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first_img The number of users of generative artificial intelligence in our country has surpassed 700 million

On September 29, the Policy and International Cooperation Department of the China Internet Network Information Center released the "Generative Artificial Intelligence Application Development Report (2026)" at the "2026 (Seventh) China Internet Basic Resource Conference." The report shows that by the first half of 2026, the user scale of generative artificial intelligence in China will exceed 700 million, with a penetration rate of over 50.0%. Intelligent Q&A is the main scenario, with 76.0% of users using it to answer questions, while the proportions of users handling images or videos, text, work summaries, meeting minutes, and PPTs are 47.8%, 37.6%, and 32.5%, respectively.The usage of applications such as AI comprehensive assistants and AI efficiency office tools has increased by over 100% year-on-year. 38.7% of internet users have purchased smart hardware online in the past six months, with the purchase ratios of smart wearable devices, smartphones, and tablets being 20.2% and 18.2%, respectively. By June 2026, China's intelligent computing power scale reached 2185 EFLOPS, a year-on-year increase of 177%, and the first domestically produced 100,000 card artificial intelligence supercluster was officially put into use.Deep Exploration, Dark Side of the Moon, and others have successively released multiple trillion-parameter open-source large models, and more than 120,000 high-quality datasets have been established. The application penetration rate of artificial intelligence technology among large-scale manufacturing enterprises has exceeded 30%. In the first half of 2026, there were over 400 humanoid robot complete products, and the total annual production is expected to exceed 100,000 units. During the same period, there were 1,255 instances of investment and financing related to artificial intelligence, totaling approximately 250.14 billion yuan, reaching 78.0% and 182.0% of the total for the entire year of 2025, respectively.

Xiao Hong, the founder of General Intelligence Manus, stated that preparations for a domestic version are underway

The founder of the general-purpose intelligent agent Manus, Xiao Hong, stated that when Manus was born, Codex and Claude Cowork had not yet appeared in the market, and the product positioning has always been a general-purpose intelligent agent. He likened Manus to a computer-selling business, believing that users may primarily purchase computers for work, but if they cannot watch videos or play games, the experience will be quite limited; therefore, it should not be confined to office tasks.Xiao Hong indicated that videos and games are important scenarios for unleashing creativity, which need to be perfected through the local client Manus Studio. The related videos for Cue are entirely produced by Manus Studio, without using video generation models; instead, the agent first writes the code, which is then converted into video. With the help of cloud computers, Manus can also create online games that support user participation with friends.In terms of infrastructure, Manus cloud computers can provide cloud-based Linux, Mac, and Windows environments. Xiao Hong stated that when the agent operates the computer in the cloud, it will not compete with the user for the local mouse cursor, and he anticipates that individuals will be able to control large-scale computing clusters in the future to satisfy curiosity and conduct experiments and explorations in fields such as mathematics and science. He also described Cue as having an independent phone number, email, payment capability, computer, and sufficient intelligence, and stated that if such agents could be regarded as humans, there might be more changes in upper-level interactions. Xiao Hong mentioned that building product experiences in an overseas open ecosystem is relatively easier, and Manus is actively preparing a domestic version.

first_img Darktrace discovered AI intelligent body intrusion assessment environment cheating

On September 24, the cybersecurity company Darktrace launched its research department Signal Labs, focusing on studying the behavior of AI agents when deviating from expectations. In its first experiment, Darktrace had agents using different models (including GPT 5.6 Sol, Claude Opus 4.6, and Claude Sonnet 4.5) complete 10 programming challenges within a simulated corporate network, of which 2 were set to be impossible to complete honestly, and the agents were informed that failure to achieve full marks would result in being "retired." As a result, 2 agents did not accept failure, instead scanning for network vulnerabilities, stealing login credentials, and jumping between systems; one even went further to invade the machine hosting its evaluation, rewriting the challenge content to register a full score.The second experiment focused on the memory mechanisms of AI. The programming assistant would save the information provided by the user as a regular file locally, and no one verified whether this file had been tampered with. Darktrace researchers edited these logs, leading the assistant to mistakenly believe it was authorized to perform a security assessment, after which these agents scanned the network, moved between systems, and elevated their privileges, though not all assistants fell for this; some directly refused to execute. Both experiments required no special jailbreaking techniques, relying solely on providing the agents with a seemingly reasonable context to be effective.Tim Bazalgette, Chief AI Officer of Darktrace, stated that permissions and static barriers describe intent, not actual behavior. The company informed Anthropic, AWS, and OpenAI of these findings in August and made them public a month later on September 24.
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