Artificial Intelligence


Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

What is Artificial Intelligence?

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

  • Knowledge
  • Reasoning
  • Problem solving
  • Perception
  • Learning
  • Planning
  • Ability to manipulate and move objects

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach.

Types of Artificial Intelligence

We need to do more than teach machines to learn. We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.


The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Deep Blue on the chess board can make only the predictions about the move of its and its opponent and few optimal moves among the possibilities. But it doesn’t have any concept of the past, nor any memory of what has happened before.

This type of intelligence involves the computer perceiving the world directly and acting on what it sees.

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: You want your autonomous car to be a reliable driver. But it’s bad if we want machines to truly engage with, and respond to, the world. These simplest AI systems won’t ever be bored, or interested, or sad.

Artificial Intelligence


This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars’ speed and direction. That can’t be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.

These observations are added to the self-driving cars’ preprogramed 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.

But these simple pieces of information about the past are only transient. They aren’t saved as part of the car’s library of experience it can learn from, the way human drivers compile experience over years behind the wheel.

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We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future. However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.

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 understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behaviour.

This is crucial to how we humans formed societies, because they allowed us to have social interactions. Without understanding each other’s motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, they’ll have to be able to understand that each of us has thoughts and feelings and expectations for how we’ll be treated. And they’ll have to adjust their behaviour accordingly.

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The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it.

This is, in a sense, an extension of the “theory of mind” possessed by Type III artificial intelligences. Consciousness is also called “self-awareness” for a reason. (“I want that item” is a very different statement from “I know I want that item.”) Conscious beings are aware of themselves, know about their internal states, and are able to predict feelings of others. We assume someone honking behind us in traffic is angry or impatient, because that’s how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences. This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.

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How AI Works?

For every incident/task /activity, a new neural network circuit gets generated inside the mind, like in the below image.

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It means that our mind works on pattern matching. Every day, in our life, we follow some patterns like how to walk on the footpath; how to hold the spoon. For every pattern there, some circuit has been created and according to that circuit, we complete our task. Suppose, we are going to a relative’s house; then most often they will greet us and ask us to sit down. That moment, only one pattern will be active in our mind that we are going to get a chair or other object which is suitable for seating.

The image below shows how the neural network of our brain looks.

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Fig: Neural Network in human brain

As of now, we are very clear about the working system of the human brain. Now, we will walk through how Artificial Intelligence works.

Artificial Intelligence works on pattern base too as a human brain works. AI works on pattern searching algorithm as shown in the below image.

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Fig: Artificial Intelligence work image

There are two images as input but we are getting an output as car and not car. Because we have written the logic to match the pattern of Car. If there is an image of a car, then it will be classified and the machine will try to identify it as pattern matching logic. If the pattern matches more than 50 percent (For matching patterns, it will keep trying  to match the pattern; maybe in the first attempt, it will identify only 10% of input pattern but as per our logic, it will keep attempting to match the pattern ) then this will return ” Yes, this is a car”; otherwise we will get an output as  this is not a car.


The ability of a machine to think or recognize is known as Artificial Intelligence. AI works on pattern matching and identification of input, just like the human brain.

Applications of AI

AI has been dominant in various fields such as −

  • Gaming − AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.
  • Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans.
  • Expert Systems − There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.
  • Vision Systems − These systems understand, interpret, and comprehend visual input on the computer. For example,
    • A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.
    • Doctors use clinical expert system to diagnose the patient.
    • Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.
  • Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in human’s noise due to cold, etc.
  • Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.
  • Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

Future of AI

Artificial Intelligence is on the verge of penetrating every major industry from healthcare to advertising, transportation, finance, legal, education, and now inside the workplace. Many of us may have already interacted with a chatbot (defined as an automated, yet personalized, conversation between software and human users) whether it’s on Facebook Messenger to book a hotel room or ordering flowers through 1-800 flowers.

As an example

As we increase the usage of chatbots in our personal lives, we will expect to use them in the workplace to assist us with things like finding new jobs, answering frequently asked HR related questions or even receiving coaching and mentoring.  Chatbots digitize HR processes and enable employees to access HR solutions from anywhere. Using artificial intelligence in HR will create a more seamless employee experience, one that is nimbler and more user driven.

Artificial Intelligence Will Transform The Employee Experience

The Intersection of Artificial Intelligence and Human Resources, HR leaders are beginning to pilot AI to deliver greater value to the organization by using chatbots for recruiting, employee service, employee development and coaching. A recent survey of 350 HR leaders conducted by ServiceNow finds 92% of HR leaders agree that the future of providing an enhanced level of employee service will include chatbots. In fact, you can think of a chatbot as your newest HR team member, one that allows employees to easily retrieve answers to frequently asked questions.


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