Gorilla Technology (NASDAQ: GRRR ), a two-decade old entity that leverages AI, edge computing, and deep learning, to provide technology solutions for clients around various industries (mainly government, logistics, transport, retail, hospitality, etc.) around the globe (although its servicing roots areAnalyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article mys ...

Financial Data and Key Metrics Changes - Cognex reported revenue exceeding $900 million with an adjusted EBITDA margin of 28% over the last ten years [5] - The company has invested around 15% of its revenue in research and development [5] Business Line Data and Key Metrics Changes - The logistics market, Cognex's largest, grew by 20% last year, indicating strong momentum [65] - The semiconductor market is the fastest-growing segment, despite some caution due to trade issues [65] - Consumer electronics are expected to see modest growth this year, while the automotive sector shrank by 14% last year [67] Market Data and Key Metrics Changes - The automotive industry remains challenging, with expectations of continued difficulties, although some recovery is anticipated [67] - The logistics market has recovered from post-COVID tightness in spending and overcapacity [65] Company Strategy and Development Direction - Cognex is focused on applying AI technology to factory automation and machine vision, aiming to lead in these areas [34] - The company is expanding its sales force to reach a broader customer base, targeting an estimated 300,000 potential customers [24] - Cognex is exploring potential acquisitions in adjacent markets, particularly in the sensor space [30] Management's Comments on Operating Environment and Future Outlook - Management noted that the shift from rules-based systems to AI has opened new opportunities for machine vision applications [13][14] - The company anticipates that automation will eventually return to the automotive sector, particularly in relation to electric vehicles [67][68] Other Important Information - Cognex has a strong company culture characterized by a "work hard, play hard" ethos, which is seen as a competitive advantage in attracting talent [9][56] - The company has over 1,000 patents in the machine vision area, which supports its innovation and market position [47] Q&A Session Summary Question: What are the key areas for future growth? - Management highlighted the importance of leading in AI technology, enhancing customer experience, and expanding the customer base as critical areas for future growth [34][35][36] Question: How does Cognex differentiate itself in the market? - Cognex leverages its extensive industry knowledge and experience to achieve high precision in machine vision applications, which is difficult for new entrants to replicate [44][45] Question: What is the outlook for the EV battery manufacturing market? - Management expressed optimism about the potential for growth in the EV battery manufacturing market, noting that Cognex's technology can significantly enhance production processes [78]

图片来源： 20VC Z Highlights Harry Stebbings： 欢迎收看20VC节目，这是一个采访世界上最佳创始人和投资者的节目。今天我们请到了AI和NLP（自然语言处理）领域的顶级专家 Noam Shazeer。Noam是Character.AI的联合创始人兼CEO，这是一家全栈AI计算平台，旨在为人们提供灵活的超级智能。 Noam，非常兴奋能和你一起聊天！我从很多不同的人那里听到了关于你的许多好话，Eric Schmidt、Sarah Wang、Prajit等人都推荐过你，非常感谢你今天 加入我们。 Noam Shazeer： 谢谢。很高兴能在这里，Harry！ Harry Stebbings： 我想先从一些背景开始，因为很少有人能在Google这样一个快速扩张的公司待上20年。首先，我想回顾一下你是如何加入Google的。听 说你加入的故事有些特别，能告诉我一下"spelling corrector"的故事吗？ Noam Shazeer： 是的，那是我在Google做的第一个项目。那时候，Google使用的是第三方软件做拼写校正，类似于当时你在文字处理软件里可能会遇到的 那种。它基于一 ...

Group 1 - Preferred Networks is recognized as a "true AI" company due to its reliance on deep learning, NLP, and generative models, along with its self-developed models and AI frameworks [1][3][4] - The company has a strong product versatility, offering solutions across various sectors including industrial automation, healthcare, and education, with over 435 global patents [5][6] - Despite its initial ambitions for international expansion, Preferred Networks has reverted to a domestic focus, raising concerns for other Japanese tech firms considering overseas ventures [2][10] Group 2 - Preferred Networks was founded in 2014 and developed the deep learning framework Chainer, which was once positioned alongside TensorFlow and PyTorch [3][11] - The company has shifted its strategy to collaborate with major Japanese corporations like Toyota and Nissan, focusing on customized AI systems rather than pursuing a broader international presence [13][18] - The company has established a new subsidiary, Preferred Elements, aimed at foundational technology development, indicating a potential shift towards a more open approach [14][16] Group 3 - PKSHA Technology, another prominent Japanese AI firm, has shown strong profitability with significant revenue growth, serving various industries including retail and finance [24][25][26] - Unlike Preferred Networks, PKSHA retains ambitions for international collaboration, partnering with companies like Microsoft and Tencent [26] - The early establishment of AI companies in Japan, such as PKSHA and Preferred Networks, was driven by a combination of engineering talent and industry demand for automation [28][30] Group 4 - The Japanese AI industry is characterized by a closed-loop system where startups primarily serve large domestic corporations, limiting their growth potential and innovation [44][45] - The government and large companies emphasize project-based AI solutions, which diminishes the drive for exploratory or innovative AI developments [44][45] - Cultural factors contribute to the lack of ambition for developing universal AI platforms, contrasting with the more aggressive approaches seen in other countries [30][43]

Core Viewpoint - WiMi Hologram Cloud Inc. has developed a Quantum Computing-Based Feedforward Neural Network (QFNN) algorithm that addresses computational bottlenecks in traditional neural network training, utilizing Quantum Random Access Memory (QRAM) for efficient data processing [1][10]. Quantum Algorithm Development - The QFNN algorithm incorporates key quantum computing subroutines, particularly in the feedforward and backpropagation processes, providing exponential speedup in both stages of neural network training [2][4]. - Classical feedforward propagation, which involves multiple matrix-vector multiplications, is enhanced by the quantum algorithm through the use of quantum state superposition and coherence, allowing computations to be performed in logarithmic time [3][6]. Computational Efficiency - The quantum algorithm significantly reduces computational complexity, shifting from a dependency on the number of connections (O(M)) in classical networks to a dependency solely on the number of neurons (O(N)) in the quantum framework [6][7]. - This reduction in complexity leads to at least a quadratic speedup in training large-scale neural networks, making it particularly advantageous for ultra-large-scale datasets [7]. Overfitting Mitigation - WiMi's quantum algorithm demonstrates inherent resilience to overfitting, a common issue in deep learning, due to the intrinsic uncertainty of quantum computing, which acts similarly to regularization techniques [8][9]. Application Prospects - The QFNN algorithm has broad application potential in fields requiring high computational speed and data scale, such as financial market analysis, autonomous driving, biomedical research, and quantum computer vision [10][11]. - Additionally, the research lays the groundwork for quantum-inspired classical algorithms that can optimize computational complexity on traditional computers, providing a transitional solution until quantum computers become widely available [10]. Future Implications - The advancement of WiMi's QFNN algorithm marks a significant milestone in the intersection of quantum computing and machine learning, suggesting that quantum neural networks will play a crucial role in the future of artificial intelligence [11][12].

实习时间： 每周至少实习3天以上，实习时间不少于3个月，不满足的请勿投递，实习考核优秀者有留用机会。 岗位职责： 1、负责数据处理、分析、统计等工作，协助研究员完成量化投资相关课题的研究； 实习生招聘 工作地点： 深圳、广州、上海、北京 ，要求线下实习 简历投递截止日期： 2025年4月30日 2、协助进行金融工程策略模型的开发与跟踪等工作； 3、完成小组安排的其他工作。 基本要求： 1、数学、统计、物理、计算机、信息工程等理工科专业，或金融工程相关专业，硕士或博士在读，特别优秀的大四 保研亦可，非应届（2026年及之后毕业）； 2、熟练掌握Python等编程语言，熟悉SQL数据库，有优秀编程能力与编程规范； 3、有责任心，自我驱动能力强， 具有良好的信息搜集能力、逻辑思维能力、分析判断能力、言语和书面表达能力、 人际沟通能力。 加分项： 4、 具备扎实的金融市场基础知识，熟悉股票、债券、期货、指数及基金等核心概念； 5、数学基础好，有科研项目经历、有学术论文被SCI或EI收录； 6、熟悉Wind、 Bloomberg、天软等金融终端； 7、熟悉机器学习、深度学习，熟悉PyTorch、Linux，有GPU服务 ...

Core Insights - Nvidia's CEO Jensen Huang highlighted the company's commitment to autonomous vehicles during his keynote at GTC 2025, linking it to the historical impact of AlexNet on deep learning and computer vision [1][3][4] Automotive Industry Impact - The introduction of AlexNet in 2012, which achieved 84.7% accuracy in the ImageNET competition, inspired Nvidia to invest heavily in self-driving technology, marking over a decade of development in this area [2][3][4] - Nvidia has established partnerships with various automakers and tech companies, including a recent expanded collaboration with GM, to enhance the development of autonomous vehicles [4] - Major automotive companies such as Tesla, Wayve, and Waymo utilize Nvidia GPUs for their data centers, while others leverage Nvidia's Omniverse for creating digital twins of factories [5] Technology Utilization - Nvidia's Drive Orin system-on-chip, based on the Ampere architecture, is employed by companies like Mercedes, Volvo, and Toyota for their automated driving systems [5] - The safety-focused operating system, DriveOS, is also being adopted by Toyota and other manufacturers, further embedding Nvidia's technology in the automotive sector [5][6]

Core Insights - Gorilla Technology Group Inc. is focused on building smart cities and enhancing security through AI-powered solutions, emphasizing a consultative approach to meet customer needs [2][3]. Company Overview - Gorilla Technology Group Inc. is headquartered in London, UK, and operates as a global solution provider in Security Intelligence, Network Intelligence, Business Intelligence, and IoT technology [5]. - The company offers a range of solutions across various sectors, including Government & Public Services, Manufacturing, Telecom, Retail, Transportation & Logistics, Healthcare, and Education, utilizing AI and Deep Learning Technologies [5]. Technology and Solutions - The company specializes in urban operations, security enhancement, and resilience through AI-driven technologies such as intelligent video surveillance, facial recognition, license plate recognition, edge computing, post-event analytics, and advanced cybersecurity [6]. - By integrating these technologies, Gorilla aims to empower Smart Cities, improving efficiency, safety, and overall quality of life for residents [6].