Core Insights - Google has undergone a significant transformation in the past year, moving from a state of perceived stagnation to a strong resurgence in AI capabilities, highlighted by the success of its Gemini applications and models [2][3][44] - The company's long-term investment in AI technology, dating back over two decades, has laid a robust foundation for its current advancements, showcasing a strategic evolution rather than a sudden breakthrough [3][6][45] Group 1: Historical Context and Development - Google's AI journey began with Larry Page's vision of creating an ultimate search engine capable of understanding the internet and user intent [9][47] - The establishment of Google Brain in 2011 marked a pivotal moment, focusing on unsupervised learning methods that would later prove essential for AI advancements [12][18] - The "cat paper" published in 2012 demonstrated the feasibility of unsupervised learning and led to the development of recommendation systems that transformed platforms like YouTube [15][16] Group 2: Key Acquisitions and Innovations - The acquisition of DeepMind in 2014 for $500 million solidified Google's dominance in AI, providing access to top-tier talent and innovative research [22][24] - Google's development of Tensor Processing Units (TPUs) was a strategic response to the limitations of existing hardware, enabling more efficient processing of AI workloads [25][30] Group 3: Challenges and Strategic Shifts - The emergence of OpenAI and the success of ChatGPT in late 2022 prompted Google to reassess its AI strategy, leading to a restructuring of its AI teams and a renewed focus on a unified model, Gemini [41][42] - The rapid development and deployment of Gemini and its variants, such as Gemini 3 and Nano Banana Pro, have positioned Google back at the forefront of the AI landscape [43][44] Group 4: Future Outlook - Google's recent advancements in AI reflect a culmination of years of strategic investment and innovation, reaffirming its identity as a company fundamentally rooted in AI rather than merely a search engine [47][48]

别再说机器人手残了！ 这款名叫 GL-Robot 的双指机械臂，真的不简单—— 从鸡蛋到哑铃，它都能稳稳抓起，还能感知力度，再也不怕捏碎或手滑！ 长久以来，机器人抓取技术一直面临一个核心矛盾：抓取力强大往往失去精细触觉，而控制精准又难以适应复 杂物体。如今，这一僵局被打破了。 浙江大学机器人研究所的科研 团队带来了一项重大突破 ——GL-Robot ， 一款具备六自由度、欠驱动并集 成力感知的双指机械臂。它不仅抓得稳，更抓得 "聪明"，实现了类似人类手指的灵活适应与触觉反馈，仿佛 有了"肌肉记忆" 。 ▍ 能 "刚"能"柔" ， GL-Robot的机械艺术 GL-Robot的核心创新之一，在于它那双巧妙的手指。 平行抓取模式和包络抓取模式 面对螺丝或笔等小型物体时，前两节指骨以同步方式运动，执行精确的 " 平行抓取 " 。而当抓取较大物体 时，近端指骨首先接触并停止运动，此时连杆机构自动解耦，驱动中段与远端指节继续弯曲，形成全面的 " 包络抓取 " 。 这种独创的机构，让 GL-Robot仅凭一个电机驱动，就实现了高适应性、高稳定性和高负载的抓取，是其实现 智能化的首要步骤。 ▍ 赋予触觉：无需传感器的 " ...

Core Insights - The research addresses the significant issue of tropospheric delay in electromagnetic wave propagation due to variations in air density and water vapor content, which affects Very Long Baseline Interferometry (VLBI) and Global Navigation Satellite Systems (GNSS) positioning [1][3] - A hybrid deep learning model combining Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks has been developed to accurately predict zenith tropospheric delay (ZTD) [1][3] Research Findings - The team conducted spectral analysis on years of GNSS observations from the Nanshan station, revealing that ZTD changes exhibit clear annual and semi-annual cycles, with higher values in summer and lower in winter, closely related to temperature and water vapor content [3] - The hybrid neural network model effectively captures both short-term fluctuations and long-term trends in atmospheric delay, achieving a prediction error of approximately 8 millimeters and a correlation coefficient of 96%, outperforming traditional statistical models and single neural networks [3] Applications and Implications - High-precision predictions of tropospheric delay can significantly enhance the atmospheric phase correction accuracy in VLBI observations, improving radio source positioning and baseline calculation results [3] - The research demonstrates the potential of artificial intelligence in atmospheric correction for radio telescopes, laying a technical foundation for the future operation of the QTT 110-meter telescope and multi-station interferometric observations in high-frequency bands [3]