The First Summer: Dawn of AI
The roots of AI can be traced back to the mid-20th century. The period from 1940 to 1960 saw a technological revolution, accelerated by World War II. By the 1950s, scientists, mathematicians, and philosophers had assimilated the concept of AI into their cultural and intellectual framework, combined with a desire to bring together machines and living beings. In 1950, Alan Turing proposed a “learning machine”, laying the conceptual foundation for AI. A Turing machine is an abstract mathematical model for understanding the limits and capabilities of computation. Turing test assesses a machine's ability to exhibit intelligent behaviour indistinguishable from that of a human. The test remains a pivotal point of philosophical discussions about intelligence, consciousness, and mind. However, computers lacked a crucial element for intelligence— they could execute commands but couldn't store them. Additionally, computing was prohibitively expensive in the early 1950s.
In 1956, John McCarthy coined the term "artificial intelligence" at the Dartmouth Conference, often considered the birthplace of AI. The pioneers of AI were inspired by the idea of creating machines that could replicate human intelligence and perform tasks that typically required human intelligence, such as problem-solving, reasoning, and decision-making. Early successes and advocacy of leading researchers led to a surge in funding and AI flourished.
The optimism reached its pinnacle in the late 1950s and 1960s, often referred to as the first AI summer, a time of grand expectations. During this period, researchers were fueled by the belief that machines could replicate human intelligence. At the same time, integrated circuits were developed which combined multiple transistors on a single semiconductor chip. Computers could now store more information, and became faster and cheaper. There was abundant funding from the government, particularly in the US and UK for AI research. Researchers worked on symbolic AI, using rules and logic to create intelligent systems. Some successes included the creation of programs that could play chess and the earliest examples of a chatbot.
In the late 1950s, an early type of neural network designed for pattern recognition, the perceptron, was designed. The groundwork for neural networks was laid in the 1940s with concepts of converting continuous input to discrete output and strengthening connections between neurons. It was one of the earliest attempts to create a machine that could learn from experience. The development of perceptron was considered a breakthrough that gained wide media attention.