作者 | 凌敏 没有什么比"群星闪耀"更适合形容近期的 TTS（Text-To-Speech，文本转语音）模型领域了。 开年以来，从科技巨头到创业公司再到研究机构，都在发力 TTS 模型。2 月，字节跳动海外实验 室推出一款轻量级 TTS 模型 MegaTTS3-Global；3 月，出门问问联合香港科技大学、上海交通 大学、南洋理工大学、西北工业大学等顶尖学术机构，共同开源新一代语音生成模型 Spark- TTS；同月，OpenAI 推出基于 GPT-4o-mini 架构的 TTS 模型。 与 AI 领域其他热门技术相比，TTS 似乎格外低调，但它却是智能硬件、数字人等场景的"隐形基 石"。凭借广泛的应用领域和开阔的商业前景，TTS 在最近一年取得了长足的进步，并悄然改变 着行业规则。 最近，TTS 模型又有重磅"上新"，Speech-02语音模型一出手，就将 OpenAI、ElevenLabs 甩在 了后面，登顶 Arena 榜单，成为全球第一。 | Creator | | Model | Arena ELO | 95% CI | # Appearances | | --- | --- | --- | - ...

作者 | AICon 全球人工智能开发与应用大会 策划 | 李忠良 编辑 | 宇琪 随着技术的快速发展，大模型在各行业的应用潜力日益凸显，但如何将大模型能力高效转化为实际业 务价值，仍是企业面临的核心挑战。 近日 InfoQ《极客有约》X AICon 直播栏目特别邀请了 华为云 AI 应用首席架构师郑岩 担任主持人， 和 蚂蚁集团高级技术专家杨浩、明略科技高级技术总监吴昊宇 一起，在 AICon 全球人工智能开发 与应用大会 2025 上海站 即将召开之际，共同探讨大模型如何驱动业务提效。 部分精彩观点如下： 在 5 月 23-24 日将于上海举办的 AICon 全球人工智能开发与应用大会 上，我们特别设置了 【大模型 助力业务提效实践】 专题。该专题将围绕模型选型与优化、应用场景落地及效果评估等关键环节，分 享行业领先企业的实战经验。 查看大会日程解锁更多精彩内容： https://aicon.infoq.cn/2025/shanghai/schedule 以下内容基于直播速记整理，经 InfoQ 删减。 场景探索 郑岩：在探索大模型应用场景时，企业常会遇到"看起来很美但落地难"的需求，各位在实际项目中是 ...

Group 1 - The core viewpoint of the article emphasizes the practical experiences of AI entrepreneurs transitioning from ideas to implementation, highlighting AI as a significant opportunity in the current era [1][6]. - The live broadcast will feature discussions on AI development, specifically the journey from demo to actual launch, addressing the challenges faced in this process [4][6]. - The event will include insights from multiple AI founders, focusing on various aspects of AI development, including product independence, system architecture, and collaborative research and development [5][6][7]. Group 2 - The live session is scheduled for May 15, from 20:00 to 21:30, providing a platform for participants to engage with industry experts [4]. - Participants are encouraged to submit questions for the speakers, which will be addressed during the live broadcast [10].

Core Viewpoint - Microsoft is set to lay off 3% of its global workforce, affecting over 6,500 employees, as part of a strategic shift to optimize resources and increase investment in emerging artificial intelligence platforms [1][2]. Group 1: Layoff Details - The layoffs are part of a significant strategic adjustment, aimed at streamlining operations and enhancing profitability to support AI-focused initiatives [1]. - This round of layoffs is the largest since 10,000 employees were let go earlier in 2023, impacting all levels, regions, and teams within the company [2]. - Employees affected by performance-related layoffs will face a two-year rehire ban, and a new "good attrition" metric has been introduced to track employee departures [3]. Group 2: Financial Performance - Despite the layoffs, Microsoft reported strong quarterly earnings with revenue of $70.1 billion, a 13% year-over-year increase, and a net profit of $25.8 billion, up 18% [2]. Group 3: AI Investment Focus - Microsoft is heavily investing in AI, integrating AI capabilities into its core products like Microsoft 365, Azure, and Dynamics 365 to attract more enterprise customers [1]. - The company’s CEO, Satya Nadella, highlighted that a significant portion of the code in their codebase is now generated by software, indicating a shift towards AI-driven development [1]. Group 4: Impact on Key Projects - Key personnel involved in significant projects, such as the TypeScript performance enhancement initiative, have also been laid off, raising questions about the decision-making process behind the layoffs [5][13]. - The TypeScript performance optimization project aims for up to a 10x performance improvement and is still in progress, despite the departure of core team members [9][12].

Core Viewpoint - The WizardLM team, including key member Can Xu, has left Microsoft to join Tencent's Hunyuan division, amidst speculation regarding the timing of their departure coinciding with Microsoft's global layoffs [1][2]. Group 1: Team Departure and Background - Can Xu announced his departure from Microsoft, clarifying that it was his personal decision and not the entire WizardLM team [1]. - Most core members of the WizardLM team have reportedly already left Microsoft prior to the announcement, and their departure is not directly related to the layoffs affecting approximately 6,000 employees [2]. - The WizardLM team was established in early 2023, focusing on the development of advanced large language models (LLMs) [4]. Group 2: Team Members and Contributions - Key members of the WizardLM team include Qingfeng Sun and Can Xu, both of whom have significant backgrounds in AI research and have contributed to various projects at Microsoft [5]. - Can Xu has led the development of several models under the WizardLM series, with over 40 papers published in top international conferences and more than 3,300 citations on Google Scholar [5]. Group 3: Model Development and Achievements - The WizardLM team introduced the Evol-Instruct method, which generates diverse instruction data using LLMs, outperforming human-created datasets in evaluations [6][9]. - The WizardLM model has achieved notable performance metrics, including a 97.8% score compared to ChatGPT on the Evol-Instruct test set [10]. - In a ranking of large language models, WizardLM was placed fourth globally, marking it as the top open-source model from a Chinese team [13][14]. Group 4: Tencent's AI Strategy - Tencent has restructured its AI model development framework, focusing on "computing power, algorithms, and data," and plans to invest approximately 124.9 billion USD in AI development this year [24][26]. - The company has established new technical departments dedicated to large language models and multimodal models to enhance its AI capabilities [24][25]. Group 5: Challenges and Community Impact - Following the release of the WizardLM-2 models, Microsoft retracted them due to missing toxicity testing, which has raised concerns within the AI community [19][21]. - The CEO of Hugging Face expressed that Microsoft's actions have negatively impacted various open-source projects and the community at large [21][23].

分享嘉宾 | 尹一峰 审校 | 李忠良 策划 | AICon 全球人工智能与开发大会 RAG 要不要做语义检索，有很多讨论，还没有定论。在 InfoQ 举办的 AICon 全球人工智能与开发大会上 Hugging FaceMachine Learning Engineer 尹 一峰为我们带来了精彩专题演讲"RAG 基本范式的选择与系统设计"，深入探讨基于语义搜索（Semantic Search）的 RAG 系统的重要性，揭示它为何在 当前技术背景下被严重低估，分析语义搜索的本质及其在 RAG 系统中的关键作用，并分享如何基于这一本质设计出高效的系统架构。 此外，演讲还将讨论 KG 驱动的 RAG 系统，并指出它并非适用于所有数据类型，帮助听众理解如何根据不同的数据特性选择最合适的 RAG 范式。 内容亮点： 以下是演讲实录（经 InfoQ 进行不改变原意的编辑整理）。 RAG 简介 我们需要了解为什么需要 RAG（Retrieval-Augmented Generation，检索增强生成）。原因很简单，因为 LLM 本身存在一些问题。RAG 作为一种辅助 工具，其存在是因为 LLM 本身有不足之处。 LLM ...

Core Viewpoint - The WizardLM team, creators of advanced large language models, has transitioned from Microsoft to Tencent's AI development organization, Hunyuan, aiming to enhance LLM training technology and develop superior AI models [1][3][31]. Group 1: Team Transition and Background - The WizardLM team, consisting of six key members, has left Microsoft amid speculation regarding layoffs affecting 3% of the workforce, although their departure is reportedly unrelated to these layoffs [4][6]. - The team was established in early 2023, focusing on the development of advanced large language models, with notable members including Qingfeng Sun and Can Xu, both of whom have significant experience in AI research [7][9][10]. - The team has previously contributed to the development of models such as WizardLM, WizardCoder, and WizardMath, and has published over 40 papers in top international conferences [10][13]. Group 2: Model Development and Achievements - WizardLM has released models that outperform Google's Gemma 3 series and have ranked among the top four global large language models in competitions [3][16]. - The core algorithm, Evol-Instruct, allows for the efficient generation of complex instruction data, leading to superior performance in human evaluations compared to traditional methods [13][14][17]. - The WizardLM-30B model achieved a 97.8% score compared to ChatGPT in specific tests, showcasing its advanced capabilities [14]. Group 3: Tencent's AI Strategy - Tencent has restructured its AI development framework, focusing on "computing power, algorithms, and data," and plans to invest approximately 124.9 billion USD in AI development [28][30]. - The company has established new technical departments dedicated to large language models and multimodal models, aiming to enhance AI capabilities in natural language processing and data integration [28][29]. - Following the acquisition of the WizardLM team, Tencent's ambition in the AI sector is expected to grow, with the team continuing to develop and release AI models [31].

Core Viewpoint - Vibe Coding has emerged as a significant trend in Silicon Valley, emphasizing a shift from traditional coding to describing requirements in natural language, allowing AI to generate code automatically [1][2][3]. Group 1: Concept and Evolution - The concept of Vibe Coding was introduced by Andrej Karpathy, highlighting a process where developers interact with AI to create applications without needing to write code themselves [1][2]. - Vibe Coding allows individuals without technical backgrounds to participate in programming, making the idea of "everyone is a programmer" more attainable [1][4]. - The capabilities of large models have evolved, enabling them to accurately understand user needs and generate runnable projects, marking a shift from code completion to comprehensive project development [2][4]. Group 2: Tools and Applications - Various tools have emerged in the Vibe Coding space, including Cursor, GitHub Copilot, and CodeBuddy, which is developed by Tencent [5][6]. - CodeBuddy's Craft mode can autonomously generate and modify multi-file code, enhancing the development process by allowing developers to focus on user experience rather than coding details [6][9]. - CodeBuddy has been widely adopted within Tencent, with 85% of developers using it, resulting in an average coding time reduction of over 40% and a productivity increase of 16% [20]. Group 3: Challenges and Future Outlook - Despite the advantages, challenges such as code quality and maintainability persist, with increasing code change rates leading to potential issues in code structure and readability [16][17]. - The rise of AI-generated code has led to a significant increase in code change rates, projected to be double that of pre-AI levels by 2024 [16][17]. - The future of Vibe Coding looks promising, with a growing number of startups indicating that a substantial portion of their code is AI-generated, suggesting a potential mainstream adoption of this approach in software development [21].

Core Viewpoint - The banking industry is undergoing a profound transformation driven by digitalization and intelligent technology, with fintech subsidiaries reshaping service models and competitive landscapes [1] Company Overview - Beiyin Jinke, established on May 16, 2019, as a technology subsidiary of Beijing Bank, aims to support the bank's digital transformation through efficiency and cost control [2] - The company focuses on four development goals: serving the parent bank, refining products, optimizing technology, and strengthening capabilities [2] Function and Role - Beiyin Jinke acts as a bridge between business and technology departments, facilitating communication and project delivery to enhance the quality and speed of digital transformation [3] - The company is committed to building a high-level, sustainable digital talent team to support the bank's transformation [3] Digital Talent Development - The company has established a "triangular mechanism" combining technology, products, and projects to shape a digital talent hierarchy [4] - The "All in AI" strategy aims for comprehensive AI integration, with all employees participating in AI application development [4] ACT Model - The "ACT" model categorizes digital talent into three types: Application Talent (A), Collaboration Talent (C), and Technical Talent (T), each playing a crucial role in bridging business and technology [6] Corporate Culture - The corporate culture emphasizes five core values: integrity, innovation, self-drive, co-creation, and responsibility, with a focus on agility and collaboration [8] - The company promotes a culture of rapid development and innovation, allowing teams to work with more flexibility and less bureaucratic hindrance [8] Organizational Structure - Beiyin Jinke has a stable workforce of approximately 1,300 employees, with a focus on nurturing young talent and leveraging experienced middle management [11] - The organization aims to maintain a high density of talent, enhancing both recruitment and internal development processes [12] Training and Development Mechanisms - The company has established a comprehensive training ecosystem, offering over 90 self-developed courses and accumulating 150,000 learning hours [14] - Annual innovation competitions are organized to foster creativity and resolve conflicts within teams [15] Digitalization Achievements - The company has implemented a fully digitalized HR system, including a "Human Resource Dashboard" for efficient management and communication [18] - AI recruitment assistants have been introduced to streamline the hiring process, enhancing efficiency and precision in talent management [19] Future Outlook - With the rapid advancement of AI technologies, Beiyin Jinke is committed to exploring new opportunities in the AI era, collaborating with industry partners to navigate future challenges [22]

Core Insights - The article emphasizes that AI and Agents are no longer optional but essential for transforming customer insights, decision-making efficiency, and service experience in financial marketing [1][3][5] - It highlights the evolution of financial marketing from traditional methods to the current intelligent 3.0 era, where AI technologies are the driving force behind marketing transformation [3][4][15] Industry Evolution - Financial marketing has evolved from a traditional 1.0 era reliant on physical branches and customer managers to a digital 2.0 era with CRM and online channels, but issues like data silos and fragmented experiences persist [3][4] - The current shift to intelligent 3.0 is characterized by the integration of AI technologies, which provide unprecedented customer insights and enhance decision-making processes [3][4][5] AI Value Proposition - AI offers unparalleled customer insights by analyzing both structured and unstructured data, enabling the identification of hidden customer needs [3][4] - It facilitates real-time and precise decision-making by integrating various data points to generate optimal marketing strategies tailored to individual customers [4][5] - AI-driven solutions improve service execution through automation, allowing for consistent and efficient customer interactions [5][11] Current Challenges in Financial Marketing - High customer acquisition costs and low conversion rates are significant challenges, with customer acquisition costs (CAC) often exceeding thousands [6][7] - Personalization remains a challenge, as many financial institutions struggle to provide truly individualized experiences [7][8] - Complex product offerings lead to customer confusion, making it difficult for them to make informed purchasing decisions [7][8] - Regulatory compliance poses challenges for innovation, requiring a balance between risk management and marketing efficiency [8][9] AI and Agent Solutions - The article proposes the creation of a robust "intelligent marketing platform" that integrates data, AI algorithms, and service applications to enhance marketing effectiveness [11][12] - Key technological advancements include large language models (LLM), knowledge graphs, and privacy-preserving computing, which collectively enhance AI's capabilities in financial marketing [12][13] Future Outlook - The future of financial marketing will focus on "intelligent density," where the effective use of AI technologies will create competitive advantages in understanding customers and optimizing experiences [15][16] - The industry is encouraged to embrace AI-driven transformations to secure long-term competitive positioning in the evolving market landscape [16]