Two robots box while German Chancellor Friedrich Merz visits Unitree Robotics in Zhejiang Province, China. In the exhibition hall at Unitree Robotics in Hangzhou, Friedrich Merz smiled and applauded the martial arts display by a platoon of humanoid warriors. But when a robot boxer advanced toward him, punching the air with its red-gloved fists, the German chancellor flinched, a look of alarm crossing his face as he appeared to realize the danger posed by an autonomous fighting machine. It was also a moment that crystallized for Merz the power of China's technology, according to a person familiar with his thinking. He saw it, too, as a sign of how far behind Germany has fallen and how European Union regulation holds back their efforts to catch up, the person said, asking not to be named discussing the chancellor's private views. The trip, last month, has triggered a broader reckoning that is starting to settle in across Europe: Maybe de-risking from China is just too big a task.

An engineer at the AgiBot factory in Shanghai, China, where the 5,000th mass-produced humanoid robot had rolled off the production line. An engineer at the AgiBot factory in Shanghai, China, where the 5,000th mass-produced humanoid robot had rolled off the production line. How close are we to the sci-fi vision of autonomous humanoid robots? C hen Liang, the founder of Guchi Robotics, an automation company headquartered in Shanghai, is a tall, heavy-set man in his mid-40s with square-rimmed glasses. His everyday manner is calm and understated, but when he is in his element - up close with the technology he builds, or in business meetings discussing the imminent replacement of human workers by robots - he wears an exuberant smile that brings to mind an intern on his first day at his dream job. Guchi makes the machines that install wheels, dashboards and windows for many of the top Chinese car brands, including BYD and Nio. He took the name from the Chinese word, "steadfast intelligence", though the fact that it sounded like an Italian luxury brand was not entirely unwelcome. For the better part of two decades, Chen has tried to solve what, to him, is an engineering problem: how to eliminate - or, in his view, liberate - as many workers in car factories as technologically possible. Late last year, I visited him at Guchi headquarters on the western outskirts of Shanghai. Next to the head office are several warehouses where Guchi's engineers tinker with robots to fit the specifications of their customers. Chen, an engineer by training, founded Guchi in 2019 with the aim of tackling the hardest automation task in the car factory: "final assembly", the last leg of production, when all the composite pieces - the dashboard, windows, wheels and seat cushions - come together. At present, his robots can mount wheels, dashboards and windows on to a car without any human intervention, but 80% of the final assembly, he estimates, has yet to be automated. That is what Chen has set his sights on. As in much of the world, AI has become part of everyday life in China . But what most excites Chinese politicians and industrialists are the strides being made in the field of robotics, which, when combined with advances in AI, could revolutionise the world of work.

Machine comprehension (MC) has gained significant popularity over the past few years and it is a coveted goal in the field of natural language understanding. Its task is to teach the machine to understand thecontent ofagivenpassage andthenanswer arelated question, which requires deep comprehension and accurate information extraction towards the text.

Hustlers are cashing in on China's OpenClaw AI craze The AI tool has become the country's latest tech obsession. Feng Qingyang had always hoped to launch his own company, but he never thought this would be how--or that the day would come this fast. Feng, a 27-year-old software engineer based in Beijing, started tinkering with OpenClaw, a popular new open-source AI tool that can take over a device and autonomously complete tasks for a user, in January. He was immediately hooked, and before long he was helping other curious tech workers with less technical proficiency install the AI agent. Feng soon realized this could be a lucrative opportunity. By the end of January, he had set up a page on Xianyu, a secondhand shopping site, advertising "OpenClaw installation support."

Modern image generation (IG) models have been shown to capture rich semantics valuable for image understanding (IU) tasks. However, the potential of IU models to improve IG performance remains uncharted. We address this issue using a token-based IG framework, which relies on effective tokenizers to map images into token sequences. Currently, pixel reconstruction (e.g., VQGAN) dominates the training objective for tokenizers. In contrast, our approach adopts the feature reconstruction objective, where tokenizers are trained by distilling knowledge from pretrained IU encoders. Comprehensive comparisons indicate that tokeniz-ers with strong IU capabilities achieve superior IG performance across a variety of metrics, datasets, tasks, and proposal networks.

Stochastic gradient methods have been a widespread practice in machine learning.

Empirically, MiTSformer achieves consistent SOTAonfivemixedtime series analysis tasks, including classification, extrinsic regression,anomalydetection,imputation,andlong-termforecasting.