数智周报将整合本周最重要的企业级服务、云计算、大数据领域的前沿趋势、重磅政策及行研报告。】 观点 科大讯飞刘庆峰：自主可控的AI基础设施已初步成型 科大讯飞董事长刘庆峰在第九届全球深商盛典暨中国企业家俱乐部20年活动上表示，全国产算力平台上，国产大模型在参数小一倍的情况下可对标国际领先 水平。在芯片受限的背景下，自主可控的AI基础设施已初步成型。 "大空头"Michael Burry：科技巨头赚取巨额利润的时代将终结，AI时代的关键指标是ROIC 知名投资者、"大空头"Michael Burry警告，大型科技公司靠相对少的投资赚取巨额利润的时代正在结束，AI是主因。他认为投资人应关注投入资本回报率 (ROIC)，而非营收增长或市场规模。他指出，AI正推动微软、谷歌和Meta等公司，从过去轻资产的软件模式，转向由数据中心、芯片和能源主导的资本密 集型的硬件公司。即使AI帮助科技巨头扩大了市场，ROIC下降仍可能在未来数年对股价造成压力。 贝莱德投资者调查：尽管投资者看好人工智能前景，但将投资重点转向能源和基础设施供应商 贝莱德的调查报告显示，尽管投资者看好人工智能前景，但将投资重点转向能源和基础设施供应商；贝莱 ...

Core Insights - DeepSeek has released a significant technological achievement by open-sourcing a new paper and module called Engram, which introduces a "lookup-computation separation" mechanism to enhance the performance of large language models in various tasks [2][5]. Summary by Sections Introduction of Engram - Engram is a scalable, lookup-based memory module designed to improve the efficiency of language models by separating memory retrieval from computational tasks [10][18]. Need for Engram - Traditional large language models rely on Transformer and Mixture-of-Experts (MoE) architectures, which combine memory and computation in a way that can lead to inefficiencies. Engram aims to address this by allowing models to handle factual memory and logical reasoning separately [8][9]. Core Technology of Engram - Engram utilizes modernized hashed N-gram embeddings, allowing for O(1) time complexity in memory retrieval, which significantly reduces computational costs while maintaining high retrieval speed [11][13]. Relationship with MoE - Engram provides a new axis of sparsity that complements MoE by offering static memory retrieval capabilities, thus optimizing parameter efficiency. In a 27 billion parameter model, Engram can utilize a large number of parameters for memory while consuming minimal computational resources during inference [15][16]. Performance Metrics - Engram has shown improved performance metrics across various benchmarks, such as achieving a loss of 1.950 on the Pile dataset and an accuracy of 60.4% on MMLU with 5-shot learning, outperforming both Dense and MoE models [17]. Community Reception - The Engram technology has received positive feedback from the community, with users highlighting its potential to separate memory pattern retrieval from neural computation, marking a new direction in model architecture design [18][19][21]. Future Implications - Observers speculate that Engram will be a core component of DeepSeek's upcoming V4 model, indicating a significant architectural advancement in memory and reasoning collaboration [22][23].