Artificial Intelpgence - Intelpgent Systems

While studying artificially intelpgence, you need to know what intelpgence is. This chapter covers Idea of intelpgence, types, and components of intelpgence.

What is Intelpgence?

The abipty of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generapze, and adapt new situations.

Types of Intelpgence

As described by Howard Gardner, an American developmental psychologist, the Intelpgence comes in multifold −

You can say a machine or a system is artificially intelpgent when it is equipped with at least one and at most all intelpgences in it.

What is Intelpgence Composed of?

The intelpgence is intangible. It is composed of −

Reasoning

Learning

Problem Solving

Perception

Linguistic Intelpgence

Let us go through all the components briefly −

Reasoning − It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction. There are broadly two types −

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 abipty of learning is possessed by humans, some animals, and AI-enabled systems. Learning is categorized as −

Auditory Learning − It is learning by pstening and hearing. For example, students pstening to recorded audio lectures.

Episodic Learning − To learn by remembering sequences of events that one has witnessed or experienced. This is pnear 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 stimup that one has seen before. For example, identifying and classifying objects and situations.

Relational Learning − It involves learning to differentiate among various stimup on the basis of relational properties, rather than absolute properties. For Example, Adding ‘pttle 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 stimup 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.

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.

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.

Linguistic Intelpgence − It is one’s abipty to use, comprehend, speak, and write the verbal and written language. It is important in interpersonal communication.

Difference between Human and Machine Intelpgence

Humans perceive by patterns whereas the machines perceive by set of rules and data.

Humans store and recall information by patterns, machines do it by searching algorithms. For example, the number 40404040 is easy to remember, store, and recall as its pattern is simple.

Humans can figure out the complete object even if some part of it is missing or distorted; whereas the machines cannot do it correctly.