Artificial Intelligence, What Is It? An In-Depth Look

Technology continues to advance at a dizzying pace every day. Thanks to this, machines can do almost anything that humans can do. According to experts, this situation seems inevitable towards the end of our century. In other words, machines will be able to do everything humans can. There will be different types of machines specific to each job. So, how will this be possible? The thing that will make this possible is called AI in the tech world. AI stands for artificial intelligence. 

Even now, it is changing our world. However, what AI promises will change our world much more. That’s why the interest in artificial intelligence technology is increasing daily. Everyone is trying to understand how this tech will change our world. That’s why there are so many articles on artificial intelligence online. Have you ever researched to understand AI better? In any case, this article will tell you everything you need to know about it. So, let’s take a closer look at what AI is.

History of AI

In the early 1900s, the famous mathematician Alan Turing asked whether machines could think. The journey to develop AI tech started with the spark that this question had caused. Let’s begin to describe the history of AI by describing this term’s emergence. This term first appeared in 1956. However, AI research began earlier this decade. Early studies were conducted in the early 1950s. Artificial Intelligence research during this period explored topics such as problem-solving and symbolic methods. 

Early in the next decade, the U.S. Department of Defense took an interest in AI studies. Around this time, the U.S. Department of Defense tried to develop programs to imitate basic human reasoning. For this purpose, the department received computer training from experts. In this process, experts developed Artificial Neural Networks based on nerve cells to imitate the brain. These nerve cells are called neurons. They are from the smallest units of the brain.

history of ai

DARPA was founded in 1958. As a result of the studies mentioned above, DARPA completed street mapping projects in the 1970s. You have all heard of Siri, Cortana, and Alexa. Darpa produced smart personal assistants in 2003, long before them. These early works shaped the automation and formal reasoning we see in computers today. Computers include decision support systems and intelligent search systems designed to complement and strengthen human capabilities. 

After 1980, the concept of machine learning emerged. We may list the factors that played a role in the development of this concept as follows:

  • Increasing valuable data to be used in AI development as a result of studies
  • The development of tech that may allow you to process this high amount of data obtained

Today’s AI Tech

All these developments we have mentioned above have caused something important. These are the advances that form the basis of the emergence of the concept of deep learning today.

We have all seen dystopian movies where robots took over the world due to the development of Artificial Intelligence. However, we don’t have to be afraid of that. Don’t believe me? Here are the jobs that AI can’t replace. Today’s AI tech is far from capable of doing that. The most important benefit that it has provided today is its customized development on a sectoral basis. Today, AI can deliver outstanding benefits tailored to any industry. 

Advanced improvements have occurred in AI tech today. These improvements are happening in several important areas. The first of these fields is data volumes. Data volumes have increased significantly in AI. In addition, AI algorithms have also improved considerably. Advanced algorithms have taken this tech to a higher level. AI’s computational power has also increased much more than before. 

What is AI, In Detail

Artificial Intelligence mimics human understanding. It uses this ability to collect new data and solve various complex tasks. It evaluates the data and iteratively improves and develops itself. So, although it’s not very advanced right now, AI can learn. Systems and machines with all these capabilities can perform many tasks that humans can do today. Let’s explain these tasks with a few examples. 

examples of ai tasks
  • Recommendation Engines

These engines collect data on users’ viewing and search habits. It offers suggestions that may be suitable for the preferences of its users by processing this data. It improves by adding new data to its structure with each search and viewing.

  • Smart Assistants

They pull critical data from large user-defined datasets. In this way, they further improve the timing. It uses AI tech to complete these tasks.

  • Chatbots

It tries to understand customers’ problems more quickly. This way, it can give more efficient and relevant answers. It uses AI to perform all these tasks.

Limiting AI to any particular form or function wouldn’t be right. It’s about ultra-powered thinking, data analysis ability, and operation. It is essential to understand that the purpose of AI is not to replace humans. It serves a much greater purpose than that. Artificial Intelligence is to develop and contribute to human capabilities. For this reason, the resources that every business dedicates to AI studies constitute a significant part of their total budget. That makes it a highly valuable commercial asset.

These types of abilities include the ability to perceive language and speech and to think strategically. There are some other essential capabilities that these systems have. Let’s take a closer look at these abilities.

AI’s Capabilities

We may classify these abilities into several groups. These are as follows:

  • Ability to perceive human language and speech
  • Human-like strategic thinking abilities
  • Lifelong learning just like a human
  • Ability to learn by connotation
  • Having two different memories, long and short term
  • Moving data from short-term memory to long-term memory, if necessary
  • Classifying learned skills according to their relevance
  • Ability to add new skills to existing ones through learning
  • Ability to transfer or teach its capabilities to other systems that have appropriate equipment and capability
  • Capability to reuse abilities
  • Ability to develop different and new learning methods
  • Ability to improve decision-making mechanisms with new skills learned
  • Having a software architecture that can reuse and accumulate these capabilities

AI Categories by Abilities

Above, we have listed the different capabilities that AI systems can have. However, not every AI has all these capabilities. AIs have three different tiers. These tiers represent the abilities they have. We may list these categories as follows:


It stands for Artificial Narrow Intelligence. It is also called narrow AI. ANI can use human-like problem-solving skills in limited areas. It can only use these abilities in line with the users’ goals previously determined.


It stands for Artificial General Intelligence. This type of AI is human-level AI. They are also called strong or deep AI. They can accomplish cognitive tasks as complex as humans. It completes tasks using learned skills. It also completes tasks by self-discovering how to do them. The fact that these systems are using their capabilities for the first time does not affect their performance in completing tasks.


It stands for Artificial Super Intelligence. Such systems are also more advanced than AGI. In other words, it may develop abilities beyond even human intelligence. It can decide to create and use these capabilities on its own.

How Do AI Systems Work?

AI has a start and an endpoint based on its work. When developing it, developers write algorithms that maintain a process from this starting point to the endpoint. You may also consider this as a process that will solve a problem. In this case, AI processes a problem from its inception to its solution. However, while developing it, you will need vast amounts of data for the algorithms you write. An adult human brain uses its experience and knowledge to solve the problems it encounters. Like that, you should feed and grow the AI you will develop with more data. The more data you feed the algorithms you have written while developing it, the more advanced AI will get. Thanks to this data, you will have more successful results. In addition, it will solve these problems much faster.

As you know, we live in the internet age. In this period, smartphones, computers, sensors, and smart home appliances collect data in many processes. In addition, they can also store this data. The size of the data collected in these environments is not easy to describe. That’s why we call this age also the age of big data. So, why is this data important to AI? As we mentioned, big data is an incomparable resource for training AI. ​​You will develop.

Stages of Development of AI Algorithms

The development of an AI algorithm takes place in four steps. We can list these steps as follows:

  • Data collection
  • Generalization
  • Testing
  • Machine Learning (Continuous learning)
stages of development of ai algortihms

If we compare these stages with humans, you may think of them as follows. The first two stages are the training phase. The next step is to test what you have learned. The final stage represents the life-long learning ability of the human brain. These processes are similar to human learning, as AI is modeled similarly to the human brain. Speaking of its similarity with the human brain, let’s mention something else important. It has artificial neural networks, just like the human brain. So, what are Artificial Neural Networks? Let’s explain this now.

We may understand what they are from their names. These networks are created by mimicking the working process of human nerve cells. Then, these networks are used in the computer environment with algorithms developed for specific tasks. So, what are the elements that make up these networks? Let’s explain them.

  • Neurons are the processing elements of these networks.
  • Dendrites serve as aggregation functions in these networks.
  • The cell body acts as a transfer function in these networks.
  • Axons are artificial neuron outputs.
  • Synapses are the weights of this structure.

We may give the example of Dense Neural Networks as the simplest structure. It processes information in multiple layers. These structures are not customized according to the data. It is the architecture type you will first encounter in Deep Learning. 

Convolutional Neural Networks

It is specialized for visual data. It provides feature extraction from the image using filters. That kind of neural network is more successful than Dense Neural Networks. These networks learn which filter weights to use in the training process. Let’s explain this situation with an example.

If you have noticed while reading this article, you are reading the words sequentially. Then you reach the correct meaning that this article wants to convey. Humans use specialized RNN-LSTM neural networks to process sequential data. To understand a sentence, you need to remember or forget parts you have read in the past. Likewise, cells in recurrent neural networks can remember or forget information from the past for a certain period.

Since birth, people learn most of the data by interacting with the environment. This type of learning is called Reinforcement Learning. However, AI interacts with the surroundings randomly. As a result, it gets a response. Thus, it understands what behaviors can achieve its goal by exhibiting.

AI & Human Brain

Artificial Intelligence is a branch of science. It tries to produce computers that can think, learn, and get trained like humans. The most important model for realizing this purpose is the human brain. The human brain has very complex systems and algorithms. However, we do not notice these processes as we carry out our daily work. In this case, our brain uses some algorithms while performing a function. In this process, our brain first examines past information. Then, it analyzes this data for the decision-making phase. Our brain performs this chain of operations in a short time. It isn’t easy to make computers complete this process with the same success rates in a short time. However, there is a branch of science that tries to realize this. It is called AI. This branch of science is looking for ways to install such a system on computers and machines.

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This branch of science also uses different branches to better understand the human brain. These are branches of science such as mathematics, philosophy, biology, and psychology. These branches of science are fundamental in understanding the human brain better. They also help to understand something else important. It is what kinds of systems, similar to the human brain, may or may not be installed on a computer. This way, it reveals ways to create artificial systems identical to the human brain. We may give the topics we mentioned in the previous sub-titles of this article as an example of this situation. These examples include Convolutional Neural Networks, neurons, dendrites, and synapses. Imitating these features of the human brain is the best way to build a human-like system for computers.

What Can A Machine Do With AI That A Human Cannot?

Machines are adept at performing physical and monotonous tasks that humans cannot or would have difficulty doing. Thanks to AI, these smart machines may be able to solve complex algorithms that humans have difficulty solving. Also, they may perform mathematical operations efficiently. Besides these, machines have many capabilities, such as collecting and keeping data in their memory. 

So, can AI learn? Thanks to the developed algorithms, they may learn just like humans. They may learn something by repeating it over and over. As a result of repeating over and over, they may walk just like humans. Or, they may learn to play chess similarly and master this game due to repetition. In this way, they may discover many processes similar to the ones we mentioned above. Today, they can learn and become able to perform complex operations better than a human due to repetition. The best part of this situation is that they reduce the margin of error to almost zero. However, it is worth noting that they can only do what they need to learn or do. Let’s explain this with a simple example.

An AI developed to play chess cannot play checkers and will never learn to play checkers. It only has algorithms for playing chess and learning this game. That is, AI cannot learn everything like humans. They also cannot think like humans. They can only do and learn what they need to do and learn. However, with the further development of tech, machines with a capacity close to the human brain will occur in our lives. Even today, AI can perform many tasks, such as speech recognition and understanding, image processing, natural language processing, and reasoning.

What Are the Uses of AI?

AI is making a breakthrough due to improving processor speeds and increasing the digital data world. It may create new algorithms by processing this data. Thus, it may manage to make humanoid decisions in analyzing new cases. Thanks to these features, it may help people in many different areas. So what are these fields? Let’s explore together the fields where AI works the best today. It is most commonly found in popular search engines. Other fields are as follows:

uses of ai
  • Computer processing ability
  • Improving the ability of robots to move
  • Daily use devices
  • Technological weapons
  • Defense industry
  • Medical surgeries
  • Game Industry
  • Smart Cars
  • Purchase Estimators
  • Fraud Prevention
  • Security systems
  • Smart home systems

AI is still in development today. However, it continues to develop and become stronger every year. Technological developments accelerate this process even more. Therefore, it continues to serve more and more people in different fields daily.

Terms Related to AI

You may be hearing some terms related to AI here and there. They can look and sound complicated, but they are pretty straightforward. Here are some of those terms;


A solution consists of a series of process steps designed to solve a problem or achieve a specified purpose. In AI, it is humanoid thinking algorithms developed to solve the problem.

Machine Learning

AI completes its training with a specific data set. However, it continues to learn after this training phase. This learning process develops its abilities. So, its capabilities are not limited to the day it completes its education with a particular data set. You may liken it to a person continuing to develop professional skills after graduation.

Deep Learning

It creates patterns by imitating the human brain among big data. It is a type of machine learning that discovers patterns within the big picture, starting with the minimum rule, and automates the algorithm creation process. Where it looks or what it concludes in examining the data is not dependent on any previous plan. It uses some elements to reach the hidden information in the data and make sense of it. We may list these elements as follows:

  • Statistics
  • Neural Networks
  • Operations Research
  • Physic Methods

With these methods, it may find solutions to new problems it encounters.

Image Recognition

That technique aims to make sense of the images that computers see with the help of cameras. It is also called computer vision. You may liken this technique to the brain’s interpretation of human eyes’ images. It is based on pattern recognition and deep learning to recognize what an image or video is. Machines may process, analyze and understand visuals. They may also capture images or videos in real time and interpret their surroundings.


NLP stands for Nature Language Process. Almost everyone knows Google Now or Siri. These assistants work based on NLP artificial intelligence. NLP is the field of AI that enables it to understand human speech and speak like a human. NLP also has the next phase. That phase is performing tasks using normal, everyday language. That is called natural language interaction, which allows these assistants to communicate with computers.

What is AI In Short

Today, computers and machines can do many things that humans can do. However, processes that require complex cognitive abilities are excluded from these tasks. At least, that was the case until a short time ago. Today, thanks to Artificial Intelligence technology, it seems possible for machines to perform these processes independently. In this article, we have examined this interesting and promising tech in-depth. We have shared the history and development of this promising tech with you. In this context, we compared it with the human brain. Then, we explained how machines do things quickly that humans cannot do, thanks to this tech. Based on this, we also explained the usage areas of AI. We’ve included the necessary terms for those unfamiliar with this tech or science.

Frequently Asked Questions About

1. Reactive Machines
2. Limited Memory
3. Theory of Mind
4. Self-awareness

Christopher Strachey managed to write the first successful AI program in 1951. This program ran on the Ferranti Mark I computer at the University of Manchester. Christopher Strachey designed it to play a complete game of checkers.

It helps turn huge data into usable information by detecting patterns. It is also useful to make successful predictions. We may give three examples of this:
1. Apple’s Siri
2. Amazon’s suggested purchases
3. Facebook’s newsfeed

John McCarthy had created it. He was a computer scientist. He also developed the Lisp programming language family.

Developing an advanced AI program is not cheap in today’s terms. However, the cost advantages such systems provide are quite high. Besides, thanks to the advancements in the tech world, it will be possible to develop it with less and less cost.

Gizem Akmanlı

Posts: 194

Jr. SEO Content Editor at Dopinger, Gizem Akmanlı graduated from Literature department. With her interest in content production and editing, she managed a blog for 5 years, then turned to digital marketing and started to develop herself in the field of SEO. She is a supporter of sustainable living... Read More

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