人工智能ai的有关专业术语
“Any fool can know. The point is to understand.” — Albert Einstein
“任何傻瓜都知道。 重点是要了解。” - 艾尔伯特爱因斯坦
演算法 (Algorithms)
Algorithms are mathematical instructions written by data scientists that tell the machine how to go about finding solutions to a problem. When a small selection of data (called training data) is run through an algorithm repeated, each time tweaked by a data scientist until its results are reliably accurate, the result is a model that the machine can use for additional learning by itself. A Chatbot is a computer program designed to simulate conversation with human users, especially over the Internet. It is an assistant that communicates with us through text messages or voice and integrates as a virtual companion into websites, applications, or instant messengers.
算法是由数据科学家编写的数学指令，告诉机器如何去寻找问题的解决方案。 当少量数据(称为训练数据)通过重复的算法运行时，每次由数据科学家对其进行调整，直到其结果可靠地准确为止，结果就是机器可以自己用于其他学习的模型。 Chatbot是一种计算机程序，旨在模拟与人类用户的对话，尤其是在Internet上。 它是一种助手，可以通过短信或语音与我们进行通信，并可以作为虚拟伴侣集成到网站，应用程序或即时通讯程序中。
数据 (Data)
Data fuels AI. It allows AI systems to reveal patterns, trends, and associations with confidence. Some data is structured, which means it’s been organized into a format computer can easily read and analyze, such as a database or an Excel file. Other data is unstructured, like tweets, PDFs, and video files.
数据助长了人工智能。 它允许AI系统充满信心地揭示模式，趋势和关联。 一些数据是结构化的，这意味着它已经被组织成一种易于阅读和分析的格式计算机，例如数据库或Excel文件。 其他数据是非结构化的，例如推文，PDF和视频文件。
深度学习 (Deep learning)
Deep learning (DL) is a group of neural networks (which are, in turn, groups of machine learning models). Deep learning can find patterns in complex data structures like images, video, and sound. Many of its models need no explicit training in order to find a solution, making them excellent for solving problems too big and complex for humans to engineer. Deep learning has been used to train self-driving vehicles, detect fraud, and even make “DeepFake” videos of popular celebrities.
深度学习(DL)是一组神经网络(依次是机器学习模型组)。 深度学习可以在复杂的数据结构(例如图像，视频和声音)中找到模式。 它的许多模型都不需要进行明确的训练就能找到解决方案，从而使其非常适合解决人类无法解决的问题。 深度学习已用于训练自动驾驶汽车，检测欺诈行为，甚至制作流行名人的“ DeepFake”视频。
机器学习 (Machine learning)
Machine learning (ML) is the engine of an AI system. It describes machines that learn without explicit instructions on how to perform tasks. It often depends on models: trained artifacts that guide machines when interpreting new data. Models represent patterns of data and help a machine learning system make predictions without being told how to do so.
机器学习(ML)是AI系统的引擎。 它描述了无需明确说明如何执行任务即可学习的机器。 它通常取决于模型：训练有素的工件，它们在解释新数据时指导机器。 模型代表数据模式，并帮助机器学习系统进行预测而不会被告知如何做。
自然语言处理 (Natural language processing)
Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. NLP can train computers to process large amounts of human text, like newspapers or conversations, comprehending the intent and meaning of that data. With NLP, a machine can then reply to humans with nuance and understanding. A common example of NLP would be a customer service chatbot.
自然语言处理(NLP)是计算机程序能够理解人类所说的人类语言的能力。 NLP可以训练计算机处理大量的人类文本，例如报纸或对话，以理解数据的意图和含义。 借助NLP，一台机器可以以细微差别和理解来回复人类。 NLP的一个常见示例是客户服务聊天机器人。
神经网络 (Neural networks)
Neural networks are groups of machine learning models. They simulate the human brain’s densely interconnected brain cells. They can learn things, recognize patterns, and make decisions without having to be explicitly programmed. Neural networks are capable of finding patterns within data that are so complex that no human could program its analysis.
神经网络是一组机器学习模型。 它们模拟人脑的紧密互连的脑细胞。 他们可以学习事物，识别模式并做出决策，而无需进行显式编程。 神经网络能够发现数据中的模式，这些模式是如此复杂，以至于没有人能够对其分析进行编程。
强化学习 (Reinforcement learning)
Reinforcement learning is a type of ML model that doesn’t give the machine any data at all, labeled or unlabeled. Instead, the machine tries different actions and receives reward signals (like cookies for a dog!) when it makes correct moves. In this way, the system is trained to solve a problem, with no human work required.
强化学习是一种ML模型，它根本不会给机器提供任何数据，无论是带标签的还是无标签的。 取而代之的是，机器做出正确的动作时会尝试不同的动作并接收奖励信号(如狗的饼干！)。 这样，系统就无需任何人工就可以解决问题。
语音识别 (Speech recognition)
Speech recognition is a technology that can recognize spoken words, which can then be converted to text or carry out a spoken command. A subset of speech recognition is voice recognition, which is the technology for identifying a person based on their voice.
语音识别是一种可以识别口语单词的技术，然后可以将其转换为文本或执行口头命令。 语音识别的一个子集是语音识别，这是一种基于语音识别人的技术。
监督学习 (Supervised learning)
Supervised learning is a type of ML model that provides the machine with a set of highly accurate data that’s been labeled by a human. The machine uses this model to recognize related things in untrained data sets.
监督学习是一种ML模型，可为机器提供一组由人类标记的高度准确的数据。 机器使用此模型来识别未经训练的数据集中的相关事物。
无监督学习 (Unsupervised learning)
Unsupervised learning is a type of ML model that doesn’t give the AI any labeled data. Instead, it gives the AI unlabeled data, and the AI suggests various ways to cluster and organize it. This is valuable when the data is so large or complex that humans can’t identify its patterns themselves.
无监督学习是一种ML模型，不会给AI提供任何标记数据。 取而代之的是，它为AI提供了未标记的数据，并且AI建议了各种方法来对其进行聚类和组织。 当数据太大或太复杂以至于人类自己无法识别其模式时，这将非常有价值。
视觉识别 (Visual recognition)
Visual recognition, also known as computer vision, is an AI sub-field focused on training computers to understand and interpret pictures and videos. Visual recognition models learn how to identify objects, people, or individual attributes in an image. For example, a model could help evaluate automobile accidents, identify the type of vehicle involved and its damage, then estimating its cost to repair.
视觉识别，也称为计算机视觉，是一个AI子领域，专注于训练计算机以理解和解释图片和视频。 视觉识别模型学习如何识别图像中的对象，人物或单个属性。 例如，一个模型可以帮助评估汽车事故，确定所涉车辆的类型及其损坏，然后估算其维修成本。
翻译自: https://medium.com/analytics-vidhya/11-artificial-intelligence-terminology-you-need-to-know-166c46a36fe1
人工智能ai的有关专业术语