AI in the Enterprise Lessons from seven frontier companiesContents A new way to work 3 Executive summary 5 Seven lessons for enterprise AI adoption Start with evals 6 Embed AI into your products models 13 Get AI in the hands of experts 16 Unblock your developers 18 Set bold automation goals 21 Conclusion 22 More resources 24 2 AI in the EnterpriseA new way 
 to work As an AI research and complex, interconnected workflows and systems. We’re seeing AI deliver significant, measurable improvements on three fronts: 01 Workforce performance Helping people deliver higher-quality outputs in shorter

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普通人学 AI 指南 作者：郭震 日期：2024 年 6 月 8 日 Contents 1 AI 大模型基础 4 1.1 AIGC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 AGI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.2 单位 B 和 T . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 AI 工具梳理 6 2.1 问答 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 ChatGPT . . . . . . . . . . 8 2.2.6 Midjourney . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 AI 视频工具 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3.1 Sora (OpenAI 公司) . . . .

IntelligenceTrends – Artificial Intelligence (AI) May 30, 2025 Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey2 Context We set out to compile foundational trends related to AI. A starting collection of several ’ At the time, the pace of change catalyzed by the internet was unprecedented. Consider now that AI user and usage trending is ramping materially faster…and the machines can outpace us. The pace and OpenAI’s ChatGPT with its extremely easy-to-use / speedy user interface. In addition, relatively new AI company founders have been especially aggressive about innovation / product releases / investments

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model DeepSeek-AI research@deepseek.com Abstract We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models. The model checkpoints are available at h t t p s : / / g i t h u b .

垂直领域优化：针对特定领域 （如医疗、法律）进行优化， 提供高精度结果。  长文本处理：擅长处理长文本 和复杂文档，适合专业场景。  定制化能力：支持用户自定义 训练和微调，适应特定需求。 Open AI o3 mini  小型化设计：轻量级模型， 适合资源有限的环境。  快速响应：优化推理速度， 适合实时交互场景。  通用性强：适用于多种自 然语言处理任务，如对话 生成和文本理解。 成数据提取并写入文件“2025春运数据.txt” Open AI o3mini 响应速度快，能够高效提 取所有需求链接，输出完 整可运行python脚本，代 码运行后生成文件，但数 据采集结果为空。 DeepSeek R1 能够提取所有网址并进行 筛选、去重，所撰写代码 运行后完成数据爬虫任务， 所获取数据准确，少量数 据有所遗漏。 提示词 测试结果受到数据样本、测试环境、AI抽卡、提示词模板等因素影响， 爬虫数据采集  目前DeepSeek R1、Open AI o3mini、Kimi k1.5支持联网查询网址，Claude 3.5 sonnet暂不支持；  四个模型均能根据上传的网页代码，对多个网址链接进行筛选、去重，完全提取出符合指令要求的所有网址链接并形成列表；  在复杂爬虫任务上，DeepSeek R1与Open AI o3min生成的代码均能正常执行数据采集任务，o3响应速度更快，R1数据采集结果更加完

write prompts, however this whitepaper focuses on writing prompts for the Gemini model within Vertex AI or by using the API, because by prompting the model directly you will have access to the configuration optimized for your specific model, regardless of whether you use Gemini language models in Vertex AI, GPT, Claude, or an open source model like Gemma or LLaMA. Besides the prompt, you will also need to zero-shot stands for ’no examples’. Prompt Engineering February 2025 14 Let’s use Vertex AI Studio (for Language) in Vertex AI,6 which provides a playground to test prompts. In Table 1, you will see an example

Jammy Zhou November 16th, 2019Bringing together the Arm ecosystemLinaro AI Initiative Provide the best-in-class Deep Learning performance by leveraging Neural Network acceleration in IP and SoCs from the the Arm ecosystem, through collaborative seamless integration with the ecosystem of AI/ML software frameworks and librariesArm NN open source project ● Linaro-hosted https://www.mlplatform.org/ ● Git or WIP: Hexagon DSP (via llvm), Ascend NPU, and more Green: Linaro 96BoardsLinaro for TVM ● Linaro AI/ML group can be a good fit for TVM collaborations on Arm based platforms to support more devices with

project. A goal is to nurture the TVM community and contribute new infrastructure and features. octom|.ai @octoml Q octoML Founding Team - The Octonauts - 四人全外日 Luis Ceze Jason Knight Tensorflow. 5 ， Improve scheduling of batch matrix multiplies. 时”Early autotuning templates improve performance by ~20% e What we're working on: This prevents most compute layers from being fused. Reshape Seattle WA Register Todayl! QQ octoML tvmconf.org 15 Q OctoML Questions? We are hiring see octoml.ai for more detailsl