闫德利 腾讯研究院资深专家 一、AI已在诸多任务领域超越人类 AI发展日新月异，在许多任务上已经陆续超越人类基线水平。如2015年图像分类，2018年中等水平阅读 理解，2020年视觉推理、英语语言理解，2023年多任务语言理解、竞赛级数学，2024年博士级科学问 题。下图所示的8项关键任务技能中，AI仅在多模态理解和推理能力上还略逊人类一筹，但从2023年开 始就加速提升。我们有望很快见证AI 能力在现有主流基准上"全部超越人类水平"的奇点时刻。 图 选定的 AI 指数技术性能基准与人类表现对比 二、AGI的终极目标或于年内实现 我们已经构建了无数在特定任务上超越人类水平的AI系统，但它们缺乏通用性，无法应对超出预定任务 之外的问题，尚处于"狭义人工智能 （Narrow AI） "阶段。随着AI性能的大幅提升，具备跨领域能力、在 多个方面媲美甚至超越人类的、更强大的AI被提上日程。 人们常将之命名为"通用人工智能（AGI）" 。 各国高度重视AGI。2023年4月28日中共中央政治局会议提出："要重视通用人工智能发展"；英国《国家 人工智能战略》 （2021 ） 对AGI进行了专门强调，指出"必须认真对待A ...

近期 Founder Park 拜访了新创基金 Creekstone Ventures 的两位合伙人钟陆欢和李一豪。对于宏 观周期，技术周期，应用方向的洞见，和时代特性的创业者，开诚布公的交流。 理想，好奇，真诚，驱动着 Creekstone 在变化中探索，为更多优秀创始人服务和助力，也期待与 更多心怀梦想的同行人 Founder Park： 「减法」怎么理解？ " 01. 新基金聚焦 AI，和创始人连接更紧密 Founder Park： 介绍一下新基金目前的情况。 Creek Stone钟陆欢： 目前基金设立已基本完成，如果顺利，计划月底进行首次募集。这期基金规模预计 在数千万美金。 Founder Park： 现阶段有已经确定要投资的项目吗？ Creek Stone钟陆欢： 有的，目前有两个项目已经在协议阶段，分别是一家 AI Coding 公司和一家 AI 眼镜 公司。 我们之前在弘毅就投过 ToC 的 AI Coding项目，在企业级上也一直很看好。这次投的是一个ToB的企业级 Coding，我们和创始人关系不错，他刚从大厂离职出来，时机也合适，就决定作为新基金的第一个投资 项目。几家机构一起投的。至 ...

Core Viewpoint - The CEO of Jumpspace, Jiang Daxin, emphasizes that any shortcomings in the multimodal field will delay the exploration of AGI (Artificial General Intelligence) [1][8][10] Group 1: Company Overview - Jumpspace has maintained a low profile compared to its competitors in the "Six Little Dragons" despite its unique positioning in the market [2][3] - The company has released 22 self-developed foundational models in the past two years, with over 70% being multimodal models, earning it the title of "multimodal king" in the industry [4] Group 2: Multimodal Development - The development stage of multimodal technology differs from that of language models, with the former still in its early exploratory phase [5][9] - Jumpspace's approach involves a challenging technical route that integrates understanding and generation within a single large model [5][14] Group 3: Future Trends and Applications - The next trends in model development include enhancing pre-trained foundational models with reinforcement learning to improve reasoning capabilities [10][18] - Jumpspace is focusing on the integration of understanding and generation in the visual domain, which is crucial for effective model performance [14][20] Group 4: Strategic Partnerships and Market Position - The company is collaborating with major enterprises like Oppo and Geely to apply its agent technology in key application scenarios [6][24] - Jumpspace aims to become a supplier for vertical industries rather than directly targeting consumer or business markets, leveraging existing user bases and scenarios from partners [24][25]

Core Viewpoint - The domestic large model industry is attempting to break through its current challenges and reconstruct a new order, but the unstable market environment poses significant risks [1] Group 1: Open Source Trends - DeepSeek has disrupted the industry's perception of open-source models, prompting OpenAI's CEO to reconsider the validity of open-source strategies [1] - Domestic large model companies like Alibaba, Baidu, and SenseTime are accelerating their open-source initiatives [1] - Open-source is seen as a key strategy to reduce dependency on foreign software and hardware, but the commercial viability of open-source projects remains complex [2][5] Group 2: Challenges in Implementation - Developers face significant technical adaptation and maintenance costs, despite open-source models lowering the technical barrier [4] - The integration of large models into existing systems requires extensive customization, which can be resource-intensive for companies [4] - The complexity of data acquisition, cleaning, and labeling poses additional challenges for businesses, particularly small and medium-sized enterprises [4] Group 3: Investor Sentiment - Investors are cautious about the open-source model due to the unclear profitability and traditional software sales evaluation methods not being applicable [5] - The potential for significant financial loss if investments in proprietary models are undermined by open-source alternatives is a concern for investors [4][5] Group 4: Business Models - Chinese large model companies are adopting a "free-to-use plus value-added services" model to build a commercial framework around open-source models [6][8] - Companies like Baidu are leveraging their cloud services to monetize the usage of their open-source models, creating a win-win situation for developers and the company [8] - The success of open-source models may depend more on the quality of cloud services than on the models themselves, as seen in the strategies of Meta and Hugging Face [9][10] Group 5: Future Outlook - Open-source is viewed as a pathway for the Chinese large model industry to overcome technological barriers, but commercial sustainability is equally important [10] - The increasing tariff barriers from the U.S. add pressure to the large model industry, making the choice of cloud platforms more critical than the open-source models themselves [10]

Group 1: Founder Psychology and Roles - Eric Schmidt emphasizes the difference between founders and professional managers, stating that founders are visionaries while professional managers are "amplifiers" who help scale ideas [4][10] - Schmidt reflects on his experience at Google, noting that he was not a typical entrepreneur but rather a professional manager who contributed during the company's scaling phase [3][4] - He discusses the challenges of attracting talent, highlighting that many talented individuals often choose to start their own companies instead of joining established firms [3][5] Group 2: Market Dynamics and Startup Ecosystem - Schmidt points out that many startups are often acquired for their talent rather than their products, indicating a market structure that can be inefficient [6][7] - He notes that the majority of startups fail, with traditional venture capital experiences suggesting that 4 out of 10 will fail completely, and 5 will become "zombies" with no growth potential [7] - The conversation highlights the importance of competition for startups, suggesting that true leadership is demonstrated when facing challenges from larger companies [11][12] Group 3: AI and Future Trends - Schmidt believes that AI is currently underestimated rather than overhyped, citing the scaling laws that drive AI advancements [33][34] - He discusses the potential of AI to transform business processes and scientific breakthroughs, emphasizing the importance of understanding how humans will coexist with advanced AI systems [35][39] - The conversation touches on the competitive landscape between the U.S. and China in AI development, with China investing heavily in AI as a national strategy [41][42] Group 4: Talent Acquisition and Management - Schmidt stresses the importance of attracting top talent by creating an environment where individuals feel they are solving significant problems [18][20] - He differentiates between "rockstar" employees who drive change and "mediocre" employees who are self-serving, advocating for the retention of the former [21][22] - The discussion includes insights on how to identify and nurture high-potential talent within organizations [24][25] Group 5: Challenges in AI Development - Schmidt highlights the challenges of defining reward functions in reinforcement learning, which is crucial for AI's self-learning capabilities [51] - He warns about the potential pitfalls of over-investing in AI infrastructure without a clear path to profitability, suggesting that many companies may face economic traps [47][48] - The conversation concludes with a call for companies to focus on the most challenging problems in AI, as solving these will yield the most significant rewards [52][53]

Core Viewpoint - The podcast discusses the impact of AI on talent acquisition and the evolving job market, emphasizing the need for companies to adapt to the AI era and for individuals to position themselves as valuable talent in this new landscape [2][5]. Group 1: AI and Talent Dynamics - The transition from the mobile internet era to the AI era will accelerate the exit of top talent from the mobile internet sector, while simultaneously ushering in a new wave of talent from the post-00s generation who are more adept at utilizing AI [5][6]. - AI will enable individuals to acquire knowledge and skills much faster than before, compressing years of experience into a few months of learning [6][7]. - The demand for traditional mobile internet talent is shifting, with companies needing to reassess the value and capabilities of these individuals in the context of AI [7][8]. Group 2: The Role of Trust in Executive Search - The distinction between headhunters and executive search professionals lies in the trust factor; executive search focuses on building trust with company owners and boards, while traditional headhunting is often seen as transactional [4][3]. - AI is expected to replace roles that primarily sell information, but the human element of trust in executive search remains irreplaceable [4][3]. Group 3: Future Job Market and Skills - The future job market will require individuals to embrace AI, with two types of people emerging: those who adopt AI and those who support others in adopting it [12][11]. - The emergence of new roles, such as AI product managers and digital employees, will reshape the workforce, necessitating a blend of traditional skills and AI proficiency [28][29]. - The traditional career progression may be disrupted, with individuals potentially achieving higher positions more quickly due to AI's efficiency [23][24]. Group 4: Challenges and Opportunities in Global Expansion - The current wave of talent leaving for overseas opportunities is primarily composed of business owners, high-level executives from large companies, and those seeking a fresh start [30][31]. - There is a significant gap in top-tier talent for companies looking to expand internationally, with many of the current expatriates not representing the best talent available [32][33]. - Companies must carefully consider their readiness for global expansion, as the challenges of finding qualified talent and managing costs can be substantial [36][37]. Group 5: The Importance of Adaptation - Companies must leverage the changing talent landscape to bring in individuals who are flexible and willing to adapt to new roles and expectations in the AI era [39][40]. - Embracing change and being open to new opportunities is crucial for both companies and individuals to thrive in the evolving job market [41][42].

Core Insights - The rise of edge cloud technology is revolutionizing data processing by shifting capabilities closer to the network edge, enhancing real-time data response and processing, particularly in the context of AI inference [1][5] - The demand for AI inference is significantly higher than for training, with estimates suggesting that inference computing needs could be 10 times greater than training needs [1][3] - Companies are increasingly focusing on the post-training phase and deployment issues, as edge cloud solutions improve the efficiency and security of AI inference [1][5] Group 1: AI Inference Demand - AI inference is expected to account for over 70% of total computing demand for general artificial intelligence, potentially reaching 4.5 times the demand for training [3] - The founder of NVIDIA predicts that the computational requirements for inference will exceed previous estimates by 100 times [3] - The transition from pre-training to inference is becoming evident, with industry predictions indicating that future investments in AI inference will surpass those in training by 10 times [4][6] Group 2: Edge Cloud Advantages - Edge cloud environments provide significant advantages for AI inference due to their proximity to end-users, which enhances response speed and efficiency [5][6] - The geographical distribution of edge cloud nodes reduces data transmission costs and improves user experience by shortening interaction chains [5] - Edge cloud solutions support business continuity and offer additional capabilities such as edge caching and security protection, enhancing the deployment and application of AI models [5][6] Group 3: Cost and Performance Metrics - Future market competition will hinge on cost/performance calculations, including inference costs, latency, and throughput [6] - Running AI applications closer to users improves user experience and operational efficiency, addressing concerns about data sovereignty and high data transmission costs [6] - The shift in investment focus within the AI sector is moving towards inference capabilities rather than solely on training [6]

本期，我们非常荣幸地于4月16日邀请虞晶怡老师，为我们开启一次AI的思想远航。 徐一平 腾讯研究院 高级研究员 王强 腾讯研究院 资深专家 以生成式AI为代表的新技术浪潮日新月异，正带来一场深刻的技术、商业与社会变革，推动人类社会从 信息社会向智能社会转变。全世界热切期待AI到来的同时，也非常关心人工智能将带来哪些新机遇、新 挑战。 为此，我们发起了一项 《AI & Society 百人百问》 研讨，广泛邀请AI技术大咖、AI独角兽创始人、AI 投资人，以及社会学家、心理学家、国际关系专家、科幻作家等，用多元视角，深入研讨人工智能技术 引发的广泛影响，发掘AI时代的共识和非共识，共同推动人工智能始终朝着"助人发展，与人为善"的方 向可持续发展。 虞晶怡，上科大讲席教授、副教务长、信息学院院长。在加入上海科技大学前，他任职美国特拉华大学计算机与信息科学系正教 授。他于2000年获美国加州理工学院应用数学及计算机学士学位， 2005年获美国麻省理工大学计算机与电子工程博士学位。他 长期从事计算机视觉、计算成像、计算机图形学、生物信息学等领域的研究工作。他是IEEE Fellow、OSA Fellow、美国NSF ...

Core Viewpoint - CloudWalk Technology, one of the "AI Four Little Dragons," reported a significant decline in performance for 2024, with revenue dropping to 398 million yuan, a year-on-year decrease of 36.69%, marking the lowest revenue point in seven years [1][2] Financial Performance - The company recorded a net profit of -722 million yuan, with losses expanding compared to the previous year, marking eight consecutive years of losses [1][2] - Revenue projections made during the company's IPO in 2022 were significantly overestimated, with actual revenues from 2022 to 2024 being 526 million yuan, 628 million yuan, and 398 million yuan respectively, resulting in a negative compound annual growth rate [2][3] - The company's gross margin fell by 16.39 percentage points to 35.81% in 2024, while its peers like SenseTime maintained a gross margin of 42.90% [3] Workforce and R&D - CloudWalk underwent significant layoffs in 2024, reducing its workforce from 801 to 453 employees, a decrease of 43%, with R&D personnel decreasing by 51% [5][6] - The company’s R&D expenses fell by 18.27% to 472 million yuan, yet the R&D expense ratio increased to 118.72% due to declining revenue [5][6] - The departure of key technical personnel, including a core technology staff member, raises concerns about the company's ability to maintain its technological edge [5][6] Strategic Challenges - The company is facing challenges in keeping pace with technological advancements, particularly in the development of large models, which has hindered its competitive position against major players like Baidu and Alibaba [3][4] - CloudWalk's cash flow from operating activities has been negative for three consecutive years, totaling nearly 1.2 billion yuan in cash outflows [3][4] Industry Context - The struggles faced by CloudWalk are not isolated, as other members of the "AI Four Little Dragons" are also experiencing similar issues, including prolonged losses and workforce reductions [6]

当人工智能的浪潮席卷全球，北京正以科技创新之姿，成为AI大模型领域的战略高地。从智源研究院的"悟道"大模型问世，到"天使投资人"模式孵化顶尖 学者，再到月之暗面、DeepSeek、智谱等人工智能独角兽崛起，这座城市不仅汇聚了前沿技术，更以开放生态孕育突破性成果。 如今，北京正积极打造"全球开源之都"，一大批研发机构、企业积极拥抱开源，而开源也已深入到汽车、机器人等众多行业。发展AI将是一场科技长征， 新京报AI研究院将深度访谈此次AI浪潮的亲历者与见证人，讲述AI竞争新格局与背后的故事。 开栏语 清程极智联合创始人师天麾。受访者供图 人工智能是年轻的事业，也是年轻人的事业。 清程极智联合创始人师天麾正成为这句话的一个生动的注脚，而他的经历也是当前中国年轻一代AI高端人才的缩影——高中拿下信息学奥林匹克竞赛金 奖保送清华大学，大学确定了系统和高性能计算的研究方向，博士毕业后成为中国科学院计算技术研究所课程讲师、中国信通院万卡智算集群服务能力推 进方阵技术专家。 多个身份标签加持，互联网大厂曾向师天麾抛出高薪的橄榄枝，他最终却选择自己创业，理由也很简单，"做一些不同的事"。在他眼中，大厂"老板安 排"和KPI均是 ...