Artificial Intelligence 简明教程

Artificial Intelligence - Research Areas

人工智能领域在深度和广度方面很大。进行当中，我们研究了人工智能领域中普遍的繁荣的研究范围——

The domain of artificial intelligence is huge in breadth and width. While proceeding, we consider the broadly common and prospering research areas in the domain of AI −

research areas of ai

Speech and Voice Recognition

这两个术语在机器人技术、专家系统和自然语言处理中很常见。虽然这些术语可以互换使用，但它们的目的是不同的。

These both terms are common in robotics, expert systems and natural language processing. Though these terms are used interchangeably, their objectives are different.

Speech Recognition

Voice Recognition

The speech recognition aims at understanding and comprehending WHAT was spoken.

The objective of voice recognition is to recognize WHO is speaking.

It is used in hand-free computing, map, or menu navigation.

It is used to identify a person by analysing its tone, voice pitch, and accent, etc.

Machine does not need training for Speech Recognition as it is not speaker dependent.

This recognition system needs training as it is person oriented.

Speaker independent Speech Recognition systems are difficult to develop.

Speaker dependent Speech Recognition systems are comparatively easy to develop.

Working of Speech and Voice Recognition Systems

通过麦克风说出用户输入的内容，转到系统的声卡。转换器将模拟信号转化为语音处理的等效数字信号。数据库用于比较声音模式以识别单词。最后，逆向反馈被提供给数据库。

The user input spoken at a microphone goes to sound card of the system. The converter turns the analog signal into equivalent digital signal for the speech processing. The database is used to compare the sound patterns to recognize the words. Finally, a reverse feedback is given to the database.

此源语言文本成为翻译引擎的输入，翻译引擎将其转换为目标语言文本。它们是由交互式 GUI、大型词汇数据库等提供支持的。

This source-language text becomes input to the Translation Engine, which converts it to the target language text. They are supported with interactive GUI, large database of vocabulary, etc.

Real Life Applications of Research Areas

在人工智能为普通人日常生活提供服务的应用程序中，存在很多领域——

There is a large array of applications where AI is serving common people in their day-to-day lives −

Sr.No.

Research Areas

Real Life Application

1

Expert Systems Examples − Flight-tracking systems, Clinical systems.

2

Natural Language Processing Examples: Google Now feature, speech recognition, Automatic voice output.

3

Neural Networks Examples − Pattern recognition systems such as face recognition, character recognition, handwriting recognition.

4

Robotics Examples − Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving, etc.

5

Fuzzy Logic Systems Examples − Consumer electronics, automobiles, etc.

Task Classification of AI

人工智能领域分为以下内容：

The domain of AI is classified into Formal tasks, Mundane tasks, and Expert tasks.

task domains of ai

Task Domains of Artificial Intelligence

Mundane (Ordinary) Tasks

Formal Tasks

Expert Tasks

Perception Computer VisionSpeech, Voice

MathematicsGeometryLogicIntegration and Differentiation

EngineeringFault FindingManufacturingMonitoring

Natural Language Processing UnderstandingLanguage GenerationLanguage Translation

Games GoChess (Deep Blue)Ckeckers

Scientific Analysis

Common Sense

Verification

Financial Analysis

Reasoning

Theorem Proving

Medical Diagnosis

Planing

Creativity

Robotics Locomotive

人类从出生开始学习 mundane (ordinary) tasks 。他们通过感知、说话、使用语言和运动来学习。他们先学习基础任务，再学习专业任务。

Humans learn mundane (ordinary) tasks since their birth. They learn by perception, speaking, using language, and locomotives. They learn Formal Tasks and Expert Tasks later, in that order.

对于人类来说，最容易学习的是平凡的任务。在尝试让机器执行平凡的任务之前，平凡的任务也被认为容易学习。早期，所有的 AI 工作都集中在平凡的任务域。

For humans, the mundane tasks are easiest to learn. The same was considered true before trying to implement mundane tasks in machines. Earlier, all work of AI was concentrated in the mundane task domain.

后来，事实证明，对于平凡的任务，机器需要更多的知识、更复杂的知识表示和更复杂的算法。这就是为什么 why AI work is more prospering in the Expert Tasks domain 现在需要专家知识，但是专业任务域不需要常识，更容易表示和处理。

Later, it turned out that the machine requires more knowledge, complex knowledge representation, and complicated algorithms for handling mundane tasks. This is the reason why AI work is more prospering in the Expert Tasks domain now, as the expert task domain needs expert knowledge without common sense, which can be easier to represent and handle.