progress comes from bigger models versus smarter algorithms, based on how much computing power you'd need to reach top performance without any improvements. Source: Epoch AI (3/24) Impact of Improved Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25); USA Food & Drug Administration, ‘FDA Announces Completion of First AI-Assisted Scientific Review Pilot and Aggressive Agency-Wide Timeline’ (5/25); NITRD.gov (5/25) New AI-Enabled Medical Devices Approved by USA Food & Drug Administration – 1995-2023, per Stanford HAI & USA FDA Number of AI Medical Devices Approved Education /

：只输出新闻文本所属的种类，不需要额外解释。 USER 美国太空探索技术公司（SpaceX）的猎鹰 9 号运载火箭（Falcon 9）在经历美国联邦航空管理局（Federal Aviation Administration，FAA）短暂叫停发射后，于当地时间 8 月 31 日凌晨重启了发射任务。 11. 宣传标语生成：让模型生成贴合商品信息的宣传标语。 SYSTEM 你是一个宣传标语专家，请根据用户需

a travel guide. I will write to you about my location and you will suggest 3 places to visit near me. In some cases, I will also give you the type of places I will visit. My suggestion: "I am in Amsterdam a travel guide. I will write to you about my location and you will suggest 3 places to visit near me in a humorous style. My suggestion: "I am in Manhattan." Travel Suggestions: Output 1. Behold the would take the difference of years between my partner and me and add those up. (20+(9-3)). Let’s help the model to think a little bit more like me. Prompt Engineering February 2025 31 Table 12 is an example

points? 最重要的十个要点 What are the trends shown in this data? 找趋势 Can you describe the data? 描述数据 Show me the top trends in a visual format. 以视觉形式显示趋势 Can you clean this dataset? 清洗数据 Can you create a heatmap 创作10个不同的图展示数据 Can you write me an article based on this dataset or statistic results? 根据结果写文章 Can you explain this dataset in one paragraph? 用一段话来解释一下这个数据集 What insights do you see here? Give me a numbered list 3 年 7 月 D e e p S e e k 成 立 2 0 2 3 年 1 1 月 2 日 首个开源代码大模型 DeepSeek Coder发布 2 0 2 3 年 1 1 月 2 9 日 推出670亿参数的通用大模型 D e e p S e e k L L M ， 包 括 7 B 和67B的base及chat版本 发 布 新 一 代 推 理 模 型 D e e p S e

workflow execution and have access to the user. Translate ‘hello’ to Spanish, French and Italian for me! ... Manager Task Spanish agent Task French agent Task Italian agent 18 A practical guide to building {message.content}") async def for in print "Translate 'hello' to Spanish, French and Italian for me!" Translation step: Declarative vs non-declarative graphs

 Some frameworks are declarative, requiring Issues and Repairs Sales Orders 21 A practical guide to building agents For example, here’s how you’d implement the decentralized pattern using the Agents SDK for 
 a customer service workflow that handles

open-source models. The model checkpoints are available at h t t p s : / / g i t h u b . c o m / d e e p s e e k - a i / D e e p S e e k - V 2 . 0 20 40 60 80 100 Activated Parameters (Billions) 55 60 65 70 . 30 C Full Formulas of MLA 31 D Ablation of Attention Mechanisms 31 D.1 Ablation of MHA, GQA, and MQA . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 D.2 Comparison Between MLA and MHA but their performance does not match MHA (we provide the ablation of MHA, GQA and MQA in Appendix D.1). For DeepSeek-V2, we design an innovative attention mechanism called Multi-head Latent Attention

or its Affiliates. All rights reserved. Dynamic codegen: kernel dispatch (proposal) Relay op: conv2d Default function FTVMStrategy A generic function CPU strategy func GPU strategy func OpStrategy Inc. or its Affiliates. All rights reserved. How to register a strategy? @conv2d_strategy.register("cpu") def conv2d_strategy_cpu(attrs, inputs, out_type, target): strategy = OpStrategy() layout strategy.register_specialized_implement(wrap_compute_conv2d(topi.x86.conv2d_winograd), topi.x86.conv2d_winograd,

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operators like TF quantized_conv2d • Underlying calculations are different than FP32 conv2d • Sometimes operators are aggressively fused • TFLite fuses quantized_conv2d, bias, relu and requantize 𝑟𝑒𝑎𝑙_𝑣𝑎𝑙𝑢𝑒 operators that satisfy many framework operators • qnn.quantize, qnn.dequantize, qnn.requantize • qnn.conv2d, qnn.dense • qnn.concatenate • qnn.add, qnn.mul • QNN operators will be lowered to Relay operators its Affiliates. All rights reserved. QNN Conv2D Operator • Calculations are different from FP32 Conv2D https://discuss.tvm.ai/t/tf-lite-quantized-conv2d-operator-conversion/2651/8 𝑟𝑒𝑎𝑙_𝑣𝑎𝑙𝑢𝑒

（c）鲁棒性弱风险。由于深度神经网络存在非线性、大规模等特点，人 工智能易受复杂多变运行环境或恶意干扰、诱导的影响，可能带来性能下降、 决策错误等诸多问题。- 4 - 人工智能安全治理框架 （d）被窃取、篡改的风险。参数、结构、功能等算法核心信息，面临被 逆向攻击窃取、修改，甚至嵌入后门的风险，可导致知识产权被侵犯、商业机 密泄露，推理过程不可信、决策输出错误，甚至运行故障。 （e）输出不可靠风险。生成式人工智能可能产生 （c）训练数据标注不规范风险。训练数据标注过程中，存在因标注规则 不完备、标注人员能力不够、标注错误等问题，不仅会影响模型算法准确度、 可靠性、有效性，还可能导致训练偏差、偏见歧视放大、泛化能力不足或输出 错误。 （d）数据泄露风险。人工智能研发应用过程中，因数据处理不当、非授 权访问、恶意攻击、诱导交互等问题，可能导致数据和个人信息泄露。 3.1.3 系统安全风险 （a）缺陷、后门被攻击利用风险。人工智能算法模型设计、训练和验证 证机制，导致认证鉴权失效。 （c）不当使用引发信息泄露风险。政府、企业等机构工作人员在业务工 作中不规范、不当使用人工智能服务，向大模型输入内部业务数据、工业信息， 导致工作秘密、商业秘密、敏感业务数据泄露。 （d）滥用于网络攻击的风险。人工智能可被用于实施自动化网络攻击或- 6 - 人工智能安全治理框架 提高攻击效率，包括挖掘利用漏洞、破解密码、生成恶意代码、发送钓鱼邮件、 网络扫描、社会工程学攻击等，降低网络攻击门槛，增大安全防护难度。