From the Turing Test to the Deep Learning revolution.
The history of Artificial Intelligence is a captivating story of ambition, breakthroughs, and setbacks. Its philosophical roots trace back centuries, but its formal beginning is often marked by Alan Turing's 1950 paper "Computing Machinery and Intelligence," which introduced the 'imitation game,' now known as the Turing Test, as a benchmark for machine intelligence. The field was officially born at the 1956 Dartmouth Workshop, where the term "Artificial Intelligence" was coined. This event brought together pioneers who were optimistic about creating thinking machines within a generation. The early years, from the late 1950s to the 1970s, were a period of discovery, characterized by successes in limited domains. Programs were developed that could solve algebra problems, prove logical theorems, and play games like checkers. However, the initial optimism waned as researchers hit a wall of computational complexity and a lack of data. The difficulty of representing common-sense knowledge proved immense, leading to the first 'AI winter' in the mid-1970s, a period of reduced funding and interest. The 1980s saw a resurgence with the rise of 'expert systems'—AI programs that captured the knowledge of human experts in specific domains like medical diagnosis. This boom eventually faded, leading to a second AI winter. The modern era of AI began in the late 1990s and exploded in the 2010s, fueled by three key factors: the availability of massive datasets (Big Data), the development of powerful parallel computing hardware (especially GPUs), and breakthroughs in machine learning algorithms, particularly deep learning.