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  2. Huxiu

    New jobs created by AI have raised the bar, and young people are losing the chance to gain experience in entry-level positions

    The article points out that among the 11 new occupations announced by the Ministry of Human Resources and Social Security in September, positions such as digital twin and embodied intelligent robot application mostly require an algorithm or programming background, while low-threshold data collectors earn only 3,500–6,000 yuan per month. A study by the Stanford Digital Economy Lab based on ADP payroll data found that in occupations with high AI exposure, the employment level of workers aged 22–25 is about 19% lower than that of their peers in occupations with low exposure, with the gap mainly coming from reduced hiring. A September report by the McKinsey Global Institute estimates that by 2035, about 11 million workers in the United States will need to change occupations, of whom about 14% have a direct transfer path with a relatively small skills gap.

    The article points out that among the 11 new occupations announced by the Ministry of Human Resources and Social Security in September, positions such as digital twin and embodied intelligent robot application mostly require an algorithm or programming background, while low-threshold data collectors earn only 3,500–6,000 yuan per month. A study by the Stanford Digital Economy Lab based on ADP payroll data found that in occupations with high AI exposure, the employment level of workers aged 22–25

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    #hot-signal#English text is being prepared.#Agents#Embodied AI

Saturday

Thursday

  1. AIhub

    Collection of Interviews from the 2026 AAAI / ACM SIGAI Doctoral Consortium

    AIhub has compiled a series of interviews from the 2026 AAAI/SIGAI Doctoral Consortium. The interviewed doctoral students come from institutions such as NTU, the Hong Kong University of Science and Technology, IIT Hyderabad, Oregon State University, ShanghaiTech University, Singapore Management University, Johns Hopkins University, McGill University, and Mila. Their research topics cover multimodal learning and embodied intelligence, reinforcement learning-driven management of gig transportation labor, interpretability and concept learning, causal decision-making systems, networked multi-agent systems, formal verification for autonomous driving, resource-constrained image generation, theory of mind, theoretical foundations of large language models, time-series anomaly detection, multiplicity in responsible AI, and improving the transparency and trustworthiness of AI systems.

    AIhub has compiled a series of interviews from the 2026 AAAI/SIGAI Doctoral Consortium. The interviewed doctoral students come from institutions such as NTU, the Hong Kong University of Science and Technology, IIT Hyderabad, Oregon State University, ShanghaiTech University, Singapore Management University, Johns Hopkins University, McGill University, and Mila. Their research topics cover multimodal learning and embodied intelligence, reinforcement learning-driven management of gig transportation

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    #ai-vertical#Industry news

Monday

Friday

  1. The Paper

    Economic Daily: Saying Goodbye to Wild Growth, AI Slowing Down Is Not a Bad Thing

    Leading overseas AI companies such as OpenAI have collectively proposed slowing the pace of iteration of frontier large models and temporarily shelving some expansion plans, while Chinese regulators have simultaneously tightened IPO review standards for embodied intelligence companies. The article argues that slowing down does not mean stagnation, and tightening does not mean hitting the brakes; the purpose of these moves is to steer AI from wild growth toward sustainable development and direct resources toward service innovation and scenario applications.

    Leading overseas AI companies such as OpenAI have collectively proposed slowing the pace of iteration of frontier large models and temporarily shelving some expansion plans, while Chinese regulators have simultaneously tightened IPO review standards for embodied intelligence companies. The article argues that slowing down does not mean stagnation, and tightening does not mean hitting the brakes; the purpose of these moves is to steer AI from wild growth toward sustainable development and direct

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    #chinese#Industry news#Safety / alignment#Embodied AI