Artificial Intelligence (AI) is a branch of information technology that builds intelligent machines. These machines can perform some tasks that normally require human intelligence.


Background of Artificail intelligence

Artificial intelligence (AI) is a reproduction of intelligence similar to humans with the help of machines like computers. So it is the science of producing intelligent machines that can perform different tasks.

Every person, animal, and artificial machine have different intelligence. All they perform their tasks according to their intelligence.

After World War II, many researchers started to construct intelligence machines. In 1947, Alan Turing, an English mathematician was the first who started working on it. His great work was that he started on AI by paying more attention to computer programming rather than making machines.

It is an interdisciplinary science that has several approaches.

The advances in machine learning and in-depth learning have led to a paradigm shift in almost all areas of the technology industry.

with AI, there is the ability of a digital computer or a computer-controlled robot to perform tasks that are usually associated with intelligent beings.

This term is often used for the project to develop systems that contain intellectual processes.

These characterize people, such as arguing, discovering meaning, and generalizing from learning from past experiences. Since the development of the digital computer in the 1940s, it has been shown that computers can be programmed to perform very complex tasks. For example, finding proof of mathematical theorems or chess – with great skill.

Despite the constant development of the computer’s processing speed and storage capacity, there are still no programs that can achieve human flexibility in wider areas or tasks that require a great deal of everyday knowledge.

On the other hand, some programs have reached the level of performance of human experts and professionals in performing certain specific tasks.

Thus artificial intelligence in this limited sense can be found in a variety of applications such as medical diagnosis, computer search engines, and speech or handwriting recognition.

What is Intelligence?

Everything is attributed to intelligence. Even the most complex insect behavior is never seen as an indication of intelligence.

What is the difference? Consider the behavior of the Sphex ichneumoneus excavator. When the female wasp returns to its hole with food, she first places it on the doorstep, searches for intruders in her hole, and enters her feed only when the coast is clear.

The true nature of the wasp’s instinctive behavior is evident when the liner is moved a few centimeters from the entrance to its building. When shown, the entire process is repeated as many times as the liner is moved.

Psychologists generally characterize human intelligence not only by a trait but by combining many different skills. The investigator working on AI has largely paid attention to the following components of intelligence: learning, perception, thinking, problem-solving, and language use.

Learning Processes

This part of AI programming pays attention to gathering data and creating rules to convert the data into actionable information. The rules called algorithms provide computer devices step-by-step instructions for performing a specific task.

Argumentation Processes

This aspect of AI programming focuses on choosing the right algorithm to achieve the desired result.


It is a solving Problems. In AI, it can be a systematic search through several possible measures to achieve a goal or solution.

The Problem-solving methods can be divided into specific or general purposes.

A particular method is tailored for a specific problem

this often uses very specific features for the situation where the problem is embedded. On the other hand, a general method applies to various problems.

One important technique used in AI is end-of-agent analysis: a slow or slow decrease of the difference between the current status and the final goal.

The program selects actions from a list of assets. In the case of a simple robot, it can include;


Many different problems have been solved with programs of artificial intelligence. Some examples are finding the winning move (or a series of moves) in a board game, providing mathematical proofs, and manipulating “virtual objects” in a computer-generated world.


In perception, the surroundings are scanned with different real or artificial sensory organs.

The scene is divided into separate objects in different spatial conditions.

The analysis is complicated by the fact that an object may look different depending on the angle of view, direction, and intensity of the lighting in the scene and the object’s contrast with the surrounding field.


A language is a system of signs that have meaning by convention. In that sense, the language need not be limited to the spoken word. For example, traffic signs form the smallest language; It is about an agreement that means “danger ahead” in some countries. An important feature of full-fledged human languages ​​- unlike bird calls and traffic signs – is their productivity. A productive language can formulate an unlimited number of sentences.

It is relatively easy to write computer programs that seem to be able to answer questions and explanations fluently in very limited contexts in a human language.

Although none of these programs really understand the language, they can, in principle, reach the point where their language skills cannot be distinguished from an ordinary person.

What is real understanding if it is not acknowledged that even a computer uses this language as a native language? There is no universally accepted answer to this difficult question. According to a theory, someone depends or understands not only a person’s behavior but also their history: to understand, you must have learned and trained the language to be part of the language community to interact with other language users.

Why is Artificial Intelligence Important?

AI automates repeated learning and recognition through data. However, AI differs from hardware-controlled, automated automation. Instead of automating manual tasks, AI performs reliable, large amounts of automated tasks reliably and without fatigue.

Human research is still necessary for this type of automation to set up the system and ask the right questions.

AI adds intelligence to existing products. In most cases, AI is not sold as a standalone application. Products you already use have previously been enhanced with AI features, just as Siri was added as a feature to a new generation of Apple products.

Automation, conversation platforms, bots, and intelligent machines can be combined with large amounts of data to improve many technologies at home and work, from security information to investment analysis.

AI adapts itself through progressive learning algorithms so that data can take over programming.

AI looks at structure and accuracy in data so that the algorithm gains a skill thus the algorithm becomes a classifier or predictor. Just as the algorithm can learn chess, it can teach itself which produces the following recommendations online.

And the models are adapted when they receive new data. Backpropagation is an AI technique that allows the model to adapt through training and added data if the first answer is not completely accurate.
AI examines more and deeper data utilizing neural networks with many concealed layers.

It was almost impossible to build a five-level fraud detection system a few years ago. All this has changed with incredible computing power and big data. You require a lot of data to get deep learning models as they learn directly from the data. The more data you can enter, the more accurate it will be.

AI achieves incredible precision across deep neural networks-previously unlikely. For example, your interactions with Alexa, Google Search, and Google Photos are based on thorough learning – and the more we use them, the more accurate they become. In the medical field, AI techniques for deep learning, image classification, and object detection can now be used to find cancer on MRI with the same accuracy as highly qualified radiologists.

AI makes the best of data. If algorithms are self-learning, the information itself can become intangible property. The answers are in the information; You just need to use AI to get them out. Since the role of data is more important than ever, it can provide a competitive advantage. If you have the best data in a competitive industry, the best data will win, even if everyone uses similar techniques.

How Artificial Intelligence is Used?

Every industry is in high demand of AI skills – especially systems for answering questions that can be used for legal aid, patent research, risk reporting, and medical research. Other uses of AI include:

Artificial Intelligence in Healthcare

AI applications can provide personalized medical and x-ray measurements. Personal health workers can act as life advisors and remind you to take your pills, exercise for a healthier life.

Artificial Intelligence in Production

AI can scrutinize factory IoT data as it flows from linked devices to forecast expected load and demand with frequent networks, a type of deep learning network used with sequence data.

Artificial Intelligence in Sale

AI offers virtual shopping opportunities that offer personalized recommendations and discuss buying options with consumers. Inventory management and website layout technology are also improved with AI.

Artificial Intelligence in Banking

Artificial intelligence improves the speed, precision, and efficiency of human efforts. AI technologies can be used in financial institutions to determine which transactions are likely to be fraudulent, to create fast and accurate credit scores, and to automate intensive data management tasks.

Work with Artificial Intelligence

Artificial intelligence is not there to replace us. It enlarges our skills and enables us to do better at what we do. Because  AI algorithms learn in a different way than humans, they observe things differently. You can see the actions and patterns that we lack. This human-AI partnership offers many opportunities. It is possible:

  • Include analyzes for industries and domains where they are currently underutilized.
  • Improve the performance of existing analytics techniques such as computer vision and time series analysis.
  • Overcoming financial barriers, including language and translation barriers.
  • Expand existing skills and improve our work.
  • Give us a better view, better understanding, better memory, and much more.

What are the Challenges of using Artificial Intelligence?

Artificial intelligence will change every industry, but we must understand its boundaries.

The main limitation of AI is that it learns from data. There is no other way to process knowledge. So if there are any errors in the data it will affect the results. Any additional levels of prediction or analysis must be added separately.

Today’s AI systems are trained to perform a clearly defined task. If a system that plays poker cannot play chess or baseball. The fraud system cannot drive or provide legal advice. In fact, an AI system that detects anti-fraud cannot properly identify tax fraud or warranty claims.

In other words, these systems are very specialized. They concentrate on a single task and in no way behave like humans.

Similarly, self-learning systems are not like the autonomous system. The imaginary AI technology that you see in movies and on TV is still science fiction. However, computers that can test complex data to learn and complete certain tasks are becoming more common.

How Artificial Intelligence Works

AI combines large amounts of data with fast, iterative processing and intelligent algorithms so that the software can automatically learn from patterns or functions in the data. AI is a broad research area that includes many theories, methods, and techniques.

The following important sub-areas:

Machine learning automates analytical modeling. It uses methods from neural networks, statistics, surgical research, and physics to find hidden insights in data without having to program precisely, where to search, or what to close.

A neural network is a type of machine learning that consists of interconnected units (eg neurons) that process information by responding to external inputs and sending information between each unit. The process requires multiple passages of data to find connections and retrieve meaning from undefined data.

Deep learning uses massive neural networks with many layers of processing units, which use advances in computing power and improved training techniques to learn complex patterns in large amounts of data.

Image and voice recognition are among the most common applications.
Cognitive computing is a division of AI that attempts for natural, human-like communication with machines. Using AI and cognitive computers, the ultimate goal is for a machine to simulate human processes through its ability to interpret images and languages ​​- and then speak coherently in response.

Computer vision is based on pattern recognition and thorough learning to see what’s in a photo or video. If machines can process, analyze, and understand images, they can take pictures or videos in real-time and interpret the environment.

Natural Language Processing (NLP) is the computer’s ability to analyze, understand, and produces human language, including speech. The next phase of NLP is natural language interaction, which allows people to interact with computers in common everyday language to perform tasks.

In Addition, different Technologies Create and Support AI

Graphics processors are the key to AI as they provide the high processing power required for iterative processing. Training neural networks require big data plus computing power.

The Internet of Things generates huge amounts of data from connected devices, most of which have not been analyzed. By automating models with AI we can use more of them.

Advanced algorithms are developed and combined in new ways to analyze more data faster and at multiple levels. This intelligent processing is the key to identifying and predicting rare events, understanding complex systems and optimizing unique scenarios.

APIs or application programming interfaces are portable code packages with which existing products and software packages can be expanded with AI features. You can add image recognition and questionnaire features to home security systems that describe data, create subtitles and headlines, or create interesting patterns and insights into data.


In summary, the purpose of AI is to provide software that can explain the reasoning for input and output. AI will give human-like interactions with help of software. It also gives decision support for specific tasks, but it is not a replacement for humans.

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