Over the decades, start-up companies and individuals taking on personal projects have adopted the habit of integrating Artificial Intelligence and Machine Learning into almost every project. AI and ML are meant to make things efficient and work faster—but you might be doing harm than good if you have a faulty AI system? In this story, we will discuss the benefits of AI in organizations and individual projects, reasons why AI should not be in every project, and how to build a solid AI system.

几十年来，从事个人项目的初创公司和个人已经习惯了将人工智能和机器学习集成到几乎每个项目中的习惯。 AI和ML旨在使事情变得高效和更快地工作，但是，如果AI系统出现故障，您可能弊大于利？ 在这个故事中，我们将讨论AI在组织和单个项目中的好处，为什么AI不应该出现在每个项目中的原因以及如何构建可靠的AI系统。

As AI is being democratized, more and more organizations are looking towards building models and making data predictions, but few are doing what it takes to implement a proper AI system.

随着AI的民主化，越来越多的组织正在寻求建立模型和进行数据预测的机会，但是很少有人在做适当的AI系统。

Gartner predicts that through 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them

Gartner预测，到2022年，由于数据，算法或负责管理这些项目的团队的偏见，有85％的AI项目将交付错误的结果

Just like a design system for products or brand books for messaging, there needs to be components and a process that guides consistency in how you collect data and build AI models.

就像用于产品的设计系统或用于消息传递的品牌书籍一样，需要有组件和过程来指导您如何收集数据和构建AI模型的一致性。

What is an AI System?

什么是AI系统？

An AI system is process infrastructure to use and scale your AI in an efficient way. Much like a design system, an AI system is a whole process that is built and maintained by an organization. A solid AI system is accurate, unbiased, secure, and saves time from collecting data to deploying algorithms.

人工智能系统是一种流程基础架构，可以有效地使用和扩展您的人工智能。 就像设计系统一样，人工智能系统是由组织构建和维护的整个过程。 可靠的AI系统准确，公正，安全，并节省了从收集数据到部署算法的时间。

建立AI系统之前要解决的4件事 (4 Things to Address Before Building an AI System)

Artificial Intelligence and Machine Learning can accomplish so many complex tasks but there are many concerns that organizations and individuals should know about before jumping into implementing AI and ML in their products and services.

人工智能和机器学习可以完成许多复杂的任务，但是在开始在其产品和服务中实现AI和ML之前，组织和个人应该了解许多问题。

• Use Cases: No system, no matter how intelligent, can do everything. AI should be directed at solving case-specific problems and not general problems. In every scenario, before one attempts to build Artificial Intelligent systems, one must make sure there is a problem that is to be solved and later work towards solving that particular problem. It is absolutely impractical to attempt to solve all the problems of the world using AI.

  用例：没有系统，无论多么智能，都可以做任何事情。 AI应该针对解决特定于案例的问题而不是一般性问题。 在每种情况下，在尝试构建人工智能系统之前，必须确保存在要解决的问题，并随后着手解决该特定问题。 试图使用AI解决世界上的所有问题是绝对不切实际的。

• Privacy: At all times, data privacy laws should be obeyed. In the attempt to build robust intelligent systems, organizations and individuals may tend to require lots of data from clients, customers, employees and others but not every data is accessible for use. Some data is protected by a significant number of laws called the data privacy laws and infringement on these laws have serious legal ramifications. Before attempting to collect or use any sort of data from clients or employees, make sure you have the permission to do so. If you do not have the required permissions to collect and use certain data, ask your clients or employees to sign a contract explicitly giving you the permission to collect or use their data.

  隐私：任何时候都应遵守数据隐私法。 在尝试构建强大的智能系统时，组织和个人可能倾向于从客户，客户，员工和其他人那里获取大量数据，但并非所有数据都可以使用。 某些数据受到大量称为数据隐私法的法律的保护，违反这些法律将产生严重的法律后果。 在尝试从客户或员工那里收集或使用任何类型的数据之前，请确保您具有这样做的权限。 如果您没有收集和使用某些数据所需的权限，请要求您的客户或员工明确签署合同，以授予您收集或使用其数据的权限。

• Bias And Inequality: Any organization or individual that seeks to develop products or provide services that are powered by Artificial Intelligence or Machine Learning has the sole responsibility of making sure that the product or service they are developing provides the required services to people of all kinds that have the legal rights to access that system. The system should at no point in time be biased with respect to gender, skin color, race or ethnicity and services provided should be equally provided in deserving proportions. An Intelligent system is only as good as the data used to train it. To make sure bias and inequality in intelligent systems is well taken care of, make sure to train it with good data containing all required labels.

  偏见与不平等：任何寻求开发以人工智能或机器学习为动力的产品或提供服务的组织或个人，均应全权负责确保他们正在开发的产品或服务向各类人提供所需的服务。拥有访问该系统的合法权利。 该系统在任何时候都不应在性别，肤色，种族或民族方面有偏见，应按应有的比例平均提供所提供的服务。 智能系统仅与用于训练它的数据一样好。 为确保妥善解决智能系统中的偏见和不平等问题，请确保使用包含所有必需标签的良好数据进行训练。

• Security: Security in AI involves the process of leveraging AI to identify and stop cyber threats with less human intervention than is typically expected or needed with traditional security approaches. The importance of this kind of security however is to protect the confidential data of anyone using that system. If any intelligent system rather compromises the security of users’ confidential data, that system needs to be checked.

  安全性： AI的安全性涉及利用AI来识别和阻止网络威胁的过程，而人工干预的程度比传统安全方法通常预期或需要的要少。 但是，这种安全性的重要性在于保护使用该系统的任何人的机密数据。 如果有任何智能系统损害了用户机密数据的安全性，则需要检查该系统。

如何知道是否应该使用AI (How to Know If You Should Use AI)

Artificial Intelligence and Machine Learning have been around for quite some time and I know everyone by now knows the potential and capabilities of these technologies. Okay, I’ll throw in some stats just in case!