AI Will Likely Do in Ten Years, per ChatGPT Source: ChatGPT (5/15/25) AI = Circa 2035?36 AI Development Trending = Unprecedented37 Machine-Learning Model* Trending = In 2015... Industry Surpassed Academia Models by Sector – 2003-2024, per Stanford HAI Annual New Notable Machine-Learning Models AI Development Trending = Unprecedented38 AI Developer Growth (NVIDIA Ecosystem as Proxy) = +6x to 6MM Developers to reach 2 million.’ Source: NVIDIA blog posts, press releases, & company overviews +6x AI Development Trending = Unprecedented Global Developers in NVIDIA Ecosystem (MM) – 2005-2025, Per NVIDIA39

costs and inference efficiency of DeepSeek 67B (Dense) and DeepSeek-V2. Contents 1 Introduction 4 2 Architecture 6 2.1 Multi-Head Latent Attention: Boosting Inference Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2.3 Training and Inference Efficiency . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Alignment 16 4.1 Supervised Fine-Tuning Language Models (LLMs) (Anthropic, 2023; Google, 2023; OpenAI, 2022, 2023) have undergone rapid development, offering a glimpse into the dawn of Artificial General Intelligence (AGI). In general, the intelligence

value faster and with greater buy-in from 
 users and stakeholders. Our approach: iterative development OpenAI is organized around three teams. Our Research Team advances the foundations of AI, developing are best-placed to improve 
 it with AI. 06 Unblock your
 developers Automating the software development lifecycle can multiply 
 AI dividends. 07 Set bold 
 automation goals Most processes involve scale up to significant business impact. But scaling up also meant using more tokens. To increase efficiency, OpenAI and Indeed 
 worked together to fine-tune a smaller GPT model that was able to deliver

Set Convolution, Max Pool etc. ˃ Any Network, Any Image Size ˃ High Frequency & High Compute Efficiency ˃ Supported on U200 – 3 Instances U250 – 4 Instances Amazon F1 ˃ ~1536 DSPs @ 700MHz Execution WB WR SCHEDULER CTRL SIGNALS MISC CALC AVG POOL MAX POOL ROI POOL ELEMENT WISE ... Efficiency > 50% for mainstream neural networks >> 4© Copyright 2018 Xilinx Inference Flow >> 5 MxNet

GPU /NiiOS ! 驱动万物智能 8000% 7000% 6000% 5000% 4000% 3000% 2000% 1000% 0o0% GEMM Hardware Efficiency @ Intel Apollo Lake GPU 60.39% 512,512,512 国OpenVINO 国TVM 68.89% 1024 1024, 1024 PART Five

Scientist and SDE positions 2. Internship for students interested in ML system. 3. Research & Development 3. Please contact Yida (wangyida [AT] amazon [DOT] com) if interested. We are hiring! 1 2

Apache TVM recently. Independent governance, allowing competitors to collaborate. Open Code Open Development Open GovernanceAcknowledgement Apache (incubating) TVM community Our awesome community members

More on this table format, the importance of tracking prompt engineering work, and the prompt development process is in the Best Practices section later in this chapter (“Document the various prompt

需要考虑的因素 任务目标、目标受众、文章类型、字数要求、特殊要求 在分析阶段，首先明确 任务目标和关键问题 通过四个关键步骤：分析（Analysis）、构思（Ideation）、发展（Development） 和评估（Assessment），为提示语链的设计提供系统化的指导。 构思阶段注重创新性思 维，探索多种解决方案 在发展阶段，逐步深化 构思并形成具体的内容 方案 最后的评估阶段用于反

需要考虑的因素 任务目标、目标受众、文章类型、字数要求、特殊要求 在分析阶段，首先明确 任务目标和关键问题 通过四个关键步骤：分析（Analysis）、构思（Ideation）、发展（Development） 和评估（Assessment），为提示语链的设计提供系统化的指导。 构思阶段注重创新性思 维，探索多种解决方案 在发展阶段，逐步深化 构思并形成具体的内容 方案 最后的评估阶段用于反