Core Viewpoint - The humanoid robot industry is experiencing rapid growth and significant controversy, with opinions divided on its viability and future potential [1][3]. Industry Challenges - There is a lack of consensus on the technical routes within the industry, with ongoing debates about the effectiveness of reinforcement learning versus world models, and whether to focus on data or models [4]. - The industry faces a critical data shortage, with most research relying on only a few hundred million tokens, while a minimum of 10 billion to 1 trillion tokens is needed for effective model training [8][9]. - The current data bottleneck severely limits the variety of tasks robots can perform and their ability to generalize in real-world scenarios [9][11]. Solutions and Innovations - The industry is exploring cloud-based solutions to address data collection and training challenges, with platforms like Huawei Cloud's CloudRobo enabling the creation of digital twins of physical environments for data generation [11][13]. - CloudRobo utilizes a self-developed engine for data reconstruction and augmentation, allowing for the simulation of various robot forms and generating vast amounts of training data [16]. - The training platform allows robots to engage in virtual labor, significantly reducing trial-and-error costs and accelerating skill acquisition [18][21]. Standardization Efforts - The industry is also grappling with a lack of standardization among robot manufacturers, prompting the development of the R2C (Robot to Cloud) protocol to facilitate interoperability [23]. - The R2C protocol aims to create a unified interface for robots, similar to USB connections in computers, promoting ecosystem collaboration and standardization [23]. Future Outlook - While cloud solutions present promising pathways for overcoming current challenges, they may not be universally applicable, especially in scenarios requiring real-time processing and high safety standards [25][27]. - The industry recognizes the need for foundational infrastructure to support the deployment of humanoid robots in practical applications, emphasizing the importance of building robust systems before advancing to complex robotics [30][31].

Core Viewpoint - Huawei has launched the world's strongest computing supernodes and clusters, emphasizing their critical role in the future of artificial intelligence infrastructure [1][3]. Group 1: Supernodes and Clusters - Huawei's latest supernode products, Atlas 950 SuperPoD and Atlas 960 SuperPoD, support 8192 and 15488 Ascend cards respectively, leading in key metrics such as card scale, total computing power, memory capacity, and interconnect bandwidth [3]. - The Atlas 950 SuperCluster and Atlas 960 SuperCluster have computing scales exceeding 500,000 cards and reaching 1 million cards, making them the strongest computing clusters globally [3]. Group 2: Technological Innovations - Huawei has introduced the first general-purpose computing supernode, TaiShan 950 SuperPoD, which can replace various large and small machines, including Exadata database machines [4]. - The company has developed a new interconnect protocol, Lingqu (UnifiedBus), to address the challenges of large-scale supernode interconnect technology and is inviting industry partners to collaborate on related products [4]. Group 3: Chip Development and Ecosystem - Over the next three years, Huawei plans to launch multiple Ascend chips, including the 950PR, 950DT, 960, and 970 chips, with specific release timelines [5]. - The developer ecosystem for Huawei's Kunpeng and Ascend has surpassed 3.5 million, with over 5,600 partners and more than 15,500 certified solutions [5]. - In existing computing centers in China, the Ascend solution accounts for approximately 85% of the domestic solutions deployed [5].