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清华系具身大脑团队累计融资数亿规模,对标美国头部公司,已在行业头部厂商落地|硬氪首发
3 6 Ke· 2025-05-20 01:33
Core Insights - Qianjue Technology has recently completed a new round of Pre-A+ financing, raising several hundred million yuan, with investments from Junshan Investment, Xiangfeng Investment, and Shixi Capital [1] - The funding will primarily be used for core technology evolution, product standardization, and enhancing industrial delivery capabilities [1] - The company is a Tsinghua University incubated firm specializing in embodied intelligence technology, with a team that has a strong research background and engineering transformation capabilities [1] Company Overview - Qianjue Technology is the only domestic company comparable to the U.S. Physical Intelligence, having achieved practical long-term task execution capabilities in general embodied intelligence [1][2] - The "embodied brain" system developed by Qianjue Technology emphasizes multi-modal real-time perception and autonomous execution without relying on preset strategies, aligning closely with Physical Intelligence's π0.5 model [2] Technology and Innovation - The "embodied brain" serves as the core technology architecture, responsible for central decision-making, which directly influences the robot's autonomous execution capabilities and expands application boundaries [1][5] - The system has demonstrated capabilities in complex environments, with the ability to adapt to over twenty types of embodied hardware forms, showcasing its long-duration autonomous decision-making abilities [2][5] Commercialization Progress - Qianjue Technology's embodied brain has achieved stable operation in various scenarios, including home services, logistics, and commercial operations, collaborating with leading embodied robot manufacturers and tech companies [6] - The company has built the world's largest pure real-sampling home scene dataset, supported by key projects in Chinese brain science, enhancing its model training and demonstrating strong generality and cross-task adaptability [6] - With the completion of the new financing round, Qianjue Technology aims to accelerate technological evolution and product deployment, promoting the large-scale popularization and application of embodied intelligence [6]
湖南95后女博士,力挑谷歌,要造思考时"不发烧"的AI
创业邦· 2025-03-19 09:28
Core Viewpoint - Lu Xi Technology aims to challenge the dominance of the Transformer architecture in AI by developing a brain-like computing ecosystem, introducing the NLM model that significantly reduces energy consumption while enhancing inference efficiency [2][3][4]. Group 1: Company Overview - Lu Xi Technology was founded in 2023 by two women born in the 1990s, marking it as the first domestic company focused on brain-like computing [2]. - The NLM model, launched in 2024, is the first domestically developed large model using a non-Transformer architecture based on brain-like technology [2][12]. - The company has received approval from the National Internet Information Office for its generative AI services and deep synthesis algorithm services [2][12]. Group 2: Technology and Innovation - The NLM model boasts a reduction in energy consumption by over 80% while improving inference efficiency several times compared to traditional models [12][13]. - Lu Xi Technology's brain-like architecture mimics the human brain's neural structure, allowing for efficient computation and storage by activating only relevant neurons [4][12]. - The company is developing a range of products based on the NEURARK brain-like architecture, including foundational models and industry-specific models, to meet diverse market needs [12][15]. Group 3: Market Position and Strategy - Lu Xi Technology aims to break the dependency on NVIDIA chips by developing its own FPGA and ASIC chips tailored for large models [10][12]. - The company collaborates with various state-owned enterprises and industry leaders to deploy its models across multiple sectors, including healthcare and disaster management [15]. - The company is targeting a significant increase in model parameter scale, aiming to reach 600 billion parameters by 2025, which would bring it closer to the complexity of the human brain [16].