Air strikes have targeted the headquarters of the Iran-aligned Popular Mobilisation Forces (PMF) in Iraq's capital, Baghdad, as the country becomes a two-way battlefield between armed factions and the United States during its war with Iran . The US carried out strikes against the Shia paramilitary umbrella group, also known locally as Hashed al-Shaabi, late on Sunday after attacks on a US diplomatic and logistics centre at Baghdad International Airport. The attack was carried out after Iraqi security officials said four explosions were heard near Camp Victory, a US logistics centre at the capital's main airport. Al Jazeera's Assed Baig, reporting from Baghdad, said some drones "breached air defences and caused damage, more symbolic damage than anything else". "At the same time, Iraqi security forces have set up checkpoints around Baghdad to try and stop these drone strikes because some of these factions are launching drones from the vicinity of Baghdad," he said.

While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the languagemodel likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation dual-NMT. Experiments show that dual-NMT works very well on English French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task.

In a place denied access to basic forensic technology--and where people disappear into Israeli detention--the fate of thousands remains unknown. One of them is an autistic teenager. In the early morning dark, Abeer Skaik turned to her husband, Ali Al-Qatta, and said that today would be the day they would find their son. Ali nodded in silence, and she handed him the stack of flyers. Each bore a photograph of 16-year-old Hassan smiling widely, his shoulders loose, wearing a plain red T-shirt. He is looking directly at the camera, unguarded. On top of the page, in large letters, Abeer had written a single word in bold red ink: --an appeal. Abeer watched as Ali stepped into a car with a few close friends and drove away. They started the 30-kilometer trip south, from al-Tuffah, east of Gaza City, to the European Hospital in Khan Younis. They had heard that a group of people detained by Israel, including children, would be released there. The gate was already crowded. Families stood shoulder to shoulder, wrapped in blankets against the cold, clutching photographs and ID cards. Ali distributed the flyers among his friends. When the buses of released detainees arrived, he and the others moved slowly through the narrow gaps between clusters of people. Some of those who had just been released were being pulled into embraces. Ali waited at the edge of each reunion. "Have you seen my son?" he asked. One after another, people shook their heads.

Don't Listen to Anyone Who Thinks Secession Will Solve Anything Americans increasingly fantasize about a divorce between red and blue states--but they dread the thought of civil war. You can't have one without the other. It's become almost like a histamine response: After a shocking national event like the assassination of Charlie Kirk, or Donald Trump's deployment of the military to Los Angeles last June, mentions of the term " civil war " and calls for secession surge online. This kind of talk flared again in January, when two citizens were shot and killed by immigration agents on the streets of Minneapolis, and governor Tim Walz mobilized the Minnesota National Guard to be ready to support local law enforcement. "I mean, is this a Fort Sumter?" Walz said in an interview with The Atlantic, invoking the battle that sparked the Civil War.

In its early days, the AI initiative known as Project Maven had its fair share of skeptics at the Pentagon. Today, many of them are true believers. The rise of AI warfare speaks to the biggest moral and practical question there is: Who--or what--gets to decide to take a human life? And who bears that cost? In 2018, more than 3,000 Google workers protested the company's involvement in "the business of war" after finding out the company was part of Project Maven, then a nascent Pentagon effort to use computer vision to rifle through copious video footage taken in America's overseas drone wars. They feared Project Maven's AI could one day be used for lethal targeting. In my yearslong effort to uncover the full story of Project Maven for my book,, I learned that is exactly what happened, and that the undertaking was just as controversial inside the Pentagon. Today, the tool known as Maven Smart System is being used in US operations against Iran . How the US military's top brass moved from skepticism about the use of AI in war to true believers has a lot to do with a Marine colonel named Drew Cukor. In early September 2024, during the cocktail hour at a private retreat for tech investors and defense leaders, Vice Admiral Frank "Trey" Whitworth found his way to Drew Cukor. Now Project Maven's founding leader and his skeptical successor were standing face-to-face. Three years earlier, Whitworth had been the Pentagon's top military official for intelligence, advising the chairman of the Joint Chiefs of Staff and running one of the most sensitive and potentially lethal parts of any military process: targeting.

How the artists in this year's survey do or, more often, don't acknowledge those who paved the way for them. Machado makes pieces that one might call documents of reverence, excavated burial grounds. If nothing else, the 2026 Whitney Biennial, curated by Marcela Guerrero and Drew Sawyer (at the Whitney Museum through August 23rd), introduces viewers to what I call ChatGPT art--facsimiles of facsimiles by makers who have little if any relationship to what they're putting out there, aside from its being a product in service of a career. Indeed, it's difficult to think of the people who grew up with and apparently condone the use of A.I. sources in the creation of "art" as artists themselves, especially if you define art as a creative expression of thoughts or feelings that have changed, and contributed to the vision of, the artists who made it. It's true that, nearly from the beginning, postmodern art challenged the notion of originality, or, more specifically, the weight of originality--often with great joy and wit and not a little fear.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN [7, 19] that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection. Our method can thus naturally adopt fully convolutional image classifier backbones, such as the latest Residual Networks (ResNets) [10], for object detection. We show competitive results on the PASCAL VOC datasets (e.g., 83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20 faster than the Faster R-CNN counterpart.

Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks.