Alan Turing's paper, “Computer Machinery and Intelligence” published in 1950, is a foundational work in Artificial Intelligence and Computational Theory. It addresses the question “Can machines think?” and introduced the ‘Imitation Game’ (also known as the Turing Test), a conceptual experiment between 3 entities that aimed to evaluate a machines capacity to exhibit intelligent behaviour indistinguishable from that of a human.
The Imitation Game involves three participants:
A human interrogator
A human respondent
A machine respondent
The human interrogator, who cannot see both participants, begins to ask a series of questions to determine which of the respondents are human. If the machine can successfully imitate the human responses to the extent that the interrogator cannot determine that it is easily distinguishable, Turing argues, that it should be capable of “thinking”.
To begin this set of work, Turing argues that the usual meanings of “machine” and “think” are “absurd” when used to discuss machine intelligence. He introduces the Imitation Game to assess a machine’s ability to mimic human responses through observable communication outcomes. The paper also addresses anticipated criticisms, including objections from theological, mathematical, and philosophical perspectives.
This work is significant not only for laying the foundation for Artificial Intelligence research but also for challenging assumptions about perception, intelligence and the nature of consciousness.
Arthur Samuel, an American scientist and pioneer in Computer Gaming and Artificial Intelligence, made significant development on his Checkers program while working on IBM's 701 computer series. Samuel pursued advancements through his game and famously coined the term "machine learning.”
The program started with a set of basic rules and heuristics for evaluating moves of the game. Over time, it improved through two key learning methods: rote learning and reinforcement learning, where it remembered past game positions and outcomes, and generalisation, where it adjusted an evaluation function that scored board positions based on their likelihood of leading to a win. The rule-based systems that aided the computers' ability to play and learn through trial and error, similar to human brains, fore-fronted the use of neural networks sophisticated enough to compete with human intelligence, which leads us onto IBM’s Deep Blue.
IBM’s Deep Blue was a chess-playing supercomputer that made history by defeating the Russian Chess Grandmaster, Garry Kasparov (Гарри Каспаров) in 1997. Deep Blue's triumph over Kasparov marked a pivotal moment in computing, signalling a future where supercomputers and artificial intelligence could emulate aspects of human thought.
Bayliss, E (2024) Infographic on the Turing Test [Digital Artwork]
Chess.com Team (2018). Kasparov vs. Deep Blue | The Match That Changed History. [online] Chess.com. Available at: https://www.chess.com/article/view/deep-blue-kasparov-chess.
Croes, R (1998) Optiebeurs schaakvierkamp Kasparov tegen Van der Wiel (op de rug gezien), Bestanddeelnr. Available at: https://commons.wikimedia.org/wiki/File:Optiebeurs_schaakvierkamp_Kasparov_tegen_Van_der_Wiel_(op_de_rug_gezien),_Bestanddeelnr_934-2483.jpg
IBM (2024). Deep Blue | IBM. [online] www.ibm.com. Available at: https://www.ibm.com/history/deep-blue.
IBM (n.d.). The games that helped AI evolve | IBM. [online] www.ibm.com. Available at: https://www.ibm.com/history/early-games.
Turing, A.M. (1950). Computing Machinery and Intelligence.
Wikipedia (2012) IBM 702 Series. Available at: https://commons.wikimedia.org/wiki/File:BRL61-IBM_702.jpg
Wikipedia Contributors (2019). Deep Blue (chess computer). [online] Wikipedia. Available at: https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer).
