{"id":537,"date":"2026-05-28T13:09:49","date_gmt":"2026-05-28T13:09:49","guid":{"rendered":"https:\/\/server.ua\/en\/blog\/?p=537"},"modified":"2026-05-28T13:09:49","modified_gmt":"2026-05-28T13:09:49","slug":"how-modern-artificial-intelligence-appeared","status":"publish","type":"post","link":"https:\/\/server.ua\/en\/blog\/how-modern-artificial-intelligence-appeared","title":{"rendered":"How Modern Artificial Intelligence Appeared"},"content":{"rendered":"\n
\"The
The evolution of ideas that led to modern artificial intelligence<\/figcaption><\/figure>\n\n\n\n

Not so long ago, computers were completely blind. For a machine, any digital image was not a cat, a car, or a human face, but simply an endless table of pixel numbers. Teaching hardware to \u201csee\u201d the real world was considered an almost impossible task. The objects around us constantly change angle, lighting, scale, hide in shadows, or overlap with other things. The ordinary datasets of a few thousand photographs that scientists had were catastrophically insufficient to explain all this visual variety to a machine.<\/p>\n\n\n\n\n\n\n\n

The creators of the first successful artificial intelligence systems later wrote quite directly: without millions of examples for training, nothing would work. But such large-scale databases simply did not exist in the world.<\/p>\n\n\n\n

At that time, programmers tried to control everything manually. They literally spelled out rules in code, almost on their fingers: what exactly the computer should look at. The program was forced to search an image for sharp color changes, contours, corners, or geometric figures, hoping that the correct image would be glued together from these fragments. It was the \u201cStone Age\u201d of computer vision. The machine could calculate the mathematical statistics of lines in a photo, but it absolutely did not understand the meaning of the scene.<\/p>\n\n\n\n

The whole industry rested on the belief that a human first had to come up with which details in the image were important, and only then give the computer a command to search for them. The breakthrough of 2012 destroyed this logic completely.<\/p>\n\n\n\n

Perceptron and the First AI Winter<\/h2>\n\n\n\n

In general, this whole story began back in the late 1950s. In 1957, American scientist Frank Rosenblatt invented the perceptron \u2013 the first mathematical model of an artificial neural network. Just a year later, he and his colleagues built a huge analog machine for this idea, with a bunch of wires and switches. The device had three types of nodes that imitated biological processes: \u201csensation,\u201d \u201cassociation,\u201d and \u201creaction.\u201d It was the first attempt to copy the work of the human brain in metal.<\/p>\n\n\n\n

The main achievement was the idea itself, not the accuracy of the device. During the first demonstrations, this bulky machine, after several dozen attempts, learned to distinguish cards with marks on the left from cards with marks on the right. To the public, it looked like pure science fiction. The machine was not simply executing a rigid algorithm written by a human \u2013 it was improving its own results based on mistakes. That was when the industry received its most important idea: intelligence does not need to be programmed down to the smallest details; it can simply be trained, like a child.<\/p>\n\n\n\n

But the first model had a serious mathematical limit. Rosenblatt\u2019s neural network was \u201csingle-layered\u201d \u2013 it could solve only the most primitive logical tasks. Soon, other well-known scientists published a book where they described all the limitations of this system in detail and quite soberly.<\/p>\n\n\n\n

The problem was that at that moment no one in the world knew how to create and, most importantly, effectively train more complex, multilayer neural networks. As a result, the useful statement \u201cthe first model is too simple\u201d turned, in public perception, into a harsh verdict: \u201cneural networks are a dead end.\u201d<\/p>\n\n\n\n

By the early 1970s, the scientific community had almost completely abandoned this direction. Interest faded, project funding stopped. This period in the history of computer science was officially called the \u201cAI winter.\u201d The first model was not wrong \u2013 it simply appeared too early. The idea was ahead of its time, because the technologies and capacities needed to scale it did not yet exist.<\/p>\n\n\n\n

Multilayer Networks and the Backpropagation Method<\/h2>\n\n\n\n

The solution suggested itself: if one layer of artificial neurons can draw only a simple separating line, they need to be layered on top of one another. This way, the system will be able to understand more complex, tangled connections. However, the mere fact of creating a multilayer network did not solve the main issue \u2013 how to make it learn? How to understand which exact one of thousands of virtual neurons made a mistake during a test?<\/p>\n\n\n\n

The turning point came in 1986 thanks to a group of scientists, among whom was Geoffrey Hinton \u2013 the person who today is called the \u201cgodfather of modern AI.\u201d They described the backpropagation algorithm. It worked like this: the network made a prediction, analyzed its error at the output, and then this signal \u201crolled\u201d backward through all the layers of the network, automatically adjusting the settings of each neuron.<\/p>\n\n\n\n

The most important thing here was the new promise: a complex neural network could now independently, without human help, find hidden patterns in data.<\/p>\n\n\n\n

The technology got another chance at life, but there was still no real commercial application. Training a tiny model on paper is one thing; launching a heavy system on millions of real images is a completely different story. Neural networks simply lacked two things: gigantic arrays of data for examples and colossal computing power. The algorithm gave machines the ability to learn, but the world had not yet prepared fuel for them.<\/p>\n\n\n\n

A Clear Example: How Networks Learned to See<\/h2>\n\n\n\n

The first serious and practical \u201ceye\u201d of artificial intelligence was the convolutional neural network LeNet-5, created in the late 1990s by a team led by Yann LeCun. They made the model recognize handwritten digits, and this is where the principles on which all modern computer vision rests appeared.<\/p>\n\n\n\n

Instead of analyzing one large picture as a whole, the network was taught to look at it through small virtual \u201cwindows\u201d that slid across the image step by step. The first layers of the neural network noticed the simplest things \u2013 strokes, lines, and contrasting edges. The next layers glued these lines together, already identifying more complex elements: clear silhouettes, groupings of shadows, or the specific texture of a surface. The final layers assembled these fragments into a complete picture and gave the final result \u2013 for example, that the photo showed a cat.<\/p>\n\n\n\n

\"The
The main stages of object recognition by a neural network<\/figcaption><\/figure>\n\n\n\n

This proved that visual features of objects do not need to be invented from scratch \u2013 a computer can learn them itself, simply by looking at the right examples.<\/p>\n\n\n\n

The technology turned out to be so successful that it was even built into industrial scanners that read check numbers in banks. However, a global revolution still did not happen. For a wide range of complex tasks, neural networks still lost to classical mathematical programs. There was little data on the internet, computers worked slowly, so the first successes remained just a beautiful announcement of the future.<\/p>\n\n\n\n

AlexNet: The Moment the World Changed<\/h2>\n\n\n\n

The breaking point was 2012. By that time, ImageNet had already appeared in the industry \u2013 a gigantic virtual database where scientists collected and manually labeled more than 14 million photographs of all kinds of things, from keychains to dogs. Every year, a global computer vision championship was held on this database. Programs from around the world tried to recognize what was shown in the photographs, but the results improved very slowly. The industry had hit a dead end, trying to squeeze the maximum out of old mathematical formulas.<\/p>\n\n\n\n

Then a small university team entered the competition: Alex Krizhevsky, Ilya Sutskever, and their supervisor Geoffrey Hinton. Sutskever insisted that if a neural network was given a gigantic amount of data, it would produce a magical result. Krizhevsky wrote unique code that allowed the neural network to be trained not on a regular computer processor, but on powerful gaming video cards (GPUs), which can process thousands of numbers simultaneously. They assembled and trained their winning model literally in a bedroom, on an ordinary home computer with two video cards.<\/p>\n\n\n\n

Their neural network, which was named AlexNet, was enormous for its time. To prevent it from going crazy from such an amount of information and simply stupidly memorizing pictures, the developers used tricks. They used dropout technology \u2013 during training, they randomly \u201cswitched off\u201d half of the neurons, forcing the system to constantly look for new, more reliable connections and insure itself. They also artificially multiplied the photographs: mirrored them, cropped the edges, and changed the brightness so that the model would get used to the chaos of real life.<\/p>\n\n\n\n

The championship result stunned the industry. The AlexNet neural network crushed all classical programs by a huge margin, making almost half as many errors as its closest competitors. Explaining such a triumph by luck or coincidence was impossible. Leading scientific journals called it the main technological breakthrough of the decade.<\/p>\n\n\n\n

\"A
Neural network development scale<\/figcaption><\/figure>\n\n\n\n

From that point on, the whole world stopped arguing about whether neural networks had a future. The industry understood: the old approach was dead. Now we do not write code that explains to a machine what a wheel or animal fur looks like. We build the right network structure, give it a bunch of video cards, a billion pictures \u2014 and it learns to see on its own.<\/p>\n\n\n\n

The Era of Depth: VGG, Inception, and ResNet<\/h2>\n\n\n\n

After 2012, deep learning technology began developing at a wild pace. Scientists realized that the more layers a neural network has, the better it understands complex concepts.<\/p>\n\n\n\n

Soon, the VGG model was created in Oxford. If AlexNet had only 8 layers of neurons, VGG increased their number to 16-19. The architecture was made as simple and consistent as possible, proving that network depth itself has colossal power.<\/p>\n\n\n\n

Google took a different path with its GoogLeNet model. Instead of simply building a long line of layers, they taught the network to analyze an image at different scale levels simultaneously inside a single block. This made the system twice as efficient while using significantly less computer memory.<\/p>\n\n\n\n

But soon developers faced a paradox: if networks were made even deeper, they suddenly began to learn worse, and accuracy dropped. Because of the huge number of layers, mathematical signals during training simply \u201cfaded out\u201d along the way.<\/p>\n\n\n\n

This problem was solved by the ResNet architecture in 2015. Scientists came up with a brilliant trick: they allowed the signal to jump directly over several layers of neurons. If some layer of the network turned out to be useful, the model used it; if not, it simply passed the information further without losses. This made it possible to build a giant neural network with as many as 152 layers. It recognized objects in photographs better than an average human. Artificial intelligence finally reached an industrial level.<\/p>\n\n\n\n

The Era of Transformers: From Image Recognition to ChatGPT<\/h2>\n\n\n\n

When neural networks learned to see photographs almost perfectly, scientists decided to transfer this success to human language. For a long time, translation and text work were difficult for machines because algorithms read sentences sequentially \u2013 word by word. By the time a computer reached the end of a long paragraph, it simply \u201cforgot\u201d what had been discussed at the beginning.<\/p>\n\n\n\n

Everything changed in 2017, when Google published a paper with the telling title \u201cAttention Is All You Need.\u201d They presented a new architecture called Transformer. The main feature of this model is the artificial attention mechanism. Instead of reading text in order, a Transformer looks at the whole sentence or even the whole text at once. It instantly calculates how words are connected by meaning, regardless of how far apart they stand from each other. For language, this became the same kind of breakthrough as sliding windows had been for images twenty years earlier.<\/p>\n\n\n\n

OpenAI took this technology as a foundation and created the first models of the GPT series. They combined two things: first, they fed the neural network almost the entire text internet so that it simply learned to understand the logic of language and guess the next word in a sentence, and then carefully fine-tuned it for specific tasks. The scale grew catastrophically, and the GPT-3 model already had 175 billion virtual connections. It could maintain a dialogue, write program code, or create articles simply by reading a short instruction from a user.<\/p>\n\n\n\n

When ChatGPT, familiar to everyone, appeared in November 2022, it was not some new scientific discovery. It was the perfect product assembly of everything scientists had been developing for years. The model was simply taught to communicate in the format of a convenient chat, and thousands of human experts were involved to evaluate its answers (RLHF technology), forcing the system to be polite, understandable, and useful. Today, these systems have become multimodal: the latest version, GPT-5.5, is already a single digital organism that simultaneously and without delay hears your voice, sees an image through a smartphone camera, and instantly responds with text.<\/p>\n\n\n\n

\"Timeline
Key stages of neural network development<\/figcaption><\/figure>\n\n\n\n

And this is where the circle closes. The story from the first crushing victory of the AlexNet neural network over images to the triumph of ChatGPT over text is one and the same line. It rests on a single philosophical principle: there is no need to try to write a million rigid rules for a computer. You simply need to give a flexible neural network the right structure, a colossal amount of data, and powerful hardware<\/a> \u2013 and the machine will learn to understand our world on its own.<\/p>\n","protected":false},"excerpt":{"rendered":"

Not so long ago, computers were completely blind. For a machine, any digital image was not a cat, a car, or a human face, but simply an endless table of pixel numbers. Teaching hardware to \u201csee\u201d the real world was considered an almost impossible task. The objects around us constantly change angle, lighting, scale, hide […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[146,197,134],"class_list":["post-537","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-ai","tag-neural-network","tag-technology-development"],"_links":{"self":[{"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/posts\/537","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/comments?post=537"}],"version-history":[{"count":1,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/posts\/537\/revisions"}],"predecessor-version":[{"id":542,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/posts\/537\/revisions\/542"}],"wp:attachment":[{"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/media?parent=537"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/categories?post=537"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/tags?post=537"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}