It's actually really old!
The idea of creating artificial life dates back to Greek myths (like Talos). More importantly, the formal study of Logic by figures like Aristotle and later George Boole (Boolean Algebra, 1854) provided the abstract mathematical language (True/False, 1/0) needed for computing. This provided the fundamental abstract language for the entire field.
Alan Turing laid the theoretical groundwork. His concept of the Turing Machine (1936) provided the abstract model of computation, proving a single machine could perform any calculation. In 1950, he proposed the Turing Test, the first criteria for judging machine intelligence, asking, "Can machines think?"
This summer workshop, organized by John McCarthy (who coined the term "Artificial Intelligence"), Marvin Minsky, and others, is considered the formal birthplace of the field. The central goal was established: every aspect of learning or intelligence can be simulated by a machine.
Frank Rosenblatt developed the Perceptron, the first basic artificial neural network capable of learning from data. This was the first attempt at modeling brain-like structure, paving the way for future neural networks.
Early proof-of-concept installations: ELIZA (the first chatbot) simulated conversation, and Shakey the Robot was the first general-purpose mobile robot that could reason about its surroundings, marking the start of physical embodiment in AI.
Initial optimism about imminent General AI led to unrealistic expectations. When machines failed to deliver on early, grand promises due to limitations in computing power and memory, government funding was significantly reduced following critical reports like the Lighthill Report. This period was an "AI Winter," where the building project slowed to a crawl.
IBM's Deep Blue became the first computer to defeat a reigning world chess champion (Garry Kasparov) in a match. This was a critical milestone, proving that machines could outperform humans in complex, strategic problems using brute force and clever search algorithms.
The convergence of three factors fueled the current AI boom: Big Data (the essential resources), vastly improved GPU/Computational Power (the necessary infrastructure), and breakthroughs in Deep Learning (the advanced methodology). Key moments include the Transformer architecture (2017) and the rise of large language models like GPT, fundamentally changing the landscape of machine intelligence.