Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI 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. 

At it’s core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines.

The expansive goal of artificial intelligence has given rise to many questions and debates. So much so, that no singular definition of the field is universally accepted.  

The major limitation in defining AI as simply “building machines that are intelligent” is that it doesn’t actually explain what artificial intelligence is? What makes a machine intelligent?

The concept of Artificial Intelligence (AI) was born in the summer of 1956 at Dartmouth College in Hanover, New Hampshire. Half of a century has passed, and AI has turned into an important field whose influence on our daily lives can hardly be overestimated. The original view of intelligence as a computer program – a set of algorithms to process symbols – has led to many useful applications now found in internet search engines, voice recognition software, cars, home appliances, and consumer electronics, but it has not yet contributed significantly to our understanding of natural forms of intelligence. Since the 1980s, AI has expanded into a broader study of the interaction between the body, brain, and environment, and how intelligence emerges from such interaction.

This advent of embodiment has provided an entirely new way of thinking that goes well beyond artificial intelligence proper, to include the study of intelligent action in agents other than organisms or robots. For example, it supplies powerful metaphors for viewing corporations, groups of agents, and networked embedded devices as intelligent and adaptive systems acting in highly uncertain and unpredictable environments. In addition to giving us a novel outlook on information technology in general, this broader view of AI also offers unexpected perspectives into how to think about ourselves and the world around us. In this chapter, we briefly review the turbulent history of AI research, point to some of its current trends, and to challenges that the AI of the 21st century will have to face.

Artificial intelligence applications in use: in the 21st century

Use of Artificial Intelligence in applications is growing gradually.

  • AI in business: Robotic process automation is used and applied in highly repetitive tasks which are usually performed by humans. It is used to serve customers and give better service. Automation of job positions is now a talking point in academia and IT consultancies too.
  • Use of AI in education: In the work of grading, calculating marks is charged up by the use of AI. It can drive efficiency. It can follow the admin’s task. With the help of AI, a teacher is able to handle 50 students at a time.
  • AI for vehicle service: Autonomous vehicles are there with long-ranging radars or cameras which are done by the help of AI. Each machine collects different information. It can direct cars to gas stations; it can find quickest routes, etc.
  • AI use in healthcare: Use of applications like Bill screen, NuMedi, Medecision has made man’s life easy by diagnosing health issues and giving effective treatments for them.

Artificial Intelligence Programming languages:

Python, C++, Java, Lisp, Prolog are the AI Programming languages which are very much in use nowadays. As it is a branch of engineering that aims at making the computers think intelligently with the help of those languages. AI Researchers have developed specialized programming languages for the best use.

Robotics and Artificial Intelligence:

Artificial intelligence is probably the most exciting field in Robotics. But at the same time, it is controversial too. AI Robotsis the most effective example of this technology. Some Robots have the ability to interact in a limited capacity. Learning robots can recognize your action. Some AI Robots can interact socially.

  • Kismet, a robot in M.I.T’s AI lab can connect to human body language and can recognize voice inflexion. This AI Robot can understand the tone of speech too.
  • Sophia, a humanoid Robot can understand speech, make conversation. It can follow faces and sustain eye contact too. It is the first AI Robot having the citizenship of any country. It is made by Hong Kong-based company Hanson Robotics.

Types of Artificial Intelligence Algorithms

Artificial intelligence algorithms can be broadly classified as :

1. Classification Algorithms

Classification algorithms are part of supervised learning. These algorithms are used to divide the subjected variable into different classes and then predict the class for a given input. For example, classification algorithms can be used to classify emails as spam or not. Let’s discuss some of the commonly used classification algorithms.Dreaming to study abroad,this is the best programme for you.

    a) Naive Bayes

Naive Bayes algorithm works on Bayes theorem and takes a probabilistic approach, unlike other classification algorithms. The algorithm has a set of prior probabilities for each class. Once data is fed, the algorithm updates these probabilities to form something known as posterior probability. This comes useful when you need to predict whether the input belongs to a given list of classes or not.

    b) Decision Tree

The decision tree algorithm is more of a flowchart like an algorithm where nodes represent the test on an input attribute and branches represent the outcome of the test.

    c) Random Forest

Random forest works like a group of trees. The input data set is subdivided and fed into different decision trees. The average of outputs from all decision trees is considered. Random forests offer a more accurate classifier as compared to Decision tree algorithm.

   d) Support Vector Machines

SVM is an algorithm that classifies data using a hyperplane, making sure that the distance between the hyperplane and support vectors is maximum.

    e) K Nearest Neighbours

KNN algorithm uses a bunch of data points segregated into classes to predict the class of a new sample data point. It is called “lazy learning algorithm” as it is relatively short as compared to other algorithms.

2. Regression Algorithms

Regression algorithms are a popular algorithm under supervised machine learning algorithms. Regression algorithms can predict the output values based on input data points fed in the learning system. The main application of regression algorithms includes predicting stock market price, predicting weather, etc. The most common algorithms under this section are 

    a) Linear regression

It is used to measure genuine qualities by considering the consistent variables. It is the simplest of all regression algorithms but can be implemented only in cases of linear relationship or a linearly separable problem. The algorithm draws a straight line between data points called the best-fit line or regression line and is used to predict new values.

    b) Lasso Regression

Lasso regression algorithm works by obtaining the subset of predictors that minimizes prediction error for a response variable. This is achieved by imposing a constraint on data points and allowing some of them to shrink to zero value.

    c) Logistic Regression

Logistic regression is mainly used for binary classification. This method allows you to analyze a set of variables and predict a categorical outcome. Its primary applications include predicting customer lifetime value, house values, etc

    d) Multivariate Regression

This algorithm has to be used when there is more than one predictor variable. This algorithm is extensively used in retail sector product recommendation engines, where customers preferred products will depend on multiple factors like brand, quality, price, review etc.

    e) Multiple Regression Algorithm

Multiple Regression Algorithm uses a combination of linear regression and non-linear regression algorithms taking multiple explanatory variables as inputs. The main applications include social science research, insurance claim genuineness, behavioural analysis, etc. 

3. Clustering Algorithms

Clustering is the process of segregating and organizing the data points into groups based on similarities within members of the group. This is part of unsupervised learning. The main aim is to group similar items. For example, it can arrange all transactions of fraudulent nature together based on some properties in the transaction. Below are the most common clustering algorithms.

    a) K-Means Clustering

It is the simplest unsupervised learning algorithm. The algorithm gathers similar data points together and then binds them together into a cluster. The clustering is done by calculating the centroid of the group of data points and then evaluating the distance of each data point from the centroid of the cluster. Based on the distance, the analyzed data point is then assigned to the closest cluster. ‘K’ in K-means stands for the number of clusters the data points are being grouped into. 

    b) Fuzzy C-means Algorithm

FCM algorithm works on probability. Each data point is considered to have a probability of belonging to another cluster. Data points don’t have an absolute membership over a particular cluster, and this is why the algorithm is called fuzzy. 

    c) Expectation-Maximisation (EM) Algorithm

It is based on Gaussian distribution we learned in statistics. Data is pictured into a Gaussian distribution model to solve the problem. After assigning a probability, a point sample is calculated based on expectation and maximization equations. 

    d) Hierarchical Clustering Algorithm

These algorithms sort clusters hierarchical order after learning the data points and making similarity observations. It can be of two types

  • Divisive clustering, for a top-down approach
  • Agglomerative clustering, for a bottom-up approach


The advantages of Artificial intelligence applications are enormous and can revolutionize any professional sector. Let’s see some of them

1) Reduction in Human Error:

The phrase “human error” was born because humans make mistakes from time to time. Computers, however, do not make these mistakes if they are programmed properly. With Artificial intelligence, the decisions are taken from the previously gathered information applying a certain set of algorithms. So errors are reduced and the chance of reaching accuracy with a greater degree of precision is a possibility.

Example: In Weather Forecasting using AI they have reduced the majority of human error.

2) Takes risks instead of Humans:

This is one of the biggest advantages of Artificial intelligence. We can overcome many risky limitations of humans by developing an AI Robot which in turn can do the risky things for us. Let it be going to mars, defuse a bomb, explore the deepest parts of oceans, mining for coal and oil, it can be used effectively in any kind of natural or man-made disasters.

Example: Have you heard about the Chernobyl nuclear power plant explosion in Ukraine? At that time there were no AI-powered robots that can help us to minimize the effect of radiation by controlling the fire in early stages, as any human went close to the core was dead in a matter of minutes. They eventually poured sand and boron from helicopters from a mere distance.

AI Robots can be used in such situations where intervention can be hazardous.

3) Available 24×7:

An Average human will work for 4–6 hours a day excluding the breaks. Humans are built in such a way to get some time out for refreshing themselves and get ready for a new day of work and they even have weekly offed to stay intact with their work-life and personal life. But using AI we can make machines work 24×7 without any breaks and they don’t even get bored, unlike humans.

Example: Educational Institutes and Helpline centers are getting many queries and issues which can be handled effectively using AI.

4) Helping in Repetitive Jobs:

In our day-to-day work, we will be performing many repetitive works like sending a thanking mail, verifying certain documents for errors and many more things. Using artificial intelligence we can productively automate these mundane tasks and can even remove “boring” tasks for humans and free them up to be increasingly creative.

Example: In banks, we often see many verifications of documents to get a loan which is a repetitive task for the owner of the bank. Using AI Cognitive Automation the owner can speed up the process of verifying the documents by which both the customers and the owner will be benefited.

5) Digital Assistance:

Some of the highly advanced organizations use digital assistants to interact with users which saves the need for human resources. The digital assistants also used in many websites to provide things that users want. We can chat with them about what we are looking for. Some chatbots are designed in such a way that it’s become hard to determine that we’re chatting with a chatbot or a human being.

Example: We all know that organizations have a customer support team that needs to clarify the doubts and queries of the customers. Using AI the organizations can set up a Voice bot or Chatbot which can help customers with all their queries. We can see many organizations already started using them on their websites and mobile applications.

6) Faster Decisions:

Using AI alongside other technologies we can make machines take decisions faster than a human and carry out actions quicker. While taking a decision human will analyze many factors both emotionally and practically but AI-powered machine works on what it is programmed and delivers the results in a faster way.

Example: We all have played Chess games in Windows. It is nearly impossible to beat CPU in the hard mode because of the AI behind that game. It will take the best possible step in a very short time according to the algorithms used behind it.

7) Daily Applications:

Daily applications such as Apple’s Siri, Window’s Cortana, Google’s OK Google are frequently used in our daily routine whether it is for searching a location, taking a selfie, making a phone call, replying to a mail and many more.

Example: Around 20 years ago, when we are planning to go somewhere we used to ask a person who already went there for the directions. But now all we have to do is say “OK Google where is Visakhapatnam”. It will show you Visakhapatnam’s location on google map and the best path between you and Visakhapatnam.

8) New Inventions:

AI is powering many inventions in almost every domain which will help humans solve the majority of complex problems.

Example: Recently doctors can predict breast cancer in the woman at earlier stages using advanced AI-based technologies.


As every bright side has a darker version in it. Artificial Intelligence also has some disadvantages. Let’s see some of them

1) High Costs of Creation:

As AI is updating every day the hardware and software need to get updated with time to meet the latest requirements. Machines need repairing and maintenance which need plenty of costs. It’ s creation requires huge costs as they are very complex machines.

2) Making Humans Lazy:

AI is making humans lazy with its applications automating the majority of the work. Humans tend to get addicted to these inventions which can cause a problem to future generations.

3) Unemployment:

As AI is replacing the majority of the repetitive tasks and other works with robots,human interference is becoming less which will cause a major problem in the employment standards. Every organization is looking to replace the minimum qualified individuals with AI robots which can do similar work with more efficiency.

4) No Emotions:

There is no doubt that machines are much better when it comes to working efficiently but they cannot replace the human connection that makes the team. Machines cannot develop a bond with humans which is an essential attribute when comes to Team Management.

5) Lacking Out of Box Thinking:

Machines can perform only those tasks which they are designed or programmed to do, anything out of that they tend to crash or give irrelevant outputs which could be a major backdrop.

Artificial Intelligence Future:

AI field has a wide scope in computer and technical world. We have seen so many practical examples of AI which have made us surprised. There are so many scopes left open for future AI scientists.

  • Manufacturing industrial machinery: Manufacturing activities have been charged up by the invention of AI. Many organizations now prefer AI machines and shifting towards human-less manufacturing
  • Autonomous cars:AI software system is the actual brain behind the autonomous car services like Ola or Uber. It takes constant learning and monitoring of activities.
  • Retailing:We all have seen the example of Amazon Go where you do not need any sales personnel to be present there. It also helps with target marketing and product recommendation. Amazon, Flipkart, Myntra are those websitesthat utilize AI technology to the fullest to tap in on the huge market.
  • Cybersecurity: Future applications of AI will prevent hackers. Ethical hackers are there to protect important data. Launch of iPhone x with race recognition feature is a step towards AI Future.
  • Emotion bots:In 2015 a robot called “pepper” went on sale. Virtual assistants like Siri, Cortana, and Alexa show us how they can connect to humans. So, respective of all these we can say 2020 is going to be the year of chatbots.

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Author: refuge_2020

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