Many tech experts have warned that AI is on a path of destruction, but a new survey of top researchers has quantified the chances of it causing human extinction. A team of international scientists asked 2,778 AI experts about the future of the systems, with five percent reporting the tech will lead to collapse. But, a far more frightening estimation came from one in 10 researchers who said there's a shocking 25 percent chance that AI will destroy the human race. The experts cited three possible causes: AI allowing threatening groups to make powerful tools, like engineered viruses, 'authoritarian rulers using AI to control their populations and AI systems worsening economic inequality by disproportionately benefiting certain individuals.' Artificial intelligence regulation control is the only answer to protecting humans, and if AI isn't regulated, researchers estimated that there is a 10 percent chance that machines will outperform humans in all tasks by 2027, - but it would increase to a 50 percent chance by 2047.

Fox News senior congressional correspondent Chad Pergram has the latest on the foreign policy issues facing the Biden administration on Your World. Four members of an Iran-aligned Iraqi militia group -- including a high-ranking Iraqi militia commander -- were killed in a drone strike in Baghdad on Thursday, according to reports by The Associated Press and Reuters. A U.S. official has confirmed to Fox News that the U.S. was responsible for the strike which targeted an Iraqi militia leader in Baghdad who is believed responsible for attacks on U.S. forces. The official says this was a precision strike on a vehicle and not a hit on a whole facility as other outlets have reported. The strike targeted a leader of the Harakat Hezbollah al-Nujaba, an Iraqi Shi'ite military group, the U.S. official said.

Iraq's government has accused the United States-led international coalition forces of carrying out a drone strike targeting an Iran-aligned paramilitary group in the capital, Baghdad, that killed and wounded several people. The strike on Thursday targeted the Popular Mobilisation Forces (PMF), also known as Hashd al-Shaabi. Hajj Mushtaq Talib al-Saidi (Abu Taqwa), a senior PMF commander, was among those killed. The total number of casualties was not immediately clear, but the Reuters news agency reported that four PMF members were killed and six wounded. "The Iraqi armed forces hold the forces of the international coalition responsible for this attack," Prime Minister Mohammed Shia al-Sudani's office said in a statement, calling it a "dangerous escalation and aggression".

A week before Halloween last year, city of Austin employee Rachel Castignoli sent a polite but firm email to a government relations staffer at self-driving vehicle developer Cruise. "We would like you to not operate between 5 pm and 9 pm on Halloween," she wrote in bold text highlighted in yellow, documents obtained by WIRED through a public records request show. More children are killed by vehicles on Halloween than on any other night of the year, she wrote, and the city wanted to limit traffic--regardless of whether software or a human was behind the wheel. "Please acknowledge receipt of this email," Castignoli concluded, also in bold, adding "Thanks!" Castignoli's email is an example of the strange position of officials in some US cities chosen by Cruise and rivals such as Alphabet's Waymo as testing grounds for self-driving taxi services. Castignoli works for Austin's Transportation and Public Works Department, which like local agencies around the country, is responsible for what happens on city streets, setting speed limits and traffic restrictions.

Just 24 hours before he took to the podium on Wednesday, Hassan Nasrallah, the head of Lebanon's powerful armed group Hezbollah, was preparing to deliver a speech commemorating another of Israel's former arch foes, Qassem Soleimani, the Iranian commander killed in a U.S. drone strike four years ago to the day. But in the wake of the suspected Israeli assassination on Tuesday of Saleh al-Arouri, a top Hamas leader killed in the heart of Hezbollah's stronghold in southern Beirut, Mr. Nasrallah revised his comments to commemorate not just one of his closest allies, but two. In a highly anticipated speech on Wednesday that gained new significance in the wake of Mr. al-Arouri's assassination, Hezbollah's leader denounced the attack in Lebanon's capital as a "dangerous" milestone, pledging revenge for the killing and threatening to meet any wider Israeli conflict with unrestrained warfare. "If the enemy considers waging a war against Lebanon, our battle will be without boundaries or rules," Mr. Nasrallah said. "We are not afraid of war. Those who think of going to war with us will regret it. War with us will come at a very, very, very high cost."

The explosions that killed more than 100 people in Iran on Wednesday took place at an anniversary commemoration for Maj. Gen. Qassim Suleimani, the top Iranian commander who was killed by a U.S. drone strike four years ago. General Suleimani, the most powerful Iranian commander at the head of the foreign-facing arm of Iran's Revolutionary Guards Corps, was considered a hero by some in Iran and in other parts of the region for building an axis of allied militias to defend Iran's interests across the Middle East, to counter the United States and Israel, and for helping to defeat the Islamic State in Syria and Iraq. In the United States, he was regarded as a force behind international terrorism campaigns, and President Donald Trump said his killing in January 2020 was ordered "to stop a war" because General Suleimani had been plotting attacks on American diplomats and military personnel. General Suleimani was designated as a terrorist by the United States and Israel, where he helped orchestrate waves of militia attacks.

Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although some of the existing survey papers have explored various learning-based approaches, a comprehensive review emphasizing the application of AI-enabled UAV systems and their subsequent impact on multi-stage wildfire management is notably lacking. This survey aims to bridge these gaps by offering a systematic review of the recent state-of-the-art technologies, highlighting the advancements of UAV systems and AI models from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior.

Large-scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub-surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have seen numerous successes over several engineering applications, estimations on quantities of interest (QoI) - such as breakthrough time of particles reaching the edge of the system - suffer from a two distinct types of uncertainty. A run of a DFN simulator requires several parameter values to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameter choices will lead to some amount of uncertainty in the QoI, called epistemic uncertainty. Furthermore, since DFN simulators rely on stochastic processes to place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e. different seeds) of the same random process leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. We make several design choices to handle an observed heteroskedasticity in DFN simulators, where the aleatoric uncertainty changes for different inputs, since the quality makes several standard statistical methods inadmissible. Beyond the specific takeaways on which input variables affect uncertainty the most for DFN simulators, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

Femicide refers to the killing of a female victim, often perpetrated by an intimate partner or family member, and is also associated with gender-based violence. Studies have shown that there is a pattern of escalating violence leading up to these killings, highlighting the potential for prevention if the level of danger to the victim can be assessed. Machine learning offers a promising approach to address this challenge by predicting risk levels based on textual descriptions of the violence. In this study, we employed the Long Short Term Memory (LSTM) technique to identify patterns of behavior in Brazilian police reports preceding femicides. Our first objective was to classify the content of these reports as indicating either a lower or higher risk of the victim being murdered, achieving an accuracy of 66%. In the second approach, we developed a model to predict the next action a victim might experience within a sequence of patterned events. Both approaches contribute to the understanding and assessment of the risks associated with domestic violence, providing authorities with valuable insights to protect women and prevent situations from escalating.

Recent advancements in generative artificial intelligence (AI) have raised concerns about their potential to create convincing fake social media accounts, but empirical evidence is lacking. In this paper, we present a systematic analysis of Twitter(X) accounts using human faces generated by Generative Adversarial Networks (GANs) for their profile pictures. We present a dataset of 1,353 such accounts and show that they are used to spread scams, spam, and amplify coordinated messages, among other inauthentic activities. Leveraging a feature of GAN-generated faces -- consistent eye placement -- and supplementing it with human annotation, we devise an effective method for identifying GAN-generated profiles in the wild. Applying this method to a random sample of active Twitter users, we estimate a lower bound for the prevalence of profiles using GAN-generated faces between 0.021% and 0.044% -- around 10K daily active accounts. These findings underscore the emerging threats posed by multimodal generative AI. We release the source code of our detection method and the data we collect to facilitate further investigation. Additionally, we provide practical heuristics to assist social media users in recognizing such accounts.