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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · 36氪 (36Kr) 中文(ZH) ·

    Yuxin Technology: Plans to invest 39 million yuan to participate in the establishment of a special fund, mainly investing in early and mid-stage technology companies in artificial intelligence, big data, and related industrial chains

    Yuxin Technology plans to invest 39 million yuan in a new 200 million yuan fund focused on early-stage artificial intelligence and big data companies. The fund, named Deqing Yuxin, will be established in collaboration with investment firm Huijin Deqing and Yuxin Shuzhi. This move is considered a related party transaction and has been approved by the board of directors. AI

    IMPACT This investment signals continued financial commitment to early-stage AI and big data ventures by established tech companies.

  2. TOOL · Medium — fine-tuning tag English(EN) · · [2 sources]

    How to Select the Right GPU for AI Workloads: Inference, Fine-Tuning, and Training Explained

    Businesses can now access high-performance GPUs on demand through GPU as a Service (GPUaaS), eliminating the need for substantial upfront hardware investments. This service caters to various AI and data-intensive tasks, including machine learning, generative AI, deep learning training, and big data analytics. Additionally, selecting the right GPU for AI workloads involves more than just VRAM, as modern demands extend beyond memory capacity. AI

    IMPACT On-demand GPU access via GPUaaS lowers the barrier to entry for AI development and large-scale data processing.

  3. TOOL · Hugging Face Daily Papers English(EN) ·

    Cross-Paradigm Knowledge Distillation: A Comprehensive Study of Bidirectional Transfer Between Random Forests and Deep Neural Networks for Big Data Applications

    Researchers have explored bidirectional knowledge distillation between Random Forests and Deep Neural Networks, a novel approach to model compression and ensemble learning for big data. Their study introduces methods for progressive multi-stage distillation and uncertainty-aware transfer, demonstrating competitive performance and interpretability. Experiments across six datasets showed significant accuracy and regression scores, establishing a new direction for interpretable AI and scalable model deployment. AI

    IMPACT Establishes a new research direction for cross-paradigm knowledge transfer, potentially improving interpretable AI and model deployment in big data environments.