The way Artificial Intelligence AI Works

In certain cases, machines are more efficient and reliable than humans, leading to their widespread use in both personal life and industry. Automatic robots can perform specific tasks much faster and more accurately than humans.

Today, many jobs are being done by machines, which were previously done by humans. Now the question is how the machine or robot actually works?

The answer is quite simple: machines operate based on a program that contains instructions, and they only follow these instructions. This means that machines do not have the capability to make decisions on their own. However, programs are now being developed that enable machines to make their own decisions, which is known as Artificial Intelligence (AI). Just as a person learns to make better decisions over time by learning from their mistakes, Artificial Intelligence (AI) also learns from its errors and continuously improves itself. As a result, the use of AI is steadily increasing.

Those of us who use the internet are unknowingly benefiting from many advantages of Artificial Intelligence (AI). In some cases, we are even being guided by AI. For example, when we watch videos on YouTube or Facebook, it is AI that determines which videos appear in our recommendations. In other words, AI studies the types of videos a person watches and then recommends videos tailored to that individual. Additionally, AI is used in various modern technologies such as suggesting text while typing, converting voice to text, generating voice from text, answering your questions, identifying objects in images, and generating images based on your instructions.

In fact, the scope of use of AI is vast. Today's artificial intelligence is so efficient that it even surpasses human intelligence in some cases. For example, Go is an ancient popular board game in China. This Go game was organized in 2016 where the world's best Go player Lee Sedo was against Alpha go artificial intelligence. Among the total of five games played, AlphaGo won four matches, demonstrating its prowess by employing strategies that were previously unseen and not anticipated by human players. Now, let's discuss how this artificial intelligence was developed and how it operates.

Although the concept of artificial intelligence (AI) dates back to ancient times, significant work in the field began in the mid-20th century. In 1957, psychologist Frank Rosenblatt created the perceptron, which was the first neural network capable of distinguishing between different categories, such as boys and girls. This development generated considerable excitement at the time due to media coverage, sparking early enthusiasm and interest in the potential of AI. In 1998, an article published in The New York Times envisioned a future where computers would be able to walk, talk, see, write, and even have a sense of consciousness and reproduce themselves. This speculative article reflected the high hopes and expectations surrounding neural network systems at that time, although neural networks had not yet achieved the level of success envisioned in the article.

As a result, in the later period, work on artificial intelligence continued on a small scale. In the 1980s, the first self-driving car was developed, which was capable of moving at a speed of 2 kilometers per hour. In 1994, Yann LeCun developed the first neural network that could recognize handwritten numbers. Over time, artificial intelligence has continued to advance steadily, and understanding where it stands currently.

Now the question is how does artificial intelligence work?
It is not possible to give a simple answer to the question of how artificial intelligence works, because the subject of artificial intelligence is a huge concept and there are complex mathematical issues behind it.

To understand AI, we need to grasp the concept of machine learning. Machine learning is the process through which machines are taught how to complete tasks, such as identifying objects in images or understanding speech.

Teaching a machine is much like a human being, like a child learns from childhood by watching people, learning from mistakes, learning from our instructions and learning from our own experiences. Machines are taught the same way. There are basically three types of machine learning methods.

1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning

Supervised learning:
In this method, the machine is first given many samples and told what the object in the sample is, through which the machine gets an idea about that object. For example, suppose you feed the machine 10,000 images of cars taken at different angles as input and tell it is a car. Now the machine will analyze these images and understand what the car actually looks like. Then, if you give the machine a picture of another car out of these 10,000 pictures, the machine can tell that it is a car. That is, we teach children what they are by showing them different things, supervised learning is very much like that.

Unsupervised learning:
In this method, the machine learns to distinguish between similar patterns in the input. For example, if you provide the machine with many pictures of humans and pictures of cars, the machine will learn to classify images of humans separately from images of cars based on analyzing the patterns in these images. It won't explicitly tell you that there is one group of humans and another group of cars; rather, the machine will learn to categorize these two types of objects into separate groups based on the patterns it identifies.

Reinforcement learning:
In this case, the machine learns from its own mistakes. For example, imagine the machine is playing a game against itself. During this gameplay, the machine learns from its mistakes. In other words, in this scenario, the more training the machine receives, the more skilled it becomes. Initially, AlphaGo was trained using this reinforcement learning method to become proficient at the game of Go.

The methods of teaching humans and machines can be likened to some extent, but they also have notable differences. Humans typically learn over extended periods, accumulating various experiences as they age. In contrast, machines can be taught in a relatively short time span. However, teaching machines requires a substantial amount of data and training. The more data and training you provide to a machine, the more skilled it becomes.

It was understood what the methods of teaching the machine are. But the machine does not have a human brain, so how does the machine learn? The answer is neural network.

At present, AI has advanced to the point where in some fields it outperforms humans in delivering results. For instance, in 2015, Resnet (Residual neural network) surpassed humans in accuracy when identifying details within images. Given a picture, Resnet could potentially describe what's in it more accurately than a human could. If you make five guesses about what an object might be, not having the correct answer is called a Top Five Error.

Human error rates are around 5.1% while ResNet scores 96.5%. ChatGPT, which everyone knows, has been trained on all kinds of texts available on the internet. As a result, it can quickly provide responses that are sometimes more accurate than humans in certain areas. In this context, various image generators exist where AI creates images according to your instructions. Neural networks in such cases are trained using Diffusion methods. This involves initially providing the machine with directions to clarify an image, typically starting with blurred or noisy images.

Subsequently, the machine learns how well it has performed this task based on feedback. This feedback helps the machine adjust its computational calculations and gradually improve over time.

There's also voice generation technology available now that is so powerful that if trained with your voice, it can read any text exactly like your voice. It means that AI can perform tasks that may either take humans a long time or sometimes humans may not be able to perform those tasks at all. For example, AI is being used in medical fields like image generation in diagnostics.

AI is trained using millions of X-ray reports to learn patterns and identify potential issues similar to experienced doctors. Consequently, AI can easily analyze new X-rays and predict possible problems, much like a skilled physician. Artificial intelligence is also being used in the medical sector to develop new medications.

Just as a new technology brings various benefits, it also introduces new challenges, often stemming from human misuse. For example, the use of artificial intelligence has created opportunities to deceive or manipulate people. It could involve presenting your image in a misleading way or impersonating your voice for purposes of deception, among others. Another significant concern is that many people might lose their jobs due to automation. Certainly, it wouldn't be fair to label it solely as a disadvantage. Artificial intelligence, while it may take over certain jobs traditionally done by humans, also creates opportunities for new types of work.

Author: science writer, content creator, and educator.