BOND May 2025 Trends – Artificial IntelligenceTrends – Artificial Intelligence (AI) May 30, 2025 Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey2 Context We set out to compile foundational datapoints turned into this beast. As soon as we updated one chart, we often had to update another – a data game of whack-a-mole… a pattern that shows no sign of stopping…and will grow more complex as competition The pace and scope of change related to the artificial intelligence technology evolution is indeed unprecedented, as supported by the data. This document is filled with user, usage and revenue charts

LLaMA 2 70B LLaMA 3 8B LLaMA 3 70B Mistral 7B Mixtral 8x7B Mixtral 8x22B Command R Command R+ Grok-1 DBRX Qwen1.5 32B Qwen1.5 72B LLaMA 1 Family LLaMA 2 Family LLaMA 3 Family Mixtral Family . . . . . . . . . . . . . . . . 11 3 Pre-Training 11 3.1 Experimental Setups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.1 Data Construction . . . . . . . . . . . . . . . . . . . . . 31 E Discussion About Pre-Training Data Debiasing 32 F Additional Evaluations on Math and Code 33 G Evaluation Formats 34 3 1. Introduction In the past few years, Large Language

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 adding value. 03 Powering products By delivering more relevant and responsive customer experiences. 3 AI in the EnterpriseBut leveraging AI isn’t the same as building software or deploying cloud apps. The employees can focus on 
 the things only people can do. And because AI can process huge amounts of data from many sources, it can create customer experiences that feel more human because they’re more relevant

guide, you’ll have the foundational knowledge you need to confidently start building your first agent. 3 A practical guide to building agents What is an agent? While conventional software enables users to error-prone, 
 for example performing vendor security reviews. 03 Heavy reliance on unstructured data: Scenarios that involve interpreting natural language, 
 extracting meaning from documents, or interacting implement the same concepts using your preferred library or building directly from scratch. Python 1 2 3 4 5 6 weather_agent = Agent( name= instructions= tools=[get_weather], ) , "Weather agent"

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1驱动万物智能 AliOs overview 。 AliOs (www.alios.cn) is a newly designed to drive everything toward intelligence. The Alios is running in vehicles, Phone, Pad and loT terminals. Provide cloud and devices co-related Upstream Master ) 。， Optimize on INT8 & FP32 AiiOS ! 驱动万物智能 Alios TVM @ ARM CPU INT8 * Cache 芍四 Data FO Data FOData … QNNPACK Convolution 。，NHWC layout Cach， 浆百 FeU Cach- 区下 。， Cache 大站 Fe Data FO Data … FOData QNNPACK /NiiOS ! 驱动万物智能 P Cache 浆加 Data FO Data FOData … NHWC L2 da … FL2 da Alios TVM @ ARM CPU INT8 TVM /QNNPACK Speed Up @ Mobilenet V2 @ rasp 3b+ AARCH64 35

TensorCore AutocCodeGen and Mixed-Precision Training/Inference PAI (Platform of AD Alibaba Cloud Intelligence Outline 计算平台事业部 。TensorCore AutoCodeGen memory load latency 。 storage align to reduce bank conflicts of shared memory 。 Virtual threads for data reuse (on going) Performance on V100 (FP16) 计算平台事业部 COMPUTING PLATFORM 512, 16, 512 512, 32, 512

. . . . . . . 8 2.2.4 SD (Stable Diffusion) . . . . . . . . . . . . . . . . . . . . 8 2.2.5 DALLE3 . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.6 Midjourney . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.6.4 Llama3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 零代码本地部署 AI 后端 13 3.1 大模型 Llama3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.3 使用 Llama3 . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2 大模型 phi-3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.1 Ollama 安装 phi-3 . . . . . . . . . . . .

as image prompts) is the input the model uses to predict a specific output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most complicated. Many aspects of your prompt affect its efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matter. Therefore responses, and can hinder the model’s ability to provide meaningful output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. Prompt Engineering February

shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control flow: concatenate within a while loop Limitation of TVM/graph typing Any: represent an unknown dimension at compilation time. Define a tensor type: Tensor<(Any, 3, 32, 32), fp32> Define type relation: arange: fn(start:fp32, stop:fp32, step:fp32) -> Tensor<(Any) modes (op_attrs, input_tensors, out_ndims) -> out_shape_tensors ○ Data dependent (op_attrs, input_data, out_ndims) -> out_shape_tensors ○ Data independent (op_attrs, input_shapes, out_ndims) -> out_shape_tensors©