Artificial intelligence

History and Definition

AI was 1st coined by Jay ballad maker in 1955 at the Dartmouth College conference . AI is a machine having the ability to solve problems that we humans do with our natural intelligence. A computer would demonstrate a form of intelligence when it learns how to improve itself at solving these problems to elaborate further, the 1955 proposal defines seven areas of AI today they’re surely more but here are the original seven one simulating higher functions of the human brain to programming a computer to use general language, arranging hypothetical neurons in a manner enabling them to form concepts for a way to determine and measure problem complexity, self-improvement, abstraction defined as the quality of dealing with ideas rather than events, randomness and creativity.

A typical AI system observes its surrounding and then takes actions according to his past experience. AI is programmed for some special type of behavior. AI is designed with some algorithms and using these algorithms, AI performs daily tasks. The algorithms of some learning machine are capable of learning from data and then AI system improves with time by improvising new strategies and writes new algorithms.

Types of AI

 In the definition you heard the word intelligence, according to Jack Copeland who has written several books on AI some of the most important factors of intelligence are generalization learning that is learning that enables the learner to be able to perform better in situations. There are also different types of artificial intelligence in terms of approach, for example strong AI and weak AI. strong AI is simulating the human brain by building systems that think and give us an insight into how the brain works we’re nowhere near the stage yet. Weak AI is a system that behaves like a human but doesn’t give us an insight into how the brain works.

Sometime we compared artificial intelligence with natural intelligence but here AI lack some features of human being like strong common sense reasoning. There are lot of research going in the field of AI and the main purpose of all this research to create such technology which enables computer and machine to function in an intelligent manner.  


IBM’s deep blue a chess-playing AI was an example it processed millions of moves before it made any actual moves on the chessboard. It doesn’t stop there though there’s actually a new kind of middle ground between strong and weak AI this is where a system is inspired by human reasoning.

 Google’s deep learning is similar as it mimics the structure of the human brain by using neural networks but doesn’t follow its function exactly. The system uses nodes that act as artificial neurons connecting information going a little bit deeper.

 Neural networks are actually a subset of machine learning so machine learning refers to algorithms that enable software to improve its performance over time as it obtains more data and this is programming by input-output examples rather than just coding so that this makes more sense let’s use an example a programmer would have no idea how to program a computer to recognize a dog but he can create a program with a form of intelligence that can learn to do so if he gives the program enough image data in the form of dogs and let it process and learn when you give the program an image of a new dog that it’s never seen before it would be able to tell that it’s a dog with relative ease.

Expert System

Most artificial intelligence algorithms are expert systems; definition of an expert system is as follows an expert system is a system that employs human knowledge in a computer to solve problems that ordinarily inquire human expertise basically it’s the practical application of a knowledge database we’ve arguably only just got the first proven non expert system. This year deep mind’s alpha-go is not an expert system meaning that its algorithms could be used and applied to other things demis hassabis, he was the co-creator of D mind. He said that in a quote; We are thrilled to have mastered go and thus achieved one of the grand challenges of AI however the most significant aspect of all of this for us is that alpha go isn’t just an expert system built on handcrafted rules instead it uses general machine learning techniques to figure out for itself. The methods we have used a general-purpose, one day they could be extended to help us in some of society’s toughest and most pressing problems from climate modeling to complex disease analysis.

There are many tools developed in AI that provide suitable solutions in computer science field. Some of them are listed below;

  1. Many problems can be solved by searching through many possible solutions. Optimization search is very prominent in evolutionary computation.
  2. Logic is used and many different forms of logic are used in AI. AI logic include propositional logic, first order logic, default logic, non-monotonic logic.
  3. There is some incomplete knowledge where the AI system uses some powerful tools like using methods of probability theory and economics.
  4. AI applications have two types; classifiers and controllers. Controllers first fulfill the conditions and then take actions, so it is the key part of AI. Classifiers use functions which perform matching of related items and pick the closest one.
  5. An artificial neuron network is just like structure of the architecture of neurons of human brain. A simple neuron accepts input from numerous neurons and each neuron has its own weight.

There are numerous techniques of modern AI available and it became so familiar that it is no longer considered AI and this phenomenon is called effect of artificial intelligence such as self-driving car and drone. AI play important role in healthcare. AI has solved high cost problem in health and save billions of dollars. AI has developed many formulas for the treatment of severe disease like cancer and a project is being carried out to fight myeloid leukemia.  AI is doing ground-breaking discoveries in health care industries.Automotive industries are also moving into AI technologies and the evolutionary example is self-driving vehicles.

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