来源：晚点LatePost 阿里培养的校招生正在成为通义实验室研发主力。 文丨陈佳惠 编辑丨贺乾明 管艺雯 我们独家获悉，阿里云 CTO、通义实验室负责人周靖人近期成为阿里巴巴合伙人。 合伙人团队是阿里的最高集体决策机构。今年 6 月，阿里合伙人团队刚经历大幅缩编，人数降至上市以来最低的 17 人。 阿里选拔合伙人，候选人除了在阿里工作 5 年以上、高度认同公司文化、对公司发展有积极贡献、愿意为公司文化和使命传承竭尽全力，还需要四分之三 以上的合伙人赞同才能当选，也就是 17 位合伙人需要至少 13 人投赞成票。 周靖人已经在阿里工作十年。他毕业于中国科技大学，2004 年获得美国哥伦比亚大学计算机博士学位，后加入微软担任研发合伙人。2015 年，他加入阿 里出任阿里云首席科学家，之后转岗多次：刚开始在阿里云负责 iDST（数据科学与技术研究院，达摩院前身），随后负责电商的搜索推荐广告，2020 年 底转去蚂蚁，一年多之后回到阿里云担任 CTO、兼达摩院副院长。 我们了解到，周靖人成为阿里合伙人的关键因素之一，是他负责的通义实验室过去一年努力保证了 Qwen 的模型领先地位。阿里管理层给了他高度肯定， 评价 ...

Core Insights - The integration of AI in mental health is gaining traction, with AI being positioned as a potential "affordable therapist" capable of providing emotional support and guidance [1][2][3] - Major internet companies are leading the charge in AI mental health solutions, developing products that can engage users in emotional conversations and provide support [2][14] - The increasing prevalence of mental health issues like anxiety and depression highlights the need for accessible mental health resources, which AI can help address [3][4] Market Dynamics - The market for AI-driven mental health solutions is expanding, with significant investments in companies like HaoxinQing and emerging startups like XihuXincheng and Jingxiang Technology [1][14] - The demand for mental health services is rising, but there is a shortage of qualified professionals, creating an opportunity for AI to fill the gap [3][4] - AI products are still in the early stages of development, with many facing challenges in achieving effective emotional engagement and commercial viability [6][9] Product Development - AI mental health products are primarily focused on emotional support and light symptom management rather than severe mental health conditions [5][7] - Companies are exploring various applications for AI in mental health, including screening, diagnosis, and intervention, particularly targeting B2B markets such as schools and enterprises [12][14] - The development of AI models like Therabot shows promise in assisting with depression treatment, demonstrating significant symptom reduction in clinical trials [5] Challenges and Opportunities - AI mental health solutions face challenges in user engagement and the ability to provide nuanced emotional support, often relying on traditional therapy models that may not translate well to AI [6][9] - Ethical considerations and the need for a robust referral system to human professionals remain critical issues for AI in mental health [7] - The potential for AI to serve as a tool for efficiency in mental health care, particularly in managing lighter cases and providing initial support, is recognized as a viable path forward [8][12]