Trends Artificial Intelligence
Maddison Project 1100 1000 1300 1200 1400 1500 1600 1700 1800 1900 2000 Printing Press Steam Engines Telegraph Electrification Mass Steel Production Mass Production & Assembly Lines Internal Combustion to computing, calculating or counting patents. Google patents data changes somewhat between each query so numbers are rounded and should be viewed as directionally accurate. Source: USA Patent & Trademark deployment frameworks that accompany it. The productivity upside may be significant, but unevenly distributed. The geopolitical, ethical, and economic implications may evolve gradually, not abruptly. As0 码力 | 340 页 | 12.14 MB | 4 月前3
OpenAI 《A practical guide to building agents》Examples Data Enable agents to retrieve context and information necessary for executing the workflow. Query transaction databases or systems like CRMs, read PDF documents, or search the web. Action Enable instructions executes workflows in a loop 02 Multi-agent systems, where workflow execution is distributed across multiple coordinated agents Let’s explore each pattern in detail. 13 A practical guide (blocklists, input length limits, regex filters) to prevent known threats like prohibited terms or SQL injections. Output validation Ensures responses align with brand values via prompt engineering and0 码力 | 34 页 | 7.00 MB | 6 月前3
DeepSeek-V2: A Strong, Economical, and Efficient
Mixture-of-Experts Language Modelapproaches have been explored to address this issue, including Grouped-Query Attention (GQA) (Ainslie et al., 2023) and Multi-Query Attention (MQA) (Shazeer, 2019). However, these methods often compromise limit the inference efficiency. In order to reduce the KV cache, Multi-Query Atten- tion (MQA) (Shazeer, 2019) and Grouped-Query Attention (GQA) (Ainslie et al., 2023) are proposed. They require a smaller respectively: q? = ??h?, (1) k? = ? ?h?, (2) v? = ??h?, (3) 6 Grouped-Query Attention (GQA) Multi-Head Attention (MHA) Multi-Query Attention (MQA) Multi-Head Latent Attention (MLA) Keys Queries Values0 码力 | 52 页 | 1.23 MB | 1 年前3
TVM: Where Are We GoingTVM: Where are we going Tianqi ChenCurrent Deep Learning Landscape Frameworks and Inference engines DL Compilers Kenrel Libraries Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated0 码力 | 31 页 | 22.64 MB | 5 月前3
开源中国 2023 大模型(LLM)技术报告开发工具有: :帮助用户极致优化 给大模型的提示词(prompt),使得对大语 言模型提问时,可以获得更理想的输出。 :用于语义搜索、LLM 编排和语言模 型工作流的一体化嵌入数据库,可以使用 SQL、对象存储、主题建模、图形分析和多模 态索引进行矢量搜索。 :专注以 Sketch、PSD、静态 图片等形式的视觉稿作为输入,通过智能化技 术一键生成可维护的前端代码,包含视图代码、 数据字段绑定、组件代码、部分业务逻辑代码。0 码力 | 32 页 | 13.09 MB | 1 年前3
普通人学AI指南JetBrains AI AI 编程开发助手,集成在 JetBrains 系列开发工具中,提升编码效率。 9 Figure 6: AI 编程工具 2.4.3 AirOps 用于生成和修改 SQL 语句的工具,旨在简化数据库操作。 2.4.4 ChatDev 面壁智能开发的 AI 智能体开发平台,支持创建和部署智能对话系统。 2.4.5 solo Mozilla 开源项目,提供零代码网站开发功能,易于使用。0 码力 | 42 页 | 8.39 MB | 8 月前3
清华大学 DeepSeek+DeepResearch 让科研像聊天一样简单snails distributed across a vertical rocky intertidal gradient. Functional Ecology 25:177-185 Bourdeau PE(2011) Constitutive and inducible defensive traits in co-occurring marine snails distributed across0 码力 | 85 页 | 8.31 MB | 8 月前3
Google 《Prompt Engineering v7》specific aspects of the RAG system that impact what content was inserted into the prompt, including the query, chunk settings, chunk output, and other information. Once you feel the prompt is close to perfect0 码力 | 68 页 | 6.50 MB | 6 月前3
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