Industry Overview - The technology services industry is mature, with demand for services in good shape, and revenues, income, and cash flows are expected to gradually return to pre-pandemic levels, enabling stable dividends for most players [1] - The global shift toward digitization is creating opportunities in markets such as 5G, blockchain, and AI, with companies adopting generative AI, machine learning, and data science to gain competitive advantages [4] - The business software industry is benefiting from strong demand for multi-cloud-enabled software solutions as companies transition from legacy platforms to modern cloud-based infrastructure [4] Company Highlights AppLovin Corp. (APP) - AppLovin is a software-based platform for mobile app developers, enhancing marketing and monetization of apps [6] - The introduction of AI-powered AXON 2.0 technology and strategic expansion in gaming studios have significantly boosted revenue growth [7] - AppLovin's expected revenue and earnings growth rates for the current year are 16% and 84.8%, respectively, with a Zacks Consensus Estimate for current-year earnings improving by 0.1% in the last seven days [8] - The average short-term price target for AppLovin shares indicates a potential increase of 37% from the last closing price of $341.64, with a maximum upside of 90.3% [8] Duolingo Inc. (DUOL) - Duolingo operates as a mobile learning platform offering courses in 40 different languages and provides a digital language proficiency assessment exam [10] - The company utilizes AI applications to personalize and automate the learning process, with tools like Birdbrain analyzing real-time user performance [11] - Duolingo's expected revenue and earnings growth rates for the current year are 33.5% and 54.3%, respectively, with the Zacks Consensus Estimate for current-year earnings improving by 2.1% over the last 60 days [12] - The average short-term price target for Duolingo shares indicates a potential increase of 24% from the last closing price of $391.86, with a maximum upside of 53.1% [12]

Market Overview - The global market for Data Annotation Tools was estimated at US$1.9 Billion in 2024 and is projected to reach US$6.2 Billion by 2030, growing at a CAGR of 22.2% from 2024 to 2030 [1][14]. Importance of Data Annotation Tools - Data annotation tools are essential for labeling and categorizing data to train AI and machine learning models, which is crucial for applications like computer vision, natural language processing, and speech recognition [2][3]. - High-quality, accurately labeled data is vital for the performance of AI models in critical applications such as autonomous driving and medical imaging [3]. Growing Demand Across Industries - The demand for data annotation tools is increasing as industries rely on AI and machine learning to enhance efficiency and customer experiences [4][6]. - In the automotive sector, these tools are necessary for training autonomous vehicles to recognize road signs and obstacles [4]. - In healthcare, annotated data aids in developing AI models for disease detection and treatment planning [5]. - Other sectors like e-commerce and finance utilize data annotation for personalization and fraud detection [6]. Market Segmentation and Growth - The report highlights significant growth in the Text segment, expected to reach US$2.1 Billion by 2030 with a CAGR of 19.6%, while the Image/Video segment is projected to grow at a 24.3% CAGR [13]. - The U.S. market is valued at $474.1 Million in 2024, with China forecasted to grow at an impressive 28.0% CAGR to reach $1.7 Billion by 2030 [13]. Strategic Insights - The report includes analysis of market dynamics, competitive intelligence, and the impact of global trade and economic shifts on the data annotation tools market [10][16]. - Key players in the market include Alegion, Amazon Mechanical Turk, Appen, and Clickworker, among others [16][19]. - The increasing focus on AI-powered solutions and the need for high-quality labeled data are driving the growth of the data annotation tools market [16].

[Music] As a nonprofit corporation, MITER is chartered to deliver solely for the public interest. We get up every single day working on really, really hard problems that the government brings to us in a research capacity or a prototype capacity or a policy capacity. In order to do a better job serving that public, we needed to reach out to the leading technology companies in the world.and Nvidia was identified as one of those leading technology companies. We realized that there was a massive growing demand ...

Core Insights - The article discusses the author's academic and professional background in Machine Learning, Economics, and Finance, highlighting expertise in Data Science and Machine Learning applications within the banking and finance sectors [1]. Group 1: Academic Background - The author holds a PhD in Machine Learning with a focus on Economics and Finance [1]. - The author has academic affiliations with IESE Business School, ESADE Business School, and the Barcelona Supercomputing Center [1]. Group 2: Professional Experience - The author has worked at Deloitte Financial Advisory, specializing in Data Science and Machine Learning applications for clients in banking, insurance, and finance [1]. - The author currently teaches Asset Pricing and Introduction to Corporate Finance at ESADE Business School at the MSc/BSc level [1]. Group 3: Research Focus and Interests - Current research focuses on Generative AI in sustainable finance [1]. - The author's interests include machine learning and generative AI applications in finance and economics, with proficiency in Python, R, and SQL [1].

弗吉尼亚理工学院和 Meta AI 的研究团队提出了一种名为 UNIMATE 的统一模型，通过创新的模型架构解决了当前超材料 AI 设计中的关键瓶 颈，首次实现了对超材料设计三大核心要素，即三维拓扑结构、密度条件和力学性能的统一建模与协同处理。 超材料是一类由人工设计的亚波长结构组成的材料，能够突破天然材料的固有限制，实现对电磁波的精确调控，如负折射率、超透镜、隐身技术等。其 中，机械超材料作为一类拥有特殊力学性能的人造材料，在航空航天、生物医药、能源存储等多个领域展现出巨大的应用潜力。其独特之处在于性能并非 由材料的化学成分决定，而是通过精心设计的微观结构来实现。例如，具有负泊松比的超材料在拉伸时会横向膨胀，这种特性使其在柔性器件中具有重要 应用价值；而具有高能量吸收行为的超材料则适用于缓冲装置。 在材料科学与人工智能交叉领域，超材料的设计与优化一直是备受关注的研究方向。在机械超材料的设计过程中，通常涉及三个关键模态：3D 拓扑结 构、密度条件和力学性能。这三个模态之间存在着密切的关联，已知其中任意两个模态，就可以推导出第三个模态。然而，研究团队通过全面回顾现有文 献发现，大多数已有研究仅考虑其中两个模态。 ...

Core Insights - The article discusses the author's academic and professional background in Machine Learning, Economics, and Finance, highlighting expertise in Data Science and Machine Learning applications within the banking and finance sectors [1] - The current research focus is on Generative AI in sustainable finance, indicating a trend towards integrating advanced technologies in financial practices [1] Group 1: Academic and Professional Background - The author holds a PhD in Machine Learning with a focus on Economics and Finance, and has affiliations with prestigious business schools and research centers [1] - Professional experience includes working at Deloitte Financial Advisory, specializing in Data Science and Machine Learning applications for clients in banking, insurance, and finance [1] - The author teaches Asset Pricing and Introduction to Corporate Finance at ESADE Business School at both MSc and BSc levels [1] Group 2: Research Interests and Skills - Current research interests include Generative AI applications in sustainable finance, reflecting a growing intersection of technology and finance [1] - Proficient in programming languages such as Python, R, and SQL, which are essential for data analysis and machine learning applications in finance [1]

Group 1 - The individual holds a PhD in Machine Learning with a focus on Economics and Finance, indicating a strong academic background relevant to financial analysis [1] - Professional experience includes working at Deloitte Financial Advisory, specializing in Data Science and Machine Learning applications for clients in banking, insurance, and finance, highlighting expertise in applying advanced technologies in financial sectors [1] - Current teaching roles include Asset Pricing and Introduction to Corporate Finance at ESADE Business School, suggesting involvement in educating future finance professionals [1] Group 2 - Research interests focus on Generative AI in sustainable finance, indicating a trend towards integrating advanced AI technologies in financial practices [1] - Proficiency in programming languages such as Python, R, and SQL, which are essential for data analysis and financial modeling in the industry [1] - The individual has publications in Artificial Intelligence and finance journals, reflecting a commitment to contributing to academic and professional discourse in these fields [1]

AI Workloads & AMD's Solutions - AI workloads are classified into five buckets: traditional machine learning, recommendation systems, language models and generative AI, and mixed AI enabled workloads [7] - AMD offers both GPUs and CPUs to cover the span of all enterprise AI needs, supported through an open ecosystem [11] - AMD's 5 GHz EPYC processor is purpose-built as a host processor for AI accelerators, leveraging the x86 ecosystem for broad software support and flexibility [13][14] - AMD EPYC CPUs lead with 64 cores and 5 GHz operation, suitable for robust enterprise-class workloads [15] Performance & Efficiency - AMD EPYC CPUs demonstrate a 7% to 13% performance boost compared to Xeon when used as a host processor for GPUs [17] - For generative workloads, AMD EPYC CPUs show a 28% to 33% improvement compared to the competition [24] - For natural language workloads, AMD EPYC CPUs outperform the latest generation competition by 20% to 36% [25] - AMD EPYC processors are built for low power, low cost AI inference, offering fast inference, easy integration, and the ability to add AI workloads without adding significant power consumption [28] Uber's Use Case - Uber handles 33 million trips daily, serving 170 million monthly active users, requiring a robust technology stack [30] - Uber began its cloud migration journey with GCP and OCI in late 2022, focused on accelerating innovation and optimizing costs [33] - Uber is migrating more workloads to AMD CPUs in a multi-cloud environment, leveraging next-gen technologies like PCI Gen 6 and CXL [37] - Uber expects over 30% better SPECjbb2015-perf per dollar with GCP C40 chips based on Turin architecture compared to CKD [38]

Company Overview - Fortinet is a global leader in cybersecurity, focusing on the convergence of networking and security [3] - The company offers an extensive portfolio of over 50 enterprise-grade cybersecurity products [3] - Fortinet has a customer base of well over half a million, making its solutions among the most deployed in the industry [3] Upcoming Financial Results - Fortinet will hold a conference call to discuss its second quarter 2025 financial results on August 6, 2025, at 1:30 p.m. Pacific Time [1] - The conference call will be available in listen-only mode on the company's investor relations website [2] - Prepared remarks, supplemental slides, and a replay of the call will be accessible on the Quarterly Earnings page of Fortinet's website [2] Commitment to Cybersecurity - Fortinet is dedicated to enhancing global cyber resilience through collaboration with public and private sector organizations [3] - The Fortinet Training Institute provides extensive cybersecurity training and career opportunities [3] - FortiGuard Labs utilizes advanced machine learning and AI technologies to deliver top-rated protection and actionable threat intelligence [3]

Core Insights - NextNRG, Inc. has entered a strategic advisory agreement with Goldfarb Management Services, appointing Gary M. Goldfarb as Chairman of its newly established Advisory Board, aimed at enhancing its market strategies and partnerships in the energy sector [1][4]. Company Overview - NextNRG is focused on AI-driven energy innovation, transforming energy production, management, and delivery through its Next Utility Operating System, smart microgrids, wireless EV charging, and mobile fuel delivery [1][6]. - The company aims to create an integrated ecosystem utilizing AI and machine learning in renewable energy, battery storage, and on-demand mobile fuel delivery [6]. Leadership and Expertise - Gary M. Goldfarb brings over 50 years of experience in international trade and supply chain management, having held significant roles in logistics and market expansion [2][4]. - His past achievements include revitalizing The Miami Free Zone, achieving over 96% occupancy, and driving trade volume to nearly $1 billion [2]. Strategic Goals - NextNRG's mission is to reimagine energy generation and management, with a focus on powering commercial and industrial facilities through distributed energy generation and advanced smart grid technologies [3][5]. - The company is targeting high-impact opportunities across infrastructure, industrial, and municipal energy sectors as part of its corporate development strategy [5]. Product Innovations - NextNRG's portfolio includes patented wireless charging systems designed for industrial and commercial operations, eliminating the need for traditional plug-in infrastructure and enhancing operational efficiency [3][7]. - The smart microgrid technology integrates with wireless charging systems to optimize energy distribution across commercial facilities, supporting decarbonization initiatives [7]. Market Expansion - The company is expanding its fleet of fuel delivery trucks and national footprint, including the acquisition of Yoshi Mobility's fuel division and Shell Oil's trucks, solidifying its position in the on-demand fueling industry [8]. - NextNRG aims to assist fleet customers in transitioning to electric vehicles by integrating sustainable energy solutions into its mobile fueling operations [8].