1: History of AI

The History of Artificial Intelligence

The quest to create artificial minds is not a modern phenomenon. Its roots stretch back to ancient myths, such as the Greek Talos, a giant bronze automaton, and to philosophical inquiries into reasoning and symbolism. However, the formal field of Artificial Intelligence as we know it began in the mid-20th century, catalyzed by the development of the electronic computer.

A pivotal moment was the 1950 publication of Alan Turing’s “Computing Machinery and Intelligence,” which proposed the famous Turing Test as a measure of machine intelligence and argued that machines could be built to simulate thought. The term “Artificial Intelligence” itself was coined six years later at the 1956 Dartmouth Conference, organized by John McCarthy. This event, attended by pioneers like Marvin Minsky, Claude Shannon, and Nathaniel Rochester, is widely considered the birth of AI as an academic discipline.

The subsequent decades were marked by cycles of immense optimism, known as “AI summers,” and periods of reduced funding and progress, called “AI winters.” Early success in the 1950s and 60s with programs that could solve algebra problems, prove logical theorems, and even defeat a human at checkers led to predictions of human-level AI within a generation. This initial period, known as the “golden age,” was characterized by symbolic AI, which focused on high-level symbol manipulation and logic to replicate human problem-solving.

The 1970s and late 1980s saw the first major AI winters. The limitations of symbolic AI became apparent; computers lacked the computational power for more complex tasks, and systems could not handle uncertainty or learn from experience. This led to a shift towards sub-symbolic approaches, including connectionism (neural networks) and a growing emphasis on statistical methods and learning from data.

The field was reinvigorated in the 1990s and 2000s. AI began achieving tangible commercial success through “weak” or Narrow AI (ANI), exemplified by IBM’s Deep Blue defeating world chess champion Garry Kasparov in 1997. The rise of the internet provided vast amounts of data, and increased computational power, particularly through Graphics Processing Units (GPUs), enabled the training of complex neural networks. This culminated in the modern era of deep learning, marked by breakthroughs in speech recognition, image classification, and strategic game playing, such as AlphaGo's victory in 2016.