The UN's AI warnings grow louder Welcome back to In the Loop, new twice-weekly newsletter about AI. It was a busy week for our team: Tharin Pillay was on site during the UN General Assembly in New York, while Harry Booth and Nikita Ostrovsky were at the "All In AI" event in Montreal. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? The United Nations General Assembly met this week in New York. While the assembly members spent much of their time on the crises in Palestine and Sudan, they also devoted a good chunk to AI.

Why have Spain, Italy sent ships to assist the Gaza flotilla? Israel's mass surveillance: Microsoft blocks the army from using its software United States tech giant Microsoft has cancelled some services it provides to the Israeli military over concerns it is violating its terms of service by using the firm's cloud computing software to spy on millions of Palestinians, the company's vice chair and president Brad Smith confirmed. Smith wrote in a Thursday blog post that the company had "ceased and disabled a set of services" to a unit within the Israeli Ministry of Defence in response to an August 6 joint investigation by The Guardian newspaper, +972 Magazine, and Hebrew-language outlet Local Call. Unit 8200 is the Israeli military's elite cyber warfare unit responsible for clandestine operations, including collecting signal intelligence and surveillance. The investigation by journalists revealed that following a 2021 meeting between Microsoft CEO Satya Nadella and Unit 8200's leader Yossi Sariel, an agreement was reached to collaborate on moving large volumes of sensitive intelligence material into the company's Azure platform.

Trump accuses Comey of nearly starting a war as it's revealed why new MAGA star prosecutor rushed indictment Tim Allen reveals Erika Kirk's speech inspired him to forgive his father's killer 60 years after tragic death Girl found dead in D4vd's Tesla was AGED 12 when they met online. Now as masked men guard his mansion, friends unravel the truth... and tell of the chilling moment her texts stopped Someone is trying to drive a wedge between Charles and William. I'm no conspiracy theorist, but even my royal sources say something'calculated' and odd is going on. This is what's really happening, reveals REBECCA ENGLISH The $2 fruit that reverses diabetes... as 100million Americans suffer from deadly condition and most don't know it What would her mother think? Johnny Carson's Malibu home lists for $110m - and it has jaw-dropping hidden feature Selena Gomez and Benny Blanco's FULL wedding plans leaked: Top secret details, surprise celeb host and a MAJOR A-list drop out... ahead of ceremony this weekend Texas man's final words as he is executed for the'exorcism' killing of his girlfriend's 13-month-old daughter Creepy New England road is so isolated it only sees a car every few DAYS.

Toyota has opened the doors to the first phase of Woven City, its experimental town meant to act as an incubator for technologies from autonomous driving to artificial intelligence. Woven City aims to serve as a test bed for the innovative technology the world's No. 1 automaker needs to regain its competitive edge in an industry dominated by battery-powered cars with sophisticated software. Still, it was unclear during a Thursday tour for media and stakeholders how the city might fit that requirement. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

Chinese drone experts have flown to Russia to conduct technical development work on military drones at a state-owned weapons manufacturer that is under Western sanctions, according to two European security officials and documents. The Chinese experts have visited arms maker IEMZ Kupol on more than half a dozen occasions since the second quarter of last year. During that time, Kupol also received shipments of Chinese-made attack and surveillance drones via a Russian intermediary, according to the documents and two officials. In September last year, it was documented that Kupol had developed a new drone, the Garpiya-3, in China with the help of local specialists, with specific details of the extensive involvement of Chinese experts in tests and technological work on military-use drones inside Russia now being reported for the first time. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

We develop a new high-dimensional statistical learning model which can take advantage of structured correlation in data even in the presence of randomness. We completely characterize learnability in this model in terms of VCN${}_{k,k}$-dimension (essentially $k$-dependence from Shelah's classification theory). This model suggests a theoretical explanation for the success of certain algorithms in the 2006~Netflix Prize competition.

Abstract-- 3D object localisation based on a sequence of camera measurements is essential for safety-critical surveillance tasks, such as drone-based wildfire monitoring. Localisation of objects detected with a camera can typically be solved with dense depth estimation or 3D scene reconstruction. However, in the context of distant objects or tasks limited by the amount of available computational resources, neither solution is feasible. In this paper, we show that the task can be solved using particle filters for both single and multiple target scenarios. The method was studied using a 3D simulation and a drone-based image segmentation sequence with global navigation satellite system (GNSS)-based camera pose estimates. The results showed that a particle filter can be used to solve practical localisation tasks based on camera poses and image segments in these situations where other solutions fail. The particle filter is independent of the detection method, making it flexible for new tasks. The study also demonstrates that drone-based wildfire monitoring can be conducted using the proposed method paired with a pre-existing image segmentation model.

Short form video platforms are central sites for health advice, where alternative narratives mix useful, misleading, and harmful content. Rather than adjudicating truth, this study examines how credibility is packaged in nutrition and supplement videos by analyzing the intersection of authority signals, narrative techniques, and monetization. We assemble a cross platform corpus of 152 public videos from TikTok, Instagram, and YouTube and annotate each on 26 features spanning visual authority, presenter attributes, narrative strategies, and engagement cues. A transparent annotation pipeline integrates automatic speech recognition, principled frame selection, and a multimodal model, with human verification on a stratified subsample showing strong agreement. Descriptively, a confident single presenter in studio or home settings dominates, and clinical contexts are rare. Analytically, authority cues such as titles, slides and charts, and certificates frequently occur with persuasive elements including jargon, references, fear or urgency, critiques of mainstream medicine, and conspiracies, and with monetization including sales links and calls to subscribe. References and science like visuals often travel with emotive and oppositional narratives rather than signaling restraint.

Universit e Jean Monnet Saint-Etienne, T elecom Saint-Etienne, F-42023 Saint-Etienne, France Email: {firstname.surname}@univ-st-etienne.fr Abstract--Quality of Experience (QoE) modeling is crucial for optimizing video streaming services to capture the complex relationships between different features and user experience. We propose a novel approach to QoE modeling in video streaming applications using interpretable Machine Learning (ML) techniques over raw time series data. Unlike traditional black-box approaches, our method combines Kolmogorov-Arnold Networks (KANs) as an interpretable readout on top of compact frequency-domain features, allowing us to capture temporal information while retaining a transparent and explainable model. We evaluate our method on popular datasets and demonstrate its enhanced accuracy in QoE prediction, while offering transparency and interpretability. Quality of Experience (QoE) is a crucial aspect in today's digital landscape, as it directly affects how users perceive and interact with applications and services. Defined as'the overall acceptability of an application or service, as perceived subjectively by the end user' [1], QoE is a complex and multifaceted concept that cannot be solely defined by traditional Quality of Service (QoS) metrics such as bandwidth, delay, or jitter. In the context of video streaming, for instance, perceptual quality is a critical component of QoE.

Short-form video platforms like TikTok present unique challenges for misinformation detection due to their multimodal, dynamic, and noisy content. We present ShortCheck, a modular, inference-only pipeline with a user-friendly interface that automatically identifies checkworthy short-form videos to help human fact-checkers. The system integrates speech transcription, OCR, object and deepfake detection, video-to-text summarization, and claim verification. ShortCheck is validated by evaluating it on two manually annotated datasets with TikTok videos in a multilingual setting. The pipeline achieves promising results with F1-weighted score over 70\%.