Core Viewpoint - The field of embodied intelligence is experiencing unprecedented attention, yet key issues remain unresolved, including data scarcity and differing technical approaches [1][2][3] Group 1: Data and Technical Approaches - The industry is divided into two factions: the "real machine" faction, which relies on real-world data collection, and the "synthetic" faction, which believes in the feasibility of synthetic data for model training [5][12] - Galaxy General, representing the synthetic faction, argues that achieving generalization in embodied intelligence models requires trillions of data points, which is unsustainable through real-world data alone [8][9] - The "real machine" faction challenges the notion that real-world data is prohibitively expensive, suggesting that with sufficient investment, data collection can be scaled effectively [12][14] Group 2: Model Architecture - Discussions around the architecture of embodied intelligence models highlight a divide between end-to-end and layered approaches, with some experts advocating for a unified model while others support a hierarchical structure [15][19] - The layered architecture is seen as more aligned with biological evolution, while the end-to-end approach is criticized for potential error amplification [19][20] - The debate extends to the relevance of VLA (Vision-Language Alignment) versus world models, with some experts arguing that VLA is currently more promising due to its data efficiency [21][22] Group 3: Industry Trends and Infrastructure - The scaling law in embodied intelligence is beginning to emerge, indicating that expanding model and data scales could be effective [24] - The industry is witnessing an acceleration in the deployment of embodied intelligence technologies, with various companies sharing their experiences in human-robot interaction and industrial applications [24][29] - Cloud service providers, particularly Alibaba Cloud, are emphasized as crucial players in supporting the infrastructure needs of embodied intelligence companies, especially as they transition to mass production [29][31] Group 4: Alibaba Cloud's Role - Alibaba Cloud has been preparing for the exponential growth in data and computational needs associated with embodied intelligence, having developed capabilities to handle large-scale data processing and model training [33][35] - The company offers a comprehensive suite of cloud-based solutions to support both real and synthetic data production, enhancing efficiency and reducing costs [35][36] - Alibaba Cloud's unique position as a model provider and its engineering capabilities are seen as significant advantages in the rapidly evolving embodied intelligence landscape [37][41]