近日， AI记忆公司穗升科技宣布完成数百万美元天使轮融资，由红杉中国种子基金领投，安克创新联合创 始人高韬跟投， 高鹄资本担任独家财务顾问 。公司首款产品预计将于 2026 年正式面向欧美市场发布。 创始团队强势集结：深耕全球 AI 与智能硬件十余年 穗升科技成立于 2025 年 8 月，由 前安克创新全球 CMO、中国区总裁王时远 创立。王时远于 2015 年 加入安克创新，长期负责全球品牌与营销，2020 年起担任中国区总裁，是推动安克品牌全球化的重要操 盘者。 团队核心成员来自 安克创新、华为、腾讯等头部科技公司 ，具备丰富的智能硬件、AI 算法、AI Agent 产 品研发与全球商业化经验。核心技术负责人曾长期在欧美主导多款 AI 产品落地，拥有成熟的跨国团队管 理与产品交付能力。 以"AI 记忆"为切入点，探索软硬件结合的下一逻辑 欧美市场仍处早期阶段：软硬件双擅长的玩家稀缺，全球化团队迎来结构性机会 相比国内激烈竞争，AI 记忆赛道在欧美仍处发展早期。 海外的部分团队擅长软件体验，但缺乏硬件交付能力；而中国团队在供应链、产品迭代速度上具备优势， 但在欧美市场的品牌建设与渠道经验上相对薄弱。穗升科技 ...

硬氪独家获悉，前安克创新全球CMO、中国区总裁王时远离职后创业，成立"穗升科技"；公司聚焦 AI录音硬件赛道，依托软硬件结合方案实现记忆管理与行动的闭环。 "我们认为，'记忆'会在未来AI生态中发挥关键作用。基于AI生态构建结构化、可调用的用户个人记 忆，能作为上下文（context）为AI提供支撑，让Agent更精准地服务用户。"王时远告诉硬氪，硬件 只是收集、储存用户记忆的入口和载体，"其中，声音仅作为短期的数据输入源之一；从中长期来 看，多模态信息的输入方式将会日趋成熟并逐步普及。" 与国内相比，AI记忆赛道在欧美呈现出不同的竞争格局。王时远向硬氪分析指出，目前该领域在海外 仍处早期发展阶段，真正具备软硬件协同能力的成熟玩家十分稀缺。尽管部分海外企业擅长软件体验 与垂直场景，国内团队则在市场响应速度上更具优势，但能同时在硬件迭代与软件体验上保持领先的 参与者并不多见。 这正是王时远和团队眼中的战略机会。无论是技术架构还是应用场景均未固化，这也为硬件创新、软 件迭代及生态构建留下充分的想象和探索空间。 王时远表示，团队熟悉欧美市场的营销与销售模式，计划以此为基础，构建起"产品销售—数据积累 —产品迭代"的 ...

硬氪独家获悉，前安克创新全球CMO、中国区总裁王时远离职后创业，成立"穗升科技"；公司聚焦AI录音硬件赛道，依托软硬件 结合方案实现记忆管理与行动的闭环。 近日，穗升科技已完成数百万美金天使轮融资，本轮融资由红杉中国种子基金领投、安克创新联合创始人高韬跟投，高鹄资本担 任独家财务顾问。首款产品预计将于2026年在欧美市场正式发布。 以下文章来源于硬氪 ，作者黄楠 硬氪 . 专注全球化、硬科技报道。36kr旗下官方账号。 首款产品计划于2026年面向欧美市场发布。 文 ｜ 黄楠 编辑 ｜ 袁斯来 来源｜ 硬氪（ID：south_36kr） 封面来源 ｜ Pixabay 穗升科技于2025年8月成立。创始人王时远2015年加入安克创新任全球CMO、主导全球品牌与营销工作，2020年起转任中国区总 裁。核心高管长期在欧美负责AI研发工作，曾领导全球化团队完成多产品的AI应用落地。此外，还有多位团队核心成员来自安克 创新、华为、腾讯等头部科技大厂，兼具智能硬件、AI算法、AI智能体等软硬件产品开发经验及全球商业化经验。 从产品策略层面来看，穗升科技聚焦于"AI记忆"赛道。当下，国内市场的AI录音类产品正陷入同质化困境 ...

近日，穗升科技已完成数百万美金天使轮融资，本轮融资由红杉中国种子基金领投、安克创新联合创始 人高韬跟投，高鹄资本担任独家财务顾问。首款产品预计将于2026年在欧美市场正式发布。 穗升科技于2025年8月成立。创始人王时远2015年加入安克创新任全球CMO、主导全球品牌与营销工 作，2020年起转任中国区总裁。核心高管长期在欧美负责AI研发工作，曾领导全球化团队完成多产品 的AI应用落地。此外，还有多位团队核心成员来自安克创新、华为、腾讯等头部科技大厂，兼具智能 硬件、AI算法、AI智能体等软硬件产品开发经验及全球商业化经验。 从产品策略层面来看，穗升科技聚焦于"AI记忆"赛道。当下，国内市场的AI录音类产品正陷入同质化困 境，硬件形态趋于相似，软件功能也多局限在会议纪要、翻译等基础场景。王时远认为，这本质上是一 种跟随策略。 因此，穗升科技选择以差异化路径切入，通过一款便捷无感的硬件设备、搭配具备记忆功能的AI，系 统性地提升用户在工作和生活中的效率。 作者｜黄楠 编辑｜袁斯来 硬氪独家获悉，前安克创新全球CMO、中国区总裁王时远离职后创业，成立「穗升科技」；公司聚焦 AI录音硬件赛道，依托软硬件结合方案实现 ...

Core Insights - The article emphasizes that memory is becoming a critical breakthrough in the evolution of AI, transitioning from "instant answer tools" to "personalized super assistants" [1][4] - A new machine learning paradigm called "Nested Learning" has been proposed, allowing large language models to learn new skills without forgetting old ones, marking significant progress towards AI that mimics human memory [3][4] Group 1: Shifts in AI Landscape - The focus of large models is shifting from size and speed to memory capabilities and understanding user needs, indicating a new competitive landscape in AI [4][5] - Current large models struggle with long-term memory due to inherent limitations in their architecture, leading to issues like forgetting critical user information during interactions [6][7] Group 2: Memory Mechanisms - Existing models typically have context windows of 8k-32k tokens, which can lead to early information being "pushed out" during long conversations, causing loss of context [6] - The lack of a shared memory mechanism among multiple agents results in "memory islands," where users must repeatedly provide information, diminishing the user experience [7] Group 3: Innovations in Memory - Companies like Google, OpenAI, and Anthropic are focusing on enhancing memory capabilities in AI models, responding to industry demands for long-term, stable, and evolving memory systems [7][10] - Red Bear AI has developed "Memory Bear," a product that addresses the memory limitations of traditional models by implementing a human-like memory architecture [10][11] Group 4: Memory Bear's Architecture - "Memory Bear" utilizes a hierarchical, dynamic memory structure inspired by the human brain's hippocampus and cortex, allowing for efficient memory management [11][13] - The system distinguishes between explicit memory (easily codified information) and implicit memory (subjective understanding), enhancing its ability to recall and utilize user-specific data [15][16] Group 5: Practical Applications and Impact - "Memory Bear" has shown significant improvements in various applications, such as AI customer service, where it creates dynamic memory maps for users, enhancing interaction quality and reducing the need for repetitive information sharing [20][21] - In marketing, "Memory Bear" tracks user behavior to create personalized marketing strategies, moving beyond traditional recommendation systems [22] - The technology has also improved knowledge acquisition efficiency in organizations and personalized education experiences, demonstrating its versatility across sectors [23][24] Group 6: Industry Consensus and Future Directions - The consensus in the industry is that memory capabilities are essential for advancing AI technology and applications, with increasing investments and explorations into human-like memory systems [24]

Core Viewpoint - The article discusses the ongoing competition in the AI industry regarding the development of long-term memory systems, highlighting different approaches taken by companies to enhance user experience and product differentiation in the AI landscape [1]. Group 1: From "Behavior Data" to "AI Memory" - Current AI products, such as assistants and virtual companions, primarily operate on a one-time interaction basis, which diminishes user trust and engagement [4]. - Long-term memory should be a core design element from the outset, rather than an afterthought, as emphasized by Artem Rodichev from Ex-human [4]. - Effective memory systems must balance the retention of significant events, updates based on user interactions, and user control over memory management [4]. - The true challenge in product differentiation lies not in replicating features but in how products learn and adapt through memory [4]. - Mainstream personal assistant systems categorize memory into short-term, mid-term, and long-term layers, enhancing understanding of user behavior over time [4]. - The interconnectedness of these memory layers creates a "behavioral compounding" effect, making it difficult for competitors to replicate this contextual depth [4]. - Companies are making strategic choices regarding what to remember, for whom, and for how long, aiming to establish a competitive edge through unique memory systems [4]. Group 2: Routes to Achieve AI's "Lifetime Memory" - Various product routes have emerged around AI long-term memory, each emphasizing different strategic narratives such as privacy, cost efficiency, speed, and integration [5].

Core Insights - The article discusses insights from Andrej Karpathy, emphasizing that the transformation brought by AI will unfold over the next decade, with a focus on the concept of "ghosts" rather than traditional intelligence [5][16]. Group 1: AI Evolution and Cycles - AI development is described as "evolutionary," relying on the interplay of computing power, algorithms, data, and talent, which together mature over approximately ten years [8][9]. - Historical milestones in AI, such as the introduction of AlexNet in 2012 and the emergence of large language models in 2022, illustrate a decade-long cycle of significant breakthroughs [10][22]. - Each decade represents a period for humans to redefine their understanding of "intelligence," with past milestones marking the machine's ability to "see," "act," and now "think" [14][25]. Group 2: The Concept of "Ghosts" - Karpathy introduces the idea of AI as "ghosts," which are reflections of human knowledge and understanding rather than living entities [30][31]. - Unlike animals that evolve through natural selection, AI learns through imitation, relying on vast datasets and algorithms to simulate understanding without genuine experience [30][41]. - The notion of AI as a "ghost" suggests that it mirrors human thought processes, raising philosophical questions about the nature of intelligence and consciousness [35][36]. Group 3: Learning Mechanisms - Karpathy categorizes learning into three types: evolution, reinforcement learning, and pre-training, with AI primarily relying on pre-training, which lacks the depth of human learning [40][41]. - The fundamental flaw in AI learning is the absence of "will," as it learns passively without the motivations that drive human learning [42][43]. - The distinction between AI and true "intelligent agents" lies in the ability to self-question and reflect, which current AI systems do not possess [43][44]. Group 4: Memory and Self-Reflection - AI's memory is likened to a snapshot, lacking the continuity and emotional context of human memory, which is essential for self-awareness [45][46]. - Karpathy suggests that the evolution of AI towards becoming an intelligent agent may involve developing a self-referential memory system that allows for reflection and understanding of its actions [48][50]. - The potential for AI to simulate "reflection" marks a significant step towards the emergence of a new form of consciousness, where it begins to understand its own processes [49][50].

Core Viewpoint - The article discusses the evolution and potential of AI products, particularly focusing on the role of mobile manufacturers like OPPO in developing AI capabilities that leverage personal data and memory systems to enhance user experience [6][7][12]. Group 1: AI Product Landscape - The AI industry is characterized by innovative products that aim to disrupt existing paradigms, yet many of these products struggle with user retention and engagement [3][4]. - There is a notable absence of mobile manufacturers in discussions about key players in the AI space, despite their significant user bases and potential for innovation [5][6]. Group 2: OPPO's AI Initiatives - OPPO has introduced "Little Memory," an AI product focused on memory systems, which was upgraded in October 2023 as part of ColorOS 16 [7][12]. - The development of AI products at OPPO is informed by a deep understanding of user needs and the importance of personal data accumulation [6][7]. Group 3: Memory and Personalization - The concept of an AI phone is evolving towards a personalized AI operating system that serves as a super assistant, utilizing extensive personal data to provide tailored services [12][14]. - Memory systems are crucial for enhancing user experience, allowing for the collection and organization of fragmented information across various applications [15][21]. Group 4: User Engagement and Feedback - User engagement with memory features has revealed diverse use cases, from academic study aids to personal finance management, indicating a broad spectrum of user needs [57][58]. - The feedback loop from users has been instrumental in refining the memory functionalities, leading to improvements in summarization and contextual understanding [43][48]. Group 5: Future Directions - The future of AI memory systems involves expanding capabilities to include proactive features that anticipate user needs and provide personalized insights [90][91]. - The integration of memory across devices and applications is seen as a key direction for enhancing user experience and maintaining relevance in a rapidly evolving tech landscape [67][70].

Core Insights - OpenAI CEO Sam Altman and former Apple designer Jony Ive are attempting to create a "screenless AI device" aimed at transforming human-computer interaction, but the project is currently facing multiple challenges [1][3] - The device is intended to be a portable, always-on AI companion that can perceive the world through cameras and microphones, aiming to surpass existing voice assistants like Echo and Siri [2][3] Technical Challenges - A significant hurdle for the project is the lack of computational power, as OpenAI struggles to meet the demands of ChatGPT, let alone support a consumer-grade AI device that operates continuously [3][5] - Another challenge is defining the AI's "personality," balancing between being friendly and not overly intrusive, which has proven difficult for previous attempts at similar devices [3][4] Market Context - Previous attempts to create AI companion devices, such as Humane's AI pin and the Friend AI pendant, have faced criticism for performance issues and awkward interactions, leading to market skepticism [4][5] - Despite these challenges, OpenAI's valuation has soared to $500 billion, surpassing Elon Musk's SpaceX, indicating strong investor confidence in its potential to expand beyond software into a complete AI ecosystem [5] Strategic Moves - OpenAI has acquired a subsidiary of Jony Ive's design firm and is actively recruiting hardware talent from Apple and Meta, signaling a commitment to a "soft-hard integration" approach similar to Apple's strategy [5]

Core Viewpoint - The article discusses the emerging importance of "memory" in AI models, suggesting that the ability to possess human-like memory will be a key factor in the next wave of AI advancements [2][6][35]. Group 1: Importance of Memory in AI - The concept of "memory" is evolving from short-term to long-term or lifelong memory, allowing AI to learn continuously and adapt to new tasks without forgetting previous knowledge [3][7]. - Recent developments in AI memory capabilities have been highlighted by major players like Anthropic, Google, ByteDance, and OpenAI, all of which have introduced memory features in their AI systems [4][6][35]. - The demand for memory capabilities is driven by both technical and application needs, as AI models are increasingly expected to function as long-term partners rather than just tools [20][21][23]. Group 2: Current Trends and Developments - Various AI companies are exploring different approaches to implement memory, including parameterized memory, context memory, and external databases [26][28][30]. - The industry is witnessing a surge in interest and investment in memory-related research, with many companies racing to develop and integrate these capabilities into their products [6][35]. - The competition among AI firms is intensifying, with the potential for breakthroughs in memory capabilities to redefine the market landscape, similar to past pivotal moments in AI development [35][36]. Group 3: Future Outlook - The timeline for achieving widespread and effective memory capabilities in AI is estimated to be one to two years for basic functionalities, while addressing governance and privacy issues may take three to five years [36][37]. - The future of AI memory capabilities remains uncertain, with various players in the industry vying for dominance, indicating that any company could emerge as a leader in this space [38].