ai驱动数据安全治理_人工智能驱动的Microsoft工具简介

ai驱动数据安全治理

介绍 (Introduction)

Microsoft is nowadays one of the major providers for AI powered cloud services. In fact, according to a RightScale's survey carried out in 2018, Microsoft Azure Cloud services are currently second just to Amazon AWS (Figure 1).

如今，微软已成为AI驱动的云服务的主要提供商之一。 实际上，根据RightScale在2018年进行的调查，Microsoft Azure云服务目前仅次于Amazon AWS(图1)。

In this article, I will be considering Microsoft as case study as Microsoft CEO Satya Nadella recently shared Microsoft interest to make AI a vital part of their business [1].

在本文中，我将以微软为案例研究，因为微软首席执行官Satya Nadella最近分享了微软将AI成为其业务的重要组成部分的兴趣[1]。

“To be the leader in it, it's not enough just to sort of have AI capability that we can exercise—you also need the ability to democratise it so that every business can truly benefit from it.”                     - Satya Nadella

“要成为其中的领导者，仅仅拥有我们可以行使的AI能力是不够的–您还需要使它民主化的能力，以便每个企业都能真正从中受益。” -萨蒂亚·纳德拉( Satya Nadella)

I will now introduce you to some of the different Microsoft tools which are currently available and some alternatives provided by the completion. Finally, we will focus on what are going to be next steps in research.

现在，我将向您介绍一些当前可用的各种Microsoft工具以及完成功能提供的一些替代方法。 最后，我们将重点研究下一步的工作。

Microsoft AI工具 (Microsoft AI Tools)

Microsoft Azure currently provides a wide range of services which can be used to create any sort of AI powered solutions. Some of the most important ones are:

Microsoft Azure当前提供了广泛的服务，可用于创建任何类型的基于AI的解决方案。 一些最重要的是：

• Azure Machine Learning Service
  Azure机器学习服务
• Azure Machine Learning Studio
  Azure机器学习工作室
• Auto Machine Learning (ML)
  自动机器学习(ML)
• Azure Internet of Things (IoT)
  Azure物联网(IoT)

Azure机器学习服务 (Azure Machine Learning Service)

Azure Machine Learning enables you to create, train and test Machine Learning and Deep Learning models using Microsoft Cloud Services.

Azure机器学习使您可以使用Microsoft Cloud Services创建，训练和测试机器学习和深度学习模型。

In this way, you don't have to worry anymore about memory and computational power constraint of your local machine because all the work is executed on Microsoft Servers.

这样，您不必再担心本地计算机的内存和计算能力约束，因为所有工作都在Microsoft服务器上执行。

When using Azure Machine Learning, all the main Python libraries come preinstalled (eg. Tensorflow, PyTorch, scikit-learn), therefore reducing set-up time to a minimum. This enables developers to quickly create new models without any type of constraint or environmental set up.

使用Azure机器学习时，所有主要的Python库都已预先安装(例如Tensorflow，PyTorch，scikit-learn)，因此将设置时间减少到最少。 这使开发人员无需任何类型的约束或环境设置即可快速创建新模型。

Azure机器学习工作室 (Azure Machine Learning Studio)

Azure Machine Learning Studio enables users to perform Machine Learning tasks without needing any programming experience.

Azure机器学习Studio使用户无需任何编程经验即可执行机器学习任务。

ML models are created and tested just using a visual interface by dragging and dropping all the model components (Figure 2). Once a model is ready to be deployed in the real world, it can then be easily exported out of the Azure ML Studio platform.

只需使用可视界面通过拖放所有模型组件即可创建和测试ML模型(图2)。 一旦准备好在现实世界中部署模型，就可以轻松地将其导出到Azure ML Studio平台之外。

Right now, Azure Machine Learning Studio is mainly suitable for Clustering, Classification and Regression tasks. Additionally, if you want, it is possible to add code in Python or R in Azure Machine Learning Studio to add more workflow functionalities.

目前，Azure Machine Learning Studio主要适合于群集，分类和回归任务。 此外，如果需要，可以在Azure Machine Learning Studio中以Python或R添加代码以添加更多工作流功能。

自动机器学习(ML) (Auto Machine Learning (ML))

Automated Machine Learning is currently one of the hottest topics in AI.

自动化机器学习是当前AI中最热门的主题之一。

Nowadays Data Scientists and Machine Learning Engineers spend lots of their time trying to identify the best Machine Learning model and Hyper-parameters to use for each different prediction task.

如今，数据科学家和机器学习工程师花费大量时间试图确定最佳的机器学习模型和超参数，以用于每个不同的预测任务。

AutoML aims instead to automate this process by creating software able to correctly identify and test Machine Learning models (Figure 3).  

相反，AutoML旨在通过创建能够正确识别和测试机器学习模型的软件来自动化该过程(图3)。

Automated Machine Learning is still an area in large development and it can be used right now (with satisfactory results) just for a limited number of tasks.

自动化机器学习仍然是大型开发领域，它现在可以用于有限的任务(效果令人满意)。

AutoMl can be currently implemented using Microsoft Tools using either Azure Machine Learning or ML.NET. Right now, just classification and forecasting/regression problems can solved using Microsoft services.

当前可以使用Microsoft工具通过Azure机器学习或ML.NET来实现AutoMl 。 目前，使用Microsoft服务可以解决分类和预测/回归问题。

AutoML can instead be implemented in Python using libraries such as Auto-sklearn, TPOT and H2O. Application of AutoML in fields like Unsupervised Learning are currently still under development.

AutoML可以被替代地使用Python中的库，如实现自动sklearn ， TPOT和H2O 。 AutoML在无监督学习等领域的应用目前仍在开发中。

Azure物联网(IoT) (Azure Internet of Things (IoT))

Azure is able to deliver both pre-customized and fully customizable solutions for IoT services (Figure 4). In this way, Azure is able to provide solutions for both beginners and experts in IoT.

Azure能够提供针对物联网服务的预定制和完全可定制的解决方案(图4)。 通过这种方式，Azure能够为物联网的初学者和专家提供解决方案。

Microsoft Azure enables you to easily scale IoT systems to include devices from different manufacturers and also provides analytics and Machine Learning services support.

Microsoft Azure使您可以轻松扩展IoT系统，以包括来自不同制造商的设备，还提供分析和机器学习服务支持。

If you are looking for a more detailed explanation on how the Internet of Things is going to change our life and how it can be implemented using cloud services, take a look at my previous blog post.

如果您正在寻找有关物联网将如何改变我们的生活以及如何使用云服务实现它的更详细的解释，请查看我以前的博客文章 。

人工智能研究 (Research in Artificial Intelligence)

Microsoft is now demonstrating a huge interest in AI. Right now, some of its services such as Cortana, Skype or Office 365 are already starting to make extensive use of AI. Additionally, just in 2018, Microsoft acquired 5 AI companies.

微软现在对AI表现出极大的兴趣。 现在，它的某些服务(例如Cortana，Skype或Office 365)已经开始广泛使用AI。 此外，仅在2018年，微软就收购了5家AI公司。

Microsoft, also decided to create an organisation called Microsoft Research AI to work on future developments of AI products. Some of the main topics currently under research are: Bias in AI, Ethics, and Interpretability.

微软还决定创建一个名为Microsoft Research AI的组织，致力于AI产品的未来开发。 当前正在研究的一些主要主题是：人工智能，伦理和可解释性方面的偏见。

人工智能的偏见 (Bias in AI)

According to Rich Caruana, a senior researcher at Microsoft, Microsoft is currently working on creating a bias-detection tool [6].

微软高级研究员Rich Caruana表示，微软目前正在开发一种偏差检测工具[6]。

Every Machine Learning model is trained using some input data. Although, at times, the input data might contain some form of bias which might compromise the model's ability to correctly make predictions (eg. favourite a class compared to another). Testing a trained model using a Bias-detection tool could, therefore, be of great help in minimising this risk.

每个机器学习模型都使用一些输入数据进行训练。 尽管有时输入数据可能包含某种形式的偏差，这可能会损害模型正确做出预测的能力(例如，将某个类与另一个类相比)。 因此，使用偏差检测工具测试经过训练的模型可能对最大程度地降低这种风险有很大帮助。

In the meantime, other companies such as Facebook and IBM are also currently working on implementing similar tools for their corresponding businesses [6, 7].

同时，Facebook和IBM等其他公司目前也正在为其相应的业务实施类似的工具[6，7]。

伦理 (Ethics)

In April 2019, the European Commission published a list of Ethics Guidelines for Trustworthy AI. These principles, in addition to the previous application of GDPR (General Data Protection Regulation), defined quite a clear path of how end users should be given access to fair/unbiased products and how their personal data should be protected.

2019年4月，欧盟委员会发布了可信赖AI的道德准则清单。 这些原则，除了先前的GDPR(通用数据保护法规)应用之外， 还为如何使最终用户获得公平/公正的产品以及如何保护其个人数据定义了一条非常清晰的路径。

Big companies such as Google, Facebook and Microsoft have already started working towards this direction. Techniques such Differential Privacy and Federated Learning have been created in order to protect users' privacy in AI applications.

像Google，Facebook和Microsoft这样的大公司已经开始朝这个方向努力。 为了保护AI应用程序中用户的隐私，已经创建了差异隐私和联合学习等技术。

“Artificial Intelligence brings great opportunity, but also great responsibility. We’re at that stage with AI where the choices we make need to be grounded in principles and ethics – that’s the best way to ensure a future we all want.”                                                         - Satya Nadella [8]

“人工智能带来了巨大的机会，但也带来了巨大的责任。 我们处在AI的那个阶段，我们做出的选择必须以原则和道德为基础-这是确保所有人都希望的未来的最佳方法。” -萨蒂亚·纳德拉[8]

可解释性 (Interpretability)

Use of AI in decision-making applications (such as medicine or law) has recently caused some concerns both for individuals and authorities. This is because, when working with complex Machine Learning models or deep neural networks, it is currently not possible (at least to a full extent) to understand the decision-making process the algorithm performs when having to carry out a predetermined task.

最近，在决策应用程序(例如医学或法律)中使用AI引起了个人和当局的关注。 这是因为，当使用复杂的机器学习模型或深度神经网络时，当前(至少在最大程度上)无法理解算法在必须执行预定任务时执行的决策过程。

One possible solution to this problem is Explainable AI (XAI). The main aims of XAI are to make machines explain themselves and to reduce the impact of biased algorithms.

解决此问题的一种可能方法是可解释AI(XAI)。 XAI的主要目的是使机器自我解释并减少有偏见的算法的影响。

Different algorithms have been implemented in last few years in order to make models more explainable. Some examples are: Reversed Time Attention Model (RETAIN), Local Interpretable Model-Agnostic Explanations (LIME) and Layer-wise Relevance Propagation (LRP). These can be implemented using Python libraries such as ELI5, Skater, SHAP and Microsoft InterpretML.

为了使模型更易于解释，最近几年实施了不同的算法。 一些示例包括：反向时间注意力模型(RETAIN)，本地可解释模型不可知性解释(LIME)和分层分层相关性传播(LRP)。 可以使用Python库(例如ELI5，Skater，SHAP和Microsoft InterpretML)实现这些功能。

If you are interested in finding out more about Explainable AI, you can find more information here.

如果您想了解有关Explainable AI的更多信息，可以在此处找到更多信息。

联络人 (Contacts)

If you want to keep updated with my latest articles and projects follow me and subscribe to my mailing list. These are some of my contacts details:

如果您想随时了解我的最新文章和项目，请关注我并订阅我的邮件列表 。 这些是我的一些联系方式：

• Linkedin

  领英

• Personal Blog

  个人博客

• Personal Website

  个人网站

• Medium Profile

  中档

• GitHub

  的GitHub

• Kaggle

  卡格勒

参考书目 (Bibliography)

[1] Microsoft CEO Satya Nadella On The Extraordinary Potential Of AI. Forbes, Bob Evans. Accessed at: https://www.forbes.com/sites/bobevans1/2018/06/04/microsoft-ceo-satya-nadella-on-the-extraordinary-potential-of-ai/#3c3c6383162f

[1] Microsoft首席执行官Satya Nadella关于AI的非凡潜力。 福布斯， 鲍勃·埃文斯 。 访问网址 ： https : //www.forbes.com/sites/bobevans1/2018/06/04/microsoft-ceo-satya-nadella-on-the-extraordinary-potential-of-ai/#3c3c6383162f

[2] Top cloud providers 2018: How AWS, Microsoft, Google, IBM, Oracle, Alibaba stack up. ZDNet, Larry Dignan. Accessed at: https://www.zdnet.com/article/top-cloud-providers-2018-how-aws-microsoft-google-ibm-oracle-alibaba-stack-up/

[2] 2018年顶级云提供商：AWS，Microsoft，Google，IBM，Oracle，阿里巴巴如何堆叠。 ZDNet， Larry Dignan 。 访问网址 ： https : //www.zdnet.com/article/top-cloud-providers-2018-how-aws-microsoft-google-ibm-oracle-alibaba-stack-up/

[3] Code free Data Science Microsoft Azure Machine Learning. Accessed at: https://gilberttanner-homepage.cdn.prismic.io/gilberttanner-homepage/b391e301d2372a1c42bed40506a6ab5e7c072bb3_azure-ml-studio-1.jpg

[3]无代码数据科学Microsoft Azure机器学习。 访问网址 ： https : //gilberttanner-homepage.cdn.prismic.io/gilberttanner-homepage/b391e301d2372a1c42bed40506a6ab5e7c072bb3_azure-ml-studio-1.jpg

[4] Tutorial: Use automated machine learning to predict taxi fares. Microsoft Azure Documentation. Accessed at: https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-auto-train-models

[4]教程：使用自动机器学习来预测出租车费用。 Microsoft Azure文档。 访问以下网址 ： https : //docs.microsoft.com/zh-cn/azure/machine-learning/service/tutorial-auto-train-models

[5] Smart Devices and Analytics Spur Innovation in the Internet of Things. Eric Wetjen, MathWorks. Accessed at: https://www.mathworks.com/company/newsletters/articles/smart-devices-and-analytics-spur-innovation-in-the-internet-of-things.html

[5]智能设备和分析促进物联网的创新。 Eric Wetjen，MathWorks。 访问以下网址 ： https ： //www.mathworks.com/company/newsletters/articles/smart-devices-and-analytics-spur-innovation-in-the-internet-of-things.html

[6] Microsoft Creating Tool to Weed Out AI Bias. Dominique Adams, DIGIT. Accessed at: https://digit.fyi/microsoft-ai-bias-detection/

[6] Microsoft创建工具以消除AI偏见。 Dominique Adams，DIGIT。 访问网址 ： https : //digit.fyi/microsoft-ai-bias-detection/

[7] IBM launches tool aimed at detecting AI bias. Zoe Kleinman, BBC. Accessed at: https://www.bbc.co.uk/news/technology-45561955

[7] IBM推出了旨在检测​​AI偏差的工具。 英国广播公司(BBC)的佐伊·克莱曼(Zoe Kleinman)。 访问网址 ： https : //www.bbc.co.uk/news/technology-45561955

[8] Guidelines released for ethical and trustworthy AI. Cornelia Kutterer - Senior Director EU Government Affairs, AI, Privacy & Digital Policies, Microsoft. Accessed at: https://blogs.microsoft.com/eupolicy/2019/04/09/ethical-guidelines-trustworthy-ai/

[8]发布了有关道德和可信赖AI的指南。 Cornelia Kutterer-欧盟政府事务高级总监，微软AI，隐私和数字政策 。 访问网址 ： https : //blogs.microsoft.com/eupolicy/2019/04/09/ethical-guidelines-trustworthy-ai/

翻译自: https://www.freecodecamp.org/news/ai/

ai驱动数据安全治理