What is Artificial Intelligence? How Does AI Work, Applications and Future?

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The intelligence demonstrated by machines is known as Artificial Intelligence. Artificial Intelligence has grown to be very popular in today’s world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life.
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Table of Content
Introduction to Artificial Intelligence
The short answer to What is Artificial Intelligence is that it depends on who you ask.
A layman with a fleeting understanding of technology would link it to robots. They’d say Artificial Intelligence is a terminator like-figure that can act and think on its own.
If you ask about artificial intelligence to an AI researcher, (s)he would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. So to summarise, Artificial Intelligence meaning is:
Artificial Intelligence Definition
An intelligent entity created by humans.
Capable of performing tasks intelligently without being explicitly instructed.
Capable of thinking and acting rationally and humanely.
How do we measure if Artificial Intelligence is acting like a human?
Even if we reach that state where an AI can behave as a human does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity with the:
Turing Test
Let’s take a detailed look at how these approaches perform:
What is the Turing Test in Artificial Intelligence?
The basis of the Turing Test is that the Artificial Intelligence entity should be able to hold a conversation with a human agent.
The human agent ideally should not able to conclude that they are talking to an Artificial Intelligence
. To achieve these ends, the AI needs to possess these qualities:
Natural Language Processing to communicate successfully.
Knowledge Representation to act as its memory.
Automated Reasoning to use the stored information to answer questions and draw new conclusions.
Machine Learning to detect patterns and adapt to new circumstances.
Cognitive Modelling Approach
As the name suggests, this approach tries to build an Artificial Intelligence model-based on Human Cognition. To distil the essence of the human mind, there are 3 approaches:
Introspection
: Using MRI to observe how the brain functions in different scenarios and replicating that through code.
The Laws of Thought Approach
The Laws of Thought are a large list of logical statements that govern the operation of our mind. The same laws can be codified and applied to artificial intelligence algorithms. The issues with this approach, because solving a problem in principle (strictly according to the laws of thought) and solving them in practice can be quite different, requiring contextual nuances to apply. Also, there are some actions that we take without being 100% certain of an outcome that an algorithm might not be able to replicate if there are too many parameters.
The Rational Agent Approach 
A rational agent acts to achieve the best possible outcome in its present circumstances.
According to the Laws of Thought approach, an entity must behave according to the logical statements. But there are some instances, where there is no logical right thing to do, with multiple outcomes involving different outcomes and corresponding compromises. The rational agent approach tries to make the best possible choice in the current circumstances. It means that it’s a much more dynamic and adaptable agent.
Now that we understand how Artificial Intelligence can be designed to act like a human, let’s take a look at how these systems are built.
How Artificial Intelligence (AI) Works?
Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using it’s computational prowess to surpass what we are capable of. 
To understand How Aritificial Intelligence actually works, one needs to deep dive into the various sub domains of Artificial Intelligence and and understand how those domains could be applied into the various fields of the industry.
Machine Learning
: ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data, saves a human time for businesses and helps them make a better decision.
Deep Learning
: Deep Learning ia an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
Neural Networks
: Neural Networks work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underying variabes and processes the data as a human brain does.
Natural Language Processing
c: NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
Computer Vision
: Computer vision algorithms tries to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
Cognitive Computing
: Cognitive computing algorithms try to mimic a human brain by anaysing text/speech/images/objects in a manner that a human does and tries to give the desired output.
What are the Types of Artificial Intelligence?
Not all types of AI all the above fields simultaneously. Different Artificial Intelligence entities are built for different purposes, and that’s how they vary. AI can be classified based on Type 1 and Type 2 (Based on functionalities). Here’s a brief introduction the first type.
3 Types of Artificial Intelligence
Artificial Narrow Intelligence (ANI)
Let’s take a detailed look.
What is Artificial Narrow Intelligence (ANI)?
This is the most common form of AI that you’d find in the market now.
These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather.
This is the only kind of Artificial Intelligence that exists today. They’re able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters.
What is Artificial General Intelligence (AGI)?
AGI is still a theoretical concept.
It’s defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.
We’re still a long way away from building an AGI system. An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem, communicating with each other to mimic human reasoning. Even with the most advanced computing systems and infrastructures, such as Fujitsu’s K or IBM’s Watson, it has taken them 40 minutes to simulate a single second of neuronal activity. This speaks to both the immense complexity and interconnectedness of the human brain, and to the magnitude of the challenge of building an AGI with our current resources.
What is Artificial Super Intelligence (ASI)?
We’re almost entering into science-fiction territory here, but ASI is seen as the logical progression from AGI.
An Artificial Super Intelligence (ASI) system would be able to surpass all human capabilities. This would include decision making, taking rational decisions, and even includes things like making better art and building emotional relationships.
Once we achieve Artificial General Intelligence, AI systems would rapidly be able to improve their capabilities and advance into realms that we might not even have dreamed of. While the gap between AGI and ASI would be relatively narrow (some say as little as a nanosecond, because that’s how fast Artificial Intelligence would learn) the long journey ahead of us towards AGI itself makes this seem like a concept that lays far into the future.
Strong and Weak Artificial Intelligence
Extensive research in Artificial Intelligence also divides it into two more categories, namely Strong Artificial Intelligence and Weak Artificial Intelligence. The terms were coined by  John Searle in order to differentiate the performance levels in different kinds of AI machines. Here are some of the core differences between them.
Weak AI
Example: Advanced Robotics
What is the Purpose of Artificial Intelligence?
The purpose of Artificial Intelligence is to aid human capabilities and help us make advanced decisions with far-reaching consequences. That’s the answer from a technical standpoint. From a philosophical perspective, Artificial Intelligence has the potential to help humans live more meaningful lives devoid of hard labour, and help manage the complex web of interconnected individuals, companies, states and nations to function in a manner that’s beneficial to all of humanity.
Currently, the purpose of Artificial Intelligence is shared by all the different tools and techniques that we’ve invented over the past thousand years – to simplify human effort, and to help us make better decisions. Artificial Intelligence has also been touted as our Final Invention, a creation that would invent ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality and human suffering.
That’s all in the far future though – we’re still a long way from those kinds of outcomes. Currently, Artificial Intelligence is being used mostly by companies to improve their process efficiencies, automate resource-heavy tasks, and to make business predictions based on hard data rather than gut feelings. As all technology that has come before this, the research and development costs need to be subsidised by corporations and government agencies before it becomes accessible to everyday laymen.
Where is Artificial Intelligence (AI) Used?
AI is used in different domains to give insights into user behaviour and give recommendations based on the data. For example, Google’s predictive search algorithm used past user data to predict what a user would type next in the search bar. Netflix uses past user data to recommend what movie a user might want to see next, making the user hooked onto the platform and increase watch time. Facebook uses past data of the users to automatically give suggestions to tag your friends, based on their facial features in their images. AI is used everywhere by large organisations to make an end user’s life simpler. The uses of Artificial Intelligence would broadly fall under the data processing category, which would include the following:
Searching within data, and optimising the search to give the most relevant results
Logic-chains for if-then reasoning, that can be applied to execute a string of commands based on parameters
Pattern-detection to identify significant patterns in large data set for unique insights
Applied probabilistic models for predicting future outcomes
What are the Advantages of Artificial Intelligence?
There’s no doubt in the fact that technology has made our life better. From music recommendations, map directions, mobile banking to fraud prevention, AI and other technologies have taken over. There’s a fine line between advancement and destruction. There’s always two sides to a coin, and that is the case with AI as well. Let us take a look at some advantages of Artificial Intelligence-
Advantages of Artificial Intelligence (AI)
Reduction in human error
AI-enabled robots are being used in a customer service capacity in retail and hospitality industries
These robots leverage Natural Language Processing to interact with customers intelligently and like a human
More these systems interact with humans, more they learn with the help of machine learning
Packaging 
AI enables quicker, cheaper, and more accurate packaging
It helps in saving certain motions that a robot is making and constantly refines them, making installing and moving robotic systems easily
Open Source Robotics 
Robotic systems today are being sold as open-source systems having AI capabilities. 
In this way, users can teach robots to perform custom tasks based on a specific application
Eg: small scale agriculture
Top Used Applications in Artificial Intelligence
Google’s AI-powered predictions (E.g.: Google Maps)
Ride-sharing applications (E.g.: Uber, Lyft)
AI Autopilot in Commercial Flights
Spam filters on E-mails
Smart personal assistants (E.g.: Siri, Alexa)
Fraud protection and prevention.
Career Trends in Artificial Intelligence
Jobs in AI have been steadily increasing over the past few years and will continue growing at an accelerating rate.
57% of Indian companies are looking forward to hiring the right talent to match up the Market Sentiment
. On average,
there has been a 60-70% hike in salaries of aspirants who have successfully transitioned into AI roles
. Mumbai stays tall in the competition followed by Bangalore and Chennai. As per research, the demand for AI Jobs have increased but efficient workforce has not been keeping pace with it.
As per WEF, 133 million jobs would be created in Artificial Intelligence by the year 2020
.
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) which defines one of the core tenets of Artificial Intelligence – the ability to learn from experience, rather than just instructions.
Machine Learning algorithms automatically learn and improve by learning from their output. They do not need explicit instructions to produce the desired output.  They learn by observing their accessible data sets and compares it with examples of the final output. The examine the final output for any recognisable patterns and would try to reverse-engineer the facets to produce an output.
What is Deep Learning?
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning concepts are used to teach machines what comes naturally to us humans. Using Deep Learning, a computer model can be taught to run classification acts taking image, text, or sound as an input.
Deep Learning is becoming popular as the models are capable of achieving state of the art accuracy. Large labelled data sets are used to train these models along with the neural network architectures.
Simply put, Deep Learning is using brain simulations hoping to make learning algorithms efficient and simpler to use. Let us now see what is the difference between Deep Learning and Machine Learning.
What is the relationship between AI, ML, and DL?
As the above image portrays, the three concentric ovals describe DL as a subset of ML, which is also another subset of AI. Therefore, AI is the all-encompassing concept that initially erupted. It was then followed by ML that thrived later, and lastly DL that is now promising to escalate the advances of AI to another level.
What is NLP?
A component of Artificial Intelligence, Natural Language Processing is the ability of a machine to understand the human language as it is spoken. The objective of NLP is to understand and decipher the human language to ultimately present with a result. Most of the NLP techniques use machine learning to draw insights from human language.
Also Read:  Most Promising Roles for Artificial Intelligence in India
What is Computer Vision?
Computer Vision is a field of study where techniques are developed enabling computers to ‘see’ and understand the digital images and videos. The goal of computer vision is to draw inferences from visual sources and apply it towards solving a real-world problem.
There are many applications of Computer Vision today, and the future holds an immense scope.
Facial Recognition for surveillance and security systems
Retail stores also use computer vision for tracking inventory and customers
Autonomous Vehicles
Computer Vision in medicine is used for diagnosing diseases
Financial Institutions use computer vision to prevent fraud, allow mobile deposits, and display information visually
What are Neural Networks?
Neural Network is a series of algorithms that mimic the functioning of the human brain to determine the underlying relationships and patterns in a set of data.
The concept of Neural Networks has found application in developing trading systems for the finance sector. They also assist in the development of processes such as time-series forecasting, security classification, and credit risk modelling.
Future of Artificial Intelligence
As humans, we have always been fascinated by technological changes and fiction, right now, we are living amidst the greatest advancements in our history. Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Artificial intelligence is not only impacting the future of every industry and every human being but has also acted as the main driver of emerging technologies like big data, robotics and IoT. Considering its growth rate, it will continue to act as a technological innovator for the foreseeable future. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.
Getting certified in AI will give you an edge over the other aspirants in this industry. With advancements such as Facial Recognition, AI in Healthcare, Chat-bots, and more, now is the time to build a path to a successful career in Artificial Intelligence. Virtual assistants have already made their way into everyday life, helping us save time and energy. Self-driving cars by Tech giants like Tesla have already shown us the first step to the future. AI can help reduce and predict the risks of climate change , allowing us to make a difference before it’s too late. And all of these advancements are only the beginning, there’s so much more to come. 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022.
Important FAQs on Artificial Intelligence (AI) 
Ques. Where is AI used?
Ans. Artificial Intelligence is used across industries globally. Some of the industries which have delved deep in the field of AI to find new applications are E-commerce, Retail, Security and Surveillance. Sports Analytics, Manufacturing and Production, Automotive among others. 
Ques. How is AI helping in our life?
Ans. The virtual digital assistants have changed the way w do our daily tasks. Alexa and Siri have become like real humans we interact with each day for our every small and big need. The natural language abilities and the ability to learn themselves without human interference are the reasons they are developing so fast and becoming just like humans in their interaction only more intelligent and faster. 
Ques. Is Alexa an AI?
Ans. Yes, Alexa is an Artificial Intelligence that lives among us.
Ques. Is Siri an AI?
Ans. Yes, just like Alexa Siri is also an artificial intelligence that uses advanced machine learning technologies to function.
Ques. Why is AI needed?
Ans. AI makes every process better, faster, and more accurate. It has some very crucial applications too such as identifying and predicting fraudulent transactions, faster and accurate credit scoring, and automating manually intense data management practices. Artificial Intelligence improves the existing process across industries and applications and also helps in developing new solutions to problems that are overwhelming to deal with manually. 
What’s your view about the future of Artificial Intelligence? Leave your comments below.
Curious to dig deeper into AI, read our blog on some of the top Artificial Intelligence books .
Further Reading