What is Artificial Intelligence? Introduction, History & Types of AI

What is Artificial Intelligence (AI)?

AI (Artificial Intelligence) is a machine’s ability to perform cognitive functions as humans do, such as perceiving, learning, reasoning, and solving problems. The benchmark for AI is the human level concerning in teams of reasoning, speech, and vision.

In this Artificial Intelligence tutorial, you will learn the following AI basics-

Introduction to Artificial Intelligence Levels

Nowadays, AI is used in almost all industries, giving a technological edge to all companies integrating AI at scale. According to McKinsey, AI has the potential to create 600 billion dollars of value in retail bring 50 per cent more incremental value in banking compared with other analytics techniques. In transport and logistics, the potential revenue jump is 89% more.

Concretely, if an organization uses AI for its marketing team, it can automate mundane and repetitive tasks, allowing the sales representative to focus on relationship building, lead nurturing, etc. A company named Gong provides a conversation intelligence service. Each time a Sales Representative makes a phone call, the machine records, transcribes and analyzes the chat. The VP can use AI analytics and recommendation to formulate a winning strategy.

In a nutshell, AI provides cutting-edge technology to deal with complex data that a human being cannot handle. AI automates redundant jobs allowing a worker to focus on the high level, value-added tasks. When AI is implemented at scale, it leads to cost reduction and revenue increase.

History of Artificial Intelligence

Artificial Intelligence is a buzzword today, although this term is not new. In 1956, avant-garde experts from different backgrounds decided to organize a summer research project on AI. Four bright minds led the project; John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

Here, is Brief history of Artificial Intelligence:

Year Milestone / Innovation
1923 Karel Čapek plays named “Rossum’s Universal Robots, the first use of the word “robot” in English.
1943 Foundations for neural networks laid.
1945 Isaac Asimov, a Columbia University alumni, use the term Robotics.
1956 John McCarthy first used the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University.
1964 Danny Bobrow’s dissertation at MIT showed how computers could understand natural language.
1969 Scientists at Stanford Research Institute Developed Shakey. A robot equipped with locomotion and problem-solving.
1979 The world’s first computer-controlled autonomous vehicle, Stanford Cart, was built.
1990 Significant demonstrations in machine learning
1997 The Deep Blue Chess Program beat the then world chess champion, Garry Kasparov.
2000 Interactive robot pets have become commercially available. MIT displays Kismet, a robot with a face that expresses emotions.
2006 AI came into the Business world in the year 2006. Companies like Facebook, Netflix, Twitter started using AI.
2012 Google has launched an Android app feature called “Google now”, which provides the user with a prediction.
2018 The “Project Debater” from IBM debated complex topics with two master debaters and performed exceptionally well.

Goals of Artificial Intelligence

Here are the main Goals of AI:

  • It helps you reduce the amount of time needed to perform specific tasks.
  • Making it easier for humans to interact with machines.
  • Facilitating human-computer interaction in a way that is more natural and efficient.
  • Improving the accuracy and speed of medical diagnoses.
  • Helping people learn new information more quickly.
  • Enhancing communication between humans and machines.

Subfields of Artificial Intelligence

Here, are some important subfields of Artificial Intelligence:

Machine Learning: Machine learning is the art of studying algorithms that learn from examples and experiences. Machine learning is based on the idea that some patterns in the data were identified and used for future predictions. The difference from hardcoding rules is that the machine learns to find such rules.

Deep Learning: Deep learning is a sub-field of machine learning. Deep learning does not mean the machine learns more in-depth knowledge; it uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model. For instance, the Google LeNet model for image recognition counts 22 layers.

Natural Language Processing: A neural network is a group of connected I/O units where each connection has a weight associated with its computer programs. It helps you to build predictive models from large databases. This model builds upon the human nervous system. You can use this model to conduct image understanding, human learning, computer speech, etc.

Expert Systems: An expert system is an interactive and reliable computer-based decision-making system that uses facts and heuristics to solve complex decision-making problems. It is also considered at the highest level of human intelligence. The main goal of an expert system is to solve the most complex issues in a specific domain.

Fuzzy Logic: Fuzzy Logic is defined as a many-valued logic form that may have truth values of variables in any real number between 0 and 1. It is the handle concept of partial truth. In real life, we may encounter a situation where we can’t decide whether the statement is true or false.

Subfields of Artificial Intelligence

Types of Artificial Intelligence

There are three main types of artificial intelligence: rule-based, decision tree, and neural networks.

  • Narrow AI is a type of AI that helps you perform a dedicated task with intelligence.
  • General AI is a type of AI intelligence that can perform any intellectual task efficiently like a human.
  • Rule-based AI is based on a set of pre-determined rules that are applied to an input data set. The system then produces a corresponding output.
  • Decision tree AI is similar to rule-based AI in that it uses sets of pre-determined rules to make decisions. However, the decision tree also allows for branching and looping to consider different options.
  • Super AI is a type of AI that allows computers to understand human language and respond in a natural way.
  • Robot intelligence is a type of AI that allows robots to have complex cognitive abilities, including reasoning, planning, and learning.

AI Vs Machine Learning

Most of our smartphone, daily device or even the internet uses Artificial Intelligence. Very often, AI and machine learning are used interchangeably by big companies that want to announce their latest innovation. However, Machine learning and AI are different in some ways.

AI- artificial intelligence- is the science of training machines to perform human tasks. The term was invented in the 1950s when scientists began exploring how computers could solve problems on their own.

AI Vs Machine Learning

Artificial Intelligence is a computer that is given human-like properties. Take our brain; it works effortlessly and seamlessly to calculate the world around us. Artificial Intelligence is the concept that a computer can do the same. It can be said that AI is a large science that mimics human aptitudes.

Machine learning is a distinct subset of AI that trains a machine to learn. Machine learning models look for patterns in data and try to conclude. In a nutshell, the machine does not need to be explicitly programmed by people. The programmers give some examples, and the computer is going to learn what to do from those samples.

Also read difference between Deep Learning and Machine Learning Vs AI, click here.

Where is AI used? Examples

Now in this AI for beginner’s tutorial, we will learn various applications of AI:

AI has broad applications-

  • Artificial Intelligence is used to reduce or avoid repetitive tasks. For instance, AI can repeat a task continuously, without fatigue. AI never rests, and it is indifferent to the task to carry out.
  • Artificial intelligence improves an existing product. Before the age of machine learning, core products were built upon hard-code rules. Firms introduced artificial intelligence to enhance the functionality of the product rather than starting from scratch to design new products. You can think of a Facebook image. A few years ago, you had to tag your friends manually. Nowadays, with the help of AI, Facebook gives you a friend’s recommendation.

AI is used in all industries, from marketing to supply chain, finance, food-processing sector. According to a McKinsey survey, financial services and high tech communication are leading the AI fields.

AI Used Examples

Why is AI booming now?

Now in this Artificial Intelligence testing tutorial, let’s learn why AI is booming now. Let’s understand by the below diagram.

AI booming

A neural network has been out since the nineties with the seminal paper of Yann LeCun. However, it started to become famous around the year 2012. Explained by three critical factors for its popularity are:

  1. Hardware
  2. Data
  3. Algorithm

Machine learning is an experimental field, meaning it needs data to test new ideas or approaches. With the boom of the internet, data became more easily accessible. Besides, giant companies like NVIDIA and AMD have developed high-performance graphics chips for the gaming market.


In the last twenty years, the CPU’s power has exploded, allowing the user to train a small deep-learning model on any laptop. However, you need a more powerful machine to process a deep-learning model for computer vision or deep learning. Thanks to the investment of NVIDIA and AMD, a new generation of GPU (graphical processing unit) are available. These chips allow parallel computations, and the machine can separate the computations over several GPUs to speed up the calculations.

For instance, with an NVIDIA TITAN X, it takes two days to train a model called ImageNet against weeks for a traditional CPU. Besides, big companies use clusters of GPU to train deep learning models with the NVIDIA Tesla K80 because it helps to reduce the data center cost and provide better performances.

Artificial Inteligence in Graphics cards


Deep learning is the structure of the model, and the data is the fluid to make it alive. Data powers artificial intelligence. Without data, nothing can be done. The latest Technologies have pushed the boundaries of data storage, and it is easier than ever to store a high amount of data in a data center.

Internet revolution makes data collection and distribution available to feed machine learning algorithms. If you are familiar with Flickr, Instagram or any other app with images, you can guess their AI potential. There are millions of pictures with tags available on these websites. Those pictures can train a neural network model to recognize an object on the picture without the need to collect and label the data manually.

Artificial intelligence combined with data is the new gold. Data is a unique competitive advantage that no firm should neglect, and AI provides the best answers from your data. When all the firms can have the same technologies, the one with data will have a competitive advantage. To give an idea, the world creates about 2.2 exabytes, or 2.2 billion gigabytes, every day.

A company needs exceptionally diverse data sources to find the patterns and learn in a substantial volume.

Big Data in AI


Hardware is more powerful than ever, data is easily accessible, but one thing that makes the neural network more reliable is the development of more accurate algorithms. Primary neural networks are a simple multiplication matrix without in-depth statistical properties. Since 2010, remarkable discoveries have been made to improve the neural network.

Artificial Intelligence uses a progressive learning algorithm to let the data do the programming. It means the computer can teach itself how to perform different tasks, like finding anomalies becoming a chatbot.


  • AI is a full form of Artificial intelligence is the science of training machines to imitate or reproduce human tasks.
  • A scientist can use different methods to train a machine. At the beginning of the AI’s ages, programmers wrote hard-coded programs, typing every logical possibility the machine could face and how to respond.
  • When a system grows complex, it becomes difficult to manage the rules. To overcome this issue, the machine can use data to learn how to take care of all the situations from a given environment.
  • The most important feature of having a powerful AI is that it has enough data with considerable heterogeneity. For example, a machine can learn different languages as long as it has enough words to learn from.
  • AI is the new cutting-edge technology. Ventures capitalists invest billions of dollars in startups or AI projects, and McKinsey estimates AI can boost every industry by at least a double-digit growth rate.
  • General AI, Rule-based AI, Decision tree AI, Super AI are types of artificial intelligence. Many of these concepts are applied in the creation of AI chatbots. If you’re interested, you can learn more about how these principles are implemented in some of the best AI chatbots available today.

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