Artificial intelligence(AI) is used to describe machines or computers that functions mimic the human cognitive. AI is associated with the human mind, such as learning and solving problems.

In this article you will get information about what actually artificial intelligence is? And also its importance and major concepts of AI. Let's  go for detail info.

What is Artificial Intelligence?

Artificial intelligence is a term used to describe “ Intelligence” demonstrated by machines. The core concept of AI is the idea of developing intelligent machines. This means computer algorithms that work and react like humans.

AI helps machines to calculate, reason, perceive relationships, analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language,classifier, generalize and adapt new situations.

This word Artificial Intelligence was introduced for the first time by John McCarthy in 1955 at Dartmouth Conference.  Now you might wonder Why is AI more famous now when AI has been around for more then 70 years? The answer is due to the enormous growth of data now we need machines that can store, process and analyze faster.

AI challenges the human Intelligence. Intelligence can be defined as a general mental ability for problem solving and learning, reasoning, cognitive functions such as attention, perception, memory, language or planning. Let’s study AI in detail.

Why is Artificial Intelligence Important?

To make complex decisions today we need machines that can work faster and smarter then human. Everyday humans are creating a large amount of data online. To store, process and analyze such a large amount of data we need intelligent machines.

Well, AI contributes a lot in many other domains where human intelligence is involved. AI is used in speech recognition, natural language processing and translation, visual perception, learning, reasoning, planning, and so on.

Humans are trying to make intelligent machines that they can behave like humans, think like humans and work like humans. Human future depends on these intelligent systems.

A simple example of Artificial Intelligence is that your smartphone has a major application that is Google Assistance, Siri or Alexa . What we do with these applications is that we give a voice query which converts that query through NLP(Natural Language Processing) and gives its answers to you.

Artificial Intelligence vs Human Intelligence

The easiest way to think about Artificial Intelligence is in context of humans. After all, humans are the most intelligent creatures we know off. AI is a broad branch of computer science. The goal of AI is to create systems that can function intelligently and independently.

  • Humans can speak and listen to communicate through a language and this is the field of speech recognition. Much of speech recognition is statistical based, hence it is called Statistical learning.
  • Humans can write and read text in a language and this is a field of NLP(Natural language processing)
  • Humans can see with their eyes and process what they see and this is the field of Computer Vision. Computer vision falls under Symbolic Learning .
  • Humans recognize what they see around them through their eyes which create images of that world around them and this is the field of Image Processing.
  • Humans can understand their environment and move around fluidly. This is the field of Robotics.
  • Humans have the ability to see patterns such as grouping like objects and this is the field of Pattern Recognition. Machines are even better at pattern recognition because they can use more data and dimensions of data. This is the field of ML(Machine Learning).
  • The human brain is the network of Neurons and use is to learn things. If we can replicate the structure and function of the human brain, we might be able to get cognitive capabilities in machines. This is the field of NN (Neural Networks).
  • If these networks are more complex and deeper and we use those to learn complex things, that is the field of Deep Learning. there are different types of deep learning and machines which are essentially different techniques to replicate with the human brain.
  • If we get the network to scan images from left to write or top to bottom and it's a CNN (Convolution Neural Network). The CNN is used to  objects seen. This is how computer vision fixes and object recognition is accomplished through AI.
  • Humans can remember the past like when they had dinner last night. We can get neural network eliminated past and this is RNN(Recurrent Neural Network).

Generally, there are two ways that AI works. One is symbolic based and another is database for database it is called Machine Learning. We need to feed a lot of data to machines before it can learn.

For example if we had lots of data for sale vs advertising spend you can plot that data to see some kind of pattern. If the machine can learn this pattern, then it can make predictions based on what it has learned. While 1 or 2 or even 3 dimensions can be easy for humans to understand and learn. But machines can learn more dimensions, even hundreds or thousands, that's why machines can look at lots of high  dimensional data and determine patterns.

Once it can learn these patterns, it can make predictions that humans can’t even come close to. We can use all those machine learning techniques to do one or two things.


When we use some information about customers, to assign new customers to a  group like young adults then you are classifying that customers.


If we use data to predict, if there is likely to defect a competitor then you are making predication.

There is another way to think about learning algorithms used for AI. If you trained an algorithm with data, they also contain the answer then it is called Supervised Learning. For example when you trained a machine to recognize your friends by name then you will need to identify them for the computer.

If you trained the machine to figure out the patterns then its Unsupervised Learning. For example you might want to feed the  statistical data about the universe and expect the machine to come up with patterns in the data by itself.

If you give any algorithm any goal and expect the machine through trial and error to achieve that goal then it is called Reinforcement Learning. Robots attempt to climb over the world and till it exceeds is an example of reinforcement.

You can read more about AI at here

Examples of AI

  • Smart assistance (like Siri and Alexa)
  • Disease mapping and prediction tools
  • Manufacturing and drone robots
  • Amazon.com
  • Nest
  • Spam filters on email
  • Smart speakers
  • Smart Keyboards Apps
  • Social Media feeds
  • Optimized, personalized healthcare treatment recommendations
  • Conversational bots for marketing and customer service
  • Robo-advisors for stock trading
  • Song or TV show recommendations - Netflix

Top 10 Artificial Intelligence Applications

The upcoming years are creating a new modern platform for Artificial Intelligence and scientists are focusing on to gain more space in it. AI influence on different domains. The following are the top 10 artificial intelligence applications domains that contribute a lot to them.

  1. Artificial Creativity
  2. E-commerce
  3. Robotics
  4. Social Media
  5. Managing HR
  6. Healthcare
  7. Agriculture
  8. Gaming
  9. Automobiles
  10. Marketing

In Conclusion

AI technologies are constructed by mathematical processes that leverage increasing computing power to deliver  faster and more efficiently. The peace of AI development is faster day by day. AI massive breakthroughs are required in areas such as neuroscience, understanding consciousness, neural networks and deep learning algorithms. Human intelligence uses a combination of several cognitive processes and  revolves around adapting to the environment.