Core Viewpoint - Sterling Capital Management launched the Sterling Capital Hedged Equity Premium Income ETF (SCEP), which combines artificial intelligence for idea generation and human intelligence for portfolio construction to provide a U.S. equity allocation while employing options strategies for income generation and market protection [1][3]. Fund Overview - The fund began trading on December 12 and currently manages $214.1 million in assets with a management fee of 0.65% [2]. - The ETF structure was chosen for its tax efficiency, intraday trading capability, and lower costs compared to traditional mutual funds [2]. Investment Strategy - SCEP aims to deliver tax-efficient monthly income, better risk-adjusted returns through AI-driven stock selection, and reduced downside risk via protective options trading [3]. - The fund's sub-adviser, Guardian Capital, has utilized AI in equity strategies since 2018 and manages over $4 billion in assets using similar investment processes [3]. AI Utilization - Guardian's AI models forecast key investment variables, including earnings growth and dividend growth, using machine learning and deep learning techniques [4]. - The AI narrows the investment universe to a shortlist of companies with a higher probability of durable earnings and dividend growth [5][6]. Portfolio Composition - The fund's top holdings include Alphabet Inc. (6.37%), Apple Inc. (6.09%), NVIDIA Corp. (5.77%), Microsoft Corp. (5.07%), and Amazon.com, Inc. (4.58%), identified for their strong balance sheets and growth potential [7]. - The fund maintains around 21% exposure to midcap stocks, which may provide better valuations compared to large-cap alternatives [11]. Options Strategy - SCEP employs a dynamic options overlay strategy, writing covered call options on up to 100% of its portfolio to boost income while buying protective put options to cushion against market declines [8][9]. - Protective puts are structured to guard against a 10% to 30% market decline, aiming to protect income-focused investors from large drawdowns [9][10]. Tax Efficiency - The fund's structure seeks to deliver more tax-efficient income by finding losses on individual securities and options to offset gains, potentially allowing for return of capital distributions [13]. - Return of capital distributions are generally not taxable in the year received, deferring taxes until the shares are sold, which may result in a higher capital gain [14][15].

Core Insights - The PGA Tour has enhanced its partnership with Amazon Web Services (AWS) to modernize operations and improve production capabilities using AWS AI infrastructure [1][4][7] Group 1: Partnership Development - The PGA Tour and AWS have been collaborating since 2021, with AWS serving as the official cloud provider and AI partner for the Tour [10] - The expanded partnership aims to transform how golf content is created, distributed, and experienced globally [4][7] Group 2: New Features and Enhancements - A "favorite players hub" will be introduced on the Tour's app and website, allowing fans to track their favorite players' stats and storylines [4] - Real-time shot-by-shot commentary will be provided throughout the season, along with enhanced graphics and statistics for the Tour's "World Feed" [5][7] Group 3: Vision and Future - The PGA Tour aims to connect fans with players, events, and content more effectively, leveraging AWS's vision for personalized sports experiences [7][8] - AWS's commitment to supporting golf is further reinforced by its partnership with the DP World Tour, which also named AWS as its official cloud provider in 2025 [10]

Core Insights - Geoffrey Hinton has officially become the second computer scientist in history to surpass 1 million citations on Google Scholar, following his collaborator Yoshua Bengio [1][3][4]. Academic Achievements - Hinton's most cited paper, "ImageNet classification with deep convolutional neural networks," has received 188,837 citations since its publication in 2012, marking a significant milestone in deep learning [3]. - He co-authored the influential paper "Deep learning," published in 2015, which has garnered over 107,646 citations, summarizing the development and applications of deep learning [23]. - Hinton's contributions include the development of backpropagation, Boltzmann machines, deep belief networks, dropout techniques, and t-SNE, among others, which have laid the groundwork for modern AI [11][14][15][21]. Personal Background - Geoffrey Hinton was born into an academic family in London, UK, and faced high expectations from a young age, which shaped his pursuit of academic excellence [5][9]. - His early curiosity about the world led him to explore various fields, including physics, philosophy, and psychology, before committing to artificial intelligence [9][10]. Career Milestones - Hinton moved to Canada in the 1980s, where he established a long-term academic career at the University of Toronto, contributing significantly to the AI field [10]. - He received the Turing Award in 2018 alongside Bengio and Yann LeCun, recognized as the "three giants of deep learning" [21]. Recent Developments - In 2023, Hinton left Google after a decade to freely discuss the risks associated with AI, expressing concerns about the potential dangers of advanced digital intelligence [27]. - In 2024, he was awarded the Nobel Prize in Physics alongside John Hopfield for their foundational discoveries in machine learning using artificial neural networks [25].

Core Insights - Gorilla Technology Group Inc. will participate in an investor webinar on January 28, 2026, to discuss its latest milestones and strategic objectives for 2026 [1][2]. Company Overview - Gorilla Technology Group Inc. is a global solution provider specializing in AI-driven Security Intelligence, Network Intelligence, Business Intelligence, and IoT technology, with over 24 years of operating history and 29 granted patents [3]. - The company is headquartered in London, U.K., and offers a wide range of solutions across various sectors, including Government & Public Services, Manufacturing, Telecom, Retail, Transportation & Logistics, Healthcare, and Education [4]. Financial Performance - Gorilla has a growing pipeline exceeding $7 billion, driven by strong demand for GPU-as-a-Service infrastructure, AI-powered smart cities, and mission-critical security platforms [3]. - Recent milestones include a $1.4 billion multi-year partnership to deploy AI-ready data centers in Southeast Asia and continued expansion of public safety programs in Asia and Latin America [3]. - The company reaffirmed its 2025 revenue guidance of $100-$110 million with EBITDA margins of 20-25% and expects 2026 revenue to range from $137 million to $200 million, reflecting increasing scale and sustained momentum [3].

Core Insights - Magnite and Cognitiv have announced a partnership to enhance real-time data integration, improving curation capabilities within Magnite's ClearLine solution, which allows media buyers to access premium video inventory more effectively [1][2] Company Overview - Magnite is the largest independent sell-side advertising company, providing technology for publishers to monetize content across various formats including CTV, online video, display, and audio [4] - Cognitiv is a leading advanced performance partner utilizing deep learning to predict consumer behavior and optimize advertising strategies [5] Industry Context - The media landscape is becoming increasingly fragmented across multiple channels such as streaming TV, audio, display, and mobile, necessitating advanced solutions for effective media curation [2] - The complexity of the programmatic ecosystem is driving demand for AI solutions that can enhance content signals and streamline workflows for buyers [3]

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Core Viewpoint - MicroCloud Hologram Inc. has launched a quantum-enhanced deep convolutional neural network image 3D reconstruction technology system, which integrates quantum computing with deep learning to improve the precision and adaptability of 3D model generation [1][8]. Group 1: Technology Overview - The new system consists of six core modules: quantum-optimized dataset preparation, quantum-assisted feature extraction, quantum-enhanced parameter generation, quantum-accelerated 3D reconstruction, quantum-precision model evaluation, and an interactive application interface [2]. - The quantum-optimized dataset preparation module is crucial for ensuring high-quality 3D model data, which is essential for the deep learning algorithm to accurately learn morphological features [3]. - The quantum-assisted feature extraction module utilizes quantum convolutional neural networks to efficiently extract higher-level features from input images, overcoming limitations of traditional algorithms [4]. - The quantum-enhanced parameter generation module maps high-dimensional feature vectors to three-dimensional space, allowing for refined control over model attributes such as shape and size [5]. - The quantum-accelerated 3D reconstruction module generates high-precision 3D models by leveraging quantum computing's parallel processing capabilities [6]. - The quantum-precision model evaluation module optimizes algorithm parameters based on error measurements, enhancing the robustness of the 3D reconstruction model [7]. Group 2: Competitive Advantages - Compared to traditional 3D reconstruction algorithms, the new system offers significant advantages in precision and adaptability, enabling better alignment with actual needs through quantum-accelerated training on large datasets [8]. - The technology has broad application prospects across various fields, including medical diagnostics, robotics, and manufacturing, with potential integration into augmented and virtual reality technologies [9][10]. Group 3: Company Background - MicroCloud Hologram Inc. focuses on holographic technology and has a cash reserve exceeding 3 billion RMB, with plans to invest over 400 million USD in quantum computing and related technologies [11]. - The company aims to become a global leader in quantum holography and quantum computing technology [11].

Summary of Key Points from the Webinar on AI Data Center Memory Demand Industry Overview - The discussion centers around the U.S. IT Hardware industry, specifically focusing on AI data centers and their memory requirements [1][12]. - The webinar featured Gunjan Shah, a former Senior Cloud Engineer at Google, who provided insights into memory demand for AI workloads [1][12]. Core Insights Memory Demand in AI - Training AI models requires significantly more memory than inference, with medium-sized models consuming approximately 1TB of memory during training compared to much lower demands during inference [2][15]. - The rapid adoption of AI has led to a sharp increase in memory demand and prices, particularly for components like HBM (High Bandwidth Memory) and DRAM [3][21]. - Innovations in model architectures and memory technologies are expected to help manage memory demand sustainably in the long term [3][18]. Shift from HDDs to SSDs - Due to HDD shortages, many hyperscalers are transitioning to SSDs, which are 5 to 10 times more expensive but offer superior performance and lower operational costs [4][38]. - SSDs provide benefits such as reduced power consumption and minimal cooling requirements, contributing to a lower total cost of ownership (TCO) [4][40]. Emerging Memory Technologies - High Bandwidth Flash (HBF) is an emerging technology that aims to provide fast, non-volatile memory, potentially lowering energy consumption and cooling costs for AI inference workloads [5][18]. Investment Implications - Companies such as Seagate Technology (STX), Western Digital (WDC), SanDisk (SNDK), Samsung, SK Hynix, and Micron have been rated with specific price targets based on their performance in the memory market [7][8][9][10][11]. - STX is rated Outperform with a price target of $370, while WDC is rated Market-Perform with a target of $170 [8][9]. Additional Insights Memory Usage Breakdown - The memory footprint for training is heavily reliant on model weights, activations, and gradients, while inference requires only temporary tensors and KV caches [15][16]. - The demand for storage during training is significantly higher, with requirements ranging from terabytes to petabytes depending on the model size [24][25]. Market Dynamics - The demand for memory is outpacing supply, leading to increased prices for HBM, DRAM, and SSDs [21][29]. - Hyperscalers are signing multi-year purchase agreements and vertically integrating into chip design to secure memory supplies [29][36]. Comparison of AI Models - Gemini 3.0 is currently outperforming ChatGPT 5.0 in various benchmarks, attributed to its optimized training and architecture [33][34]. - The U.S. is leading in AI model development compared to China, with significant differences in performance and resource availability [35][36]. Cost Considerations - Despite the higher initial costs of SSDs, their lower operational costs and performance benefits make them more economical for performance-critical tasks over time [40][42]. - The TCO for SSDs is favorable due to lower power consumption, reduced cooling needs, and higher reliability compared to HDDs [40][42]. Conclusion - The AI data center memory landscape is evolving rapidly, driven by increasing model sizes and the need for efficient memory solutions. The shift from HDDs to SSDs and the emergence of new memory technologies are key trends to watch in this sector.

AI Engineering Resources - Stanford 提供 6 份 AI 工程师必备的速查表 [1] - 速查表涵盖监督/非监督机器学习 [1] - 速查表涉及深度学习 [1] - 速查表包含机器学习技巧与窍门 [1] - 速查表包括概率与统计 [1] - 速查表覆盖代数与微积分 [1]

AI Resources - Stanford provides 6 must-read cheat sheets for AI Engineers [1] - The cheat sheets cover Supervised/Unsupervised ML, Deep Learning, ML Tips & Tricks, Probability & Statistics, Algebra & Calculus [1] Social Sharing - The author encourages readers to reshare the content [1] - The author shares tutorials and insights on DS, ML, LLMs, and RAGs daily [1]