Core Viewpoint - The article discusses the advancements and potential of embodied intelligence, particularly focusing on the development of a "brain" for robots that can adapt and learn across various forms and tasks, highlighting the contributions of companies like Qianjue Technology and Liufeng Space [2][3][4]. Group 1: Embodied Intelligence Development - Embodied intelligence has emerged as a hot investment area, with significant advancements in creating "small brains" but challenges remain in developing a comprehensive "big brain" [3][4]. - Recent scientific research indicates substantial potential for embodied intelligence, although the foundational paradigms are still evolving [4]. - Qianjue Technology aims to create a "brain in a jar" that can be utilized by various robot forms, with plans to connect 100,000 devices to its system by next year [4][5]. Group 2: Technical Approaches - Qianjue Technology employs a decoupled approach to brain modeling, allowing for independent optimization and evolution of different brain regions, which enhances efficiency [5][14]. - Liufeng Space focuses on building world models that drive embodied brains, utilizing real-time interactive space generation technology [6][11]. - The two companies represent different paths in the development of embodied intelligence, with Qianjue emphasizing brain-like structures and Liufeng leveraging world models for practical applications [8][10]. Group 3: Data and Training - Data scarcity is a significant challenge in training embodied intelligence systems, with Qianjue Technology achieving multiple generations of pre-training, which is rare in the industry [14][17]. - Liufeng Space believes that good robot data should be treated as an asset, emphasizing the importance of diverse and abundant data for effective training [12][17]. - Both companies recognize the need for extensive data to achieve effective pre-training, with estimates suggesting that a billion clips may be necessary for comprehensive training [26][27]. Group 4: Future Outlook - The timeline for achieving a mature embodied brain technology is optimistic, with both companies suggesting that significant advancements could occur within two years [26][27]. - The potential for embodied intelligence to surpass language models is highlighted, with expectations for the emergence of numerous billion-dollar companies in this sector [27].

"人脑以仅约20瓦的功耗支撑起千亿级神经元的复杂运作，这为AI提供了极致的能效范例。借鉴人脑机制将成为突破 低功耗、长序列与通用性三大核心难题的关键。" 在"从脑机接口到脑机共生"主题论坛期间，在接受《科创板日报》等媒体采访时，对于AI与脑机接口融合的下一步发 展方向，中国科学院自动化所研究员李国齐如是阐述。 在这场会议上，天桥脑科学研究院研究院创始人雒芊芊宣布天桥脑科学研究院成立尖峰智能实验室，由李国齐团队研 发的国内首款类脑脉冲大模型"瞬悉1.0"也同步亮相。该模型在国产GPU算力平台上完成训练与推理，并与国产GPU企 业沐曦科技协同，打通了从类脑基础模型、国产算力平台到类脑芯片的全栈式研究链条。 打通国产GPU算力平台 "瞬悉1.0"被视为我国类脑计算与大模型融合的一次关键突破。该模型由李国齐团队研发，是国内首个类脑脉冲大模 型。与当前主流基于Transformer架构的大模型不同，类脑模型借鉴人脑以脉冲形式进行信息传递和处理的机制，试图 从根本上破解高能耗、长序列建模和泛化能力受限等问题。 李国齐向《科创板日报》等媒体介绍，发现式智能的一个关键能力是神经动力学。人脑以仅约20瓦的功耗支撑起千亿 级神经 ...

Core Insights - The research team at Jiangnan University has made significant advancements in multifunctional optoelectronic devices, proposing a device scheme that integrates neuromorphic computing and hardware encryption imaging through bias control [1][2] - This innovation addresses the challenges of achieving high sensitivity UV light detection, brain-like information processing, and robust security encryption within a single device, overcoming the limitations of traditional multi-component systems [1] Summary by Sections Technological Advancements - The team developed a device based on AlScN/GaN heterojunctions, enabling reconfigurable functionality through precise bias control [1] - At low bias, the device functions as a high-speed, highly sensitive UV detector, while at high bias, it exhibits significant persistent photoconductivity, simulating biological synaptic learning and memory [1] Security and Integration - The research introduces a dynamic bias protocol that acts as a physical "key" for hardware-level encryption of images, enhancing information security [1] - This technology is expected to pave the way for integrated detection, computation, storage, and encryption within a single device, providing a new physical layer solution for information security [2] Future Applications - The technology is anticipated to be applied in fields requiring high security and integration, promoting the development of safer and more efficient intelligent hardware systems by deeply integrating neuromorphic computing with sensor encryption [2]

Group 1 - The 2025 World Computing Conference was held in Changsha on November 20-21, showcasing the rapid increase in global computing power and the widespread application of large models in consumer products such as smartphones, cars, and smart home devices, leading to significant changes in production and lifestyle [1][2] - The conference highlighted ten global computing innovations, including the exponential growth of global computing capabilities, the acceleration of generative AI applications, and the commercial deployment of neuromorphic processors, marking a new era in brain-like computing [2][3] - Advanced computing-driven intelligent factories reported an average reduction of product development cycles by 28.4%, an increase in production efficiency by 22.3%, a decrease in defect rates by 50.2%, and an average reduction in carbon emissions by 20.4% [4] Group 2 - The strategic importance of advanced computing in reshaping the global industrial landscape is increasingly recognized, with the computing technology evolution driving significant changes in the information technology industry and the digital economy [4] - The World Computing Conference has evolved into an international and professional platform for communication in the computing field since its establishment in Changsha in 2019, now hosting its seventh edition [4]

在过去20多年时间里，金耀初一直在思考从自然演化和生物发育角度来研究、理解人的智能的可能性。 在他看来，人的神经系统和形态是协同发育，神经系统的发育过程跟环境、形态是密不可分的，这与近 期比较热的具身智能在原理上是相似的。 例如，当演化一个有不同模块、不同结构深度的脉冲大模型时，技术人员往往会基于Transformer架构加 入一些类脑的机制，实现不同的通道，有自上而下的通道，也有自下而上的通道，也有注意力机制的调 整，使得结构更灵活。这与人脑内有不同通道，也有很多神经调控机制，有着很大的关联。 大会上，金耀初还发布了西湖大学人工智能领域最近几年的研发成果。据他介绍，在具身智能领域，西 湖大学在大模型的辅助下，目前已经能够做到更加自主地感知、自主决策和自主执行。（文猛） 责任编辑：常福强 炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 新浪科技讯 11月9日下午消息，2025年世界互联网大会乌镇峰会期间，欧洲科学院院士、西湖大学人工 智能讲席教授金耀初指出，"传统大模型有很多局限性，包括能耗大，没有自主学习的瓶颈等，基于类 脑神经网络，将类脑计算与具身智能结合，才能让类脑大模型真正具 ...

Core Insights - The development of brain-like computing is driven by the challenges faced by existing computing systems [1][2] - The demand for intelligent computing, particularly from large models in artificial intelligence, is experiencing explosive growth [1][2] Group 1: Event Overview - The 2025 Brain-like Intelligent Computing Forum and Innovation Product Launch was held on October 24, organized by the Guangdong Institute of Intelligent Science and Technology [1] - Experts from various universities and research institutions shared insights on cutting-edge technologies in brain-like computing [1] Group 2: Technological Advancements - Tsinghua University's brain-like computing center has developed a brain-like cloud brain platform with approximately 1.6 billion neurons [2] - The platform supports both brain science and brain-like science, addressing long hardware development cycles with a trillion-level end-to-end brain-like simulation platform [2] Group 3: Product Launch - The world's first ultra-compact, mobile brain-like computing device, "Wise One" BIE-1, was officially launched [2] - The device integrates 1,152 CPU cores, 4.8TB DDR5 memory, 204TB storage, and a 40G high-speed network interface, fitting into a space the size of a mini single-door refrigerator [2] Group 4: Core Technology - The product features a unique brain-like algorithm called "Intuitive Neural Network" (INN), capable of efficiently running various model training and inference, including large models [3] - The INN allows for training of 10 billion tokens in just 30 hours, with training and inference speeds reaching 100,000 tokens/second and 500,000 tokens/second, respectively [3] Group 5: Future Goals - The Guangdong Institute of Intelligent Science and Technology aims to build embodied brain-like intelligence, focusing on visual and location positioning, as well as multi-modal continuous learning [3]

Core Viewpoint - The article discusses the recent advancements in Metal-Organic Frameworks (MOFs) and their application in creating ultra-miniature fluid chips, highlighting their potential to revolutionize computing by mimicking brain-like memory functions [1][20]. Group 1: MOF Technology and Applications - MOFs, once deemed "useless," have gained recognition after winning the Nobel Prize in Chemistry, leading to innovative applications such as fluid chips [1][20]. - The newly developed fluid chips can perform conventional calculations while also retaining previous voltage changes, resembling short-term memory similar to that of brain neurons [2][3]. - The creation of advanced fluid chips using MOF materials addresses the challenges of high-precision nano-channel devices, enabling adjustable non-linear ion transport [4][5]. Group 2: Device Structure and Functionality - Researchers constructed a layered nano-fluid transistor device (h-MOFNT) using Zr-MOF-SO₃H crystals, which features heterogeneous junctions for enhanced performance [7][8]. - The device exhibits non-linear proton transport characteristics, differing from typical diode behavior, indicating a threshold-controlled transport mechanism [12][13]. - The h-MOFNT demonstrated a memory effect, capable of retaining past voltage states, which could lead to applications in liquid-based information storage and brain-like computing [18][19]. Group 3: Historical Context and Future Potential - Historically, MOFs have been viewed as having significant theoretical potential but lacking practical applications, with over 100,000 related papers published but few achieving industrial application [25][26]. - The challenges faced by MOFs include structural stability issues and complex synthesis processes, which have hindered their widespread use [27][28]. - The emergence of MOF-based chips suggests that the material may not be "useless" but rather that suitable applications have yet to be fully explored [29].

Core Insights - The recent Nobel Prize recognition of Metal-Organic Frameworks (MOFs) has sparked new interest in their practical applications, particularly in the development of advanced fluidic chips that mimic brain-like memory functions [1][14]. Group 1: MOF Technology and Applications - Scientists at Monash University have developed ultra-miniature fluidic chips using MOFs, which not only perform conventional calculations but also retain previous voltage changes, resembling short-term memory akin to brain neurons [3][4]. - The h-MOFNT device, constructed from layered Zr-MOF-SO₃H crystals, demonstrates significant potential in simulating biological mechanisms and ion separation due to its precise nanoscale channel structures [5][6]. - The device exhibits unique non-linear proton transport characteristics, which differ from traditional diode behavior, indicating a threshold-controlled transport mechanism [9][10]. Group 2: Performance Characteristics - The h-MOFNT device shows a pronounced hysteresis effect during voltage scanning, suggesting its ability to remember past voltage states and indicating a form of fluid memory [10][11]. - Experiments with multiple h-MOFNT devices in parallel have produced a series of non-linear I-V curves, simulating electronic field-effect transistor (FET) output characteristics [11][13]. - The device's ability to maintain local electric potentials during proton transport highlights its potential for liquid-based information storage and brain-like computing [13][14]. Group 3: Historical Context and Future Potential - Historically, MOFs were considered "useless" due to their theoretical nature and lack of practical applications, despite extensive research and numerous publications [14][20]. - The emergence of MOF-based chips challenges the notion of their ineffectiveness, suggesting that the right applications and contexts for MOFs have yet to be fully realized [20][21]. - The potential for MOFs to serve as customizable materials for new functionalities presents unprecedented opportunities in various fields, including energy storage, sensing, and photonic devices [15][20].

Core Viewpoint - The article highlights the recent breakthrough in using Metal-Organic Frameworks (MOFs) to create ultra-miniature fluid chips, which can perform computations and exhibit short-term memory similar to brain neurons, challenging the previous notion that MOFs were "useless" [1][20]. Group 1: MOF Technology and Applications - MOFs, once considered theoretical with limited practical applications, have now been recognized for their potential in advanced computing technologies following their Nobel Prize acknowledgment [1][21]. - The newly developed fluid chip, made from MOF materials, can overcome limitations of traditional electronic chips by enabling advanced functionalities [3][5]. - The h-MOFNT device constructed from layered Zr-MOF-SO₃H crystals demonstrates unique ion transport properties, allowing for precise control over ionic movement [7][12]. Group 2: Device Characteristics and Performance - The h-MOFNT device exhibits non-linear proton transport characteristics, which differ from typical diode behavior, indicating a threshold-controlled transport mechanism [12][13]. - Experimental results show that the device can remember past voltage states, demonstrating fluid memory and learning capabilities, akin to electronic devices [16][18]. - The ability to create a small fluid circuit using multiple h-MOFNTs showcases the potential for complex computations and memory functions in liquid systems [16][19]. Group 3: Historical Context and Future Prospects - Historically, despite extensive research (over 100,000 related papers), the practical industrial application of MOFs has been limited due to issues like structural stability and production costs [25][27]. - The emergence of MOF-based chips suggests that the material may not be "useless," but rather that suitable applications were not previously identified [29]. - Future developments may lead to the realization of liquid-based information storage and brain-like computing systems through innovative design of heterogeneous constraint systems [19].

Core Insights - Researchers at Monash University have developed a microfluidic chip that mimics the neural pathways of the brain, potentially paving the way for next-generation computing [1] Group 1: Technology Development - The chip, the size of a coin, is made from specially designed metal-organic framework (MOF) materials and transmits ions through tiny channels, simulating the switching of electronic transistors in computers [1] - Unlike traditional computer chips, this new chip can "remember" previous signals, mimicking the plasticity of brain neurons [1] Group 2: Research Findings - The research paper was published in the journal "Science Advances," highlighting the potential of engineered nanoporous materials in the development of next-generation devices [1] - Professor Wang Huanting stated that the team observed saturated nonlinear conduction of protons in nano-fluidic devices for the first time, opening new avenues for designing ionic electronic systems with memory and learning capabilities [1] Group 3: Future Applications - The research team constructed a small fluid circuit with multiple MOF channels to validate the chip's potential, which responds to voltage changes similarly to electronic transistors while also exhibiting memory functions [1] - The chip is expected to have future applications in liquid data storage or "brain-like" computing systems [1]