Market Overview - The AI and robotics sector experienced a sideways adjustment, with the Huaxia Sci-Tech AI ETF (589010) declining by 0.9% as of April 18. Key stocks such as Chipone Technology and Anlu Technology led the decline [1] - The Robotics ETF (562500) remained flat, with a trading volume of 415 million yuan and a turnover rate of 3.57%, indicating active market trading [1] Key Events - On April 19, the world's first humanoid robot half marathon was successfully held in Beijing, with 20 robot teams participating. The TianGong Ultra robot from the Beijing Humanoid Robot Innovation Center finished first, completing the 21-kilometer race in 2 hours and 40 minutes [1] - The CTO of the Beijing Humanoid Robot Innovation Center, Tang Jian, stated that the price of humanoid robots is expected to be comparable to that of an entry-level sedan as they become more widely adopted [1] Institutional Insights - Dongwu Securities highlighted several issues with humanoid robot hardware revealed during the marathon: 1. Insufficient battery life, as most robots required battery changes mid-race except for the walking robot [3] 2. Inadequate joint cooling, with robots needing cooling sprays to maintain joint functionality during the competition [3] - The event, hosted by local government and broadcasted by CCTV, garnered significant public attention, reflecting the government's strong emphasis on the humanoid robot industry. This focus is expected to accelerate the development of the humanoid robot sector and help overcome key challenges for commercialization [3] Popular ETFs - The Robotics ETF (562500) is the largest robotics-themed ETF in the market, facilitating investors' access to the Chinese robotics industry [3] - The Huaxia Sci-Tech AI ETF (589010) serves as the brain of robotics, with a 20% fluctuation range and small-cap elasticity, aiming to capture pivotal moments in the AI industry [3] Technological Developments - On April 18, Microsoft launched the BitNet b1.58 2B4T, the largest 1-bit AI model globally, featuring 2 billion parameters and capable of efficient operation on standard CPUs like Apple's M2. The model utilizes minimal weights of -1, 0, and 1 to achieve high memory and computational efficiency, outperforming similar models from Meta and Google in various inference tasks [2]
AI与机器人盘前速递丨天工Ultra获机器人半马冠军,微软发布可在CPU超高效运行AI模型BitNet