Machine Learning 简明教程

Difference Between AI and ML

Artificial Intelligence 和 Machine Learning 是技术界常用的两个时髦用语。虽然它们经常可以互换使用，但它们并不是一回事。人工智能 (AI) 和机器学习 (ML) 是相关概念，但它们有不同的定义、应用和含义。在本文中，我们将探讨机器学习和人工智能之间的差异，以及它们之间的关系。

Artificial Intelligence and Machine Learning are two buzzwords that are commonly used in the world of technology. Although they are often used interchangeably, they are not the same thing. Artificial intelligence (AI) and machine learning (ML) are related concepts, but they have different definitions, applications, and implications. In this article, we will explore the differences between machine learning and artificial intelligence and how they are related.

What is Artificial Intelligence?

Artificial intelligence 是一个涵盖开发可执行通常需要人类智能的任务（例如感知、推理、学习和决策）的智能机器的广泛领域。简单来说，人工智能是指机器执行通常需要人工干预或智力的任务的能力。

Artificial intelligence is a broad field that encompasses the development of intelligent machines that can perform tasks that typically require human intelligence, such as perception, reasoning, learning, and decision-making. In simple terms, AI is the ability of machines to perform tasks that normally require human intervention or intelligence.

人工智能有两种类型：狭义或弱人工智能和广义或强人工智能。狭义人工智能旨在执行特定任务（例如语音识别或图像识别），而广义人工智能旨在能够执行人类可以完成的任何智力任务。目前，我们只是使用了狭义人工智能，但目标是开发可应用于广泛任务的广义人工智能。

There are two types of AI: narrow or weak AI and general or strong AI. Narrow AI is designed to perform specific tasks, such as speech recognition or image recognition, while general AI is designed to be able to perform any intellectual task that a human can do. Currently, we only have narrow AI in use, but the goal is to develop general AI that can be applied to a wide range of tasks.

Branches of AI

人工智能就像一个装着多个分支的篮子，其中重要的分支是机器学习 (ML)、机器人技术、专家系统、模糊逻辑、神经网络、计算机视觉和 Natural Language Processing (NLP) 。

AI is like a basket containing several branches, the important ones being Machine Learning (ML), Robotics, Expert Systems, Fuzzy Logic, Neural Networks, Computer Vision, and Natural Language Processing (NLP).

ai ml

以下是 AI 其他重要分支的简要概述：

Here is a brief overview of the other important branches of AI:

  1. *Robotics * − Robots are primarily designed to perform repetitive and tedious tasks. Robotics is an important branch of AI that deals with designing, developing and controlling the application of robots.

  2. Computer Vision − It is an exciting field of AI that helps computers, robots, and other digital devices to process and understand digital images and videos, and extract vital information. With the power of AI, Computer Vision develops algorithms that can extract, analyze and comprehend useful information from digital images.

  3. Expert Systems − Expert systems are applications specifically designed to solve complex problems in a specific domain, with humanlike intelligence, precision, and expertise. Just like human experts, Expert Systems excel in a specific domain in which they are trained.

  4. Fuzzy Logic − We know computers take precise digital inputs like True (Yes) or False (No), but Fuzzy Logic is a method of reasoning that helps machines to reason like human beings before taking a decision. With Fuzzy Logic, machines can analyze all intermediate possibilities between a YES or NO, for example, "Possibly Yes", "Maybe No", etc.

  5. Neural Networks − Inspired by the natural neural networks of the human brain, Artificial Neural Networks (ANN) can be considered as a group of highly interconnected group of processing elements (nodes) that can process information by their dynamic state response to external inputs. ANNs use training data to improve their efficiency and accuracy.

  6. Natural Language Processing (NLP) − NLP is a field of AI that empowers intelligent systems to communicate with humans using a natural language like English. With the power of NLP, one can easily interact with a robot and instruct it in plain English to perform a task. NLP can also process text data and comprehend its full meaning. It is heavily used these days in virtual chatbots and sentiment analysis.

人工智能的示例包括虚拟助手、自动驾驶汽车、面部识别、自然语言处理和决策系统。

Examples of AI include virtual assistants, autonomous vehicles, facial recognition, natural language processing, and decision-making systems.

What is Machine Learning?

机器学习是人工智能的一个子集，它专注于教授机器如何从数据中学习。换句话说，机器学习是一个计算机可以自动学习数据中模式和关系的过程，而不需要明确地对它们进行编程以这样做。机器学习算法旨在检测和学习数据中的模式以进行预测或决策。

Machine learning is a subset of artificial intelligence that focuses on teaching machines how to learn from data. In other words, machine learning is a process by which computers can automatically learn patterns and relationships in data without being explicitly programmed to do so. Machine learning algorithms are designed to detect and learn from patterns in data to make predictions or decisions.

机器学习有三种主要类型：监督学习、无监督学习和强化学习。监督学习是指机器在具有已知结果的标记数据上进行训练。无监督学习是指机器在未标记的数据上进行训练，并被要求查找模式或相似性。强化学习是指机器通过与环境的交互通过反复试验进行学习。

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is when the machine is trained on labeled data with known outcomes. Unsupervised learning is when the machine is trained on unlabeled data and is asked to find patterns or similarities. Reinforcement learning is when the machine learns by trial and error through interactions with the environment.

机器学习的示例包括图像识别、语音识别、推荐系统、欺诈检测和自然语言处理。

Examples of machine learning include image recognition, speech recognition, recommendation systems, fraud detection, and natural language processing.

Artificial Intelligence Vs. Machine Learning – Overview

现在我们对机器学习和人工智能有了一个基本的了解，让我们深入了解两者之间的差异。

Now that we have a basic understanding of what machine learning and artificial intelligence are, let’s dive deeper into the differences between the two.

首先，机器学习是人工智能的一个子集，这意味着机器学习是更大的人工智能领域的一部分。机器学习是一种用于实现人工智能的技术。

Firstly, machine learning is a subset of artificial intelligence, meaning that machine learning is a part of the larger field of AI. Machine learning is a technique used to implement artificial intelligence.

其次，虽然机器学习专注于开发可以从数据中学习的算法，但人工智能专注于开发可以执行通常需要人类智能的任务的智能机器。换句话说，机器学习更专注于从数据中学习的过程，而人工智能更专注于创造可以执行智能任务的机器这一最终目标。

Secondly, while machine learning focuses on developing algorithms that can learn from data, artificial intelligence focuses on developing intelligent machines that can perform tasks that normally require human intelligence. In other words, machine learning is more focused on the process of learning from data, while AI is more focused on the end goal of creating machines that can perform intelligent tasks.

第三，机器学习算法旨在从数据中学习并随着时间的推移提高其准确性，而人工智能系统旨在学习并适应新情况和新环境。机器学习算法需要大量数据才能有效地进行训练，而人工智能系统可以适应并实时学习新数据。

Thirdly, machine learning algorithms are designed to learn from data and improve their accuracy over time, while artificial intelligence systems are designed to learn and adapt to new situations and environments. Machine learning algorithms require a lot of data to be trained effectively, while AI systems can adapt and learn from new data in real-time.

最后，与人工智能相比，机器学习在能力上受到更多限制。机器学习算法只能学习其接受训练的数据，而人工智能系统可以学习并适应新情况和新环境。机器学习非常适合通过模式识别才能解决的特定问题，而人工智能更适合需要推理和决策制定、复杂且实际的问题。

Finally, machine learning is more limited in its capabilities compared to AI. Machine learning algorithms can only learn from the data they are trained on, while AI systems can learn and adapt to new situations and environments. Machine learning is great for solving specific problems that can be solved through pattern recognition, while AI is better suited for complex, real-world problems that require reasoning and decision-making.

Difference Between Artificial Intelligence and Machine Learning

下表重点介绍了机器学习与人工智能之间的重要差别 −

The following table highlights the important differences between Machine Learning and Artificial Intelligence −

Key

Artificial Intelligence

Machine Learning

Definition

Artificial Intelligence refers to the ability of a machine or a computer system to perform tasks that would normally require human intelligence, such as understanding language, recognizing images, and making decisions.

Machine Learning is a type of Artificial Intelligence that allows a system to learn and improve from experience without being explicitly programmed. It articulates how a machine can learn and apply its knowledge to improve its decisions.

Concept

Artificial Intelligence revolves around making smart and intelligent devices.

Machine Learning revolves around making a machine learn/decide and improve its results.

Goal

The goal of Artificial Intelligence is to simulate human intelligence to solve complex problems.

The goal of Machine Learning is to learn from data provided and make improvements in machine’s performance.

Includes

Artificial Intelligence has several important branches including Artificial Neural Networks, Natural Language Processing, Fuzzy Logic, Robotics, Expert Systems, Computer Vision, and Machine Learning.

Machine Learning training methods include supervised learning, unsupervised learning, and reinforcement learning.

Development

Artificial Intelligence is leading to the development of such machines which can mimic human behavior.

Machine Learning is helping in the development of self-learning algorithms.