China's People's Armed Forces Maritime Militia deployed thousands of fishing vessels in coordinated formations that could disrupt global shipping lanes, analysts warn.

He Xiaopeng launches Xpeng's next-gen Iron humanoid robot during a press conference at the company's headquarters in Guangzhou on November 5, 2025. He Xiaopeng launches Xpeng's next-gen Iron humanoid robot during a press conference at the company's headquarters in Guangzhou on November 5, 2025. It was a controversy laced with pride for He Xiaopeng. In November, He, the founder and CEO of Chinese physical AI firm XPeng, had just debuted his new humanoid robot, IRON, whose balance, posture shifts, and coquettish swagger mirrored human motion with such eerie precision that a slew of netizens accused him of faking the demonstration by putting a human in a bodysuit. To silence the naysayers, He boldly cut open the robot's leg live on stage to reveal the intricate mechanical systems that allow it to adapt to uneven surfaces and maintain stability just like the human body. "At first, it made me sad," He tells TIME in his Guangzhou headquarters.

'They don't follow the classic ideas of how we would have imagined the species evolved.' Breakthroughs, discoveries, and DIY tips sent six days a week. A tiny tropical flower is challenging a longstanding model for plant evolution. According to researchers at the Field Museum in Chicago, an oddball member of the lipstick vine family evolved to attract more pollinators spreading to other parts of the world, and not the other way around. "It was really exciting to get these results, because they don't follow the classic ideas of how we would have imagined the species evolved," explained Jing-Yi Lu, a botanist and coauthor of a study published today in the journal . Most lipstick vines look like their name implies: lengthy plants featuring vibrantly red, tubular flowers.

Predictions that AI bot swarms were a threat to democracy weren't'fanciful', said Michael Wooldridge, professor of the foundations of AI at Oxford University. Predictions that AI bot swarms were a threat to democracy weren't'fanciful', said Michael Wooldridge, professor of the foundations of AI at Oxford University. Experts warn of threat to democracy from'AI bot swarms' infesting social media Political leaders could soon launch swarms of human-imitating AI agents to reshape public opinion in a way that threatens to undermine democracy, a high profile group of experts in AI and online misinformation has warned. The Nobel peace prize-winning free-speech activist Maria Ressa, and leading AI and social science researchers from Berkeley, Harvard, Oxford, Cambridge and Yale are among a global consortium flagging the new "disruptive threat" posed by hard-to-detect, malicious "AI swarms" infesting social media and messaging channels. A would-be autocrat could use such swarms to persuade populations to accept cancelled elections or overturn results, they said, amid predictions the technology could be deployed at scale by the time of the US presidential election in 2028.

China launches new military exercises around Taiwan following record $11.1 billion U.S. arms sale, with warships conducting live-fire drills that could escalate toward war.

Three years after ChatGPT made OpenAI the leader in artificial intelligence and a household name, rivals have closed the gap and some investors are wondering if the sensation has the wherewithal to stay dominant. Investor Michael Burry, made famous in the film The Big Short, recently likened OpenAI to Netscape, which ruled the web-browser market in the mid-1990s only to lose to Microsoft's Internet Explorer. OpenAI is the next Netscape, doomed and hemorrhaging cash, Burry said recently in a post on X, formerly Twitter. 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.

Employees work on laptops in the Moore Kingston Smith office in London on Nov. 13. LONDON - When accountants at mid-tier firm Moore Kingston Smith began using artificial intelligence to speed up their work, profit margins jumped. Colleagues in another team running checks against corporate fraud created a report for customers in two hours, something that previously took two weeks. The rollout of AI is raising hopes that Britain's economy can escape the productivity problem that has dogged it for two decades, even as slow growth pushes finance minister Rachel Reeves toward tax hikes in Wednesday's budget. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

To effectively process impulse noise for narrowband powerline communications (NB-PLCs) transceivers, capturing comprehensive statistics of nonperiodic asynchronous impulsive noise (APIN) is a critical task. However, existing mathematical noise generative models only capture part of the characteristics of noise. In this study, we propose a novel generative adversarial network (GAN) called noise generation GAN (NGGAN) that learns the complicated characteristics of practically measured noise samples for data synthesis. To closely match the statistics of complicated noise over the NB-PLC systems, we measured the NB-PLC noise via the analog coupling and bandpass filtering circuits of a commercial NB-PLC modem to build a realistic dataset. To train NGGAN, we adhere to the following principles: 1) we design the length of input signals that the NGGAN model can fit to facilitate cyclostationary noise generation; 2) the Wasserstein distance is used as a loss function to enhance the similarity between the generated noise and training data; and 3) to measure the similarity performances of GAN-based models based on the mathematical and practically measured datasets, we conduct both quantitative and qualitative analyses. The training datasets include: 1) a piecewise spectral cyclostationary Gaussian model (PSCGM); 2) a frequency-shift (FRESH) filter; and 3) practical measurements from NB-PLC systems. Simulation results demonstrate that the generated noise samples from the proposed NGGAN are highly close to the real noise samples. The principal component analysis (PCA) scatter plots and Fréchet inception distance (FID) analysis have shown that NGGAN outperforms other GAN-based models by generating noise samples with superior fidelity and higher diversity.

A China Coast Guard vessel is seen on a giant screen showing news footage about the coast guard's law enforcement patrols in waters around Taiwan, outside a shopping mall in Beijing on April 1. | REUTERS TAIPEI - China is increasing military activities near Taiwan and honing its ability to stage a surprise attack, as well as seeking to undermine trust in the government with hybrid online warfare tactics, the island's defense ministry said on Thursday. Democratically-governed Taiwan, which China views as its own territory, has faced increased military pressure from Beijing over the past five years, including at least seven rounds of major war games around the island since 2022. China has been using artificial intelligence tools to weaken Taiwan's cybersecurity and to scan for weak points in critical infrastructure, the defense ministry said in a report released every two years. Beijing is also using hybrid warfare to weaken people's trust in the government and support for defense spending, and stepping up grey zone harassment, it added, referring to non-combat operations such as coast guard patrols designed to pressure Taiwan. Through both conventional and unconventional military actions, it aims to test its capabilities for attacking Taiwan and confronting foreign forces, the ministry said.

The rising aging population has increased the importance of fall detection (FD) systems as an assistive technology, where deep learning techniques are widely applied to enhance accuracy. FD systems typically use edge devices (EDs) worn by individuals to collect real-time data, which are transmitted to a cloud center (CC) or processed locally. However, this architecture faces challenges such as a limited ED model size and data transmission latency to the CC. Mobile edge computing (MEC), which allows computations at MEC servers deployed between EDs and CC, has been explored to address these challenges. We propose a multilayer MEC (MLMEC) framework to balance accuracy and latency. The MLMEC splits the architecture into stations, each with a neural network model. If front-end equipment cannot detect falls reliably, data are transmitted to a station with more robust back-end computing. The knowledge distillation (KD) approach was employed to improve front-end detection accuracy by allowing high-power back-end stations to provide additional learning experiences, enhancing precision while reducing latency and processing loads. Simulation results demonstrate that the KD approach improved accuracy by 11.65% on the SisFall dataset and 2.78% on the FallAllD dataset. The MLMEC with KD also reduced the data latency rate by 54.15% on the FallAllD dataset and 46.67% on the SisFall dataset compared to the MLMEC without KD. In summary, the MLMEC FD system exhibits improved accuracy and reduced latency.