Artificial Intelligence

The word Artificial Intelligence is not a surprise or new technology for us. It goes back from the old age since the 1950s. It started as a hypothetical situation but right now it is the most important technology in the entire world. Almost everything around us is run through AI, Deep learning, or Machine learning. Since 1950 to now AI has exponential growth in its evolution.

History of AI

History of Artificial Intelligence (AI)

Under Greek Mythology, the concept of Artificial Intelligence, machines, and mechanical men were well thought of. The best example of this is the Talos.

Talos was a giant animated bronze warrior, programmed to guard the island of Crete. However these are only hypothetical ideas, nobody knows the actual implementation of this.

In the 19th century, the concept of AI and ML comes into existence. 1950 was the most important year for the history and introduction of AI. In 1950, Alan Turing published a paper, in which he hypothesizes about the possibility of the creation of a machine, that can think like a human, so he creates a test, which is called Turing Test. In 1950-51, Alan Turing creates a test, to determine whether a machine can think intelligently or not like a human being. However, it was quite difficult to define but he made it simple through the Turing test. If a machine passes this test that means the machine can think like a human being. However, there is no such ideal machine till now, which passed the Turing Test. In 1951, a well-known computer scientist Christopher Strachey wrote a checkers program and contemporary this program was written from the chess as well. Further, these programs were improved and re-executed, but this was the first attempt to create a program that can play and compete for chess with human beings.

1956- Invention of Artificial Intelligence (AI)

Now take a forward step in the invention of AI. 1956 was the most crucial year in the invention of Artificial Intelligence. The term AI was coined by John McCarthy at the Dartmouth Conference in 1956. Further, in 1959 the first AI laboratory was established, this was the time when researches were started for AI. The first AI research lab was MIT lab, which is still working for the AI applications till time.

1960- General Motor Robot

In this year, the first robot or machine was introduced to the general motor assembly line.

1961- Invention of first Chatbot.

The first Chatbot was invented called ELIZA was introduced in 1961; however, we have SIRI, Alexa and so many Chatbot in running time.

1997- IBM deep blue

In 1977, there was a big surprise in the evolution of AI. IBM deep blue beats the world champion Garry Kasparov in the game of chess. So this was the first achievement in the history of Artificial Intelligence.

2005- DARPA Grand Challenge

This was another big achievement for AI. In 2005, Stanford Racing Team built a robotic racing car named Stanley, won the DARPA grand challenge.

2011- IBM Watson

In 2011, IBM’s question and answering system called Watson defeated the two greatest Jeopardy champions named Brad Rutter and Ken Jennings.

Why Artificial Intelligence (AI)?

Artificial Intelligence now becomes the need of human beings rather than a technology. If we saw any machine around us, we will find that almost everything is running through AI technology. So Why AI becomes so important for the human being, there are some reasons for the demand for AI is as follows:

  • More Data storage:
Big Data

We are generating more data every day through social media, IoT devices, etc. So AI is a solution to process more data and derive useful insight and grow business with the help of data. Big data helps to recommend a better product while shopping online and classify an object from an image. AI is trained on large data sets and Big data helps us to do this more easily.

  • Better Algorithm and broad Investment

AI uses effectively better algorithms based on the concept of Neural networks and concepts related to deep learning. Since it has better algorithms, it can do faster computations with relatively more accuracy, and this will increase the demand for AI. All Universities, governments, and start-ups are all investing in AI. Big multinational companies like Google, Microsoft, Salesforce, and Amazon, etc. are also investing heavily in the field of AI assuming that AI is the future of all technologies. So AI is rapidly growing in every field like study as well as in economic fields.

  • Better computational power

We have more computational power due to AI, as AI requires a lot of computing power. There are so many advances that have been made and complex deep learning models are deployed. One of the greatest technology that made it possible is GPUs. As we have more computational power, so we can implement AI in our daily aspects.

Types of Artificial Intelligence (AI)

Artificial Intelligence is divided into two different stages based on capabilities and functionalities of the machines are as follows:

Type-1 (Based on capabilities)

  • Artificial Narrow Intelligence (ANI)
  • Artificial General Intelligence (AGI)
  • Artificial Super Intelligence (ASI)

Type-2 (Based on Functionalities)

  • Reactive Machines
  • Limited Memory
  • Theory of mind
  • Self-awareness

Artificial Intelligence Type-1: (Based on Capabilities of machines)

  • Artificial Narrow Intelligence

Artificial Narrow Intelligence is also known as weak AI. It is applied only for some specific tasks. Most of the currently existing systems that claim to use AI are actually operating as weak AI focused on a narrowly defined specific problem.

Narrow/weak AI

ExampleAlexa is the best example of Artificial Narrow Intelligence or weak AI because it operates within an unlimited pre-defined range of functions. It does not have any genuine intelligence or self-awareness.


Some other examples of weak AI are the Google search engine, Sophia the humanoid, self-driving cars, and famous AlphaGo.

  • Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) is also known as strong AI. It involves a machine that passes the ability to perform any intellectual task that a human being can perform.


Machines don’t possess human-like abilities. However they have a very strong processing unit that can perform very high-level computations, but they are not yet capable of doing simple and most reasonable things that a human being can perform. A machine can review millions of documents in a few seconds or minutes only but if you ask a machine to walk up to your living room and switch on the fan, a machine will take forever to learn that because machines don’t have a reasonable way of thinking.

So we can conclude, machines have a strong processing unit but they can’t think like a human being. There is no such machine developed yet that is fully strong AI.

ExampleAlphaGo Zero has defeated AlphaGo in the game of Go.

  • Artificial Super Intelligence (ASI)

Artificial Super Intelligence (ASI) is a term referring to the time when the capabilities of computers will surpass humans. However, it is a hypothetical concept as depicted in movies or science fiction books where machines have taken over the world. Movies like Terminator depict Artificial Super Intelligence.


These do not exist yet in the real world but there are lots of people who speculate that ASI will take over the world by the year 2040.

Note- Narrow intelligence is the only thing that is existing till now which means we have only weak AI or weak Artificial Intelligence in the real world. The main difference among the above types is Narrow AI is dedicated to one task, General AI can perform like a human being and Super AI can think intelligently than human beings.

Artificial Intelligence Type-2: (Based on Functionalities of machines)

  • Reactive Machines

Purely reactive machines are the most common types of AI that don’t have the ability to form memories and use pass experiences for current decisions. IBM chess-playing supercomputer and Deep blue are the perfect examples of Reactive machines, which defeated the chess grandmaster Garry Kasparov in early 1990.

Deep blue is a technique that is used to identify the pieces on the chessboard and each new move. It can predict for the next movement of the opponent and the most optimal movement from all available possible movements. Deep learning can’t able to memorize the past moves or does not have any past memory. It ignores the rarely used chess-specific rules against repeating the same technique three times.

Similarly, Google’s AplhaGo has beaten top human experts in Go. It also can’t evaluate the potential future moves. But it can analyze better than Deep blue by using Neural Network to evaluate the game development. These machines can function only for the assigned tasks and can be easily fooled.

  • Limited Memory

Apart from Reactive machines, these machines have the ability to use past experience for current decisions. Self-driving cars have proven it already because it is not easy to predict the speed and direction of other cars and object randomly. These predictions through self-driving cars added as a programmed representation including traffic signaling, lane marking, the curve in the road, and other traffic elements. These machines help to decide when to change the lane and overtake to the other cars without any accident.

These pieces of information are instantaneous about the past as this information never saved in the car’s database of experience from where it can learn in the future. So a system that can use past experience and also can handle new situations lies in this category.

  • Theory of Mind

These types of AI machines are performing as a bridge among Reactive machines, Limited memory, and the machines that will build in the future.