AI and Science of Reasoning(3)

Sherlockian Way of Thinking

2. Age of Generative Artificial Intelligence

Deep Learning: Revolution That Changed Mind of Machines

Deep learning stands as the transformative concept that reshaped the entire course of artificial intelligence. True to its name, it signifies a system that “learns in depth,” drawing inspiration from the way the human brain processes information. Our brains consist of countless neurons intricately connected, transmitting signals and forming judgments. Likewise, deep learning constructs artificial neural networks, layering them in profound hierarchies to process data in a way faintly reminiscent of cognition itself.

In truth, the idea was not new. The notion of neural networks dates back to the mid-twentieth century, and theoretical work had continued for decades. Yet early computers lacked both the speed and the memory to train such complex networks. Deep structures demanded immense computational power and vast amounts of training data—resources that simply did not exist. Thus, for a long time, deep learning remained an elegant but dormant theory.

The turning point arrived in the early 2010s. The advent of powerful graphics processing units (GPUs) made it feasible to train large neural models efficiently, while the explosive spread of the internet and smartphones yielded an unprecedented deluge of data—images, text, voice, and video. For the first time, deep learning stepped out of theory and into the real world.

A research team led by Geoffrey Hinton at the University of Toronto entered the ImageNet competition

The year 2012 marked a watershed in AI history. A research team led by Geoffrey Hinton at the University of Toronto entered the ImageNet competition, a massive image-recognition challenge involving millions of photographs. Their deep learning model outperformed all existing algorithms by a striking margin, achieving record-breaking accuracy. This triumph heralded the deep learning revolution, igniting a global surge of research and investment in the field.

Soon deep learning penetrated every frontier: speech recognition, natural-language processing, autonomous driving, medical imaging, and more. Perhaps the most iconic moment came in 2016, when Google DeepMind’s AlphaGo faced the world champion Lee Sedol in the game of Go. Powered by deep reinforcement learning, AlphaGo trained itself to master a game long thought to require human intuition. When Lee Sedol lost four games to one, the world witnessed a profound shift—the first palpable sign that machines might surpass human intuition.

Deep learning is no longer merely a technology; it is the pathway through which machines grow human-like. It is the quiet revolution that made today’s generative AI possible. Even now, every moment, deep learning models are parsing vast oceans of data, discerning patterns, drawing inferences, and generating responses. The evolution of intelligence continues—and at its root lies the silent heartbeat of deep learning.

When Lee Sedol lost four games to one, the world witnessed a profound shift.

Transformer: Architecture That Changed Intelligence Itself

In 2017, another decisive turning point arrived. Researchers from Google Brain published a paper entitled “Attention Is All You Need,” unveiling a new neural architecture—the Transformer. This innovation revolutionized how deep learning models process information, becoming the foundation of nearly every advanced AI system that followed.

Before Transformers, natural-language processing relied chiefly on recurrent neural networks (RNNs) and long short-term memory models (LSTMs). These handled sequential data well but suffered from critical limitations: slow computation and difficulty preserving information over long text sequences—a problem known as long-term dependency.

The Transformer solved these issues at once. Its core was the attention mechanism, which calculates the relative importance of each input element to every other. Rather than following word order linearly, the model identifies relationships between tokens and assigns weighted significance accordingly. This seemingly simple yet powerful approach enabled far deeper comprehension of context without relying on recurrence.

The Transformer becomes the foundation of nearly every advanced AI system that followed.

Its greatest advantage lay in parallelism. Whereas RNNs processed data step by step, Transformers analyzed entire sequences simultaneously, drastically reducing training time. Attention mechanisms also allowed the handling of long or complex contexts with remarkable effectiveness, and the architecture’s modular simplicity made it ideal for scaling to vast datasets.

Since its publication, the Transformer has become the central framework not only in natural-language processing but also in speech recognition, image generation, and even code synthesis. Almost all leading AI models today—OpenAI’s GPT series, Google’s BERT, Meta’s LLaMA, and DeepMind’s AlphaCode—trace their lineage to this design.

The Transformer was not merely a new model; it was a new language of thought. By altering how AI attends, relates, and composes, it redefined how machines understand the world—and ushered in an era in which computers could grasp and express human language with creativity and depth.