As generative artificial intelligence has rapidly spread in recent years, interest in the various deep learning architectures that form the basis of AI technology has grown along with it. Today's large language models and generative AI systems did not suddenly appear but developed atop decades of accumulated AI research. In particular, Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), and Transformer architecture are evaluated as core technologies that must be addressed to understand modern AI technology. These models each emerged to solve specific problems, and their development process is also the history of how AI research has evolved.
One of the most important problems in early deep learning research was how to process data that has temporal flow. Images and static data were relatively easy to process, but data with temporal order — such as sentences, speech, or financial data — was difficult to process with conventional neural network structures. The model that emerged to solve this was the Recurrent Neural Network (RNN).
The core idea of RNN lies in a structure that remembers previous input information while utilizing it for the next calculation. Just as humans remember words that appeared earlier while reading a sentence and interpret the meaning of the next word, RNN also stores past information as internal state and uses it to process the next data. Thanks to this structure, RNN was utilized in various fields such as sentence analysis, speech recognition, and time-series data analysis. However, RNN had structural limitations. As sentences became longer, the Vanishing Gradient problem occurred, where initial information gradually disappeared.
The model that emerged to solve this problem was LSTM (Long Short-Term Memory). LSTM, as its name suggests, is a neural network structure designed to maintain long-term memory. While conventional RNN simply passed information, LSTM has a structure that selectively decides what information to remember and what to discard. The core device that makes this possible is the Gate Mechanism.
LSTM uses three main gates. The Forget Gate serves to remove unnecessary information, the Input Gate serves to store new information, and the Output Gate determines information to pass to the next stage. Thanks to this structure, LSTM became capable of stably processing data with long sentences or long time intervals, and was utilized as a core technology in various AI fields such as machine translation, speech recognition, and natural language processing. However, LSTM also came to have speed limitations in large-scale data training due to the structural characteristic of having to process data sequentially.
Meanwhile, in the field of image recognition, a completely different direction of neural network structure developed. This was the Convolutional Neural Network (CNN). CNN is a model designed with inspiration from the structure of the human visual cortex, with a structure specialized for analyzing spatial patterns in images. CNN uses a method of repeatedly analyzing small areas of images to extract features.
For example, CNN performs image analysis in multiple stages. In the initial stage, simple patterns such as lines and edges are recognized; in the next stage, shapes are identified; and in the final stage, entire objects are recognized. Thanks to this hierarchical structure, CNN demonstrated revolutionary performance in the field of image recognition. In particular, when AlexNet won the ImageNet competition with overwhelming performance in 2012, CNN became evaluated as a core technology opening the deep learning era. Subsequently, CNN is being utilized as a core technology in various fields such as autonomous vehicles, facial recognition systems, medical image analysis, and object detection.
However, the technology that made today's generative AI revolution possible is Transformer. First proposed in the paper "Attention Is All You Need" published by Google researchers in 2017, this structure fundamentally changed existing RNN and LSTM-based natural language processing models. The core of Transformer lies in the Attention Mechanism.
Attention is a method of calculating which words are important when understanding a sentence. For example, it is a method of simultaneously calculating the semantic relationships between words such as 'book' and 'interesting' in the sentence "The book I read at the library yesterday was very interesting." While conventional RNN had to process sentences one by one in word order, Transformer can analyze entire sentences simultaneously and calculate relationships between words.
This method fundamentally changed the structure of AI models. RNN and LSTM had limitations in parallel computation because they had to process data sequentially. In contrast, Transformer can calculate all word relationships simultaneously, enabling parallel processing and showing much higher efficiency even in large-scale data training. Also, the ability to understand long contextual relationships has been greatly improved.
Thanks to these characteristics, most of today's generative AI models are developed based on the Transformer structure. Representative examples include GPT-series models, BERT, Claude, Gemini, and LLaMA. It is not an exaggeration to say that today's generative AI revolution is effectively taking place on top of Transformer technology.
Looking at the development flow of AI technology, one direction becomes clear. Early CNN provided AI with the ability to see the world. Subsequently, RNN and LSTM created the ability to understand the flow of time. And Transformer provided AI with the ability to understand human language and knowledge structures.
This development ultimately shows that AI has evolved in the direction of understanding human language and knowledge. Deep learning research that began with image recognition gradually expanded into the domain of language and knowledge processing, and as a result, today's large language models and generative AI systems emerged.
The AI industry today is developing at a very rapid pace, but its technological foundation is relatively clear. CNN provided AI with the ability to visually recognize the world, RNN and LSTM provided the ability to understand temporal context, and Transformer made possible the ability to understand language and knowledge.
In particular, the Transformer structure designed centered on the attention mechanism has established itself as the de facto standard architecture for current generative AI models. For this reason, many researchers put it this way when explaining the core technology of modern AI in one sentence: The core of modern AI is ultimately attention.
And precisely this technology is becoming the technological foundation of today's large language models and the AI agent era.
