Core Insights - The conference "Horizon Together 2025" focuses on the transition of smart driving from commercialization to widespread adoption, emphasizing collaboration among industry leaders [1][3] - Dr. Yu Kai, CEO of Horizon, highlighted the company's journey from "high aspirations" to "collaborative growth," aiming to democratize advanced technology for a broader audience [3][5] Group 1: Technological Advancements - Horizon has introduced three technological pillars: BPU®, compilers, and algorithms, which are essential for driving advancements in smart driving and robotics [3][10] - The newly released fourth-generation BPU architecture, named Riemann, boasts a tenfold increase in key operator computing power and supports full floating-point calculations, enhancing efficiency for large language models [14][16] - The compiler technology has evolved into an "AI-driven compilation" era, significantly improving compilation speed and model performance [14][16] Group 2: Market Strategy and Collaboration - Horizon is transitioning from a traditional model to an "HSD Together" algorithm service model, allowing partners to focus on their strengths while Horizon provides comprehensive algorithm services [20][21] - The company aims to reduce the time and costs associated with product development by 90% through this collaborative approach, enabling more companies to leverage advanced smart driving capabilities [20][21] - Horizon's products have achieved significant market penetration, with the latest series reaching over one million units shipped in just 12 months [20][29] Group 3: Industry Impact and Vision - Horizon positions itself as a foundational player in the robotics era, aspiring to be the "Wintel of the robotics age," focusing on ecosystem collaboration rather than competing as a vehicle or robot manufacturer [9][24] - The company has expanded its influence beyond automotive applications, becoming a leading platform for consumer robotics with over 100 products and extensive partnerships [24][25] - Horizon emphasizes the importance of making advanced technology accessible to the masses, aiming to transform high-end innovations into everyday solutions for consumers [28][29]

在人工智能应用竞争激烈的2025年末，一匹"黑马"正以加速度闯入更多人视野。 12月10日，阿里巴巴官方信息显示，旗下AI应用"千问"自11月17日全面公测仅23天，月活跃用户数（涵盖 APP、Web、PC全端）已破3000万大关，一举成为全球增长最快的AI应用之一。 有业内观察人士指出，这展示出市场对AI应用的旺盛需求，也体现出AI应用的竞争焦点正从炫技式的"会聊 天"转向务实的"能办事"。 不只是流量狂欢 千问的快速起势是多种因素共同作用的结果，其背后既体现了技术积累的价值，也反映出战略定位在市场竞争 中的关键作用。 首先，底层模型的技术支撑即基于阿里千问（Qwen）大模型的长期积累，为快速发展奠定基础。实际上，自 2023年起，阿里便采取了积极的开源策略，其Qwen系列模型在Hugging Face等全球开发者社区获得了广泛认 可。 截至目前，阿里千问Qwen已开源300多款模型，囊括文本、编程、图像、语音、视频等全模态，覆盖0.5B到 480B等全尺寸，在全球主要模型社区的下载量已经突破6亿，衍生模型突破17万个。 其次，明确的功能场景聚焦加速了其用户增长。千问发布伊始便定位"会聊天、能办事"这一方向 ...

Core Insights - Liang Wenfeng has been recognized as one of the top ten scientists of 2025 by the prestigious journal Nature for his significant contributions to the AI field through the DeepSeek model [1][2] - DeepSeek has disrupted the AI industry by offering a cost-effective model that enhances the global presence of domestic large models, proving that high performance does not necessarily require extensive data or resources [4] Group 1: Recognition and Impact - Liang Wenfeng is described as a "Tech disruptor" by Nature, highlighting his dual identity as a financial expert and a pioneer in AI [3] - The DeepSeek model has achieved remarkable performance in the Agent evaluation, reaching the highest level among current open-source models [4] Group 2: Background of Liang Wenfeng - Liang Wenfeng was born in 1985 in Guangdong and excelled academically, eventually studying electronic information engineering at Zhejiang University [5] - He transitioned into quantitative investment in 2008, capitalizing on the emerging trend of quantitative trading in China [6] - In 2023, he established DeepSeek, focusing on general artificial intelligence (AGI) after recognizing the potential in large models ignited by ChatGPT [6] Group 3: Other Recognized Scientists - Mengran Du, another Chinese researcher, was also recognized for her discovery of the deepest known animal ecosystem on Earth, showcasing significant advancements in deep-sea research [8][10]

【环球网科技综合报道】12月9日消息，据cna援引路透社报道称，软银集团与英伟达正就一项对机器人基础模型公司 Skild AI 的重大投资展开深入谈判。此 轮融资规模预计超过10亿美元，若顺利完成，将使 Skild AI 的估值达到约140亿美元，较其今年早些时候B轮融资时的47亿美元增长近两倍。 资料显示，Skild AI 成立于2023年，由前 MetaAI研究人员创立，专注于开发通用人工智能软件系统，旨在作为各类机器人的"大脑"。该公司不涉足硬件制 造，而是通过训练基于海量数据的AI模型，赋予不同形态的机器人类似人类的感知、推理与决策能力，以解决当前通用机器人在工厂、仓储及家庭环境中 部署受限的核心瓶颈。 根据 PitchBook 数据，Skild 在2024年完成的B轮融资中已获得包括英伟达、LG风险投资部门和三星在内的战略投资者支持。更早的A轮融资于2023年完成， 筹集3亿美元，估值达15亿美元，投资方涵盖亚马逊创始人杰夫·贝佐斯、软银集团及科斯拉风险投资公司等。 消息人士透露，软银在内部试点项目中对 Skild 的技术表现印象深刻，认为其平台具备跨场景适应能力，可广泛应用于物流、制造业乃至家庭服 ...

Core Insights - Liang Wenfeng, founder of DeepSeek, has been recognized as one of the top ten scientific figures of 2025 by Nature, being labeled a "technology disruptor" for his contributions to AI [1][24] - DeepSeek's R1 model has demonstrated that the perceived gap in AI capabilities between the US and China may not be as significant as previously thought, challenging existing narratives in the AI landscape [5][7] Company Overview - DeepSeek, founded in 2023 by Liang Wenfeng in Hangzhou, has developed a powerful yet affordable AI model, R1, which excels in solving complex tasks by breaking them down into steps [5][13] - The R1 model is the first of its kind to be released with open weights, allowing researchers to download and adapt it for their own applications, significantly impacting the AI research community [7][8] - DeepSeek's commitment to transparency is evident as it was the first mainstream LLM to undergo peer review, with the company publicly sharing the technical details of R1's construction and training [8] Market Impact - The success of DeepSeek has inspired other companies in both China and the US to release their own open-source models, indicating a shift in the competitive landscape of AI development [7] - Despite R1's capabilities being comparable to leading US models, its training costs are significantly lower, with some estimates suggesting that training costs for models like Meta's Llama 3 are over ten times higher [9][15] Leadership and Vision - Liang Wenfeng's background as a former financial analyst who applied AI algorithms to the stock market has shaped his vision for DeepSeek, focusing on achieving general artificial intelligence [17][20] - The company prioritizes individual potential over experience in its hiring practices, fostering a flat organizational structure that empowers researchers to choose their research directions [20] Societal Integration - DeepSeek's models are becoming integral to daily life in China, with local governments utilizing them for chatbots and assisting citizens, reflecting a broader trend of AI integration into economic development [20] - The company is seen as a symbol of China's transformation from a follower to an innovator in the AI field, with expectations for the upcoming R2 model to further this narrative [21][23]

Core Viewpoint - IBM's CEO Arvind Krishna questions the expected returns on the massive investments made by tech giants like Google and Amazon in AI infrastructure, suggesting that such investments are unlikely to yield reasonable returns due to the high costs associated with data centers [2][3]. Investment and Costs - Goldman Sachs estimates that the global data center market currently consumes about 55 gigawatts of power, with only approximately 14% related to AI. This demand is projected to rise to 84 gigawatts by 2027 due to increasing AI needs [2]. - Krishna calculates that building a 1-gigawatt data center requires an investment of about $80 billion. If a company commits to constructing 20 to 30 gigawatts of data centers, the capital expenditure could reach $1.5 trillion, nearly equivalent to Tesla's current market value [2]. - If all major cloud providers expand to around 100 gigawatts of capacity, it would necessitate an investment of approximately $8 trillion, with the required profit scale to cover this expenditure being staggering [2][3]. Profitability Concerns - Krishna emphasizes that $8 trillion in capital expenditure would require around $800 billion in profits just to cover interest payments, making it highly unlikely for such investments to be profitable [3]. - The rapid technological advancements mean that the chips relied upon in data centers quickly become obsolete, further complicating the return on investment [3]. AI Development and Market Trends - Despite the ongoing investment surge, Krishna believes the probability of achieving general artificial intelligence with current technologies is at most 1%. He acknowledges the significant value of this technology, which could unlock trillions of dollars in productivity potential, but asserts that the technological requirements far exceed those of current large language models [5]. - Major cloud providers are accelerating their investments in AI infrastructure, with expected expenditures reaching about $380 billion this year. Alphabet has raised its 2025 capital expenditure forecast from $85 billion to between $91 billion and $93 billion, while Amazon has increased its forecast from $118 billion to $125 billion [5].

但有趣的是，就在今年年初，MCP还曾一度占据了AI界的头版头条，几乎所有从业者都高呼"MCP让AI 连接万物"、"AI终于有了属于自己的USB接口"、"Agent时代的基础设施"。然而仅仅半年时间过去后， MCP就从圈内人眼中的"小甜甜"，光速蜕变为"牛夫人"。 那么MCP为何会被捧上神坛，又为什么光速陨落呢？其实这是因为MCP的走红本身就很违和，属于 是"期望膨胀期"的典型产物。此外需要注意的是，MCP并非出道即巅峰，它的走红过程与ChatGPT、 DeepSeek截然不同。 不久前在11月25日，AI独角兽Anthropic发文庆祝MCP协议（模型上下文协议）诞生一周年。然而如今 整个AI业界对于Anthropic此举即便不说视而不见，也算得上是漠不关心了，这个消息在社交平台的讨 论度更是趋近于零。 不难发现，MCP是一个为智能体服务的协议，它给予了智能体获得"真功夫"的机会，这也是为什么 MCP会在今年年初走红。从某种意义上来说，先有"2025年是智能体之年"这个说法，后来才有MCP登 上舞台中央，而力推MCP则是一众AI大厂的默契。 在2024年的最后一天，OpenAI首席执行官山姆·奥特曼公布了20 ...

Company Overview - The company, Self-Variable Robotics, was established in December 2023, focusing on developing embodied intelligent general models to achieve universal robotics [5] - The founder and CEO, Wang Qian, is a graduate of Tsinghua University and one of the earliest scholars to introduce attention mechanisms in neural networks [1] - Co-founder and CTO, Wang Hao, holds a PhD in computational physics from Peking University and has led the development of significant multimodal models in China [3] Technology and Development - Self-Variable Robotics has established a technology path that integrates end-to-end unified models for general embodied intelligence, with a simultaneous development of software and hardware [5] - The company has developed the "WALL-A" model, which is claimed to be the largest end-to-end unified embodied intelligence model globally, surpassing existing models in multiple dimensions [8] - The company emphasizes the importance of real data for training algorithms and maintains a high proportion of PhD-level researchers within its teams [8] Commercial Applications - The company has identified commercial applications in various sectors, including hotels, elderly care, logistics, industry, and hospitals [5] - It is actively recruiting talented individuals in the field of embodied intelligence to drive the implementation of general artificial intelligence [5] Job Opportunities - The company is offering various positions, including algorithm engineers focused on reinforcement learning, world model development, and physical simulation [9][20][24] - Candidates are expected to have strong backgrounds in computer vision, artificial intelligence, robotics, and related fields, with proficiency in deep learning frameworks [13][17][23]

（来源：电能革新） AI时代来了以后，中国希望能够引进人才，但科技的发展不能总靠引进人才，应该自己创造人才，只 有是教育强国，才能变成科技强国。在这个时代，中国如何能够超前的在教育上多花点精力？现在很多 科技成果不是从学术界出来，而是从产业界，比如校企合作已经不能快速地解决中国教育的问题。 任总：因为我们是一家企业，企业的属性就是创造商业价值，学校的属性是探索人类的未来。学校在 做"0-1"的研究创新，"0-1"失败了并不要紧，它培养了一大批人才。人才踩在先辈理论基础上一步步攀 高，就会创造出新的未来。企业是把学校创造的理论变成工业的现实。 我最近见了一个伟大的企业家，他讲我们国家的水轮发动机在世界是先进的，涡轮式、冲击式的都是先 进的，但原创发明都是奥地利、法国、美国等西方国家的。包括中国的火车、轮船、纺织机械，原创发 明基本都是西方的；微积分、几何学都是西方提出的。大学其实是研究探讨"0-1"。 中国也会追上来，提供原创的。我举个例子，全世界做得最好的气象模型竟然是我们公司一个22岁的年 轻人提出来的，这个气象模型是利用欧洲气象卫星的气象数据做出的。他把整个宇宙作为风洞，把地球 作为模型。听很多人说用这个 ...

任正非在座谈会上反复强调"教育是教育，商业是商业"，这句话的分量远超字面意义。当下，不少企业打着"产教融合"的旗号，把高校实验室变成产品试验 田，将学生论文转化为商业专利，看似"协同创新"，实则是商业逻辑对教育本质的侵蚀。教育的核心是"培养可能性"——让青年在试错中建立批判性思维， 在好奇中触摸知识边界；而商业的本质是"实现可行性"——把技术转化为解决具体问题的工具。二者的关系不是"谁主导谁"，而是"各归其位的齿轮咬合"。 华为与ICPC的合作模式正是这种边界感的体现：ICPC挑战赛提供的是"质疑的土壤"，让青年在算法竞赛中挑战现有规则；华为则提供产业场景，将获奖选 手的创新思路接入工业互联网、医疗AI等实际需求。这种分工避免了两个极端：既不让教育沦为企业的"人才预科班"，也不让商业困于实验室的"技术乌托 邦"。正如任正非所言，"企业与学校的分工要清晰"，学校负责"仰望星空"，企业负责"脚踏实地"，二者通过"挑战赛发展""地区合作"等纽带连接，而非模糊 地带的利益捆绑。 二、AI落地：从"炫技"到"解渴"的务实革命 华为在资源受限下的选择更具启示性。面对芯片等关键领域的外部限制，华为没有选择"全面开花"，而是 ...