USA-Based LLM: Total Current Users Outside North America Note: LLM data is for monthly active mobile app users. App not available in select countries, including China and Russia, as of 5/25. Source: included in East Asia figures. Data for standalone app only. Source: Sensor Tower (5/25) 5/23 4/25 Mobile App Monthly Active Users, MM Details on Page 315 AI & Work Evolution = Real + Rapid 8 USA → More Note: PC units as of 2000. Desktop internet users as of 2005, installed base as of 2010. Mobile internet units are the installed based of smartphones & tablets in 2020. Cloud & data center capex

fine-tune your 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 development lifecycle can multiply 
 AI dividends. 07 Set bold 
 automation goals Most processes involve a lot of rote work, ripe for automation. Aim high. Let’s drill down into each of these, with customer dramatically reduced search time; and advisors spend more time on client relationships, thanks to task automation and faster insights. The feedback from advisors has been overwhelmingly positive. They’re more

requires rethinking how your systems make decisions and handle complexity. Unlike conventional automation, agents are uniquely suited to workflows where traditional deterministic and rule-based approaches As you evaluate where agents can add value, prioritize workflows that have previously resisted automation, especially where traditional methods encounter friction: 01 Complex 
 decision-making: Workflows payments. 31 A practical guide to building agents Conclusion Agents mark a new era in workflow automation, where systems can reason through ambiguity, take action across tools, and handle multi-step tasks

generate optimized program for new operator workloads and hardware Hardware FrameworksWhy Automation is the Future Clear winner on emerging models in product Competitive on benchmarking type model enables other optimizations: fusion, layout, parallelization Portable performance across devicesWhy Automation is the Future 1 1 1 1 0.76 0.83 1.16 1.44 Large MatMul BatchConv Small MatMul BatchMatMul

hand-written, highly optimized baselines (https://github.com/mozilla/LPCNet) by ~40% - Bonus: Real-time on mobile CPUs for free 6 TVM specifics X78Structured and Unstructured Sparsity - Lots of 'free' wins

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 zero-shot prompt to classify movie reviews. The table prompts, which also includes writing prompts for returning code. Let’s go to the Vertex AI Studio and test these prompts to look at some coding examples. Prompts for writing code Gemini can also be a developer it’s essential to read and test your code first. The moment we are all waiting for, does it really work? Prompt Engineering February 2025 44 Let’s try it first with a test folder with only a few files

非常简单，基本都是下一步。注意在安装过程中，我们需要确 保”Use WSL 2 instead of Hyper-V (recommended)” 这一功能被启用。 docker 有 UI 界面，如图 22所示： 21 Figure 22: docker 在 mac 下的 UI 界面 如何验证 docker 是否安装成功，只需要运行下面命令： docker run hello-world 如果返回消息中带有：成功，表明安装成功。

AGIEval, CLUEWSC, CMRC, and CMath. In addition, we perform language- modeling-based evaluation for Pile-test and use Bits-Per-Byte (BPB) as the metric to guarantee fair comparison among models with different MoE # Activated Params - 67B 72B 39B 70B 21B # Total Params - 67B 72B 141B 70B 236B English Pile-test (BPB) - 0.642 0.637 0.623 0.602 0.606 BBH (EM) 3-shot 68.7 59.9 78.9 81.0 78.9 MMLU (Acc.) 5-shot W. Liu, S. Dong, and B. Wang. Cmath: Can your language model pass chinese elementary school math test?, 2023. L. Xu, H. Hu, X. Zhang, L. Li, C. Cao, Y. Li, Y. Xu, K. Sun, D. Yu, C. Yu, Y. Tian, Q. Dong

Copyright 2018 Xilinx Quantization Tool – vai_q ˃ 4 commands in vai_q quantize ‒ Quantize network test ‒ Test network accuracy finetune ‒ Finetune quantized network deploy ‒ Generate model for DPU ˃ increase accuracy decent_q Pre-trained model (fp32) Quantized model (Int16/Int8/...) quantize test finetune needs to increase accuracy deploy Y N Model for DPU Origin training data Calibration

command Step 10: Get the generated .sof file programmed into hardware Step 11: Evaluate the unit test script test_vta_insn.py with python3. Hooray!©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED ACCELERATED