Foundations of Machine Learning and Breakthroughs in Transformers: The Evolutionary Path Behind AI Agents

Abstract

This article explores the evolutionary trajectory of AI Agents, highlighting the foundational role of machine learning and the transformative impact of the Transformer architecture. It first elaborates on how various machine learning paradigms—including supervised, unsupervised, and reinforcement learning—construct the cognitive, perceptual, and execution frameworks for intelligent agents. Subsequently, the study analyzes the core innovations of the Transformer model, such as self-attention mechanisms and parallel computing, demonstrating its function as the central reasoning engine for handling multimodal data and complex tasks. Furthermore, the paper traces the developmental stages of AI Agents from early rule-based systems to the current era of large model-driven and multi-agent collaborative networks. Finally, it examines pressing technical challenges, such as model interpretability and data bias, while outlining significant application opportunities in fields like intelligent manufacturing, healthcare, and transportation, providing a comprehensive theoretical perspective on the future of autonomous agents.