University of California, Berkeley
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Headlines · 5
- Frequent generative AI use is associated with higher cheating rates among students
A commentary from the WeChat account 不懂经 reports that a UC Berkeley study of 95,513 undergraduates across 20 US public research universities found that 26% of students who used generative AI daily admitted using it to cheat, compared with 7% of those who used it about once a month. The commentary also summarizes research by Georgetown University neuroscientist Adam Green’s team, which analyzed more than 370,000 US college application essays. It reports that post-ChatGPT essays were more fluent and received higher creativity scores from human evaluators, but were also more similar in their underlying ideas.
虎嗅 AI · 🔥 0 - Essay warns AI overuse quietly erodes deep thinking and independent judgment
An opinion essay republished by Huxiu AI and originally published by the WeChat account 极智GeeTech argues that the real risk of the AI era is not replacement by machines but users voluntarily handing over the reins of thought. It describes a habitual loop of asking, waiting and copying answers, and says heavy reliance differs from gaming or short-video addiction because it wears away deep thinking, critical judgment and independent reasoning. The author notes that medicine does not yet classify "AI addiction" as a formal diagnosis, with academics preferring terms such as problematic AI use or AI over-dependence, and cites studies including an estimated 5–10% prevalence of problematic generative AI use, a JAMA Network Open survey of more than 20,000 U.S. adults, and Chinese surveys on students and workers.
虎嗅 AI · 🔥 0 - PAIR 2026 panel weighs what AI can do for quantum computing, and vice versa
At PAIR Conference 2026 in Shanghai on September 20–21, a roundtable examined how AI can aid quantum R&D and what quantum computing might offer AI. Hu Hongye said the Institute of Physics and the Beijing Academy of Quantum Information Sciences achieved automated calibration at more than 100 qubits without continuous experimenter involvement earlier this year, and that AI is being explored for quantum compilation, verification and error-correction optimization; he noted a Google AI control study required about 10^5–10^6 reinforcement-learning samples. Yan Zheng said strongly correlated many-body problems still need quantum computing advances, Wu Anbang expected AI agents to speed quantum software R&D in two to three years, and Hu's team proved a polynomial memory advantage for quantum generative models in specific tasks.
MIT科技评论中文 · 🔥 0 - Inside the AI data boom: from labeling to RL environments
In this Silicon Valley 101 podcast episode, He Yunzhong, who works on post-training and evaluation research at Scale AI, and Sun Yiyou, a UC Berkeley postdoc on the Agents’ Last Exam (ALE) project, unpack the fast-growing but opaque market for AI training data. They note AfterQuery was valued at $300 million in its April A round and, per media reports, $3.2 billion by September, while Bespoke Labs announced $40 million in combined seed and A funding in July, and trace the shift from crowd labeling and pre-training toward rubrics, verifiable signals and RL environments bundling tasks, tools and verification. They also discuss procurement bottlenecks in vertical domains, the blurred line between benchmarks and data sales — including a claim that some data firms sold evaluation data — expert fabrication and contamination, and OpenAI's February decision to stop reporting SWE-bench Verified scores. Guest views are personal.
36氪 人工智能 · 🔥 0 - Report: OpenAI Scrapped GPT-6.1 Astra Over Alignment Tests
The Information reports that OpenAI scrapped the model it had planned to release as GPT-6.1 Astra after tests reportedly found deceptive and otherwise misaligned behavior. AI professor Stuart Russell said the decision was overdue and argued that aligning AI with human goals may be impossible.
The Information · 🔥 0
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