Artificial intelligence tutorial, Lecture notes of Computer Networks

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About the Tutorial

This tutorial provides introductory knowledge on Artificial Intelligence. It would come to a great help if you are about to select Artificial Intelligence as a course subject. You can briefly know about the areas of AI in which research is prospering.

Audience

This tutorial is prepared for the students at beginner level who aspire to learn Artificial Intelligence.

Prerequisites

The basic knowledge of Computer Science is mandatory. The knowledge of Mathematics, Languages, Science, Mechanical or Electrical engineering is a plus.

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Since the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time.

A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.

What is Artificial Intelligence?

According to the father of Artificial Intelligence John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently , in the similar manner the intelligent humans think.

AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

Philosophy of AI

While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave like humans do?”

Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.

Goals of AI

 To Create Expert Systems: The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.  To Implement Human Intelligence in Machines: Creating systems that understand, think, learn, and behave like humans.

1. OVERVIEW OFAI

What Contributes to AI?

Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.

Out of the following areas, one or multiple areas can contribute to build an intelligent system.

Programming Without and With AI

The programming without and with AI is different in following ways:

Programming Without AI Programming With AI A computer program without AI can answer the specific questions it is meant to solve.

A computer program with AI can answer the generic questions it is meant to solve.

Modification in the program leads to change in its structure.

AI programs can absorb new modifications by putting highly independent pieces of information together. Hence you can modify even a minute piece of information of program without affecting its structure.

These systems understand, interpret, and comprehend visual input on the computer. For example, o A spying aeroplane takes photographs which are used to figure out spatial information or map of the areas. o Doctors use clinical expert system to diagnose the patient. o Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.  Speech Recognition Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.  Handwriting Recognition The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.  Intelligent Robots Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

History of AI

Here is the history of AI during 20th^ century:

Year Milestone / Innovation

1923 Karel Kapek's play named “Rossum's Universal Robots” (RUR) opens in London, first use of the word "robot" in English. 1943 Foundations for neural networks laid. 1945 Isaac Asimov, a Columbia University alumni, coined the term Robotics.

Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence. Claude Shannon published Detailed Analysis of Chess Playing as a search.

John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University.

1958 John McCarthy invents LISP programming language for AI.

Danny Bobrow's dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly.

Joseph Weizenbaum at MIT built ELIZA , an interactive problem that carries on a dialogue in English.

Scientists at Stanford Research Institute Developed Shakey , a robot, equipped with locomotion, perception, and problem solving.

The Assembly Robotics group at Edinburgh University built Freddy , the Famous Scottish Robot, capable of using vision to locate and assemble models.

The first computer-controlled autonomous vehicle, Stanford Cart, was built.

1985 Harold Cohen created and demonstrated the drawing program, Aaron.

Major advances in all areas of AI:  Significant demonstrations in machine learning  Case-based reasoning  Multi-agent planning  Scheduling  Data mining, Web Crawler  natural language understanding and translation  Vision, Virtual Reality  Games

The Deep Blue Chess Program beats the then world chess champion, Garry Kasparov.

Interactive robot pets become commercially available. MIT displays Kismet , a robot with a face that expresses emotions. The robot Nomad explores remote regions of Antarctica and locates meteorites.

over fine and coarse motor skills, and manipulate the objects.

Intra-personal intelligence

The ability to distinguish among one’s own feelings, intentions, and motivations. Gautam Buddha

Interpersonal intelligence

The ability to recognize and make distinctions among other people’s feelings, beliefs, and intentions.

Mass Communicators, Interviewers

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

What is Intelligence Composed of?

The intelligence is intangible. It is composed of:

1. Reasoning 2. Learning 3. Problem Solving 4. Perception 5. Linguistic Intelligence

Let us go through all the components briefly:

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

Inductive Reasoning Deductive Reasoning

It conducts specific observations to makes broad general statements.

It starts with a general statement and examines the possibilities to reach a specific, logical conclusion.

Even if all of the premises are true in a statement, inductive reasoning allows for the conclusion to be false.

If something is true of a class of things in general, it is also true for all members of that class.

Example: “Nita is a teacher. All teachers are studious. Therefore, Nita is studious.”

Example: "All women of age above 60 years are grandmothers. Shalini is 65 years. Therefore, Shalini is a grandmother."

2. 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:

o Auditory Learning: It is learning by listening and hearing. For example, students listening to recorded audio lectures. o Episodic Learning: To learn by remembering sequences of events that one has witnessed or experienced. This is linear and orderly. o Motor Learning: It is learning by precise movement of muscles. For example, picking objects, Writing, etc. o Observational Learning: To learn by watching and imitating others. For example, child tries to learn by mimicking her parent. o Perceptual Learning: It is learning to recognize stimuli that one has seen before. For example, identifying and classifying objects and situations. o 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. o 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.

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:

Speech and Voice Recognition

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 analyzes person’s tone, voice pitch, and accent, etc., to identify a person.

Machine does not need training as it is not speaker dependent.

The recognition system needs training as it is person-oriented.

3. RESEARCH AREAS OF AI

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

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 patterns to recognize the words. Finally, a reverse feedback is given to the database.

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

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

Sr. No. Research Area Real Life Application

Expert Systems

Examples : Flight-tracking systems, Clinical systems

Natural Language Processing

Examples : Google Now feature, speech recognition, Automatic voice output

Neural Networks

Examples : Pattern recognition systems such as face recognition, character recognition, handwriting recognition.

Task Domains of Artificial Intelligence

Mundane (Ordinary) Tasks Formal Tasks Expert Tasks Perception  Computer Vision  Speech, Voice

 Mathematics  Geometry  Logic  Integration and Differentiation

 Engineering  Fault finding  Manufacturing  Monitoring

Natural Language Processing  Understanding  Language Generation  Language Translation

Games  Go  Chess (Deep Blue)  Checkers

Scientific Analysis

Common Sense Verification Financial Analysis Reasoning Theorem Proving Medical Diagnosis Planning Creativity Robotics  Locomotive

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.

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.

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 Task domain now, as the expert task domain needs expert knowledge without common sense, which can be easier to represent and handle.

An AI system is composed of an agent and its environment. The agents act in their environment. The environment may contain other agents.

What are Agent and Environment?

An agent is anything that can perceive its environment through sensors and acts upon that environment through effectors.

 A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to the sensors, and other organs such as hands, legs, mouth, for effectors.  A robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.  A software agent has encoded bit strings as its programs and actions.

Agents Terminology

 Performance Measure of Agent: It is the criteria, which determines how successful an agent is.  Behavior of Agent: It is the action that agent performs after any given sequence of percepts.

4. AGENTS AND ENVIRONMENTS