Introduction to Artificial Intelligence with Python: Best Answers 007ET

Artificial Intelligence with Python – Since the invention of computers or machines, their capability to perform various tasks has experienced exponential growth. 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 computers or machines as an intelligent agent as humans.

Artificial Intelligence with Python

Table of Contents

Introduction to Artificial Intelligence (AI)

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 software think intelligently, in the similar manner the intelligent humans think. AI is accomplished by studying how the 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 for developing intelligent software and systems.

While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave as 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.

The Necessity of Learning AI

As we know that AI pursues creating machines as intelligent as human beings. There are numerous reasons for us to study AI. The reasons are as follows −

AI can learn through data

In our daily life, we deal with huge amounts of data and the human brain cannot keep track of so much data. That is why we need to automate things. For doing automation, we need to study AI because it can learn from data and can do repetitive tasks with accuracy and without tiredness.

AI can teach itself

It is very necessary that a system should teach itself because the data itself keeps changing and the knowledge which is derived from such data must be updated constantly. We can use AI to fulfill this purpose because an AI-enabled system can teach itself.

AI can respond in real time

Artificial intelligence with the help of neural networks can analyze the data more deeply. Due to this capability, AI can think and respond to situations that are based on the conditions in real-time.

AI achieves accuracy

With the help of deep neural networks, AI can achieve tremendous accuracy. AI helps in the field of medicine to diagnose diseases such as cancer from the MRIs of patients.

AI can organize data to get most out of it

The data is intellectual property for the systems which are using self-learning algorithms. We need AI to index and organize the data in a way that always gives the best results.

Understanding Intelligence

With AI, smart systems can be built. We need to understand the concept of intelligence so that our brain can construct another intelligence system like itself.

What is Intelligence?

The ability 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 the natural language fluently, classify, generalize, and adapt to new situations.

Types of Intelligence

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

Sr.NoIntelligence & DescriptionExample
1Linguistic intelligence is the ability to speak, recognize, and use mechanisms of phonology (speech sounds), syntax (grammar), and semantics (meaning).Narrators, Orators
2Musical intelligence is the ability to create, communicate with and understand meanings made of sound, understanding of pitch, rhythm.Musicians, Singers, Composers
3Logical-mathematical intelligence is the ability to use and understand relationships in the absence of action or objects. It is also the ability to understand complex and abstract ideas.Mathematicians, Scientists
4Spatial intelligence is the ability to perceive visual or spatial information, change it, and re-create visual images without reference to the objects, construct 3D images, and move and rotate them.Map readers, Astronauts, Physicists
5Bodily-Kinesthetic Intelligence is the ability to use complete or part of the body to solve problems or fashion products, control over fine and coarse motor skills, and manipulate objects.Players, Dancers
6Intra-personal intelligence ability to distinguish among one’s own feelings, intentions, and motivations.Gautam Buddhha
7Interpersonal intelligence is 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 or all intelligence in it.

What is Intelligence Composed Of?

Intelligence is intangible. It is composed of −

  • Reasoning
  • Learning
  • Problem Solving
  • Perception
  • Linguistic Intelligence
Artificial Intelligence wih Python

Let us go through all the components briefly −

Reasoning in AI

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

Inductive ReasoningDeductive 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. 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.”

Learning AI

The ability of learning is possessed by humans, particular species of animals, and AI-enabled systems. Learning is categorized as follows −

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 the precise movement of muscles. For example, picking objects, writing, etc.

Observational Learning

To learn by watching and imitating others. For example, the 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.

Problem Solving

It is the process in which one perceives and tries to arrive at the 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.

Perception

It is the process of acquiring, interpreting, selecting, and organizing sensory information.

Perception presumes to sense. In humans, perception is aided by sensory organs. In the domain of AI, the perception mechanism puts the data acquired by the sensors together in a meaningful manner.

Linguistic Intelligence

It is one’s ability to use, comprehend, speak, and write verbal and written language. It is important in interpersonal communication.

What’s Involved in AI

Artificial intelligence is a vast area of study. This field of study helps in finding solutions to real-world problems.

Let us now see the different fields of study within AI −

Machine Learning

It is one of the most popular fields of AI. The basic concept of this field is to make machine learning from data as human beings can learn from his/her experience. It contains learning models on the basis of which the predictions can be made on unknown data.

Logic

It is another important field of study in which mathematical logic is used to execute computer programs. It contains rules and facts to perform pattern matching, semantic analysis, etc.

Searching

This field of study is basically used in games like chess, tic-tac-toe. Search algorithms give the optimal solution after searching the whole search space.

Artificial neural networks

This is a network of efficient computing systems the central theme of which is borrowed from the analogy of biological neural networks. ANN can be used in robotics, speech recognition, speech processing, etc.

Genetic Algorithm

Genetic algorithms help in solving problems with the assistance of more than one program. The result would be based on selecting the fittest.

Knowledge Representation

It is the field of study with the help of which we can represent the facts in a way the machine that is understandable to the machine. The more efficiently knowledge is represented; the more system would be intelligent.

Application of AI

In this section, we will see the different fields supported by AI −

Gaming

AI plays a crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machines can think of the large number of possible positions based on heuristic knowledge.

Natural Language Processing

It is possible to interact with a computer that understands the natural language spoken by humans.

Expert Systems

There are some applications that integrate machines, software, and special information to impart reasoning and advising. They provide explanations and advice to the users.

Vision Systems

These systems understand, interpret, and comprehend visual input on the computer. For example,

  • A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.
  • Doctors use clinical expert system to diagnose the patient.
  • 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, changes in human 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 them 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.

Cognitive Modeling: Simulating Human Thinking Procedure

Cognitive modeling is basically the field of study within computer science that deals with the study and simulating the thinking process of human beings. The main task of AI is to make machines think like humans. The most important feature of the human thinking process is problem-solving.

That is why more or less cognitive modeling tries to understand how humans can solve problems. After that this model can be used for various AI applications such as machine learning, robotics, natural language processing, etc. Following is the diagram of different thinking levels of the human brain −

Cognitive Modeling

Agent & Environment

In this section, we will focus on the agent and environment and how these help in Artificial Intelligence.

Agent

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

  • 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.
  • robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.
  • software agent has encoded bit strings as its programs and actions.

Environment

Some programs operate in an entirely artificial environment confined to keyboard input, database, computer file systems, and character output on a screen.

In contrast, some software agents (software robots or softbots) exist in rich, unlimited softbots domains. The simulator has a very detailed, complex environment. The software agent needs to choose from a long array of actions in real-time. A softbot is designed to scan the online preferences of the customer and show interesting items to the customer works in the real as well as an artificial environment.

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. It was created by Guido van Rossum during 1985- 1990. Like Perl, Python source code is also available under the GNU General Public License (GPL). Python is named after a TV Show called ëMonty Pythonís Flying Circusí and not after Python-the snake.

Python Programming Language

Python 3.0 was released in 2008. Although this version is supposed to be backward-incompatible, later on, many of its important features have been backported to be compatible with version 2.7. This tutorial gives enough understanding of the Python 3 version programming language. XXXXXX

Why to Learn Python 3 Programming Language ?

Python is a high-level, interpreted, interactive, and object-oriented scripting language. Python is designed to be highly readable. It uses English keywords frequently whereas other languages use punctuation, and it has fewer syntactical constructions than other languages.

Python is a MUST for students and working professionals to become great Software Engineers especially when they are working in Web Development Domain. I will list down some of the key advantages of learning Python:

Python is Interpreted 

Python is processed at runtime by the interpreter. You do not need to compile your program before executing it. This is similar to PERL and PHP.

Python is Interactive 

You can actually sit at a Python prompt and interact with the interpreter directly to write your programs.

Python is Object-Oriented 

Python supports Object-Oriented style or technique of programming that encapsulates code within objects.

Python is a Beginner’s Language 

Python is a great language for the beginner-level programmers and supports the development of a wide range of applications from simple text processing to WWW browsers to games.

Key Differences Between Python 2x and Python 3x Programming Language

Here is the main difference between Python 2 and Python 3:

Difference between Python 2.x and Python 3.x
Basis of comparisonPython 3Python 2
Release Date
20082000
Function print
print (“hello”)print “hello”
Division of IntegersWhenever two integers are divided, you get a float valueWhen two integers are divided, you always provide an integer value.
Unicode
In Python 3, the default storing of strings is Unicode.To store Unicode string values, you require to define them with “u”.
Syntax
The syntax is simpler and easily understandable.The syntax of Python 2 was comparatively difficult to understand.
Rules of ordering ComparisonsIn this version, Rules of ordering comparisons have been simplified.Rules of ordering comparison are very complex.
Iteration
The new Range() function was introduced to perform iterations.In Python 2, the xrange() is used for iterations.
Exceptions
It should be enclosed in parenthesis.It should be enclosed in notations.
Leak of variables
The value of variables never changes.The value of the global variable will change while using it inside the for-loop.
Backward compatibilityNot difficult to port python 2 to python 3 but it is never reliable.Python version 3 is not backwardly compatible with Python 2.
Library
Many recent developers are creating libraries that you can only use with Python 3.Many older libraries created for Python 2 is not forward-compatible.
Python Programming Language

Python 2 vs Python 3 Example Code

Python 3 Code

def main():
  print("Hello World!")
  
if __name__== "__main__":
  main()

Python 2 Code

def main():
  print "Hello World!"
  
if __name__== "__main__":
  main()

Characteristics of Python Programming Language

Following are important characteristics of python −

  • It supports functional and structured programming methods as well as OOP.
  • It can be used as a scripting language or can be compiled to byte-code for building large applications.
  • It provides very high-level dynamic data types and supports dynamic type checking.
  • It supports automatic garbage collection.
  • It can be easily integrated with C, C++, COM, ActiveX, CORBA, and Java.

Hello World using Python Programming Language .

Just to give you a little excitement about Python, I’m going to give you a small conventional Python Hello World program,

print "Hello, Python!"

Applications of Python

As mentioned before, Python is one of the most widely used languages on the web. I’m going to list a few of them here:

Easy-to-learn 

Python has few keywords, simple structure, and a clearly defined syntax. This allows the student to pick up the language quickly.

Easy-to-read 

Python code is more clearly defined and visible to the eyes.

Easy-to-maintain 

Python’s source code is fairly easy-to-maintain.

A broad standard library 

Python’s bulk of the library is very portable and cross-platform compatible on UNIX, Windows, and Macintosh.

Interactive Mode 

Python has support for an interactive mode which allows interactive testing and debugging of snippets of code.

Portable 

Python can run on a wide variety of hardware platforms and has the same interface on all platforms.

Extendable 

You can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient.

Databases 

Python provides interfaces to all major commercial databases.

GUI Programming 

Python supports GUI applications that can be created and ported to many system calls, libraries and windows systems, such as Windows MFC, Macintosh, and the X Window system of Unix.

Scalable 

Python provides a better structure and support for large programs than shell scripting.

Introduction to Artificial Intelligence with Python FAQ

Can you make artificial intelligence with Python?

With the python programming language, a script most commonly used by the developers can be used to build your personal AI assistant to perform task designed by the users.

What is Introduction to Artificial Intelligence?

Artificial Intelligence is an approach to make a computer, a robot, or a product to think how smart human think. … And finally this study outputs intelligent software systems. The aim of AI is to improve computer functions which are related to human knowledge, for example, reasoning, learning, and problem-solving.

Is Python good for Artificial Intelligence?

Python has been appreciated for its relentless ascent to distinction over recent years. Supported for applications going from web advancement to scripting and procedure mechanization, Python is rapidly turning into the top decision among engineers for AI, ML, and profound learning ventures.

What is AI in Python?

Python has Prebuilt Libraries like Numpy for scientific computation, Scipy for advanced computing and Pybrain for machine learning (Python Machine Learning) making it one of the best languages For AI.

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