What is Artificial Intelligence ?

Artificial intelligence (AI) is the facsimile of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing (NLP), speech recognition and machine vision. Computer science defines Artificial Intelligence research as the study of “intelligent agents” any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. A more elaborate definition characterizes AI as a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.

Artificial intelligence is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.

As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler’s Theorem says “AI is whatever hasn’t been done yet.”For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks, and military simulations.

Brief History of Artificial Intelligence

The beginnings of modern Artificial Intelligence(AI) can be traced to classical philosophers attempts to describe human thinking as a symbolic system. But the field of AI wasn’t formally founded until the Artificial Intelligence (AI) research that was founded in the summer of 1956 at Dartmouth College during a workshop event. The excitement of machines becoming as intelligent as humans quickly got funding for millions of dollars to make this dream a reality. As time went by, the early pioneers rapidly realized how complex and complicated this task would be.

But achieving an artificially intelligent being wasn’t so simple. After several reports criticizing progress in AI, government funding and interest in the field dropped off – a period from 1974–80 that became known as the “AI winter.” The field later revived in the 1980s when the British government started funding it again in part to compete with efforts by the Japanese. In the year 1966 the researchers emphasized developing algorithms which can solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966, which was named as ELIZA. In the year 1972 the first intelligent humanoid robot was built in Japan which was named as WABOT-1.

Excitement, investment, and interest in AI development boomed in the first decades of the 21st century. The excitement and enthusiasm ignited around successful AI projects in academia and industry with the assistance of more powerful computer hardware. The time of new AI projects, data structuring, and artificial intelligence programming language improvement led to the phrase “AI Summer”.

Types of Artificial Intelligence

There are four types of artificial intelligence:

  • Reactive machines,
  • Limited memory,
  • Theory of mind and
  • Self-awareness.

Lets me give you some knowledge on this four types of AI:

1. Reactive Machines:

Reactive machines are basic type of AI system which are reactive that they simply perceive the world and react to it, but they do not store ‘memories’ nor use past experiences to inform current decisions or to determine future actions. IBM’s Deep Blue, which defeated chess grandmaster Garry Kasporov in the late 1990s, this is the perfect example of this type of machine. Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities. It is just a reactive machine that sees the pieces on a chess board and reacts to them. It cannot refer to any of its prior experiences, and cannot improve with practice. Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment. All it does is look at the pieces on the chess board as it stands right now, and choose from possible next moves. This type of intelligence involves the computer perceiving the world directly and acting on what it sees. It doesn’t rely on an internal concept of the world. 

Similarly, Google’s AlphaGo, which has beaten top human Go experts, can’t evaluate all potential future moves either. Its analysis method is more sophisticated than Deep Blue’s, using a neural network to evaluate game developments.

These methods do improve the ability of AI systems to play specific games better, but they can’t be easily changed or applied to other situations. These computerized imaginations have no concept of the wider world – meaning they can’t function beyond the specific tasks they’re assigned and are easily fooled. They can’t interactively participate in the world, the way we imagine AI systems one day might. Instead, these machines will behave exactly the same way every time they encounter the same situation. This can be very good for ensuring an AI system is trustworthy.

2. Limited Memory:

Limited Memory AI, can make informed and improved decisions by studying the past data from its memory. Such an AI has a short-lived or a temporary memory that can be used to store past experiences and hence evaluate future actions. Self-driving cars are Limited Memory AI, that uses the data collected in the recent past to make immediate decisions. These observations are added to the self-driving cars’ preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. They’re included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.

For example, self-driving cars use sensors to identify civilians crossing the road, steep roads, traffic signals and so on to make better driving decisions. This helps to prevent any future accidents.

3. Theory Of Mind:

Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called “theory of mind”. The Theory Of Mind AI is a more advanced type of Artificial Intelligence. This category of machines is speculated to play a major role in psychology. This type of AI will focus mainly on emotional intelligence in understanding the people, creatures and objects in the world can have thoughts and emotions that affect their own behavior, so that human believes and thoughts can be better comprehended.

The Theory of Mind AI has not yet been fully developed but rigorous research is happening in this area.


Self awareness where machines have their own consciousness and become self-aware. This type of Artificial intelligence (AI) is a little far fetched given the present circumstances. However, in the future, achieving a stage of superintelligence might be possible. The final step of AI development is to build systems that can form representations about themselves.

Geniuses people like Elon Musk and Stephen Hawkings have consistently warned us about the evolution of AI. AI is a very vast field that covers many domains like Machine Learning, Deep Learning and so on.