The Netherlands's data protection watchdog has cautioned citizens against consulting with artificial intelligence on how to vote, warning that popular chatbots provide a "highly distorted and polarised view" of politics. The Dutch Data Protection Authority said on Tuesday that an increasing number of voters were using AI to help decide who to vote for, despite the models offering "unreliable and clearly biased" advice. The research found that the chatbots more often recommended parties on the fringes of the political spectrum when asked to identify the three choices that best matched the policy preferences of 1,500 fictitious voter profiles. In more than half of cases, the AI models identified the hard-right Party for Freedom (PVV) or left-wing Green Left-Labour Party as the top choice, the watchdog said. Parties closer to the political middle ground - such as the right-leaning People's Party for Freedom and Democracy and the centre-left Democrats 66 - were recommended much less often, according to the watchdog.

How successful is Ukraine's'gas war' against Russia? How will Putin travel to Hungary with an ICC arrest warrant? How much of Europe's oil still comes from Russia? How Russia's new tactics pose new winter threat to Ukraine The Russian drone strike was surgically precise and destroyed a giant transformer at a key power station in the Ukrainian capital. "There's nothing left to repair," Mykola Svyrydenko, who lives close to Thermal Station 5, a sprawling, Soviet-era structure with two giant steam pipes that provides electricity and heat to hundreds of thousands of Kyiv's residents, told Al Jazeera.

Trump henchman's'Nazi texts' emerge as racist message crisis tears through White House Kate and William lead bid to exile Andrew and Fergie from Royal Lodge as public anger grows at his taxpayer-funded luxury: Waleses'can't abide' Prince and are pushing for him to leave, author claims Meghan's hit a trashy new low. JILLIAN MICHAELS: The trans trend is reversing. Now it's time to admit what's really been driving this extremist fad all along Napa tycoon is accused of slamming Rolls Royce into women because he couldn't find a parking spot... now his scandalous divorce secrets are exposed I know why Prince Harry and Andrew were'cut so much slack' by the late Queen - it's to do with her father and sister, reveals ROBERT HARDMAN The Prince Harry interview that left the Royals reeling and resumed the war against them, with even Downing Street forced to issue a statement! Married Congressman had alleged affair with aide... before she set herself on fire: Bombshell revelation as police block release of 911 call and other evidence Trapped on death island: 'Marooned' Russian troops are starving to death and 5,000 have died after being cut off from other Russian forces in Ukraine Apple Martin's music debut is likened to an'off-key, drunken karaoke performance' and proof that'nepotism is killing art' - as she attempts to follow in Coldplay star dad Chris' footsteps Fox News host Jesse Watters stunned as he admits his MOM joined millions protesting Trump at'No Kings' rally RFK Jr's desperate proposition to Cheryl Hines after his sexting fiasco pushed their marriage to the brink'Woke to blame for Louvre robbery': Female museum security chief accused of being a'diversity hire' comes under fire as politicians say heist has made France'the laughing stock of the world' Bombshell twist in Nicole Kidman and Keith Urban's divorce: 'She'd get back with him in a heartbeat,' says insider who's known her for years - as her inner circle reacts to'girlfriend' rumours Bella Hadid under fire over'offensive' social media posts she made as a teenager: 'Should've known better' Ellen Greenberg's parents reveal harrowing final phone call with daughter's ex fiancé after shock suicide ruling The new drug obsessions of the posh'wines and lines' mums: They scoff at cocaine now - but these three vices are the talk of the private school gate: JANA HOCKING Channel your inner John Wayne! 'Tough guy' walk used by western movie heroes makes you appear more powerful When it comes to swagger, nobody does it quite like John Wayne. His distinctive wide-based walk helped solidify his'tough guy' persona that became iconic in his western films.

Drone attack in Sudan threatens Khartoum airport's reopening: Reports A series of drone attacks has hit areas in Sudan's capital, including near Khartoum international airport, a day before its long-awaited reopening, according to the AFP news agency and Sudanese media reports. Witnesses told AFP they heard drones over central and southern Khartoum early on Tuesday. A wave of explosions was reported near the airport between 4am and 6am (02:00-04:00 GMT). The airport has been shut since fighting erupted in April 2023 between the Sudanese army and the paramilitary Rapid Support Forces (RSF), badly damaging infrastructure. Sudan's Rakoba News, citing witnesses, reported more than eight blasts in and around the airport.

A tornado tore through Val-d'Oise, north of Paris, on Monday, toppling construction cranes, damaging properties and uprooting trees in its path. One person was killed and four others critically injured, authorities said. The town of Ermont, about 20 km (13 miles) northeast of Paris was hardest hit by the sudden twister, which caused damage in multiple districts. Interior Minister Laurent Nunez said on the X social media platform that it had been a storm of rare intensity. Drone footage shows blaze destroying the historic Bernaga Monastery in Italy.

The Business Secretary has insisted the government is making it easier for businesses by reducing red tape. Peter Kyle defended Labour's approach to business, telling the BBC it will implement changes in a way that is pro-worker and pro-business. Ahead of next month's Budget, Chancellor Rachel Reeves is launching a crackdown on needless form-filling for businesses at the first-ever Regional Investment Summit in Birmingham. The government has been criticised by firms who say increased employers' National Insurance contributions and the Employment Rights Bill add to the burdens facing businesses. The Chancellor will say at the Birmingham summit on Tuesday that the changes will save firms almost £6bn a year.

We introduce a novel regularization scheme for autoencoders based on matricial free energy. Our approach defines a differentiable loss function in terms of the singular values of the code matrix (code dimension x batch size). From the standpoint of free probability an d random matrix theory, this loss achieves its minimum when the singular value distribution of the code matrix coincides with that of an appropriately sculpted random metric with i.i.d. Gaussian entries. Empirical simulations demonstrate that minimizing the negative matricial free energy through standard stochastic gradient-based training yields Gaussian-like codes that generalize across training and test sets. Building on this foundation, we propose a matricidal free energy maximizing autoencoder that reliably produces Gaussian codes and show its application to underdetermined inverse problems.

Multimodal AI research has overwhelmingly focused on high-resource languages, hindering the democratization of advancements in the field. To address this, we present AfriCaption, a comprehensive framework for multilingual image captioning in 20 African languages and our contributions are threefold: (i) a curated dataset built on Flickr8k, featuring semantically aligned captions generated via a context-aware selection and translation process; (ii) a dynamic, context-preserving pipeline that ensures ongoing quality through model ensembling and adaptive substitution; and (iii) the AfriCaption model, a 0.5B parameter vision-to-text architecture that integrates SigLIP and NLLB200 for caption generation across under-represented languages. This unified framework ensures ongoing data quality and establishes the first scalable image-captioning resource for under-represented African languages, laying the groundwork for truly inclusive multimodal AI.

Abstract--This paper presents a novel framework for speech transcription and synthesis, leveraging edge-cloud parallelism to enhance processing speed and accessibility for Kinyarwanda and Swahili speakers. It addresses the scarcity of powerful language processing tools for these widely spoken languages in East African countries with limited technological infrastructure. The framework utilizes the Whisper and SpeechT5 pre-trained models to enable speech-to-text (STT) and text-to-speech (TTS) translation. The architecture uses a cascading mechanism that distributes the model inference workload between the edge device and the cloud, thereby reducing latency and resource usage, benefiting both ends. On the edge device, our approach achieves a memory usage compression of 9.5% for the SpeechT5 model and 14% for the Whisper model, with a maximum memory usage of 149 MB. Experimental results indicate that on a 1.7 GHz CPU edge device with a 1 MB/s network bandwidth, the system can process a 270-character text in less than a minute for both speech-to-text and text-to-speech transcription. Using real-world survey data from Kenya, it is shown that the cascaded edge-cloud architecture proposed could easily serve as an excellent platform for STT and TTS transcription with good accuracy and response time. I. INTRODUCTION In today's digital age, the need for accurate and efficient speech transcription and synthesis models has been increasing rapidly. These models play an important role in a variety of applications, such as learning new language(s), accessibility tools for people with difficulties in reading and hearing, as well as automated voice assistants [1]. Kinyarwanda and Swahili are two of the local languages spoken in East Africa. While Swahili is the most widely spoken language in Eastern Africa, the speakers range from 60 million to over 150 million [2].

Lung cancer, a malignancy originating in lung tissues, is commonly diagnosed and classified using medical imaging techniques, particularly computed tomography (CT). Despite the integration of machine learning and deep learning methods, the predictive efficacy of automated systems for lung cancer classification from CT images remains below the desired threshold for clinical adoption. Existing research predominantly focuses on binary classification, distinguishing between malignant and benign lung nodules. In this study, a novel deep learning-based approach is introduced, aimed at an improved multi-class classification, discerning various subtypes of lung cancer from CT images. Leveraging a pre-trained ResNet model, lung tissue images were classified into three distinct classes, two of which denote malignancy and one benign. Employing a dataset comprising 15,000 lung CT images sourced from the LC25000 histopathological images, the ResNet50 model was trained on 10,200 images, validated on 2,550 images, and tested on the remaining 2,250 images. Through the incorporation of custom layers atop the ResNet architecture and meticulous hyperparameter fine-tuning, a remarkable test accuracy of 98.8% was recorded. This represents a notable enhancement over the performance of prior models on the same dataset.