The world's carmakers are struggling to compete with China Global carmakers are facing a reckoning as US, European and Japanese brands lose ground to Chinese rivals setting the pace not only in electric vehicles, but also in batteries, design and software. The BBC visited factory floors in Beijing and Hefei on the sidelines of Auto China 2026 - the world's largest car show - and found striking levels of automation and software development speed, leaving foreign brands that once dominated the Chinese market struggling to keep up. We have no chance against this, Honda chief executive Toshihiro Mibe told Japanese media after visiting a highly automated factory in Shanghai. Ford chief executive Jim Farley has also warned that Western carmakers, are in a fight for our lives as Chinese rivals expand globally. After decades spent investing in joint ventures with Chinese partners to build vehicles, foreign carmakers are now changing the nature of those partnerships to stay competitive.

The Chinese company is adapting to the demise of Moore's Law, which guides chip production. It could complicate US chip dominance. Tingbo He, president of Huawei's chip-design subsidiary HiSilicon, says her company's engineers have developed a novel way to optimize semiconductors--and she believes it will close the performance gap between Chinese and Western chips over the next few years. Huawei's method, in short, focuses on speeding up computations across chips, circuits, and entire computing systems, rather than squeezing ever-more components onto a single piece of silicon. "We found a new path," He said at the IEEE International Symposium on Circuits and Systems in Shanghai last weekend.

People visit an Alibaba booth during the World Artificial Intelligence Conference in Shanghai on July 26, 2025. China is restricting overseas travel for top AI professionals in private firms such as Alibaba Group and DeepSeek, suggesting an escalation in measures intended to safeguard its technology and catch up to the U.S. in a pivotal sphere. Government agencies have begun imposing restrictions on individuals involved in advanced AI work and considered strategically important to the country, people familiar with the matter said. That means they need approval from relevant authorities before embarking on overseas travel, the people said, asking for anonymity to discuss a sensitive issue. Beijing has for years imposed travel restrictions on key personnel from prominent college researchers to nuclear scientists and executives at state firms.

In its first week, the seventy-ninth edition of the festival unveiled standout new works by James Gray, Paweł Pawlikowski, and Ryûsuke Hamaguchi. Attend the Cannes Film Festival long enough, and you will grow wearily accustomed to the reality that some of the best films to première there are routinely overlooked for prizes. Lee Chang-dong magnificently unsettling psychological chiller, "Burning," failed to ignite the excitement of the 2018 jury. The tragicomic glories of Maren Ade's " Toni Erdmann," from 2016, were just as inexplicably unrewarded. Jurors shut out David Cronenberg's "A History of Violence," in 2005; Hou Hsiao-hsien's "Flowers of Shanghai," in 1998; Krzysztof Kieślowski's "Three Colors: Red," in 1994; Martin Scorsese's "Alice Doesn't Live Here Anymore," in 1975; and--the tradition goes way back--Vittorio De Sica's "Umberto D.," in 1952.

One-person company SoloNest sounder Karen Dai preparing for a coffee chat at a conference room in Shanghai on April 12. | AFP-JIJI Shanghai - Young Chinese, many who fear age discrimination in their workplace after turning 35, are increasingly starting one-person companies that have artificial intelligence do most of the work. Smaller startups are already in vogue in Silicon Valley and elsewhere, with rapidly advancing AI tools seen as a welcome teammate even as they threaten layoffs at existing firms. More young people in China are subscribing to the model, as cities pledge millions of dollars in funding and rent subsidies for such ventures, in alignment with Beijing's political goal of technological self-reliance. 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.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We hear from Xinwei Song about the two main research threads she's worked on so far, plans to expand her investigations, and what inspired her to study AI. Could you start with a quick introduction - where are you studying, and what is the topic of your research? My research primarily focuses on strategic interactions in networked multi-agent systems. Could you give us an overview of the research you've carried out so far during your PhD? My research to date consists of two main threads, which complement each other in exploring strategic interactions from different perspectives.

This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most existing OPE methods, we aim to estimate the entire return distribution. To this end, we introduce a quantile-based approach for OPE using deep quantile process regression, presenting a novel algorithm called Deep Quantile Process regression-based Off-Policy Evaluation (DQPOPE). We provide new theoretical insights into the deep quantile process regression technique, extending existing approaches that estimate discrete quantiles to estimate a continuous quantile function. A key contribution of our work is the rigorous sample complexity analysis for distributional OPE with deep neural networks, bridging theoretical analysis with practical algorithmic implementations. We show that DQPOPE achieves statistical advantages by estimating the full return distribution using the same sample size required to estimate a single policy value using conventional methods. Empirical studies further show that DQPOPE provides significantly more precise and robust policy value estimates than standard methods, thereby enhancing the practical applicability and effectiveness of distributional reinforcement learning approaches.

Ritzy Bay Area town torn apart after teacher's daughter, 16, was behind wheel when four friends died in high-speed crash... then she posted a TikTok video that poured fuel on the flames Two CIA officers killed in Mexico when their car skidded off ravine and exploded after meeting about bust of'largest ever drug lab' Nancy Guthrie sheriff's appalling past revealed: Beat handcuffed suspect so badly he needed intensive care, used VILE language about woman and lied in sworn statement Trump confronts Xi as US forces seize Chinese ship carrying mysterious'gift' to Iran New'Hollywood dose' pill: A-listers hooked on'youth elixir' that dermatologists say is anti-ageing, shrinks pores, smooths wrinkles... and even banishes rosacea Days after we got engaged, the love of my life told me he'd killed a man and buried him in a bog. I reported him to police... but then I made this irreversible mistake Ark of the Covenant's final resting place pinpointed by archaeologists as fresh search begins Fury as murderer marries pen pal behind bars... as teenage victim's mom says: 'I'm serving a life sentence without my son' Insiders claim failed AI rollout could be to blame for Tim Cook's departure from Apple - as one says'the AI era requires a different kind of leadership' Life-threatening cantaloupe recall in four states upgraded to FDA's highest risk level... 'reasonable probability of death' AMANDA PLATELL: Why Sarah Ferguson - with the ghost of Princess Diana at her side - is ready to sensationally blow up the Royal Family. She knows ALL their secrets... Team USA Olympics star Noah Lyles slammed for'horrible' reaction to his wife's wedding dress reveal In honour of the Queen's (purple!) reign: Kate mirrors late monarch's colourful wardrobe and wears her pearl earrings and necklace US troops board second tanker as Iran is accused of breaking ceasefire'numerous times' How to lose weight when perimenopause sabotages your metabolism: I'm a trainer but when I hit 46, I piled on the pounds overnight. The new'posh' drug that's easier to order than Uber Eats - and why all my middle-class friends have ditched booze and cocaine for it: JANA HOCKING Autistic woman, 24, worked hard to build independent life for herself... now she's PARALYZED thanks to selfishness of stranger READ MORE: McDonald's is testing humanoid ROBOTS in Shanghai During last year's shambolic Beijing robot half marathon, humanoid machines tripped, shuffled, and occasionally shattered into pieces as they collapsed under the strain. But 12 months later, supporters looked on in awe as a new generation of speedy robotic racers left the human athletes in the dust.

We prove that conditional diffusion models whose reverse kernels are finite Gaussian mixtures with ReLU-network logits can approximate suitably regular target distributions arbitrarily well in context-averaged conditional KL divergence, up to an irreducible terminal mismatch that typically vanishes with increasing diffusion horizon. A path-space decomposition reduces the output error to this mismatch plus per-step reverse-kernel errors; assuming each reverse kernel factors through a finite-dimensional feature map, each step becomes a static conditional density approximation problem, solved by composing Norets' Gaussian-mixture theory with quantitative ReLU bounds. Under exact terminal matching the resulting neural reverse-kernel class is dense in conditional KL.

Large language models (LLMs) can generate survey responses at low cost, but their reliability varies substantially across questions and is unknown before data collection. Deploying LLMs in surveys still requires costly human responses for verification and correction. How should a limited human-labeling budget be allocated across questions in real time? We propose an adaptive allocation algorithm that learns which questions are hardest for the LLM while simultaneously collecting human responses. Each human label serves a dual role: it improves the estimate for that question and reveals how well the LLM predicts human responses on it. The algorithm directs more budget to questions where the LLM is least reliable, without requiring any prior knowledge of question-level LLM accuracy. We prove that the allocation gap relative to the best possible allocation vanishes as the budget grows, and validate the approach on both synthetic data and a real survey dataset with 68 questions and over 2000 respondents. On real survey data, the standard practice of allocating human labels uniformly across questions wastes 10--12% of the budget relative to the optimal; our algorithm reduces this waste to 2--6%, and the advantage grows as questions become more heterogeneous in LLM prediction quality. The algorithm achieves the same estimation quality as traditional uniform sampling with fewer human samples, requires no pilot study, and is backed by formal performance guarantees validated on real survey data. More broadly, the framework applies whenever scarce human oversight must be allocated across tasks where LLM reliability is unknown.