An Introduction to Artificial Intelligence – AI
An Introduction to Artificial Intelligence – AI

Artificial intelligence is a field of computer science which deals with tools and techniques to train machines so that they can solve complex problems with maximum success rate. These machines learn the hidden parameters from the available data-set and then solves the unknown problems based on the learned patterns.

What is Artificial Intelligence?

“In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.”


“The study of how to make computers do things at which, at the moment, people are better.”

Rich and Knight, 1992, 2009
  • According to Tesler’s theorem Intelligence is whatever machines haven’t done yet” also quoted as “AI is whatever hasn’t been done yet” and also known as AI Effect.
  • The AI field was founded on the bases of an assumption that human intelligence “can be so precisely described that a machine can be made to simulate it” presented at Dartmouth Conference
  • Some people consider that AI as a threat to humanity and possible risk for mass unemployment


An artificially intelligent system learns the patterns in the data and forms the general rules for the solution of a particular problem. In future, when a new instance of the problem occurs, it matches the patterns in the new instance with the learned patterns to predict the possible solution. If the predicted solution matches the actual/true solution then well and good otherwise it optimizes/tweaks the learned patterns to correct the parameters thus improving the system for future instances.

Types of Artificial Intelligence Models:

  • Search and Optimization: Intelligently searching through many possible solutions
  • Logic Based: mainly used for knowledge representation and problem solving
  • Probability Based: mainly used when there is incomplete or uncertain information
  • Classification Based: Matching patterns to classify data instances in different decision classes/categories
  • Artificial Neural Network Based Models: devised from the concept of neurons in the human brain. Learning algorithm adjust the weights of the neurons until the system successfully predicts the possible solution based on the data
  • Deep Feed Forward Neural Networks: A neural network with multiple hidden layers can deeply learn the hidden parameters
  • Deep Recurrent Neural Networks: mainly used to deal with sequential data (words/characters in text, frames in video etc.)

Sub Fields of Artificial Intelligence:

  • Evolutionary Computation
  • Vision
  • Robotics
  • Expert Systems
  • Speech Processing
  • Natural Language Processing
  • Machine Learning
  • Neural Networks
  • Deep Learning

Applications of Artificial Intelligence:

  • Healthcare: predicting possible medicine
  • Automotive: self driving cars
  • Finance and Economics: fraud prevention
  • Cyber-security: spam filtration
  • Government: mass surveillance using face recognition
  • and many more…


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