Early frameworks like the EU's AI Act focused on highly visible applications - banning high-risk uses in health, employment and law enforcement to prevent societal harms. But countries now target the underlying building blocks of AI. China restricts models to combat deepfakes and inauthentic content. Citing national security risks, the US controls the exports of the most advanced chips and, under Biden, even model weights - the "secret sauce" that turns user queries into results. These AI regulations are hiding in dense administrative language - "Implementation of Additional Export Controls" or "Supercomputer and Semiconductor End Use" bury the ledes. But behind this complex language is a clear trend: regulation is moving from AI applications to its building blocks.

Vipers can strike within 100 milliseconds of launching at their prey. Breakthroughs, discoveries, and DIY tips sent every weekday. A venomous snake bite is not something you ever want to encounter on a hiking or camping trip. For those brave scientists who study snakes-aka herpetologists -the mechanics behind the reptiles' fast fangs are more fascinating than fear-inducing. Snakes must move incredibly quickly to sink their fangs into prey before the victim flinches.

Understanding how life is adapting to urban environments represents an important challenge in evolutionary biology. In this work, we investigate a widely cited example of urban adaptation, Culex pi...

Tearful Kim Kardashian, 45, reveals doctors found brain aneurysm after MRI... as she blames stressful Kanye West divorce As royal insiders dish the dirt, this is what I'm told is the truth about Prince Andrew's daughters This is the exact plan I followed to supercharge my weight loss... and the surprising jab side-effect that cured me of my REAL problem: SUSAN ANDERSON Finance guru storms out of podcast with illegal migrants $420K in debt who insist they'deserve' new car and pool Dakota Johnson reveals her biggest'red flag' in men after Chris Martin split'Gaslighting' and'black out' fights: Kristen Bell and Dax Shepard's'volatile' marriage laid bare by insiders The secret calls and frantic meetings over Congressman's alleged affair with aide who set herself on fire in scandal that could upend Trump's future Pete Hegseth dealt another blow as judge shoots down effort to rebrand Pentagon with'warrior ethos' There's a taboo most men find repulsive... but if they can handle it, says JANA HOCKING, it's the biggest turn on ever The real reason behind Cracker Barrel's disastrous logo change... and it makes complete sense Astonishing new video shows Louvre robbers escaping in a mechanical delivery basket with £76m-worth of jewels - after evading CCTV that was'pointing the wrong way' Elon Musk's ex Grimes baffles fans with bizarre circular face tattoo as they insist inking looks like RINGWORM Putin ally accuses Trump of an'act of war' against Russia after US president imposed new oil sanctions French girl Lola, 12, who was'raped and murdered by Algerian woman' begged'please don't hurt me' before she was brutally killed, court hears Dave Grohl on'thin ice' with wife Jordyn Blum as insiders reveal her strict list of rules to save their marriage... and his plans for daughters to build relationship with his love child Amazon's delivery drivers will be forced to wear AI GLASSES that give them turn-by-turn directions to shave seconds off deliveries READ MORE: Amazon workers claim'kill switch' triggered massive outage In a bid to shave seconds off deliveries, Amazon will soon force its delivery drivers to wear smart glasses. The futuristic glasses use artificial intelligence ( AI) to feed drivers turn-by-turn directions leading up to customers' doorsteps. They're also fitted with cameras so drivers can scan packages and capture proof of delivery. Amazon claims the dystopian device will make deliveries'as safe and seamless as possible'. However, it seems not everyone agrees.

If AI takes over many jobs, how will people make a living? If AI takes over many jobs, how will people make a living? Once the AI bubble pops, we'll all suffer. Could that be better than letting it grow unabated? The Guardian's journalism is independent.

Blackouts hit Russia's Belgorod as Ukrainian drone attacks surge Residents of Russia's Belgorod region say blackouts, air-raid sirens and the sound of gunfire aimed at incoming Ukrainian drones are becoming increasingly common, as Kyiv retaliates against repeated bombardments of its cities with cross-border strikes of its own. It's so loud and so terrifying, says Nina, a Belgorod resident who asked us to change her name. I was coming back from the clinic when a siren went off. As usual, I received Telegram alerts about a drone attack. Then bursts of automatic gunfire broke out, I ran into a nearby courtyard and tried to hide under an arch, she recalls.

In networked environments, users frequently share recommendations about content, products, services, and courses of action with others. The extent to which such recommendations are successful and adopted is highly contextual, dependent on the characteristics of the sender, recipient, their relationship, the recommended item, and the medium, which makes peer influence probabilities highly heterogeneous. Accurate estimation of these probabilities is key to understanding information diffusion processes and to improving the effectiveness of viral marketing strategies. However, learning these probabilities from data is challenging; static data may capture correlations between peer recommendations and peer actions but fails to reveal influence relationships. Online learning algorithms can learn these probabilities from interventions but either waste resources by learning from random exploration or optimize for rewards, thus favoring exploration of the space with higher influence probabilities. In this work, we study learning peer influence probabilities under a contextual linear bandit framework. We show that a fundamental trade-off can arise between regret minimization and estimation error, characterize all achievable rate pairs, and propose an uncertainty-guided exploration algorithm that, by tuning a parameter, attains any pair within this trade-off. Our experiments on semi-synthetic network datasets show the advantages of our method over static methods and contextual bandits that ignore this trade-off.

Neural activity forecasting is central to understanding neural systems and enabling closed-loop control. While deep learning has recently advanced the state-of-the-art in the time series forecasting literature, its application to neural activity forecasting remains limited. To bridge this gap, we systematically evaluated eight probabilistic deep learning models, including two foundation models, that have demonstrated strong performance on general forecasting benchmarks. We compared them against four classical statistical models and two baseline methods on spontaneous neural activity recorded from mouse cortex via widefield imaging. Across prediction horizons, several deep learning models consistently outperformed classical approaches, with the best model producing informative forecasts up to 1.5 seconds into the future. Our findings point toward future control applications and open new avenues for probing the intrinsic temporal structure of neural activity.

In recent developments, deep learning methodologies applied to Natural Language Processing (NLP) have revealed a paradox: They improve performance but demand considerable data and resources for their training. Alternatively, quantum computing exploits the principles of quantum mechanics to overcome the computational limitations of current methodologies, thereby establishing an emerging field known as quantum natural language processing (QNLP). This domain holds the potential to attain a quantum advantage in the processing of linguistic structures, surpassing classical models in both efficiency and accuracy. In this paper, it is proposed to categorise QNLP models based on quantum computing principles, architecture, and computational approaches. This paper attempts to provide a survey on how quantum meets language by mapping state-of-the-art in this area, embracing quantum encoding techniques for classical data, QNLP models for prevalent NLP tasks, and quantum optimisation techniques for hyper parameter tuning. The landscape of quantum computing approaches applied to various NLP tasks is summarised by showcasing the specific QNLP methods used, and the popularity of these methods is indicated by their count. From the findings, it is observed that QNLP approaches are still limited to small data sets, with only a few models explored extensively, and there is increasing interest in the application of quantum computing to natural language processing tasks.

Feedback is one of the most powerful influences on student learning, with extensive research examining how best to implement it in educational settings. Increasingly, feedback is being generated by artificial intelligence (AI), offering scalable and adaptive responses. Two widely studied approaches are directive feedback, which gives explicit explanations and reduces cognitive load to speed up learning, and metacognitive feedback which prompts learners to reflect, track their progress, and develop self-regulated learning (SRL) skills. While both approaches have clear theoretical advantages, their comparative effects on engagement, confidence, and quality of work remain underexplored. This study presents a semester-long randomised controlled trial with 329 students in an introductory design and programming course using an adaptive educational platform. Participants were assigned to receive directive, metacognitive, or hybrid AI-generated feedback that blended elements of both directive and metacognitive feedback. Results showed that revision behaviour differed across feedback conditions, with Hybrid prompting the most revisions compared to Directive and Metacognitive. Confidence ratings were uniformly high, and resource quality outcomes were comparable across conditions. These findings highlight the promise of AI in delivering feedback that balances clarity with reflection. Hybrid approaches, in particular, show potential to combine actionable guidance for immediate improvement with opportunities for self-reflection and metacognitive growth.