Artificial Intelligence 简明教程
Artificial Intelligence - Intelligent Systems
在研究人工智能时，你需要知道什么是智能。本章涵盖了智能的理念、类型和构成要素。
Types of Intelligence
正如美国发展心理学家 Howard Gardner 所描述的，可以分为多种类型−
Intelligence |
Description |
Example |
Linguistic intelligence |
用语音（语音声音）、语言（语法）和语义（含义）的机制来说话、识别和使用的能力。 |
Narrators, Orators |
Musical intelligence |
用声音形成含义、并与之交流和理解含义的能力，理解音高和韵律。 |
Musicians, Singers, Composers |
Logical-mathematical intelligence |
在没有动作或对象的情况下，使用和理解关系的能力。理解复杂抽象的概念。 |
Mathematicians, Scientists |
Spatial intelligence |
感知视觉或空间信息、改变它并重新创造视觉图像（不涉及对象），构建 3D 图像，并移动和旋转它们的能力。 |
Map readers, Astronauts, Physicists |
Bodily-Kinesthetic intelligence |
使用全部或部分身体来解决问题或设计产品的能力，控制高级及低级运动技能并控制物体。 |
Players, Dancers |
Intra-personal intelligence |
区分自身情感、意图和动机的能力。 |
Gautam Buddhha |
Interpersonal intelligence |
识别和区分他人情感、信仰和意图的能力。 |
Mass Communicators, Interviewers |
您可以说，当一台机器或系统至少配备了一种至多全部智能时，它就是 artificially intelligent 。
What is Intelligence Composed of?
智能是无形的。它由以下部分组成：
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Reasoning
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Learning
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Problem Solving
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Perception
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Linguistic Intelligence
让我们简单了解一下所有组成部分：
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Reasoning − 这是使我们能够提供判断、做出决策并进行预测的基础的一组流程。大致有以下两种类型−
Inductive Reasoning |
Deductive Reasoning |
进行具体观察以形成概括的陈述。 |
从概括的陈述开始，并检验可能性以得出具体、合乎逻辑的结论。 |
即使陈述中所有前提条件都为真，归纳推理也允许结论为假。 |
如果某事对某类事物普遍适用，那么它对该类事物的所有成员也适用。 |
Example − "Nita is a teacher. Nita is studious. Therefore, All teachers are studious." |
Example − "All women of age above 60 years are grandmothers. Shalini is 65 years. Therefore, Shalini is a grandmother." |
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Learning − It is the activity of gaining knowledge or skill by studying, practising, being taught, or experiencing something. Learning enhances the awareness of the subjects of the study. The ability of learning is possessed by humans, some animals, and AI-enabled systems. Learning is categorized as − Auditory Learning − It is learning by listening and hearing. For example, students listening to recorded audio lectures. Episodic Learning − To learn by remembering sequences of events that one has witnessed or experienced. This is linear and orderly. Motor Learning − It is learning by precise movement of muscles. For example, picking objects, Writing, etc. Observational Learning − To learn by watching and imitating others. For example, child tries to learn by mimicking her parent. Perceptual Learning − It is learning to recognize stimuli that one has seen before. For example, identifying and classifying objects and situations. Relational Learning − It involves learning to differentiate among various stimuli on the basis of relational properties, rather than absolute properties. For Example, Adding ‘little less’ salt at the time of cooking potatoes that came up salty last time, when cooked with adding say a tablespoon of salt. Spatial Learning − It is learning through visual stimuli such as images, colors, maps, etc. For Example, A person can create roadmap in mind before actually following the road. Stimulus-Response Learning − It is learning to perform a particular behavior when a certain stimulus is present. For example, a dog raises its ear on hearing doorbell.
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Problem Solving − It is the process in which one perceives and tries to arrive at a desired solution from a present situation by taking some path, which is blocked by known or unknown hurdles. Problem solving also includes decision making, which is the process of selecting the best suitable alternative out of multiple alternatives to reach the desired goal are available.
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Perception − It is the process of acquiring, interpreting, selecting, and organizing sensory information. Perception presumes sensing. In humans, perception is aided by sensory organs. In the domain of AI, perception mechanism puts the data acquired by the sensors together in a meaningful manner.
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Linguistic Intelligence − It is one’s ability to use, comprehend, speak, and write the verbal and written language. It is important in interpersonal communication.