AI and Science of Reasoning(4)

Sherlockian Way of Thinking

Rise of GPT: Machines That Learn Language and Perceive World

When deep learning and Transformer technology converged, the stage was set for a new kind of intelligence—one capable of mastering language itself. At the forefront stood OpenAI’s GPT (Generative Pre-trained Transformer) series. True to its name, GPT models are “pre-trained” on vast corpora of text, learning the structure and rhythm of language before being fine-tuned for a multitude of tasks.

True to its name, GPT models are “pre-trained” on vast corpora of text.

The story began in 2018 with GPT-1, the first experimental model built on the Transformer framework. Modest in size—about 100 million parameters—it nonetheless displayed a surprising ability to generate coherent text, moving beyond mere reaction to genuine contextual understanding.

Then came GPT-2 in 2019, a revelation. Expanded to 1.5 billion parameters, it produced prose so fluent that even experts struggled to distinguish it from human writing. Headlines declared that “AI writes like a person.” Its potential for misuse led to delayed public release, a testament to both awe and apprehension surrounding its power.

GPT-3 (2020) marked a leap of another order. With 175 billion parameters, it demonstrated extraordinary versatility: reasoning across topics, summarizing, translating, composing code, and engaging in creative dialogue. Many users felt that GPT-3 did not merely mimic language—it seemed to understand it. Though it possessed no consciousness, its ability to infer and predict within context brought machines closer than ever to human-like expression.

Then came GPT-4 (2023), refined for accuracy and reliability, and finally GPT-4o (“omni,” 2024), capable of processing not only text but also images and speech—a true multimodal model. Users could now speak to it, show it a photograph, and receive thoughtful, integrated responses. GPT had evolved from a linguistic engine into a companion capable of cross-modal reasoning—an interlocutor in the full sense of the word.

The evolution of GPT is more than a chronicle of technical progress; it signals the emergence of an intelligence that communicates with the world through language. No longer merely writing text, it now mirrors the very structure of human thought.

Mechanism of Generative AI: Art of Probabilistic Reasoning

At first glance, models like GPT appear to think. In truth, their operation is entirely different from human cognition. They do not form intentions or ponder meanings; they predict. Generative AI functions as a vast probabilistic reasoning engine—a machine of inference rather than consciousness.

Models like GPT do not form intentions or ponder meanings; they predict.

Its learning begins with the linguistic record of humanity: billions upon billions of words drawn from books, news articles, conversations, blogs, and code. Through this immense exposure, it internalizes statistical regularities—learning, for instance, that certain words tend to follow others in specific contexts. This phase, called pre-training, provides the foundation for all that follows.

When generating text, the model predicts the next word based on probability. Given a prompt such as “Today the weather is …”, it calculates the likelihoods of possible continuations—clear, cloudy, hot—and selects the most probable (or samples from a weighted distribution). Word by word, sentence by sentence, it builds coherent text.

To the human ear, the result often feels like reasoning, even emotion. Yet beneath the surface lies pure mathematics: patterns distilled from vast experience. The AI does not understand—it recomposes. It reconstructs linguistic flows that have proved plausible countless times before.

Thus, generative AI is not a thinking being but a machine that generates the appearance of thought. Understanding and creativity remain human dominions, yet this probabilistic engine grows ever more adept at imitating the language of the mind. In its evolving fluency lie both boundless potential and profound responsibility—for what we build into it, and for how we choose to use it.