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家居行业首个具身智能大模型!萤石蓝海大模型获CIC灼识咨询权威市场地位确认
萤石蓝海大模型持续进化,在2025年6月迎来了全新升级的2.0版本,聚焦技术能力与场景应用的双重落 地,通过多维融合、模态扩展与专项记忆三大技术路径,实现感知、理解、记忆能力的三重增强,同时 助力垂直领域,做到专项能力、定向优化、精准服务,解决用户的痛点和难点。 近日,萤石自研的"萤石蓝海大模型"正式获得由知名权威咨询机构CIC灼识咨询颁发的"家居行业首个具 身智能大模型"市场地位确认证书。 根据中国计算机协会,具身智能大模型指一种基于物理身体进行感知和行动的智能系统,通过智能体与 环境交互获取信息、理解问题、做出决策并实现行动,产生智能行为和适应性。家居行业具身智能大模 型定义为应用于家居行业且获得完整国家备案的人工智能大模型。 萤石蓝海大模型自2024年6月首次亮相便确立了面向物联网场景的具身智能方向,并于9月完成生成式人 工智能服务安全备案。其命名"蓝海"寓意深刻——正如地球生命起源于浩瀚的蓝色海洋,萤石期待在复 杂多变的家居物联海洋中,孕育出全新的智能形态,最终奔向通用人工智能(AGI)的广阔未来。本次 获得的"家居行业首个具身智能大模型"市场地位确认,正是对其践行这一理念的阶段性肯定。 在通用大模型 ...
萤石云开发者大会:开启碎片化AI场景新蓝海
Zheng Quan Ri Bao Wang· 2025-06-26 14:31
Core Insights - The article highlights the advancements made by Hangzhou Yingshi Network Co., Ltd. (Yingshi) in the field of AI and visual IoT, particularly through the launch of the upgraded Yingshi Blue Ocean Model 2.0 and the EZVIZ HomePlay OS, aimed at enhancing user experience and addressing fragmented AI scenarios [1][2][3] Group 1: Product Developments - Yingshi launched the upgraded Yingshi Blue Ocean Model 2.0, focusing on enhancing perception, understanding, and memory capabilities through multi-dimensional integration and specialized memory [2] - The EZVIZ HomePlay OS has undergone a comprehensive upgrade, enabling developers to create intelligent applications quickly and cost-effectively, addressing challenges posed by fragmented AI scenarios [3] Group 2: Market Applications - Yingshi is building a home AI elderly care service system, leveraging cloud services, AI capabilities, and age-friendly devices to monitor and assist the elderly, with an average of 4.3 interactions per day [4] - The company is also innovating in the retail sector by developing capabilities for unmanned self-service scenarios, addressing industry pain points such as labor shortages and high costs [5] Group 3: Strategic Vision - Yingshi aims to empower developers to become builders of vertical intelligent scenarios through a dual approach of technology support and ecosystem collaboration, targeting a trillion-dollar market in fragmented AI scenarios [5]