ABSTRACT
Before the term artificial intelligence (AI) was coined by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon in 1955, AI research had been going on for a while. In his 1948 paper “Intelligent Machinery,” Alan Turing describes what we today call computers, as well as human-level AI. Two years later, Alan Turing published the paper “Computing Machinery and Intelligence,” introducing the “imitation game,” a test of a machine's ability to exhibit intelligent behavior which became known as the “Turing test.” Warren S. McCulloch and Walter Pitts published (1943) “A Logical Calculus of the Ideas Immanent in Nervous Activity” to mimic the brain. The authors discussed networks of simplified artificial “neurons” and how they might perform simple logical functions. Eight years later, Marvin Minsky and Dean Edmunds built SNARC (Stochastic Neural Analog Reinforcement Calculator), the first artificial neural network, using 3000 vacuum tubes to simulate a network of 40 neurons. In 1957, Frank Rosenblatt developed the Perceptron, an early artificial neural network enabling pattern recognition based on a two-layer computer-learning network. More than a decade later, Arthur Bryson and Yu-Chi Ho (1969) described a backpropagation learning algorithm for multi-layer artificial neural networks. This was an important precursor to the success of deep learning in the 2010s when big data became available and computing power was sufficiently advanced to accommodate the training of large networks. In a similar vein, Minsky's Society of Mind (1986) posited that minds are mental agents, each one made of many smaller processes. The mental agent by itself can only do some simple thing that needs no mind or thought at all. Yet when we join these agents in societies, there results social, or swarm, intelligence.
