Partnership enriches the bidstream through the power of AINEW YORK, Jan. 06, 2026 (GLOBE NEWSWIRE) -- Magnite (MGNI), the largest independent sell-side advertising company, and Cognitiv, the leading advanced performance partner powered by deep learning, today announced a real-time data integration to expand curation capabilities available across ClearLine, Magnite’s unified activation and curation solution. This collaboration gives media buyers more effective ways to plan, test, and activate custom curated ...

bir Karabü'den bahsedeceğim ya da başka bir de işte nasıl firmalar bütçelerini daha karabük olarak nitlendirdiğimiz bir şey için çarşı ediyorlar ya da nasıl bir sürü do işlem alanında yıllarını vermiş araştırmacılar eee sırf bu şirketlerin açığı yüzünden maalesef e hayata küsü ya da çalışmalarını eee devam ettiremiyor ve doğru tahmin ettiniz diye tahmin ediyorum. Eee ve bugün biraz yapay zeka konuşacağız. eee, bir adım adım yapay zeka oluştururken e hangi adımlara dikkat ediyoruz. Eee, ya da evet yani bir y ...

SHENZHEN, China, Dec. 18, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep convolutional neural network image 3D reconstruction technology system. This system first utilizes quantum convolutional neural network to complete the feature extraction of input images, then generates the core parameters of the 3D model through quantum fully connected layers, and finally imports these parameters int ...

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]

AI Engineering 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]

AI发展历程 - 10年前，AI无法区分猫和狗，但公司相信深度学习潜力巨大，可能成为人类的巨大胜利 [1] - 公司在AI领域进行了大量实验，虽然部分实验失败，但通过不断尝试和探索，取得了重要进展 [1] - 公司在文本预测方面取得了有趣发现，并持续深入研究 [1] - 过去三年是公司发展的巨大时期，取得了显著进展 [2] 未来展望 - 公司认为AI发展仍处于起步阶段，未来有更大的发展空间 [2] - 公司对AI的未来充满信心，并计划继续投入研发 [2] 战略方向 - 公司坚信规模化发展的重要性，并持续扩大投入 [2] - 公司在过去10年中积累了大量经验和想法，为未来的发展奠定了基础 [2]

AI Development & Progress - AI 在过去 10 年取得了显著进展，从无法区分猫狗到深度学习的巨大潜力 [1] - 公司坚信深度学习是人类的巨大胜利 [1] - 过去 3 年是 AI 发展的巨大时期 [2] - 公司在 AI 领域进行了大量实验，有成功也有失败 [1] - 公司在文本预测方面取得了有趣的发现 [1] Future Outlook - 公司对 AI 的未来充满信心，认为目前只是开始 [2] - 公司将继续扩大规模并不断学习 [2]

WEST LAFAYETTE, Ind., Dec. 10, 2025 (GLOBE NEWSWIRE) -- Intellicule, a software company whose solutions determine the 3D structures of biomolecules imaged with cryogenic-electron microscopy (cryo-EM), has received a $217,941 Small Business Innovation Research (SBIR) Phase I grant from the National Institutes of Health.Daisuke Kihara, who leads Intellicule, said the grant will be used to develop software technology that could impact precision medicine.“It will have the potential to accelerate the development ...