Core Insights - The collaboration between Baidu Technology and the "Super League" aims to enhance the information experience and service delivery for the event, resulting in the launch of the intelligent service agent "iSuper League" on the Xinhua Daily news client [1][4]. Group 1: Product Features - "iSuper League" allows users to quickly access information and services related to the Super League, including match schedules, game details, and event Q&A [3]. - The intelligent agent provides detailed data on team performance, including wins, losses, draws, goals scored, goals conceded, and goal difference in the league standings [4]. Group 2: Technology and Development - The implementation of "iSuper League" is powered by Baidu Technology's deep technical capabilities, utilizing a large model to build the DuerOS as its technical foundation [4]. - The architecture of "iSuper League" is based on a four-layer system that includes perception, understanding, decision-making, and output, facilitating a seamless connection between user needs and intelligent responses [4]. Group 3: Broader Implications - The initiative not only revitalizes the sports event sector with new intelligent capabilities but also highlights the core value of deep integration between AI technology and vertical scenarios [4]. - Baidu Technology plans to extend its AI capabilities across various industries, creating customized AI solutions to enhance the quality of human life [4].

Core Viewpoint - The research teams from Peking University and Renmin University have made significant breakthroughs in the field of general humanoid robot motion generation, introducing the innovative data-model collaborative scaling framework, Being-M0 [1][2]. Group 1: Motion Generation Dataset - The team has created the industry's first motion generation dataset, MotionLib, with over one million action sequences, significantly enhancing data acquisition efficiency through an automated processing pipeline [4][7]. - MotionLib includes over 1 million high-quality action sequences, achieving a scale 15 times larger than the current largest public dataset, thus overcoming the scale bottleneck in motion generation [10]. Group 2: Large-Scale Motion Generation Model - The proposed large-scale motion generation model demonstrates significant scaling effects, validating the feasibility of the "big data + big model" approach in human motion generation [5][13]. - Experiments show a strong positive correlation between model capacity and generation quality, with a 13B parameter model outperforming a 700M parameter model in key metrics [13][14]. Group 3: Motion Redirection Across Platforms - The Being-M0 team has innovatively integrated optimization and learning methods to efficiently transfer motion data to various humanoid robots, enhancing cross-platform adaptability [6][20]. - A two-phase solution is proposed for cross-modal motion transfer, ensuring high-quality generated data while maintaining real-time performance [21]. Group 4: Future Directions - The Being-M0 project aims to continuously iterate on humanoid robot capabilities, focusing on embodied intelligence, dexterous manipulation, and full-body motion control, ultimately enhancing the general capabilities and autonomy of robots [22].