Violent threats remain a significant problem across social media platforms. Useful, high-quality data facilitates research into the understanding and detection of malicious content, including violence. In this paper, we introduce a cross-platform dataset of 30,000 posts hand-coded for violent threats and sub-types of violence, including political and sexual violence. To evaluate the signal present in this dataset, we perform a machine learning analysis with an existing dataset of violent comments from YouTube. We find that, despite originating from different platforms and using different coding criteria, we achieve high classification accuracy both by training on one dataset and testing on the other, and in a merged dataset condition. These results have implications for content-classification strategies and for understanding violent content across social media.

The increasing presence of automated vehicles (AVs) presents new challenges for crash classification and safety analysis. Accurately identifying the SAE automation level involved in each crash is essential to understanding crash dynamics and system accountability. However, existing approaches often overlook automation-specific factors and lack model sophistication to capture distinctions between different SAE levels. To address this gap, this study evaluates the performance of three advanced tabular deep learning models MambaAttention, TabPFN, and TabTransformer for classifying SAE automation levels using structured crash data from Texas (2024), covering 4,649 cases categorized as Assisted Driving (SAE Level 1), Partial Automation (SAE Level 2), and Advanced Automation (SAE Levels 3-5 combined). Following class balancing using SMOTEENN, the models were trained and evaluated on a unified dataset of 7,300 records. MambaAttention demonstrated the highest overall performance (F1-scores: 88% for SAE 1, 97% for SAE 2, and 99% for SAE 3-5), while TabPFN excelled in zero-shot inference with high robustness for rare crash categories. In contrast, TabTransformer underperformed, particularly in detecting Partial Automation crashes (F1-score: 55%), suggesting challenges in modeling shared human-system control dynamics. These results highlight the capability of deep learning models tailored for tabular data to enhance the accuracy and efficiency of automation-level classification. Integrating such models into crash analysis frameworks can support policy development, AV safety evaluation, and regulatory decisions, especially in distinguishing high-risk conditions for mid- and high-level automation technologies.

The next time you order a takeaway, it might be flown directly to your door. Today, Deliveroo has launched its first drone delivery service for customers in Ireland. Drones travelling at speeds of up to 50 miles per hour (80 kph) will carry food from restaurants to customers in as little as three minutes. Upon arrival, the drone will hover above the customer's home and gently lower the food to the ground on a tether before returning to the delivery hub. Launching in Blanchardstown, on the outskirts of Dublin, the trial will cover a 1.8-mile (3km) radius, reaching up to 150,000 people.

Peers voted by 221 to 116 on Wednesday to insist on an amendment to force AI companies to be transparent about what material they use to train their models. He added: "We will not let the government forget their promise to support our creative industries. We will not back down and we will not quietly go away. This is just the beginning." Resistance to the changes in the Lords has been led by Beeban Kidron, a cross-bench peer and film director, whose amendments have been repeatedly backed by the upper chamber.

New satellite images and drone footage show serious damage inflicted on aircraft at several Russian airbases during Ukraine's surprise drone strike on Sunday. The images of two Russian airbases in north-western and central Russia, taken on Wednesday morning, show 12 aircraft damaged or destroyed. Meanwhile, drone footage, released by the Security Service of Ukraine (SBU) on Wednesday, showed attacks on these two bases as well as two more targeted elsewhere. Ukraine claims that it targeted 41 strategic bombers in the operation, adding that "at least" 13 were destroyed. Security officials say the shock incursion took 18 months to plan and saw many drones smuggled into Russia.

Russian President Vladimir Putin has told Donald Trump in a telephone conversation that Moscow would have to respond to the recent Ukrainian drone attacks, the US president said. Trump said on Wednesday that the two men "discussed the attack on Russia's docked airplanes, by Ukraine, and also various other attacks that have been taking place by both sides." Putin "did say, and very strongly, that he will have to respond to the recent attack on the airfields", Trump said in a social media post. Al Jazeera's Kimberly Halkett said that Trump described his 85-minute phone call with Putin as "a good conversation but not one that would lead to immediate peace". "You have to remember that Donald Trump, when he came into office, was very confident that he could end this war on day one, but here we are now in June and the fact is … this is far from resolved," she said from the White House.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This is our 100th episode! A super special look at AI and the environment, we interviewed four experts for this DeepDive episode. We chatted about water stress, the energy usage of AI systems and data centres, using AI for fossil fuel discovery, the geo-political nature of AI, GenAI vs other ML algorithms for energy use, demanding transparency on energy usage for training and operating AI, more AI regulation for carbon consumption, things we can change today like picking renewable hosting solutions, publishing your data, when doing "responsible AI" you must include the environment, considering who are the controllers of the technology and what do they want, and more… Hannah Smith is Director of Operations for Green Web Foundation and co-founder of Green Tech South West. She has a background in Computer Science.

President Donald Trump's recent visit to the UAE marked a pivotal moment for UAE-U.S. bilateral relations, shining a spotlight on a shared vision for the future. As the UAE and the "New Gulf" pivot from oil to cutting-edge technologies, our partnership with the U.S., rooted in decades of trust, has become a beacon of what's possible when nations collaborate. This trust has paved the way for a bold new chapter: a strategic economic alliance poised to create tens of thousands of high-tech, energy and manufacturing jobs, driving prosperity in both of our countries. At the heart of this collaboration lies the new U.S.-UAE AI Acceleration Partnership. This initiative will advance cooperation in artificial intelligence and other transformative technologies while spurring investment flows between our nations.

The modern world is built on mathematics. Math lets us model complex systems such as the way air flows around an aircraft, the way financial markets fluctuate, and the way blood flows through the heart. And breakthroughs in advanced mathematics can unlock new technologies such as cryptography, which is essential for private messaging and online banking, and data compression, which lets us shoot images and video across the internet. But advances in math can be years in the making. DARPA wants to speed things up.

Political online participation in the form of discussing political issues and exchanging opinions among citizens is gaining importance with more and more formats being held digitally. To come to a decision, a careful discussion and consideration of opinions and a civil exchange of arguments, which is defined as the act of deliberation, is desirable. The quality of discussions and participation processes in terms of their deliberativeness highly depends on the design of platforms and processes. To facilitate online communication for both participants and initiators, machine learning methods offer a lot of potential. In this work we want to showcase which issues occur in political online discussions and how machine learning can be used to counteract these issues and enhance deliberation.