So, in Anjuum Khanna’s simple words let’s define AI. As the name speaks it is known as “artificial intelligence” or “machine intelligence”. So Artificial intelligence (AI) is a special feature of machines, in comparison to the natural intelligence displayed by humans and other animals. In computer science, AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. So when a machine is involved in a function like “problem-solving” or “learning” it is also known as artificial intelligence.

As intelligence is a step above the common task so a task which is common is not intelligence. As per me, this word is a word which is full of disputes. So intelligence requires frequent innovation. Let us understand this with a small example. As optical character recognition is frequently excluded from “artificial intelligence”, has become a routine technology. At one point in time, this was the part of Artificial Intelligence. Right now these technologies are defined as artificial intelligence understanding human speech, competing at the highest level in strategic game systems (such as chess), autonomous cars, intelligent routing in the content delivery network and military simulations.

After this explanation let’s go to history, where we will see how and when AI was defined. Back in the 1950s, the fathers of the field Minsky and McCarthy described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. This is a very simple definition which in Anjuum Khanna’s words communicate that any task which is done with intelligence by the human being is performed by machine can be called as artificial intelligence. So after many disputes in history, we have settled on few criteria like planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity which undoubtedly belong to AI.

Type of AI
As per me (Anjuum Khanna), segregation is required to see the development stages of any product or technology. We can easily define AI into two categories:-

Narrow AI is what we see all around us in computers today. Intelligent systems that have been taught or learned how to carry out specific tasks without being explicitly programmed how to do so.
Let me explain through few examples this type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past. Unlike humans, these systems can only learn or be taught how to do specific tasks, which is why they are called narrow AI.

Artificial general intelligence is a futuristic intelligence and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or to reason about a wide variety of topics based on its accumulated experience. This is the sort of AI more commonly seen in movies, but this technology doesn’t exist today.
As per the survey conducted by AI developers in between 2040 & 2050 this technology will start developing and by 2075 will achieve 90% of development. However few groups are still confused about its development as till the time we don’t have the hold on the functionality of the human brain we can’t even start with general intelligence.

For the better understanding of Artificial intelligence as per me (Anjuum Khanna) we should understand few basic technologies of this concept.

Machine Learning: –
In Anjuum Khanna’s definition, machine learning is a computer system which can feed large amounts of data, which is then used by the machine to learn how to carry out a specific task, such as understanding speech or captioning a photograph.

Neural networks:-
These are brain-inspired networks of interconnected layers of algorithms, called neurons, that feed data into each other, and which can be trained to carry out specific tasks by modifying the importance attributed to input data as it passes between the layers.

These are two supplementary topics which need to understand with artificial intelligence. One more of AI research is evolutionary computation. This is basically natural selection and sees genetic algorithms undergo random mutations and combinations between generations in an attempt to evolve the optimal solution to a given problem. This approach has even been used to help design AI models, effectively using AI to help build AI.

The most important question that comes to our mind is how AI will change this world. And I m (Anjuum Khanna) having my own thought process on the same. So let’s understand this with an example.

All of the major cloud platforms such as Amazon Web Services, Microsoft Azure and Google Cloud Platform provide access to GPU arrays for training and running machine learning models, with Google also gearing up to let users use its Tensor Processing Units — custom chips whose design is optimized for training and running machine-learning models.

All of the necessary associated infrastructure and services are available from the big three, the cloud-based data stores, capable of holding the vast amount of data needed to train machine-learning models, services to transform data to prepare it for analysis, visualization tools to display the results clearly, and software that simplifies the building of models.

These cloud platforms are even simplifying the creation of custom machine-learning models, with Google recently revealing a service that automates the creation of AI models, called Cloud AutoML. This drag-and-drop service builds custom image-recognition models and requires the user to have no machine-learning expertise.

Cloud-based, machine-learning services are constantly evolving, and at the start of 2018, Amazon revealed a host of new AWS offerings designed to streamline the process of training up machine-learning models.

For those firms that don’t want to build their own machine learning models but instead want to consume AI-powered, on-demand services — such as voice, vision, and language recognition — Microsoft Azure stands out for the breadth of services on offer, closely followed by Google Cloud Platform and then AWS. Meanwhile, IBM, alongside its more general on-demand offerings, is also attempting to sell sector-specific AI services aimed at everything from health care to retail, grouping these offerings together under its IBM Watson umbrella — and recently investing $2bn in buying The Weather Channel to unlock a trove of data to augment its AI services.

AI applications
To know more about AI we need to learn through examples. Here are some examples to see its impact on all major industries.

AI in healthcare: – This is the most critical industry as it requires precision and accuracy. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It understands natural language and is capable of responding to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema.

AI in business: – Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers.

AI in education: – AI can automate grading, giving educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers. It can find out the gaps and help in resolving them.

AI in finance: – AI in personal finance applications, such as Mint or Turbo Tax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, the software performs much of the trading on Wall Street.

AI in law:- The discovery process, sifting through documents, in law is often overwhelming for humans. Automating this process is a more efficient use of time. Startups are also building question-and-answer computer assistants that can sift programmed-to-answer questions by examining the taxonomy and ontology associated with a database.

AI in manufacturing: – This is an area that has been at the forefront of incorporating robots into the workflow. Industrial robots used to perform single tasks and were separated from human workers, but as the technology advanced that changed.

Here we have seen many directions in which AI has worked and has improved deliverables. This technology is growing day by day and showing improvement in many fields.