Artificial intelligence (AI) and machine learning are often used interchangeably, but machine learning is a subset of the broader category of AI.
人工智能（AI）和机器学习经常被交替使用，但机器学习是更广泛的人工智能范畴的一个子集。
Put in context, artificial intelligence refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data.
具体来说，人工智能指的是计算机模仿人类思维并在现实世界环境中执行任务的一般能力，而机器学习指的是使系统能够通过经验和数据识别模式、做出决策并自我完善的技术和算法。
Computer programmers and software developers enable computers to analyze data and solve problems — essentially, they create artificial intelligence systems — by applying tools such as:
计算机程序员和软件开发人员通过应用以下工具，使计算机能够分析数据和解决问题，即创建人工智能系统：
machine learning 机器学习
deep learning 深度学习
neural networks 神经网络
computer vision 计算机视觉
natural language processing 自然语言处理
Below is a breakdown of the differences between artificial intelligence and machine learning as well as how they are being applied in organizations large and small today.
以下是人工智能和机器学习之间的区别，以及它们在当今大小企业中的应用情况。
What Is Artificial Intelligence? 什么是人工智能？#
Artificial Intelligence is the field of developing computers and robots that are capable of behaving in ways that both mimic and go beyond human capabilities. AI-enabled programs can analyze and contextualize data to provide information or automatically trigger actions without human interference.
人工智能（Artificial Intelligence）是一个开发计算机和机器人的领域，这些计算机和机器人能够模仿人类的行为，也能超越人类的能力。人工智能程序可以分析数据并将其与上下文联系起来，从而提供信息或自动触发行动，而无需人工干预。
Today, artificial intelligence is at the heart of many technologies we use, including smart devices and voice assistants such as Siri on Apple devices. Companies are incorporating techniques such as natural language processing and computer vision — the ability for computers to use human language and interpret images — to automate tasks, accelerate decision making, and enable customer conversations with chatbots.
如今，人工智能已成为我们使用的许多技术的核心，包括智能设备和语音助手，如苹果设备上的 Siri。公司正在将自然语言处理和计算机视觉（计算机使用人类语言和解释图像的能力）等技术应用于自动化任务、加速决策制定以及与聊天机器人进行客户对话。
What Is Machine Learning? 什么是机器学习？#
Machine learning is a pathway to artificial intelligence. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions.
机器学习是人工智能的一种途径。人工智能的这一子类使用算法从数据中自动学习洞察力和识别模式，并应用这种学习做出越来越好的决策。
By studying and experimenting with machine learning, programmers test the limits of how much they can improve the perception, cognition, and action of a computer system.
通过对机器学习的研究和实验，程序员测试了他们能在多大程度上改善计算机系统的感知、认知和操作。
Deep learning, an advanced method of machine learning, goes a step further. Deep learning models use large neural networks — networks that function like a human brain to logically analyze data — to learn complex patterns and make predictions independent of human input.
深度学习是一种先进的机器学习方法，它更进一步。深度学习模型使用大型神经网络（其功能类似于人脑对数据进行逻辑分析的网络）来学习复杂的模式，并独立于人类输入进行预测。
How Companies Use AI and Machine Learning 企业如何使用人工智能和机器学习#
To be successful in nearly any industry, organizations must be able to transform their data into actionable insight. Artificial Intelligence and machine learning give organizations the advantage of automating a variety of manual processes involving data and decision making.
要想在几乎所有行业取得成功，企业必须能够将数据转化为可操作的洞察力。人工智能和机器学习为企业带来了将涉及数据和决策制定的各种人工流程自动化的优势。
By incorporating AI and machine learning into their systems and strategic plans, leaders can understand and act on data-driven insights with greater speed and efficiency.
通过将人工智能和机器学习纳入系统和战略计划，领导者可以更快、更高效地理解数据驱动的见解并采取行动。
AI in the Manufacturing Industry 制造业中的人工智能#
Efficiency is key to the success of an organization in the manufacturing industry. Artificial intelligence can help manufacturing leaders automate their business processes by applying data analytics and machine learning to applications such as the following:
效率是制造业企业成功的关键。人工智能可以通过将数据分析和机器学习应用于以下应用，帮助制造业领导者实现业务流程自动化：
Identifying equipment errors before malfunctions occur, using the internet of things (IoT), analytics, and machine learning
利用物联网 (IoT)、分析和机器学习，在故障发生前识别设备错误
Using an AI application on a device, located within a factory, that monitors a production machine and predicts when to perform maintenance, so it doesn’t fail mid-shift
在工厂内的设备上使用人工智能应用程序，监控生产机器并预测何时进行维护，从而避免机器在中班时出现故障
Studying HVAC energy consumption patterns and using machine learning to adjust to optimal energy saving and comfort level
研究暖通空调能耗模式，利用机器学习调整到最佳节能和舒适水平
AI and Machine Learning in Banking 银行业的人工智能和机器学习#
Data privacy and security are especially critical within the banking industry. Financial services leaders can keep customer data secure while increasing efficiencies using AI and machine learning in several ways:
数据隐私和安全在银行业尤为重要。金融服务领导者可以通过多种方式，在利用人工智能和机器学习提高效率的同时，确保客户数据的安全：
Using machine learning to detect and prevent fraud and cybersecurity attacks
利用机器学习检测和预防欺诈和网络安全攻击
Integrating biometrics and computer vision to quickly authenticate user identities and process documents
整合生物识别技术和计算机视觉技术，快速验证用户身份和处理文件
Incorporating smart technologies such as chatbots and voice assistants to automate basic customer service functions
融入聊天机器人和语音助手等智能技术，实现基本客户服务功能的自动化
AI Applications in Health Care 人工智能在医疗保健领域的应用#
The health care field uses huge amounts of data and increasingly relies on informatics and analytics to provide accurate, efficient health services. AI tools can help improve patient outcomes, save time, and even help providers avoid burnout by:
医疗保健领域使用大量数据，并越来越依赖于信息学和分析来提供准确、高效的医疗服务。人工智能工具可以通过以下方式帮助改善患者的治疗效果、节省时间，甚至帮助医疗服务提供者避免职业倦怠：
Analyzing data from users’ electronic health records through machine learning to provide clinical decision support and automated insights
通过机器学习分析用户电子健康记录中的数据，提供临床决策支持和自动洞察力
Integrating an AI system that predicts the outcomes of hospital visits to prevent readmissions and shorten the time patients are kept in hospitals
整合人工智能系统，预测医院就诊结果，防止再次入院，缩短患者住院时间
Capturing and recording provider-patient interactions in exams or telehealth appointments using natural-language understanding
利用自然语言理解能力捕捉和记录检查或远程保健预约中提供方与患者的互动
原文地址：https://ai.engineering.columbia.edu/ai-vs-machine-learning/