When ChatGPT can write papers fluently, AI painting tools can accurately capture human inspiration, and intelligent robots can complete complex industrial operations, we are witnessing a leapfrog evolution of large artificial intelligence (AI) models from “perceiving the world” to “cognizing the world”. The development of AI has gone through decades, and the emergence of large models has completely broken the limitation of traditional AI’s “single-point breakthrough” and built an intelligent system closer to human thinking mode.

Early artificial intelligence was essentially “rule-driven” weak intelligence. Whether it was simple speech recognition or basic image classification, engineers needed to set a large number of rules in advance. Machines could only complete tasks within the established framework and could not cope with unpreset scenarios — just like teaching a child to speak, you can only instill word by word, but cannot make him understand the emotions and logic behind the language. The core breakthrough of large models lies in the in-depth integration of “data-driven” and “deep learning”. By feeding massive amounts of text, images, audio and other data, the model can independently explore the laws in the data, form multi-dimensional knowledge associations, and even have certain reasoning, association and creation capabilities.

From a technical perspective, the evolution of large models is inseparable from three key supports: computing power, data and algorithms. The improvement of computing power provides a strong hardware foundation for model training. The popularization of GPU clusters enables models with hundreds of millions of parameters to complete training within a reasonable time; massive high-quality data builds a “treasure trove of knowledge” for the model, covering various fields such as human society, natural science and culture and art, allowing the model to learn extensively; the optimization of algorithms enables the model to “learn efficiently”. The emergence of the Transformer architecture solves the pain point that traditional models are difficult to process long texts and multi-dimensional data, allowing large models to achieve more accurate semantic understanding and logical reasoning.

Today, large models have penetrated into all walks of life, reshaping the way of production and life. In the medical field, large models can quickly analyze medical images, assist doctors in diagnosing difficult diseases, and even predict the development trend of diseases, providing support for precision medicine; in the education field, personalized AI tutoring can customize exclusive learning plans according to students’ learning progress and weak links, breaking the barrier of uneven educational resources; in the industrial field, intelligent scheduling large models can optimize production processes, reduce energy consumption and improve production efficiency; in the scientific research field, large models can quickly screen scientific research data, simulate experimental scenarios, and accelerate breakthroughs in fields such as new drug research and development and material innovation.

However, the development of large models also faces many challenges: the problem of data security and privacy protection is becoming increasingly prominent, and the collection and use of massive data may involve the leakage of personal privacy; the “black box effect” of the model makes it difficult for people to understand its decision-making logic, and once an error occurs, it may cause serious consequences; in addition, excessive computing power consumption and high technical thresholds also limit the popularization and application of large models. In the future, the development of large artificial intelligence models will surely move towards the direction of “more intelligent, more secure and more inclusive” — it is necessary not only to break through technical bottlenecks, improve the cognitive ability and reliability of the model, but also to establish a sound regulatory system, balance technological development and social ethics, and let AI truly become a powerful driving force for the progress of human society.