Artificial Intelligence: Historical Context and State of the Art

Abstract

Abstract The idea that intelligence is the result of a computational process and can, therefore, be automated, is centuries old. We review the historical origins of the idea that machines can be intelligent, and the most significant contributions made by Thomas Hobbes, Charles Babbage, Ada Lovelace, Alan Turing, Norbert Wiener, and others. Objections to the idea that machines can become intelligent have been raised and addressed many times, and we provide a brief survey of the arguments and counter-arguments presented over time. Intelligence was first viewed as symbol manipulation, leading to approaches that had some successes in specific problems, but did not generalize well to real-world problems. To address the difficulties faced by the early systems, which were brittle and unable to handle unforeseen complexities, machine learning techniques were increasingly adopted. Recently, a sub-field of machine learning known as deep learning has led to the design of systems that can successfully learn to address difficult problems in natural language processing, vision, and (yet to a lesser extent) interaction with the real world. These systems have found applications in countless domains and are one of the central technologies behind the fourth industrial revolution, also known as Industry 4.0 . Applications in analytics enable artificial intelligence systems to exploit and extract economic value from data and are the main source of income for many of today’s largest companies. Artificial intelligence can also be used in automation, enabling robots and computers to replace humans in many tasks. We conclude by providing some pointers to possible future developments, including the possibility of the development of artificial general intelligence, and provide leads to the potential implications of this technology in the future of humanity .