ai人工智能_人工智能（AI）简介

ai人工智能

Here you will get an introduction to artificial intelligence.

在这里，您将获得有关人工智能的介绍。

First of all let us quickly recall what we have learnt in previous blog posts to get an idea of what we already have covered and shouldn’t invest time on. So far, we’ve learnt about Machine Learning concepts, the algorithms, packages and IDEs etc. Now when we are quite familiar about the term Machine Learning, it is time to explore further in the domain. It is time to understand what Artificial Intelligence is? Many people use Machine Learning and AI interchangeably and think both of them as the same. But the fact is they are not the same as inferred by majority. Come let’s understand how these two terms (ML and AI) are not the same.

首先，让我们快速回顾一下以前在博客文章中学到的内容，以了解我们已经介绍的内容，不应浪费时间。 到目前为止，我们已经了解了机器学习的概念，算法，包和IDE等。现在，当我们对机器学习一词非常熟悉时，就该在该领域进行进一步的探索了。 是时候了解什么是人工智能了？ 许多人互换使用机器学习和AI，并认为两者是相同的。 但事实是，它们与多数人推论的并不相同。 快来让我们了解一下这两个术语(ML和AI)是如何不同的。

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As we can see in the image above it is quite clear that we see AI and ML differently instead a single domain. In other words “ML is a subset of AI.”

正如我们在上图中所看到的，很明显，我们看到AI和ML的方式有所不同，而不是单个域。 换句话说，“ ML是AI的子集。”

Artificial Intelligence can be thought of as a broader term in which a machine is expected to behave humanly. Whereas on the other hand as we are already aware that in ML we train our machine providing to do a specific task by providing it some data.

人工智能可以被认为是广义的术语，指的是机器会表现出人类的行为。 另一方面，正如我们已经知道的那样，在ML中，我们通过提供一些数据来训练提供特定任务的机器。

We know you guys might be wondering now what the heck is this Deep Learning. As for now think of it as “a subset of ML (comes under Reinforcement Learning)” and we will discuss on this too on some later posts. Let us now quickly dive into Artificial Intelligence and explore what’s interesting in it.

我们知道你们现在可能想知道深度学习到底有什么用。 现在，将其视为“ ML的子集(来自强化学习)”，我们将在以后的文章中对此进行讨论。 现在让我们快速进入人工智能并探索其中有趣的部分。

什么是人工智能？ (What is Artificial Intelligence?)

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It is believed that AI is not a new term, people say that according to Greek myths there were mechanical men in early days that act like humans.

人们相信AI并不是一个新名词，人们说，根据希腊神话，早期有机械人像人类一样行事。

Instead of understanding AI as machines that do complex calculations, it can better be explained as machines that are built to act and behave like human, contains features of human like facial recognition, voice recognition, decision making, vision system etc. The devices (machines or computers) that are AI powered are often classified into two categories namely: Applied AI and General AIs.

与其将AI理解为进行复杂计算的机器，不如将其解释为被构造为像人类一样行动和行为的机器，包含面部识别，语音识别，决策，视觉系统等人类特征。由AI驱动的计算机(或计算机)通常分为两类：应用AI和通用AI。

Applied AI: Applied AI is considered as the common AI, result of which can be spotted in the field of Stock trading, autonomous vehicles etc.

应用的AI：应用的AI被认为是常见的AI，其结果可以在股票交易，自动驾驶汽车等领域发现。

General AI: The devices that run on principles are not very common to spot in our locality but it is interesting to know that some of the buzzing topics like Machine Learning is an example of this.

通用AI：基于原理运行的设备在我们当地并不常见，但有趣的是，诸如机器学习之类的一些热门话题就是其中的一个例子。

Two thoughts on AI:

关于AI的两种想法：

1. How to make machines behave like human.
   如何使机器表现得像人一样。
2. Have machine behaviour in best possible way.
   以最佳方式使机器运转。

What are the ways to carry out AI task?

开展AI任务的方式有哪些？

There are believed to be seven different steps that help in making any AI system behave humanly. We are eager to know what those steps are that can make any machine mimic human behaviour.

据信有七个不同的步骤可以帮助使任何AI系统表现得人性化。 我们渴望知道这些步骤可以使任何机器模仿人类的行为。

• Simulating higher function of human brain.
  模拟人脑的更高功能。
• Programming a computer to use general (natural) language.
  对计算机进行编程以使用通用(自然)语言。
• Arranging hypothetical neurons in a manner so that they can form concept.
  以某种方式排列假设的神经元，以便它们可以形成概念。
• A way to determine and measure problem complexity.
  确定和衡量问题复杂性的一种方法。
• Self improvement.
  自我提升。
• Abstraction – defined as, the quality of dealing with the idea rather than events.
  抽象–定义为处理想法而非事件的质量。
• Randomness and creativity.
  随机性和创造力。

图灵测试 (Turing Test)

Turing Test was proposed by an English computer scientist Alan Turing in 1950. The main objective of Turing test was to find out if a computer is intelligent or not? Though this was proved to be not that effective to measure the system’s smartness.

图灵测试是由英国计算机科学家艾伦·图灵 ( Alan Turing)在1950年提出的。图灵测试的主要目的是找出计算机是否智能？ 尽管事实证明这对衡量系统的智能程度不是那么有效。

Turing test consist of three units – an interrogator, a computer system and a human unit both situated in different rooms. The means of communication between them was through keyboard and a screen only. The interrogator asks a question to both the units located in different rooms and tries to distinguish between the responses received from both the units (human and computer), i.e. which answer is given by the computer and which one by human, if interrogator fails to correctly identify the computer’s response because of the very minor difference (or even no difference) then the system is supposed to be intelligent.

图灵测试由三个单元组成–询问器，计算机系统和人工单元，它们都位于不同的房间中。 它们之间的通信方式仅通过键盘和屏幕进行。 询问器向位于不同房间的两个单元都询问一个问题，并试图区分从两个单元(人和计算机)接收到的响应，即，如果询问器无法正确执行，则哪个回答由计算机给出，哪个由人给出如果因为差异很小(甚至没有差异)而确定计算机的响应，则该系统应该是智能的。

Other scientists like Steven Harnad extended this as Total Turing Test in which computer must interact in all areas of human behaviour instead of language (in case of turing test) only and the test duration extended to lifetime instead of five or ten minutes only.

其他像史蒂文·哈纳德(Steven Harnad)这样的科学家将其扩展为“全面图灵测试”，其中计算机必须在人类行为的所有方面进行交互，而不仅仅是语言(在图林测试中)，并且测试持续时间可以延长到整个生命周期，而不是五到十分钟。

Huhh.. much of the theoretical knowledge. Come on let’s quickly see what computer needs to pass this test.

嗯..很多理论知识。 来吧，让我们快速查看需要什么计算机通过此测试。

1. NLP (Natural Language Processing)
   NLP(自然语言处理)
2. Knowledge Representation
   知识表示
3. Automated Reasoning
   自动推理
4. Machine Learning ability
   机器学习能力
5. Computer Vision
   计算机视觉
6. Robotics (touch of Robotism)
   机器人技术(机器人精神)

人工智能的能力(人工智能能做什么？) (Capabilities of AI (What AI can do?))

1. Computer Vision (eg. Facial Recognition)
   计算机视觉(例如面部识别)
2. Robotics
   机器人技术
3. NLP (Natural Language Processing)
   NLP(自然语言处理)
4. Expert System
   专业系统
5. Speech Understanding
   言语理解
6. Test Categorization
   测试分类
7. In games
   在游戏中
8. Surf the web
   浏览网页

哪里缺少AI？ (Where AI lacks yet?)

1. Understanding NLP robustly
   深入了解NLP
2. Interpret arbitrary visual scene
   解释任意视觉场景
3. Learn a natural language
   学习自然语言
4. Exhibit true autonomy and intelligence
   展现真正的自主权和智慧

什么是自然语言处理(NLP)？ (What is Natural Language Processing (NLP)?)

Refers to AI method of communicating with intelligent system using natural language such as English. Computer should be able to understand the natural language and give response to it.

指使用自然语言(例如英语)与智能系统通信的AI方法。 计算机应该能够理解自然语言并做出响应。

Input/output can be in the form of Speech or Written Text.

输入/输出可以采用语音或书面文本的形式。

NLP的组成部分 (Components of NLP)

1. Natural Language Understanding (NLU): Mapping the input provided in natural language into some useful representation is the art of NLU.

   自然语言理解(NLU)：将自然语言提供的输入映射到一些有用的表示形式是NLU的艺术。

2. Natural Language Generation (NLG): Process of producing meaningful phrases and sentences in form of natural language from some internal representations. This includes: test planning, sentence planning and text realization.

   自然语言生成(NLG)：从某些内部表示形式以自然语言形式生成有意义的短语和句子的过程。 这包括：测试计划，句子计划和文本实现。

NLP中使用的术语 (Terminologies used in NLP)

Phonology: Study of organising sounds.

语音学：组织声音的研究。

Morphology: Study of construction of words from primitive meaningful unit.

形态学：从原始有意义单位研究单词的构造。

Syntax: Refers to arranging words to make sentences and determine the structural role of words in sentences.

语法：指安排单词构成句子并确定单词在句子中的结构作用。

Semantics: Deals with how to combine the words to make meaningful sentence.

语义：处理如何组合单词以构成有意义的句子。

Pragmatics: Refers to using and understanding sentences in different situations.

语用学：指在不同情况下使用和理解句子。

Discourse: How effectively preceeding sentence can affect interpretation of next sentence?

语篇：在前句子如何有效地影响下一个句子的解释？

Steps of NLP

NLP的步骤

什么是深度学习？ (What is Deep Learning?)

We are pretty sure that the term Deep Learning must be haunting  you guys since you see it in the very first image attached. Do not worry, we are always here for you. In short Deep Learning is a part or subset of Machine Learning as inferred from the very first image from this blog post. Deep Learning came into existence only with the notion to facilitate the machines to move forward to achieve their goal to be able to think and act rationally or humanly i.e. help computers to achieve the motive of AI with the help of Machine Learning itself.

我们非常确定，因为您在所附的第一张图片中看到了“深度学习”这个词，所以一定会困扰你们。 不用担心，我们一直在这里为您服务。 简而言之，深度学习是机器学习的一部分或子集，从本博客文章的第一张图片可以推断出。 深度学习仅以一种概念来存在，即促进机器前进以实现其能够理性或人类思考和行动的目标，即借助机器学习本身来帮助计算机实现AI的动机。

Dear readers, that is all for today. We wish you guys enjoyed learning with us. You can post any queries or doubts related to artificial intelligence in the comment section.

亲爱的读者，今天就这些。 我们希望你们喜欢与我们一起学习。 您可以在评论部分中发布与人工智能有关的任何疑问或疑问。

翻译自: https://www.thecrazyprogrammer.com/2018/01/introduction-artificial-intelligence.html

ai人工智能