如何提交一个新的 AI 应用

support regulators.We will work with regulators to develop guidance that helps them implementthe principles in a way that aligns with our expectations for how the framework should operate.Existing legal frameworks already mandate and guide regulators' actions.For example,nearlyall regulators are bound by the Regulators'Code116and all regulators – as public bodies – arerequired to comply with the Human RightsAct.117Our proposed guidance to regulators will seekto ensure that when applying the principles,regulators are supported and encouraged to:oAdopt a proportionate approach that promotes growth and innovation by focusing on the risksthat AI poses in a particular context.oConsider proportionate measures to address prioritised risks,taking into account cross-cuttingrisk assessments undertaken by,or on behalf of,government.oDesign,implement and enforce appropriate regulatory requirements and,where possible,integrate delivery of the principles into existing monitoring,investigation and enforcementprocesses.oDevelop joint guidance,where appropriate,to support industry compliance with the principlesand relevant regulatory requirements.Connected & Automated Mobility 2025,Department for Transport,2022.Regulators’ Code,Office for Product Safety and Standards,2014.Human Rights Act,HM Government,1998A pro-innovation approach to AI regulationoConsider how tools for trustworthy AI like assurance techniques and technical standards cansupport regulatory compliance.oEngage proactively and collaboratively with government’s monitoring and evaluation of theframework.A pro-innovation approach to AI regulationCase Study 3.7:What this means for businessesA fictional company,“AI Fairness Insurance Limited”,has delayed the deployment of anew AI application as – under the current patchwork of relevant regulatory requirements –it has been challenging to identify appropriate compliance actions for AI-driven insuranceproducts.Following implementation of the UK’s new pro-innovation framework to regulate AI,wecould expect to see joint guidance produced collaboratively by the Information

Read more about how we built instant apply in our[blog post](https://cursor.com/blog/instant-apply).在我们的[博客文章](https://cursor.com/blog/instant-apply)中阅读更多关于我们如何构建即时申请的信息。Cursor’s Apply allows you to quickly integrate a codeblock suggestion from the chat into your code.Cursor的Apply允许您将聊天中的代码块建议快速集成到您的代码中。[heading3][heading3]Apply Code Blocks应用代码块[content]To apply a code block suggestion,you can press on the play button in the top right corner of each chat code block.要应用代码块建议，您可以按每个聊天代码块右上角的播放按钮。This will edit your file to incorporate the code produced by Chat.Since you can add the most context and have the most back-and-forth with the model in Chat,we recommend Chat + Apply for more complex AI-driven code changes.这将编辑您的文件以合并Chat生成的代码。由于您可以在Chat中添加最多的上下文并与模型进行最多的来回交流，因此我们建议使用Chat + Apply进行更复杂的AI驱动的代码更改。[heading3][heading3]Accept or Reject接受或拒绝[content]Once you have applied a code block,you can go through the diffs and accept or reject the changes.You can also click on the “Accept” or “Reject” buttons in the top right corner of the chat code block.应用代码块后，您可以浏览差异并接受或拒绝更改。您也可以点击聊天代码块右上角的“接受”或“拒绝”按钮。Ctrl/⌘ Enter to accept,Ctrl/⌘ Backspace to reject.Ctrl/⌘ Enter键接受，Ctrl/⌘ Backspace键拒绝。

[title]写给不会代码的你：20分钟上手Python + AI[heading1]完成了一个AI应用[heading2]之后呢？[heading3]如果希望继续精进...对于AI，可以尝试了解以下内容，作为基础AI背景知识基础理论：了解人工智能、机器学习、深度学习的定义及其之间的关系。历史发展：简要回顾AI的发展历程和重要里程碑。数学基础统计学基础：熟悉均值、中位数、方差等统计概念。线性代数：了解向量、矩阵等线性代数基本概念。概率论：基础的概率论知识，如条件概率、贝叶斯定理。算法和模型监督学习：了解常用算法，如线性回归、决策树、支持向量机（SVM）。无监督学习：熟悉聚类、降维等算法。强化学习：简介强化学习的基本概念。评估和调优性能评估：了解如何评估模型性能，包括交叉验证、精确度、召回率等。模型调优：学习如何使用网格搜索等技术优化模型参数。神经网络基础网络结构：理解神经网络的基本结构，包括前馈网络、卷积神经网络（CNN）、循环神经网络（RNN）。激活函数：了解常用的激活函数，如ReLU、Sigmoid、Tanh。