Core Insights - The article discusses the transformative impact of AI and data analytics on various industries, particularly in sports training and rehabilitation, highlighting the shift from experience-driven training to data-driven methodologies [3][4][5]. Group 1: AI in Sports Training - The integration of advanced technologies such as SIMI motion capture systems and high-speed cameras in table tennis training is revolutionizing athlete performance analysis by providing real-time data on over a hundred metrics [3]. - AI is redefining the training landscape by converting intuitive skills of top athletes into quantifiable and transferable intelligent assets, marking the beginning of a "digital dissection" era in sports [3][4]. Group 2: Cross-Industry Applications - The combination of sensor arrays, edge computing, and digital twin technology is reshaping the smart transformation pathways across multiple industries, including automotive and agriculture [6]. - The AI training systems' architecture reflects a shift towards end-to-end delivery capabilities, with a focus on integrating hardware, algorithms, and industry expertise to meet customer needs [7]. Group 3: AI Commercialization Challenges - The article highlights the tension between specializing in vertical scenarios versus developing horizontal capabilities in AI commercialization, emphasizing the importance of industry-specific knowledge for effective application [8]. - The next three years are expected to see the emergence of industry-specific models driven by strong demand for precision decision-making and data security concerns [9]. Group 4: Investment Strategy - The investment logic of Haier Capital is based on a collaborative model that promotes AI's value creation in sectors like healthcare and smart technology through ecosystem synergy [9].

以下文章来源于数据结构和算法 ，作者博哥 数据结构和算法 . 《算法秘籍》作者王一博，专注于互联网大厂热点事件和算法题讲解。 最近网上有人列出了曾经在阿里巴巴上过班并离职创业成功的十位大佬，不得不说阿里巴巴确实在 向社会输入人才。 这里面孙彤宇是阿里巴巴初创团队成员之一，1999 年阿里巴巴刚成立的时候就加入了，2003 年 受马云指派创建淘宝网，后任淘宝网总裁、阿里巴巴副总裁等职，2008 年 3 月离职。 而何小鹏在2004 年联合创立 UC 优视，打造用户超 4 亿的 UC 浏览器，后来被阿里巴巴收购，在 后来离职创办小鹏汽车，也算是在阿里上过班。 --------------下面是今天的算法题-------------- 来看下今天的算法题，这题是LeetCode的第 1514题：概率最大的路径，难度是中等。 给你一个由 n 个节点（下标从 0 开始）组成的无向加权图，该图由一个描述边的列表组成，其中 edges[i] = [a, b] 表示连接节点 a 和 b 的一条无向边，且该边遍历成功的概率为 succProb[i] 。 指定两个节点分别作为起点 start 和终点 end ，请你找出 从起点到 ...

Core Insights - The article discusses the pervasive influence of algorithms on human behavior, cognition, and emotional states, highlighting the need for awareness and proactive measures to mitigate negative impacts [2][4][8]. Group 1: Cognitive Filtering - Internet algorithms create "interest profiles" based on user behavior, leading to information silos that reduce tolerance for differing viewpoints and degrade critical thinking skills [2][3]. - Over 80% of the 792 undergraduate programs published by the Ministry of Education are not covered by algorithm recommendations, limiting students' choices and potentially exacerbating social cognitive divides [3]. Group 2: Emotional Manipulation - Algorithms exploit human weaknesses, creating addictive cycles of instant gratification through features like short video conflicts and social media feedback, which can lead to emotional polarization [4][6]. - Research indicates that teenagers spending over 3 hours daily on social media are more likely to experience mental health issues such as depression and anxiety [4]. Group 3: Identity Crisis - Algorithms reinforce user traits, leading to a fragmented self-identity, with nearly half of Generation Z feeling their online persona aligns more with expectations than their true selves [5][6]. - The pressure to maintain a curated online image can result in diminished real-world social skills and increased psychological exhaustion [6]. Group 4: Behavioral Alienation - Algorithms create dependency through negative feedback mechanisms, making users anxious when attempting to disengage, thus controlling their behavior [7]. - E-commerce data shows that while users may purchase fitness equipment after viewing related content, actual usage rates are below 30%, indicating a disconnect between perceived needs and actual desires [7]. Group 5: Rebuilding Digital Resilience - Organizations should guide and regulate algorithms for positive outcomes, while individuals can set "information fasting" periods to engage with non-algorithmic content [8]. - Educational institutions are encouraged to introduce courses on algorithm analysis to enhance students' critical thinking and information discernment skills [8]. - Regulatory measures should include transparency in algorithm logic and the establishment of cognitive health assessment metrics to prevent adverse effects on users' mental states [8].

智通财经5月21日电，美国行政管理和预算办公室主任沃特表示，穆迪调降评级是企图算准时机影响美 国通过预算法案的能力。 ...

美国管理和预算局主任Vought：我认为预算法案将在本周通过，我很乐观。 ...

美国管理和预算局主任Vought：穆迪下调评级是在试图把握时机，危及我们通过预算法案的能力。 ...

机器人动捕设备专家 20250520 摘要 • 当前机器人数据采集主要有四种模式，包括真实动捕训练本体、动捕结合 虚拟引擎、纯动捕系统和模拟合成数据，有效数据比例差异显著，直接影 响训练效果和成本。 • 海外公司如 Tesla 已批量采购 Adesso 设备，采用真人动捕训练和虚拟仿 真 DNF 模式。国内公司多处于技术验证阶段，采用遥操动捕设备和少量设 备结合真人动捕与虚拟 YDF 模式。 • 数据有效性通过真人动作初步验证和机器人反向验证姿态来衡量，行业内 尚无统一标准，涉及多传感器信息融合以确保评估结果的可靠性。 • 简单动作如抓水杯只需数小时数据积累，而通用泛化性需几十万甚至数百 万小时。数据复用关键在于重定向过程，将人的高自由度数据映射到机器 人，难点在于末端精度协调和自然衔接。 • 数据采集效率极低，1,300 秒数据需经验丰富的动捕专家使用上百万设备 连续工作十几天。核心问题在于虚拟本体软件不够成熟，与真实物体交互 面临挑战。 海外公司如 Tesla 已经开始大批量采购 Adesso 设备，并采用两种主要流程： 一是真人穿着人体动捕服训练真实本体以采集数据；二是使用虚拟仿真平台通 过 DNF 模 ...

5月20日，据"快手黑板报"消息，快手上线算法推荐系列优化功能，持续推进算法向上向善。 消息称，近期，快手平台已上线"信息茧房"自我评估、"一键破茧"两大个性化推荐优化功能，支持用户 使用平台系统功能了解个人推荐页内容类型分布情况，自主调节各类内容标签的推荐强度，通过个性化 调节丰富用户对内容的需求。 据了解，"信息茧房"自我评估功能是指根据用户的推荐页视频的内容类型、内容偏好、推荐强度分布， 以可视化形式呈现。 "一键破茧"功能是指根据用户偏好和使用特征，优化调整推荐内容类型、兴趣标签和推送强度，有效防 范"信息茧房"。用户可多维度进行评估，自主选择是否启用该功能。 据介绍，首先，用户自主操作打开快手App，点击左上角三道杠—选择常用功能中的"内容偏好"模块— 通过环状饼图即可快速查看自己的推荐页兴趣标签分布和内容类型情况，判断是否符合期望。 其次，用户在进行评估后，可以选择是否保持当前个性化推荐现状，或者新增更多内容类型标签加入到 自己的推荐页当中。 第三，滑动对应兴趣标签，可以依据个人喜好自主调整推荐页内容推送强度。同时，可点击"调解更多 内容"按钮，选择更多元的标签。 第四，为有助于用户自由控制个性化 ...

Market Overview - The main board saw significant gains with stocks like Zhejiang Wenlian (+10.05%), Yingfangwei (+10.04%), and Mengwang Technology (+10.03%) leading the charge [1] - The ChiNext board also performed well, with Huibo Yuntong rising by 20.00% and Guoke Micro increasing by 12.54% [1] - The Sci-Tech Innovation board was led by Liyang Chip, which gained 15.49% [1] - Active sub-industries included SW Marketing Agency (+3.10%) and SW Optical Components (+2.41%) [1] Domestic News - A delivery ceremony for the first 12-inch high-purity silicon carbide crystal growth furnace in Hebei was held, utilizing advanced AI algorithms and PVT technology for AR glasses [1] - MediaTek's first 2nm chip is expected to complete tape-out in September, promising a 15% performance increase and a 25% reduction in energy consumption compared to 3nm chips [1] - China's smartphone exports plummeted by 72% to just under $700 million, highlighting the impact of tariffs imposed by the Trump administration [1] - Lenovo's self-developed 5nm chip SS1101 has been benchmarked, featuring a 10-core CPU architecture and achieving single-core scores over 2000 and multi-core scores of 6700+ [1] Company Announcements - Debang Technology announced a reduction in shareholding by the National Integrated Circuit Fund, decreasing its stake from 18.65% to 17.98% after selling 950,000 shares [2] - Qiangda Circuit declared a cash dividend of 4.00 yuan per 10 shares based on a total share capital of 75,375,800 shares [2] - Weirgao announced a cash dividend of 1.34 yuan per 10 shares based on a total share capital of 134,621,760 shares [2] - Meixinsheng reported a reduction in shareholding by WI Harper Fund VII, decreasing its stake from 6.96% to 5.87% after selling 1,215,000 shares [2] Industry Insights - Rivet Industries has launched military smart glasses aimed at enhancing close combat and logistical support in defense applications [3] - Texas Instruments is nearing completion of its first semiconductor manufacturing plant in Sherman, which will produce over 100 million semiconductors daily [3] - India is preparing for the next phase of its semiconductor plan, aiming for a 5% global market share by 2030, with $10 billion in incentives for potential manufacturers [3] - The Southeast Asian smartphone market saw a 3% decline in Q1 2025, with Samsung regaining the top position with 4.3 million units shipped, while Xiaomi was the only top-five vendor to achieve year-on-year growth [3]

Group 1 - The article discusses the impact of technological advancements, particularly artificial intelligence (AI), on gender equality, highlighting the potential for AI to bridge gender gaps while also addressing inherent biases within algorithms [1][2] - It emphasizes that data used in AI systems often reflects male biases, leading to inadequate representation of women's health issues in clinical trials and AI-driven medical decisions [2][4] - The article points out that the gender disparity in AI-related fields contributes to algorithmic biases, with only 28.2% of individuals in STEM fields being women, and many AI practitioners unaware of gender bias issues [5][7] Group 2 - The article highlights the need for awareness and training among AI developers to mitigate gender biases in algorithm design, suggesting that companies should create inclusive environments for women in tech [7][10] - It discusses the challenges of distinguishing between "preference" and "bias" in algorithmic recommendations, particularly in e-commerce, where gender-based targeting can lead to discriminatory practices [8][9] - The article calls for regulatory measures to ensure gender equality in AI applications, referencing specific laws and guidelines aimed at preventing discrimination in algorithmic processes [12][13] Group 3 - The article suggests that a multi-faceted approach is necessary to address gender bias in AI, including algorithm optimization and regulatory oversight [10][14] - It mentions the importance of using statistical methods to test for gender bias in algorithms and the need for continuous updates to algorithms by developers to address potential biases [13][14] - The article concludes with recommendations for data handling and model training to ensure fairness and reduce bias in AI systems [14]