Related fields
相關領域
In addition to AI, there are several other closely related topics that are good to know at least by name. These include machine learning, data science, and deep learning.
除了人工智能，還有其他幾個密切相關的話題。其中包括機器學習、數據科學和深度學習。
Machine learning can be said to be a subfield of AI, which itself is a subfield of computer science (such categories are often somewhat imprecise and some parts of machine learning could be equally well or better belong to statistics). Machine learning enables AI solutions that are adaptive. A concise definition can be given as follows:
機器學習可以說是人工智能的一個分支，人工智能本身就是計算機科學的一個分支（這類分類往往有點不精確，機器學習的某些部分屬於統計學）。機器學習使人工智能解決方案具有自適應性。簡明定義如下：
Key terminology
關鍵術語
Machine learning
Systems that improve their performance in a given task with more and more experience or data.
通過越來越多的經驗或數據來提高任務性能的系統。
Deep learning is a subfield of machine learning, which itself is a subfield of AI, which itself is a subfield of computer science. We will meet deep learning in some more detail in Chapter 5, but for now let us just note that the “depth” of deep learning refers to the complexity of a mathematical model, and that the increased computing power of modern computers has allowed researchers to increase this complexity to reach levels that appear not only quantitatively but also qualitatively different from before.
深度學習是機器學習的一個分支，它本身就是人工智能的一個分支，人工智能本身就是計算機科學的一個分支。我們將在第5章中更深入地瞭解深度學習，但現在我們要注意的是，深度學習的“深度”指的是數學模型的複雜性，並且現代計算機的計算能力的增強使得研究者能夠增加這種複雜性，使其達到不僅在數量上而且在質量上與以前都不同的水平。
As you notice, science often involves a number of progressively more special subfields, subfields of subfields, and so on. This enables researchers to zoom into a particular topic so that it is possible to catch up with the ever increasing amount of knowledge accrued over the years, and produce new knowledge on the topic — or sometimes, correct earlier knowledge to be more accurate.
正如你所注意到的，科學經常涉及到一些逐漸增加的更特殊的子領域，子領域的子領域，等等。這使得研究人員能夠放大特定的主題，以便能夠趕上多年來積累的不斷增長的知識量，併產生關於該主題的新知識——或者有時更正較早的知識以使其更加準確。
Data science is a recent umbrella term (term that covers several subdisciplines) that includes machine learning and statistics, certain aspects of computer science including algorithms, data storage, and web application development. Data science is also a practical discipline that requires understanding of the domain in which it is applied in, for example, business or science: its purpose (what "added value" means), basic assumptions, and constraints. Data science solutions often involve at least a pinch of AI (but usually not as much as one would expect from the headlines).
數據科學是最近出現的一個總稱（涵蓋多個分支學科的術語），包括機器學習和統計學，計算機科學的某些方面，包括算法、數據存儲和 web 應用程序開發。數據科學也是一門實踐學科，它要求理解應用於商業或科學的領域：其目的、基本假設和約束。數據科學解決方案通常至少涉及一點人工智能（但通常沒有人們從頭條上所期望的那麼多）。
Robotics means building and programming robots so that they can operate in complex, real-world scenarios. In a way, robotics is the ultimate challenge of AI since it requires a combination of virtually all areas of AI. For example:
機器人技術是指建立和編程機器人，使它們能夠在複雜的現實世界中工作。在某種程度上，機器人技術是人工智能的終極挑戰，因為它需要人工智能幾乎所有領域的結合。例如：
- Computer vision and speech recognition for sensing the environment用於感知環境的計算機視覺和語音識別
- Natural language processing, information retrieval, and reasoning under uncertainty for processing instructions and predicting consequences of potential actions不確定性條件下的自然語言處理、信息檢索和推理，用於處理指令和預測潛在行為的後果
- Cognitive modeling and affective computing (systems that respond to expressions of human feelings or that mimic feelings) for interacting and working together with humans認知建模和情感計算（響應人類情感表達或模仿情感的系統），用於與人類互動和合作
Many of the robotics-related AI problems are best approached by machine learning, which makes machine learning a central branch of AI for robotics.
機器學習可以最好地解決許多與機器人技術有關的 AI 問題，這使機器學習成為 AI 機器人技術的中心分支。
Note
What is a robot?
In brief, a robot is a machine comprising sensors (which sense the environment) and actuators (which act on the environment) that can be programmed to perform sequences of actions. People used to science-fictional depictions of robots will usually think of humanoid machines walking with an awkward gait and speaking in a metallic monotone. Most real-world robots currently in use look very different as they are designed according to the application. Most applications would not benefit from the robot having human shape, just like we don't have humanoid robots to do our dishwashing but machines in which we place the dishes to be washed by jets of water.
It may not be obvious at first sight, but any kind of vehicles that have at least some level of autonomy and include sensors and actuators are also counted as robotics. On the other hand, software-based solutions such as a customer service chatbot, even if they are sometimes called `software robots´, aren´t counted as (real) robotics.
Exercise 2: Taxonomy of AI
練習2：人工智能的分類
A taxonomy is a scheme for classifying many things that may be special cases of one another. We have explained the relationships between a number of disciplines or fields and pointed out, for example, that machine learning is usually considered to be a subfield of AI.
分類法是一種對許多可能彼此特殊的事物進行分類的方案。 我們已經解釋了許多學科或領域之間的關係，例如，機器學習通常被認為是 AI 的一個子領域。
A convenient way to visualize a taxonomy is an Euler diagram. An Euler diagram (closely related to the more familiar Venn diagrams) consists of shapes that corresponds on concepts, which are organized so that overlap between the shapes corresponds to overlap between the concepts (see for example Wikipedia: Euler Diagram).
可視化分類法的一種方便方法是 Euler 圖。Euler圖（與更熟悉的Venn圖密切相關）由與概念相對應的形狀組成，這些形狀的組織方式使形狀之間的重疊對應於概念之間的重疊（參見例如Wikipedia:Euler圖）。
Notice that a taxonomy does not need to be strictly hierarchical. A discipline can be a subfield of more than one more general topic: for example, machine learning can also be thought to be a subfield of statistics. In this case, the subfield concept would be placed in the overlap between the more general topics.
注意，分類法不需要嚴格的層次結構。一門學科可以是不止一個一般性主題的一個子領域：例如，機器學習也可以被認為是統計學的一個子領域。在這種情況下，子領域概念將放在更一般的主題之間的重疊處。
Your task: Construct a taxonomy in the Euler diagram example given below showing the relationships between the following things: AI, machine learning, computer science, data science, and deep learning.
您的任務：在下面給出的 Euler 圖示例中構造一個分類法，顯示以下內容之間的關係：AI、機器學習、計算機科學、數據科學和深度學習。
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