouraged his attacker to carry out the assault in retaliation for a previous argument between Johnson and the officer.

In Gaza, the sound of drones can be heard everywhere. An analysis by Al Jazeera's digital investigations team, Sanad, has revealed that Israel is repurposing commercial drones to use as weapons of war in the Strip. And as drones become ever more accessible, the line between their civilian use and their military use is becoming increasingly blurred.

A.1 Multimodal Large Language Models Building on the foundational capabilities of groundbreaking Large Language Models (LLMs) such as GPT [3], PALM [6], Mistral [49], and LLama [108], which excel in language understanding and reasoning, recent innovations have integrated these models with other modalities (especially vision), leading to the development of Multimodal Large Language Models (MLLMs). These advanced MLLMs combine and process visual and textual data, demonstrating enhanced versatility in addressing both traditional vision tasks [21, 40, 42, 133] and complex multimodal challenges [34, 70, 136]. Among all MLLMs, proprietary models consistently perform well. OpenAI's GPT-4-Vision [82] pioneered this space by adeptly handling both text and image content. Anthropic's Claude 3 series [7] integrates advanced vision capabilities and multilingual support, enhancing its application across diverse cognitive and real-time tasks.

We only consider the feasible cases. Then we consider the sigm(...) function in Equation 1. For instance, say x is the parameter to be trained, we want x to satisfy sigm(x) = 1 or 0, we would apply gradient-descent which will cause x + / . Two basic blocks of fully connected + ReLU layer are utilized as backbone for all benchmark datasets, generating instance-level representations with a dimensionality of 512. We set one branch here for the binary-classification problems.

Having reviewed dozens of drones of all shapes, sizes, and prices, I'd recently come to the conclusion that smaller, lighter, and cheaper drones were the way to go for 90 percent of consumers. But then DJI launched its new premium-priced, jumbo-size flagship consumer drone, the Mavic 4 Pro, and made me fall in love all over again. Yes, this drone is seriously impressive. But before I deep-dive the phenomenally good camera and ridiculously long range, it's important to note that the Mavic 4 Pro will not be officially available in the US. As well as ongoing issues around flight restrictions and security, a DJI spokesperson told WIRED, "Like many global companies, we have had to adjust our market strategy as local conditions and the industry environment have evolved. While we do not have a timeline for when we can introduce the product to the US market, we are closely monitoring the situation and actively exploring every possible solution."

Anomaly detection in time series data is fundamental to the design, deployment, and evaluation of industrial control systems. Temporal modeling has been the natural focus of anomaly detection approaches for time series data. However, the focus on temporal modeling can obscure or dilute the spatial information that can be used to capture complex interactions in multivariate time series. In this paper, we propose SARAD, an approach that leverages spatial information beyond data autoencoding errors to improve the detection and diagnosis of anomalies. SARAD trains a Transformer to learn the spatial associations, the pairwise inter-feature relationships which ubiquitously characterize such feedback-controlled systems. As new associations form and old ones dissolve, SARAD applies subseries division to capture their changes over time. Anomalies exhibit association descending patterns, a key phenomenon we exclusively observe and attribute to the disruptive nature of anomalies detaching anomalous features from others. To exploit the phenomenon and yet dismiss non-anomalous descent, SARAD performs anomaly detection via autoencoding in the association space. We present experimental results to demonstrate that SARAD achieves state-of-the-art performance, providing robust anomaly detection and a nuanced understanding of anomalous events.

Recent progress in deep generative models has been fueled by two paradigms - autoregressive and adversarial models. We propose a combination of both approaches with the goal of learning generative models of text. Our method first produces a high-level sentence outline and then generates words sequentially, conditioning on both the outline and the previous outputs. We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders. This provides strong, informative conditioning for the autoregressive stage. Our quantitative evaluations suggests that conditioning information from generated outlines is able to guide the autoregressive model to produce realistic samples, comparable to maximum-likelihood trained language models, even at high temperatures with multinomial sampling. Qualitative results also demonstrate that this generative procedure yields natural-looking sentences and interpolations.

Texas high school student Elliston Berry joins'Fox & Friends' to discuss the House's passage of a new bill that criminalizes the sharing of non-consensual intimate images, including content created with artificial intelligence. Welcome to Fox News' Artificial Intelligence newsletter with the latest AI technology advancements. IN TODAY'S NEWSLETTER: - Peek-a-boo, big tech sees you: Expert warns just 20 cloud images can make an AI deepfake video of your child - 5 AI terms you keep hearing and what they actually mean - AI to monitor NYC subway safety as crime concerns rise First Lady Melania Trump, joined by U.S. President Donald Trump, delivers remarks before President Trump signed the TAKE IT DOWN Act into law in the Rose Garden of the White House on May 19, 2025 in Washington, DC. The first lady made the Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks (TAKE IT DOWN) Act a priority, traveling to Capitol Hill to lobby lawmakers and show her support for the legislation, which addresses non-consensual intimate imagery, or "revenge porn," and artificial intelligence deepfakes posted online and to social media. DEEPFAKE DANGERS: Parents love capturing their kids' big moments, from first steps to birthday candles.

The recently introduced Consistency models pose an efficient alternative to diffusion algorithms, enabling rapid and good quality image synthesis. These methods overcome the slowness of diffusion models by directly mapping noise to data, while maintaining a (relatively) simpler training. Consistency models enable a fast one-or few-step generation, but they typically fall somewhat short in sample quality when compared to their diffusion origins. In this work we propose a novel and highly effective technique for post-processing Consistency-based generated images, enhancing their perceptual quality. Our approach utilizes a joint classifierdiscriminator model, in which both portions are trained adversarially. While the classifier aims to grade an image based on its assignment to a designated class, the discriminator portion of the very same network leverages the softmax values to assess the proximity of the input image to the targeted data manifold, thereby serving as an Energy-based Model. By employing example-specific projected gradient iterations under the guidance of this joint machine, we refine synthesized images and achieve an improved FID scores on the ImageNet 64x64 dataset for both Consistency-Training and Consistency-Distillation techniques.