更新时间：2026-08-26 来源：互联网编辑：邓邓侯石点击数： 4836140次

靳东疑暗讽金星

不到半年，一人写下数百万行代码 22岁“跨界”青年“解锁”AI开源新赛道_我的网站

待产孕妇跳楼身亡

A | 22岁的张晨曦，今年6月刚大学毕业。

B |

Brain-reading AI model reveals how different brain regions are linked to cognitive functions. Photo: Courtesy of Lu Han
Chinese scientists have developed a “brain-reading” AI model that could help predict the risk of depression among adolescents up to four years in advance by analyzing how humans respond to facial expressions, a technology expected to inspire future development of embodied intelligent humanoids capable of perceiving human emotion and thoughts through nuanced facial cues.
WHO data show that around 332 million people worldwide have depression, about one-third of whom have treatment-resistant forms of the condition. In China, an estimated 95 million people suffer from depression, National Business Daily reported, citing statistics from the China Mental Health Survey.
Using data from a population-based longitudinal adolescent cohort recruited across several European countries, the research team led by Lu Han, assistant professor at the School of Artificial Intelligence, Shenzhen University, has built an AI model that predicted which 19-year-olds were more likely to develop depression at the age of 23. The predictions were backed up by an independent clinical cohort of individuals with depression. The team’s paper was published in the journal Science Advances this month.
According to Lu, the study used brain scans taken at age 19 to predict depression-related symptoms at age 23. The study focuses on adolescence because the transition from adolescence to early adulthood is a key developmental period when depressive symptoms can increase rapidly. The earlier risks are identified, the greater the opportunity for prevention, Lu told the Global Times on Monday, adding that the findings need to be further validated in middle-aged and older adults and across different ethnic groups in future research.
In this study, the researchers analyzed data from adolescents in the IMAGEN, a population-based longitudinal cohort recruited across several European countries. At age 19, participants underwent an fMRI emotional-face task, and their emotional symptoms were assessed using standardized questionnaires. Genetic data obtained from blood samples were also analyzed, and participants were followed up at age 23. The researchers examined whether neural representations of angry faces at age 19 were associated with emotional symptoms and could predict elevated emotional symptoms four years later.
According to Lu, people without depression can more easily distinguish emotional changes based on others’ facial expressions and respond accordingly – for example, responding with friendliness to a smiling expression. But people with depression cannot do this, and are more likely to assume people are angry with them.
A brain-aligned deep-learning model developed by Lu’s team suggested that those participants whose brains were less able to distinguish between different facial emotions and tended to perceive others as angry were more likely to develop symptoms of depression and anxiety in adulthood.
The hypothesis that adolescents at risk of depression may respond differently to other people’s facial expressions than those without such risk based on the negative information processing bias long observed in depression research: people at risk of depression are more likely to notice, interpret, or remember negative social information, Lu said.
The researchers focused on angry facial expressions because they signal social threat and rejection, which are closely linked to interpersonal difficulties and negativity bias associated with depression. They hope to further understand how this bias develops within the visual system.
Building on this, they created a deep learning model, which mimics how the brain processes visual information, to predict how the brain encodes abstract emotional concepts such as anger.
They found that 19-year-olds whose response to facial expressions was skewed in favour of negative emotions or memories were the most likely to develop some form of depression.
Based on these findings, Lu’s team then developed a marker that can identify possible warning signs.
According to Lu, the study found that the computational biomarker was linked to the depression-related variant rs11123030 and polygenic risk for depression, suggesting that genetic susceptibility may affect emotional perception. It also provided predictive information beyond family stress and socioeconomic factors, complementing rather than replacing environmental risk factors. Therefore, depression is neither purely genetic nor purely psychological, but a complex mental disorder arising from the interplay of genetic susceptibility, brain development, emotional and cognitive processes, and life experiences.
According to Lu, the study is also expected to advance AI by aligning deep neural networks with human brain activity and using parameter perturbations to probe neural mechanisms, allowing models to both predict and explain how biases may arise.
The findings suggest that future affective computing and embodied AI should go beyond simply labeling facial expressions, incorporating visual details while preventing prior assumptions from overriding real-time sensory input, Lu said, adding that the findings could provide valuable insights for developing more interpretable robotic perception systems that more closely emulate the way humans process emotions.
。毕业不久的他却很忙：上个月，带着自研的A2A智能协作协议项目角逐新智基座赛道报名参加GOAI全球人工智能开源大赛，等待评审的同时，正在为下一阶段路演做准备；还去了一趟广东，在深圳和佛山待了两周，寻找制造业AI落地场景商机，拿下了二三十万元的企业订单。

C | 作为OPC（一人公司）创业者，张晨曦既是CEO，也是产品经理、程序员、销售员。记者在云谷中心“Y/OUR SPACE”（阿里巴巴旗下的共享办公创业社区）见到他时，他刚从深圳回来，正坐在工位上敲代码，忙着订单交付。“跨界”AI开发者本科就读于首都经济贸易大学工商管理专业的张晨曦，是一个不折不扣的“跨界AI开发者”。大二期间，一次去美国硅谷参加“黑客松”交流的经历，彻底为张晨曦打开了人工智能的大门，“工程师手把手教我用AI做网页，我觉得特别好玩、好用，回来之后就开始自己琢磨了。

D | ”往后两年多，他每天8点起床写代码，写到晚上11点，用AI从零开始自学编程，做出一个个小项目。今年1月，张晨曦报名参加了云谷中心的一场比赛，从全国两百多名参赛选手中“杀”出重围拿到铜奖。这场比赛，带给张晨曦第一个真正的机遇。当时他带来了一个面向开发者社区的Agent匹配项目，让不同AI智能体能够互相发现、自动对接，有观众对他印象深刻，希望把他的想法和技术用到自己的业务里。双方聊了几轮，合作意向逐渐清晰，但要签约走流程，张晨曦需要一家公司来承接。今年3月，他成立了自己的一人公司，同时坚定了创业初心：深耕AI底层开源技术，探索智能主体自主协作的全新范式。而杭州开放包容的城市气质，恰好接纳了这个年轻创业者的全新构想。为什么选择云谷中心？事实上，落地杭州之初，张晨曦面临许多选择。“杭州许多板块都在发力AI产业，政策、配套、扶持都很完善，对我们创业者足够包容、足够友好。”但深耕开源底层技术的张晨曦，对创业环境有着极致的个性化要求。这时，“Y/OUR SPACE”进入他的视野。“第一次来这里，就能感觉到运营团队在设计上下了很大功夫，让大家愿意在这里交流。”这个共享办公创业社区开放的空间布局、随处可见的讨论区、常态化的沙龙分享，都让他印象深刻。运营团队对入驻者的筛选也不只是看项目，更看“这个人是不是愿意和大家‘连接’”。2025年11月启用的“Y/OUR SPACE”，首期180个工位很快被抢空。张晨曦想搬进去时，排队等工位的创业者已经排到半年后。他没放弃，先是在云谷中心另一个共享空间过渡了一段时间，又在Datawhale创始人范晶晶的帮助下，临时“蹭”了一个工位。

E | 直到2026年4月，他终于如愿搬进了“Y/OUR SPACE”。与开源同频共振搬到云谷中心之后，这种“连接感”变成了实实在在的收获。张晨曦在这里认识了一批同行者，有做解决方案架构的，有做AI音乐机器人的，有做硬件社区的，还有做科技自媒体的……不同赛道的开源创作者比邻而坐，转身即可交流、随手就能联动。“工作时，大家各做各的事，但想交流的时候随时可以聊起来。

F | 工作之外，大家约着一起健身、一起吃饭、互相聊想法，这种氛围对我来说很重要。”这份独特的生态优势，让张晨曦决定，将核心研发、项目迭代、技术交流全部扎根云谷中心。如今，张晨曦正同步推进两大核心板块，打磨Harness智能治理框架、搭建高效研发“脚手架”的同时，优化A2A智能主体协作体系。“A2A协作是终极目标，简单说就是给AI制定一套‘社交规则’，让不同AI自动合作。”张晨曦介绍。