机器之心报道 编辑：Panda 近来，世界模型（World Model）很火。多个 AI 实验室纷纷展示出令人惊艳的 Demo：仅凭一张图片甚至一段文字，就能生成一个可交互、可探索的 3D 世界。这 些演示当然很是炫酷，它们展现了 AI 强大的生成能力。 但一个关键问题随之而来：这些由 AI 生成的世界中，绝大部分事物都是模型想象和虚构的。 如果我们不满足于「创造」一个虚拟世界，而是想把我们当下生活的这个真实世界（比如我们的家、办公室、工厂和城市）完整地变成一个可交互、可计算的 3D 世界呢？ 这正是如视（Realsee）想要解答的问题。11 月 13 日，如视，这家数字空间及空间智能综合解决方案引领者，正式发布了其 空间大模型 Argus 1.0，这也是全球首 个（目前也是唯一一个）支持全景图输入，推测空间深度的大模型 。它所代表的正是与虚拟生成截然不同的另一条路径：真实复刻。而这背后，正是「空间智 能」相关技术不断演进的结果。 Argus 1.0 的目标不是「虚构」世界，而是「还原」真实的世界。 它能够以毫秒级的速度，从一个场景下的单张或多张全景/普通图像中，推理出所有图像带绝对尺 度的相机位姿、深度图和 ...

Core Viewpoints - The importance of preparing for the future rather than predicting it in an era of uncertainty [7] - AI is seen as a complement to human capabilities, enhancing efficiency and creativity rather than replacing jobs [20] - The future will be shaped by those who can collaborate with AI, rather than those who resist it [8] AI and Uncertainty - There are three key uncertainties regarding AI: the possibility of achieving general artificial intelligence, the direction of AI computing (centralized vs. decentralized), and the impact of AI on employment [10][14][16] - Current investments are heavily focused on exploring general intelligence, but the future may consist of various specialized AI systems rather than a single general system [11][13] - The trend towards edge computing is emerging, with a significant portion of computing already occurring at the edge, which offers advantages in speed, privacy, and energy efficiency [14][15] AI's Role in Employment and Industry - AI is not leading to mass unemployment but is instead enhancing productivity, with studies showing an average efficiency increase of about 25% for employees using AI [17][19] - The introduction of AI changes the nature of work, allowing humans to focus on more creative and judgment-based tasks while AI handles repetitive ones [20][41] - AI's role is to augment human capabilities rather than replace them, leading to a reorganization of job structures rather than job losses [43] Future Directions of AI - Future AI innovations will focus on four key areas: symbolic reasoning, spatial intelligence, emotional intelligence, and intelligent agents [22] - Symbolic reasoning will reintroduce structured intelligence to enhance AI's understanding and reasoning capabilities [22][23] - Spatial intelligence will enable AI to interact with and understand the real world, moving beyond text-based learning [24][27] - Emotional intelligence will allow AI to recognize and respond to human emotions, fostering deeper human-AI interactions [29][30] - Intelligent agents will evolve from mere tools to partners capable of executing tasks and collaborating with other agents [30][31] The Concept of "Cool China" - "Cool China" refers to a nation that attracts others through creativity and charm rather than force, with potential to lead in innovation and cultural influence [60][61] - China has the opportunity to produce world-class products and technologies, enhancing its global standing [62] - Cultural output will play a significant role in shaping China's soft power, allowing it to resonate with global audiences [63] - The development of attractive cities that blend technology and culture will further enhance China's appeal [64] Challenges and Responsibilities - The rise of an AI-driven society will bring challenges related to privacy, data usage, and the balance between personalization and individual rights [66][68] - AI has the potential to create a more just and efficient society, particularly in areas like social governance and resource distribution [69] - The realization of "Cool China" depends on a commitment to innovation, openness, and responsibility, shaping a respected and admired global presence [71]

Core Insights - The development of artificial intelligence (AI) is entering a new phase, transitioning from "understanding language" to "understanding the world" [1] - "Spatial intelligence" is identified as the next frontier for AI, which will enable machines to perceive, reason, and act in the real world like humans [4][9] Current Limitations of AI - Current AI systems, primarily large language models, excel in text and image generation but lack fundamental capabilities in representing and interacting with the physical world [4][6] - These models struggle with basic tasks such as estimating distance, direction, and size, and often fail to maintain coherence in generated videos [4][6] Importance of Spatial Intelligence - Spatial intelligence is crucial for human cognitive construction, driving imagination, creativity, and reasoning, and is essential for integrating perception and action [4][8] - This capability allows for everyday tasks like estimating parking distances and navigating through crowds, representing a leap from mere knowledge to true understanding [4][8] Path to Achieving Spatial Intelligence - To realize true spatial intelligence, a shift from existing large language models to a more fundamental "world model" is necessary [6] - This new model should understand semantic relationships and consistently "imagine" and "reconstruct" the world in terms of geometry, physics, and dynamic rules [6] Applications and Implications - The development of world models can redefine AI's functionality, enabling proactive planning and adaptation in various fields, including robotics and creative industries [8][9] - In creative fields, spatial intelligence will allow creators to construct virtual worlds and visualize structures instantaneously, enhancing the creative process [8][9] Future Prospects - AI with spatial intelligence will not replace humans but will enhance professional judgment, creativity, and empathy, serving humanity more deeply [9] - The transition from language to spatial understanding signifies a new era for AI, capable of genuinely comprehending reality [9]

Core Viewpoint - The development of AI requires fundamental technological innovation beyond just scaling laws, and the concept of Artificial General Intelligence (AGI) is seen more as a marketing term than a scientific one [1][7][9]. Group 1: AI Development Insights - The combination of neural networks, big data, and GPUs is identified as the "golden formula" for modern AI, which remains relevant today with the success of ChatGPT [4][5]. - Current AI systems struggle with tasks that are easy for humans, indicating a significant gap in achieving true creativity, abstract thinking, and emotional intelligence [8][9]. - The concept of "world models" is proposed as a key direction for future AI development, enabling better understanding and interaction with three-dimensional environments [10][17]. Group 2: Challenges in Robotics - The challenges in robotics are highlighted, particularly the difficulty in data acquisition and the complexity of operating in three-dimensional spaces, which is more challenging than autonomous driving [15][16]. - The "bitter lesson" of using simple models with vast data does not apply straightforwardly to robotics due to the unique nature of action data required for training [15][16]. Group 3: AI's Role in Society - The potential of AI to enhance human capabilities rather than replace them is emphasized, with a focus on ensuring that technology development respects human dignity and agency [18][19]. - The belief is expressed that in the AI era, everyone will have a place, highlighting the importance of inclusivity in the technological landscape [19].

Core Insights - The future of AI may be linked to understanding the evolutionary secrets of the human brain, as highlighted by recent developments in the AI field, including Yann LeCun's plans to establish a new AI company focused on "World Models" [1] - Fei-Fei Li emphasizes the limitations of current large language models (LLMs) and advocates for the development of "Spatial Intelligence" as a crucial step towards achieving Artificial General Intelligence (AGI) [3][4] Summary by Sections World Models - "World Models" are essential for AI to understand and predict real-world scenarios, which current AI systems struggle with, such as generating realistic videos or performing household tasks [5][6] - The concept of "World Models" arises from reflections on the limitations of LLMs and the exploration of animal intelligence, suggesting that the ability to learn these models is what current AI lacks [8] Human Perception and Intelligence - Max Bennett's research identifies three key attributes of human perception that are crucial for understanding intelligence: filling-in, sequentiality, and irrepressibility [11] - The brain's ability to fill in gaps in perception and to focus on one interpretation at a time is fundamental to how humans process information [12][20][23] Generative Models - The "Helmholtz Machine" concept illustrates how generative models can learn to recognize and generate data without being explicitly told the correct answers, demonstrating the brain's inferential processes [27] - Modern generative models, including deep fakes and AI-generated art, validate Helmholtz's theories and show that the brain's neocortex operates similarly [28] Advanced Cognitive Abilities - The neocortex not only facilitates imagination and prediction but also enables complex behaviors such as planning, episodic memory, and causal reasoning, which are desired traits for future AI systems [33] - Bennett's book, "A Brief History of Intelligence," connects neuroscience with AI, outlining the evolutionary milestones of the brain and their implications for AI development [35][37]

Core Insights - The interview with Fei-Fei Li highlights the emergence of "world models" as the next frontier in AI over the next decade, emphasizing the importance of spatial intelligence in AI development [1][28]. Group 1: Historical Context of AI - Two decades ago, AI was in a "winter" phase, with limited public interest and funding, often referred to as "machine learning" [10][14]. - Fei-Fei Li entered the AI field during this period, focusing on visual intelligence and the need for large datasets to train models effectively [11][20]. - The creation of ImageNet, which involved collecting 15 million images across 22,000 categories, marked a pivotal moment in AI, leading to the rise of deep learning [23][24]. Group 2: The Concept of World Models - "World models" are defined as systems that can generate an infinite 3D world based on input, allowing for reasoning and interaction [37]. - The Marble platform exemplifies this concept, significantly reducing production time in various industries, including film and gaming, by allowing creators to generate navigable worlds from simple descriptions [40][43]. - The integration of spatial intelligence into AI is seen as crucial for enhancing both robotic capabilities and human understanding [39][32]. Group 3: Challenges in Robotics - The primary challenge in robotics lies in data acquisition, as robots require extensive real-world interaction data, which is difficult to obtain [44][45]. - Unlike language models that operate on text, robots must navigate and interact within a 3D environment, complicating their training [45]. - The historical context of autonomous vehicles illustrates the complexities involved in developing effective robotic systems [46]. Group 4: Fei-Fei Li's Career and Vision - Fei-Fei Li's career trajectory reflects a commitment to addressing significant problems in AI, transitioning from academia to industry and now to entrepreneurship with World Labs [47]. - Her focus on collaboration and team dynamics underscores the importance of human roles in the evolving landscape of AI [47]. - Li emphasizes that every individual has a vital role in the future of AI, regardless of their profession [47].

Core Insights - Stanford University professor Li Fei-Fei identifies spatial intelligence as the next frontier for AI, emphasizing its role in transitioning AI from "language intelligence" to "physical intelligence" [1] - Shenwan Hongyuan's report highlights physical AI as a systematic engineering approach that integrates world models, physical simulation engines, and embodied intelligence, positioning it as a cornerstone for digital twins and embodied intelligence applications [1][2] - Intelligent warehousing is recognized as a key breakthrough for the commercialization of embodied intelligence and physical AI due to its high maturity and clear value realization pathways [1] Company Overview - Geek+ is identified as a core player in the physical AI industry chain, recognized for its leadership in global warehouse robotics [1][3] - The company launched a "fully unmanned picking workstation" on October 28, leveraging physical AI to advance intelligent warehousing into a new era of automation [2] - Geek+ employs a layered architecture for its intelligent base model, enabling precise object recognition and optimal picking strategies, aligning with the integration logic of physical AI [2] Market Position and Financial Performance - Geek+ has established a strong market presence, serving over 850 major clients across more than 40 countries, maintaining its position as the largest warehouse fulfillment robotics company globally for six consecutive years [2][3] - The company is projected to achieve revenue of 2.41 billion yuan in 2024, leading the Hong Kong stock market's robotics sector, with a 31% year-on-year revenue increase in the first half of 2025 [3] - Geek+ has demonstrated strong customer retention, with a 74.6% overall repurchase rate in 2024, increasing to over 80% in the first half of 2025 [3] Future Outlook - The CEO of Geek+ indicates a strategic goal of achieving a fully automated warehouse by overcoming challenges in the packing process, following the successful automation of the picking stage [4] - The industry trend points towards a clear path of "technology implementation + deep scenario cultivation," with Geek+ leading the commercialization of physical AI through its intelligent technology and global service support [5]

Core Insights - The article emphasizes that spatial intelligence is identified as the "next frontier of AI," which will drive the transition from "language intelligence" to "physical intelligence" [1] - Physical AI is becoming a foundational element for digital twins and embodied intelligence applications, with intelligent driving and embodied intelligence being the most promising areas [1] - Geek+ is recognized as a core player in the physical AI industry chain, particularly in the warehouse robotics sector, due to its understanding of industry know-how and physical processing logic [1] Physical AI Commercialization - The realization of physical AI relies on the collaboration of three key technologies: "world model, physical simulation engine, and embodied intelligent controller," with the latter being crucial for connecting virtual reasoning and physical execution [2] - Geek+ launched its "unmanned picking workstation" and full-process unmanned picking solution on October 28, leveraging physical AI to advance intelligent warehousing into a "truly unmanned" era [2] - The Geek+ model is driven by its self-developed embodied intelligent base model, Geek+Brain, which utilizes a "perception-strategy" layered architecture for optimal decision-making and physical execution [2] Market Position and Financial Performance - Geek+ has established itself as a leading core manufacturer in the physical AI industry chain, with a strong R&D team and a wide range of patents covering key areas such as robotics hardware and AI algorithms [3] - The company has a global presence with local teams in key markets, ensuring robust service capabilities and support for stable project implementation [3] - Geek+ achieved revenue of 2.41 billion yuan in 2024, maintaining its position as the top revenue-generating company in the Hong Kong robotics sector, with a 31% year-on-year growth in the first half of 2025 [3] Future Outlook - The founder and CEO of Geek+ indicated that after achieving full-process unmanned picking, the next goal is to tackle the technology for robotic packing, aiming for a fully automated warehouse [4] - The article concludes that the path to physical AI commercialization is clearly directed towards "technology implementation and scene cultivation," with Geek+ leading the way in this transformation [5]

Core Insights - The essence of AI is not "artificial" but an extension of "intelligence," enhancing human understanding of the world [3][11] - The concept of "world models" is crucial for advancing AI, particularly in spatial and visual understanding beyond language models [4][39] - The development of AI has transitioned from skepticism to widespread acceptance, with companies now identifying as AI firms [9][30] Group 1: AI Development and Historical Context - AI's evolution has been marked by significant milestones, including the creation of ImageNet, which provided a vast dataset for training models [6][23] - The combination of big data, neural networks, and GPUs has been pivotal in the modern AI landscape, leading to breakthroughs like ChatGPT [24][25] - The early days of AI were characterized by limited public interest and funding, with a resurgence occurring in the last decade [9][19] Group 2: World Models and Their Importance - World models are foundational capabilities that enable reasoning, interaction, and world creation, essential for both AI and robotics [40][41] - The development of world models aims to bridge the gap between language understanding and spatial intelligence, enhancing AI's ability to operate in real-world scenarios [39][43] - The recent launch of Marble, a product that generates navigable 3D worlds, exemplifies the application of world models in various fields, including gaming and virtual production [53][60] Group 3: Challenges in Robotics - The "Bitter Lesson" suggests that simple models with large datasets outperform complex models with limited data, but this principle faces challenges in robotics due to data scarcity [45][47] - Robotics requires not only advanced algorithms but also physical systems and real-world applications, complicating the training process [48][49] - The current state of robotics is still experimental, with significant hurdles remaining before achieving desired capabilities [47][50] Group 4: Future Directions and Innovations - Continuous innovation is necessary for AI to reach new heights, as current models still lack capabilities like abstract reasoning and emotional intelligence [35][36] - The focus on spatial intelligence and world modeling is expected to drive future advancements in AI, particularly in enhancing human-machine collaboration [39][44] - The integration of AI into various sectors, including psychology and design, highlights its potential to transform industries and improve efficiency [60][61]

Investment Rating - The report maintains an investment rating of "Outperform the Market" for the computer industry, indicating an expected return that exceeds the CSI 300 Index by 10% or more over the next six months [8]. Core Insights - The launch of the first commercial world model product, Marble, by World Labs, allows users to create editable and downloadable 3D virtual scenes from various inputs, significantly reducing scene distortion and inconsistency [1][12]. - The concept of a "world model" is introduced as a new AI system that enables machines to understand spatial relationships and interactions, moving beyond mere language descriptions [2][13]. - Major breakthroughs in world model technology have been achieved by global tech giants, including Tencent's mixed 3D world model and Google DeepMind's Genie 3, which enhances the generation of interactive virtual environments [3][14]. - Spatial intelligence is expected to empower creative tools in the short term and serve as a foundational capability for machines to understand and interact with the three-dimensional world in the medium term [4][15]. Summary by Sections Investment Recommendations - The domestic world model and physical AI industry chain is forming, with significant advancements such as the ReKep system developed by Li Feifei's team, which utilizes RGB-D cameras for 3D visual data support [5][16]. - Recommended stocks include: - Oboe Technology (leader in 3D visual perception) - Zhiwei Intelligent (robotic brain controller) - Suochen Technology (physical AI product developer) - Alter (investing in the robotics sector) [5][16]. Market Performance Review - The computer sector underperformed relative to the CSI 300 Index, with a decline of 3.72% this week, while the overall market indices showed mixed results [17][18]. - The computer industry index ranked 28th among 30 industry indices, indicating weaker performance compared to other sectors [20]. Industry News - The report highlights significant developments in quantum applications in Anhui province, aiming for 1,000 application scenarios by 2027, and the departure of Meta's chief AI scientist, who plans to establish a world model company [24][25].