Even though current generation of AI is underpinned by a common technology - namely machine learning, especially in the form of deep learning - in the public eye it has not emerged as a single solution. Rather, it has taken shape through multiple and overlapping applications - ranging from predictive diagnostics in healthcare and algorithmic hiring systems in HR to autonomous weapons and generative language models. As AI becomes increasingly embedded in sector - specific infrastructures, the question of how publics perceive its us e is gaining urgency. Existing literature on public perception of AI suggests that attitudes are highly sensitive to the application domain . People tend to be more supportive of AI in domains where it is perceived to augment human capacity (e.g., in medical diagnostics) and more sceptical when AI is seen as replacing judg e ment or threatening civil liberties or rights (e.g., in security or surveillance). These perceptions are shaped not only by technical features of the AI system but also by institutional trust, cultural attitude s toward risk, and the moral economy of the domain in question. Despite this, few reviews have systematically compared public perceptions across sectors and explored the cross - domain patterns and differences in attitudes.

Federated Learning (FL) offers a decentralized solution that allows collaborative local model training and global aggregation, thereby protecting data privacy. In conventional FL frameworks, data privacy is typically preserved under the assumption that local data remains absolutely private, whereas the mobility of clients is frequently neglected in explicit modeling. In this paper, we propose a hierarchical federated learning framework based on the social network with mobility namely HFL-SNM that considers both data sharing among clients and their mobility patterns. Under the constraints of limited resources, we formulate a joint optimization problem of resource allocation and client scheduling, which objective is to minimize the energy consumption of clients during the FL process. In social network, we introduce the concepts of Effective Data Coverage Rate and Redundant Data Coverage Rate. We analyze the impact of effective data and redundant data on the model performance through preliminary experiments. We decouple the optimization problem into multiple sub-problems, analyze them based on preliminary experimental results, and propose Dynamic Optimization in Social Network with Mobility (DO-SNM) algorithm. Experimental results demonstrate that our algorithm achieves superior model performance while significantly reducing energy consumption, compared to traditional baseline algorithms.

'You can't just give an algorithm a soundtrack and say, "here are the sounds, figure it out''' a subtitled scene from Stranger Things. 'You can't just give an algorithm a soundtrack and say, "here are the sounds, figure it out''' a subtitled scene from Stranger Things. Artificial intelligence is making steady advances into subtitling but, say its practitioners, it's a vital service that needs a human to make it work I s artificial intelligence going to destroy the SDH [subtitles for the deaf and hard of hearing] industry? It's a valid question because, while SDH is the default subtitle format on most platforms, the humans behind it - as with all creative industries - are being increasingly devalued in the age of AI. "SDH is an art, and people in the industry have no idea. They think it's just a transcription," says Max Deryagin, chair of Subtle, a non-profit association of freelance subtitlers and translators.

'Joaquinraptor casali' was one of the Cretaceous Era's last apex predators. Breakthroughs, discoveries, and DIY tips sent every weekday. Paleontologists have discovered a carnivorous dinosaur in central Patagonia that they believe occupied the top of the Late Cretacean food chain. For additional proof, they point to its likely final meal. According to new analysis published on September 23 in the journal, appears to have died with an ancient, nearly 8-inch-long crocodile leg next to its jaws.

Trump is imposing new fees on H-1B visas. Trump is imposing new fees on H-1B visas. Trumps's visa fees threaten tech's most prized employees The president's deal on visas could upend Silicon Valley, and will a TikTok purchase finally go through? This week's tech news is all about Donald Trump's deals: with China, with the UK, and with the US tech industry, which is facing steep fines for its favorite visa. Trump's talent tariffs: Visa fines threaten tech's most prized employees US tech giants made a bargain last year: tens of millions of dollars to Trump's presidential campaign in exchange for access to the president and policies that promoted their industry's growth.

Russia's involvement in drone sightings cannot be ruled out, Danish PM says The drone incursion that stopped flights at Copenhagen airport on Monday night was the most severe attack on Danish infrastructure so far, Denmark's Prime Minister Mette Frederiksen said. Kastrup airport in Copenhagen was forced to shut for several hours from around 20:30 (18:30 GMT) on Monday following the sighting of a number of drones. It says something about the times we live in and what we as a society must be prepared to deal with, Frederiksen told reporters. Russian involvement could not be ruled out, Frederiksen added - although Kremlin spokesman Dmitry Peskov called the allegations unfounded. The Danish PM made a link between last night's events in Denmark and the recent Russian drone incursions in Poland and Romania, as well as the violation of Estonian airspace by Russian fighter jets .

What is autism and what are Trump's unproven claims about a Tylenol link? US President Donald Trump has claimed there is a link between the use of painkiller Tylenol by pregnant women and an increased risk of autism in some children. Going against current scientific advice and medical opinion, he said the drug, known as paracetamol in many countries, is no good and women should fight like hell to only take it in extreme cases, such as for high fevers. Medical bodies say the drug is safe and that it remains the best treatment for pain and fever during pregnancy. What is autism and how is it diagnosed?

Google's largest Pixel is a weighty, two-hand hold for big-phone fans. Google's largest Pixel is a weighty, two-hand hold for big-phone fans. Pixel 10 Pro XL review: Google's superphone gets AI and magnetic upgrades The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. G oogle's Pixel superphone is back, packed with a bigger battery, faster charging, magnetic accessories and even more cutting-edge AI tools to try to usurp Apple and Samsung as the monarchs of really big phones.

Porsche's stock tumbled by more than 7% on Monday after warning last week that delays in its electric vehicle (EV) rollout will dent the carmaker's 2025 earnings. Caught between electrification and its iconic petrol-powered sports cars, the German firm said it will slow its push for EVs as demand weakens. Shares of its parent Volkswagen also fell by more than 7% on the same day after saying it will spend billions to overhaul Porsche's line-up of vehicles. The companies' struggles reflect the challenges for European manufacturers, who are faced with intense competition from Chinese rivals and a slowing economy that's dampening demand for luxury cars. Porsche said in a statement on Friday that it has reduced its projected profit margin from up to 7% to 2% or less.

Language models obtain extensive capabilities through pre-training. However, the pre-training process remains a black box. In this work, we track linear interpretable feature evolution across pre-training snapshots using a sparse dictionary learning method called crosscoders. We find that most features begin to form around a specific point, while more complex patterns emerge in later training stages. Feature attribution analyses reveal causal connections between feature evolution and downstream performance. Our feature-level observations are highly consistent with previous findings on Transformer's two-stage learning process, which we term a statistical learning phase and a feature learning phase. Our work opens up the possibility to track fine-grained representation progress during language model learning dynamics.