How AI slowly gained popularity

The idea of intelligent machines stretches back to ancient times. Myths from Greece spoke of mechanical beings, and early automata were created in Egypt and China. These concepts laid the imaginative groundwork for AI.

Over centuries, philosophers and mathematicians built the theoretical foundation. Aristotle introduced logic and reasoning, Ramon Llull developed symbolic systems, and Thomas Bayes created probability theory—central to modern machine learning. In the 1800s, Charles Babbage designed the Analytical Engine, a blueprint for general-purpose computing, while Ada Lovelace recognized its potential for symbolic computation.

In the 20th century, Alan Turing's work on the universal Turing machine and the Turing Test formalized key concepts of machine intelligence. John von Neumann's stored-program architecture and McCulloch and Pitts' artificial neuron model laid essential groundwork. The Dartmouth Conference in 1956 marked AI's formal birth, leading to early systems capable of problem-solving and logic. However, due to limited computing power, progress was slow and punctuated by setbacks.

By the early 2000s, AI was powering real applications like smarter search engines and better recommendation systems. A major breakthrough came in 2012, when AlexNet demonstrated deep learning's power in image recognition. Tools like TensorFlow, released in 2015, accelerated development by making machine learning more accessible.

By the 2020s, AI had entered a new phase—defined by generative and multimodal systems that could work with text, images, audio, and video. These systems support fields ranging from scientific research and medicine to logistics and the arts.