- Computing Machinery and Intelligence
Computing Machinery and Intelligence, written by Alan Turing and published in 1950 in Mind, is a seminal paper on the topic of artificial intelligence in which the concept of what is now known as the Turing test was introduced to a wide audience.
Turing's paper considers the question "Can machines think?" Since the words "think" and "machine" can't be defined in clear way that satisfies everyone, Turing suggests we "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words." To do this, he must first find a simple and unambiguous idea to replace the word "think", second he must explain exactly which "machines" he is considering, and finally, armed with these tools, he formulates a new question, related to the first, that he believes he can answer in the affirmative.
Rather than trying to determine if a machine is thinking, Turing suggests we should ask if the machine can win a game, called the "Imitation Game". It involves three participants in isolated rooms: a computer (which is being tested), a human, and a (human) judge. The human judge can converse with both the human and the computer by typing into a terminal. Both the computer and human try to convince the judge that they are the human. If the judge cannot consistently tell which is which, then the computer wins the game.
Turing writes 'What will happen when a machine takes the part of A in this game?' Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, 'Can machines think?'"
As Stevan Harnad notes, the question has become "Can machines do what we (as thinking entities) can do?" In other words, Turing is no longer asking whether a machine can "think"; he is asking whether a machine can act indistinguishably from the way a thinker acts. This question avoids the difficult philosophical problem of pre-defining the verb "to think" and focuses instead on the performance capacities that being able to think makes possible, and how a causal system can generate them.
Some have taken Turing's question to have been "Can a computer, communicating over a teleprinter, fool a person into believing it is human?"  but it seems clear that Turing was not talking about fooling people but about generating human cognitive capacity.
Turing also notes that we need to determine which "machines" we wish to consider. He points out that a human clone, while man-made, would not provide a very interesting example. Turing suggested that we should focus on the capabilities of digital machinery—machines which manipulate the binary digits of 1 and 0, rewriting them into memory using simple rules. He gave two reasons.
First, there is no reason to speculate whether or not they can exist. They already did in 1950.
Second, digital machinery is "universal." Turing's research into the foundations of computation had proved that a digital computer can, in theory, simulate the behaviour of any other digital machine, given enough memory and time. (This is the essential insight of the Church-Turing thesis and the universal Turing machine.) Therefore, if any digital machine can "act like it is thinking" then, every sufficiently powerful digital machine can. Turing writes, "all digital computers are in a sense equivalent."
This allows the original question to be made even more specific. Turing now restates the original question as "Let us fix our attention on one particular digital computer C. Is it true that by modifying this computer to have an adequate storage, suitably increasing its speed of action, and providing it with an appropriate programme, C can be made to play satisfactorily the part of A in the imitation game, the part of B being taken by a man?" This question, he believes, can be answered without resorting to speculation or philosophy. It has become a straightforward question of software engineering.
Hence Turing states that the focus is not on "whether all digital computers would do well in the game nor whether the computers are present available would do well, but whether there are imaginable computers which would do well". What is more important is to consider the advancements possible in the state of our machines today regardless of whether we have the available resource to create one or not.
Nine common objections
Having clarified the question, Turing turned to answering it: he considered the following nine common objections, which include all the major arguments against artificial intelligence raised in the years since his paper was first published.
- Theological Objection: This states that thinking is a function of man's immortal soul; therefore, a machine cannot think. "In attempting to construct such machines," wrote Turing, "we should not be irreverently usurping His power of creating souls, any more than we are in the procreation of children: rather we are, in either case, instruments of His will providing mansions for the souls that He creates."
- 'Heads in the Sand' Objection: "The consequences of machines thinking would be too dreadful. Let us hope and believe that they cannot do so." This thinking is popular among intellectual people, as they believe superiority derives from higher intelligence and the possibility of being overtaken is a threat (as machines have efficient memory capacities and processing speed, machines exceeding the learning and knowledge capabilities are highly probable). This objection is a fallacious appeal to consequences, confusing what should not be with what can or cannot be (Wardip-Fruin, 56).
- Mathematical Objections: This objection uses mathematical theorems, such as Gödel's incompleteness theorem, to show that there are limits to what questions a computer system based on logic can answer. Turing suggests that humans are too often wrong themselves and pleased at the fallibility of a machine. (This argument would be made again by philosopher John Lucas in 1961 and physicist Roger Penrose in 1989.)
- Argument From Consciousness: This argument, suggested by Professor Geoffrey Jefferson in his 1949 Lister Oration states that "not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain." Turing replies by saying that we have no way of knowing that any individual other than ourselves experiences emotions, and that therefore we should accept the test. He adds, "I do not wish to give the impression that I think there is no mystery about consciousness ... [b]ut I do not think these mysteries necessarily need to be solved before we can answer the question [of whether machines can think]." (This argument, that a computer can't have conscious experiences or understanding would be made in 1980 by philosopher John Searle in his Chinese Room argument. Turing's reply is now known as the "other minds reply". See also Can a machine have a mind? in the philosophy of AI.)
- Arguments from various disabilities. These arguments all have the form "a computer will never do X". Turing offers a selection:
Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humour, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make some one fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behaviour as a man, do something really new.
- Machines cannot make mistakes. He notes it's easy to program a machine to appear to make a mistake.
- A machine cannot be the subject of its own thought (or can't be self-aware). A program which can report on its internal states and processes, in the simple sense of a debugger program, can certainly be written. Turing asserts "a machine can undoubtably be its own subject matter."
- A machine cannot have much diversity of behaviour. He notes that, with enough storage capacity, a computer can behave in an astronomical number of different ways.
- Lady Lovelace's Objection: One of the most famous objections states that computers are incapable of originality. This is largely because, according to Ada Lovelace, machines are incapable of independent learning.
The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths.
- Argument from continuity in the nervous system: Modern neurological research has shown that the brain is not digital. Even though neurons fire in an all-or-nothing pulse, both the exact timing of the pulse and the probability of the pulse occurring have analog components. Turing acknowledges this, but argues that any analog system can be simulated to a reasonable degree of accuracy given enough computing power. (Philosopher Hubert Dreyfus would make this argument against "the biological assumption" in 1972.)
- Argument from the informality of behaviour: This argument states that any system governed by laws will be predictable and therefore not truly intelligent. Turing replies by stating that this is confusing laws of behaviour with general rules of conduct, and that if on a broad enough scale (such as is evident in man) machine behaviour would become increasingly difficult to predict. He argues that, just because we can't immediately see what the laws are, does not mean that no such laws exist. He writes "we certainly know of no circumstances under which we could say, 'we have searched enough. There are no such laws.'". (Hubert Dreyfus would argue in 1972 that human reason and problem solving was not based on formal rules, but instead relied on instincts and awareness that would never be captured in rules. More recent AI research in robotics and computational intelligence attempts to find the complex rules that govern our "informal" and unconscious skills of perception, mobility and pattern matching. See Dreyfus' critique of AI)
- Extra-sensory perception: In 1950, extra-sensory perception was an active area of research and Turing chooses to give ESP the benefit of the doubt, arguing that conditions could be created in which mind-reading would not affect the test.
Turing ends the paper by speculating about how a machine that can pass the test could be designed. His discussion emphasizes the role of machine learning.
- ^ Turing 1950, p. 433
- ^ Saygin, A. P.; Cicekli, I.; Akman, V. (2000). "Turing Test: 50 years later". Minds and Machines 10 (4): 463–518.
- ^ This describes the simplest version of the test. For a more detailed discussion, see Versions of the Turing test.
- ^ Turing 1950, p. 434
- ^ Harnad, Stevan (2008). "The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence". In Epstein, Robert; Peters, Grace. The Turing Test Sourcebook: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Kluwer. http://eprints.ecs.soton.ac.uk/12954/.
- ^ Harnad, Stevan (2001). "Minds, Machines, and Turing: The Indistinguishability of Indistinguishables". Journal of Logic, Language, and Information 9 (4): 425–445. http://cogprints.org/2615/.
- ^ Wardrip-Fruin, Noah and Nick Montfort, ed (2003). The New Media Reader. The MIT Press. ISBN 0-262-23227-8.
- ^ Harnad, Stevan (1992). "The Turing Test Is Not A Trick: Turing Indistinguishability Is A Scientific Criterion". SIGART Bulletin 3 (4): 9–10. http://cogprints.org/1584/.
- ^ a b Turing 1950, p. 442
- ^ Turing 1950 and see Russell & Norvig 2003, p. 948 where comment "Turing examined a wide variety of possible objections to the possibility of intelligent machines, including virtually all of those that have been raised in the half century since his paper appeared."
- ^ Lucas 1961, Penrose 1989, Hofstadter 1979, pp. 471–473,476–477 and Russell & Norvig 2003, pp. 949–950. Russell and Norvig identify Lucas and Penrose's arguments as being the same one answered by Turing.
- ^ "The Mind of Mechanical Man"
- ^ Searle 1980 and Russell & Norvig 2003, pp. 958–960, who identify Searle's argument with the one Turing answers.
- ^ Dreyfus 1979, p. 156
- ^ Dreyfus 1972, Dreyfus & Dreyfus 1986, Moravec 1988 and Russell & Norvig 2003, pp. 51–52, who identify Dreyfus' argument with the one Turing answers.
- Brooks, Rodney (1990), "Elephants Don't Play Chess", Robotics and Autonomous Systems 6: 3–15, doi:10.1016/S0921-8890(05)80025-9, http://people.csail.mit.edu/brooks/papers/elephants.pdf, retrieved 2007-08-30
- Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0-465-02997-3
- Dreyfus, Hubert (1972), What Computers Can't Do, New York: MIT Press, ISBN 0060110821
- Dreyfus, Hubert; Dreyfus, Stuart (1986), Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer, Oxford, UK: Blackwell
- Dreyfus, Hubert (1979), What Computers Still Can't Do, MIT Press .
- Harnad, Stevan; Scherzer, Peter (2008), "First, Scale Up to the Robotic Turing Test, Then Worry About Feeling", Artificial Intelligence in Medicine 44 (2): 83–9, doi:10.1016/j.artmed.2008.08.008, PMID 18930641, http://eprints.ecs.soton.ac.uk/14430/ .
- Haugeland, John (1985), Artificial Intelligence: The Very Idea, Cambridge, Mass.: MIT Press .
- Moravec, Hans (1976), The Role of Raw Power in Intelligence, http://www.frc.ri.cmu.edu/users/hpm/project.archive/general.articles/1975/Raw.Power.html
- Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2, http://aima.cs.berkeley.edu/
- Searle, John (1980), "Minds, Brains and Programs", Behavioral and Brain Sciences 3 (3): 417–457, doi:10.1017/S0140525X00005756, http://members.aol.com/NeoNoetics/MindsBrainsPrograms.html
- Turing, Alan (October 1950), "Computing Machinery and Intelligence", Mind LIX (236): 433–460, doi:10.1093/mind/LIX.236.433, http://loebner.net/Prizef/TuringArticle.html
- Saygin, A.P.Turing Test: 50 years later. Minds and Machines. 2000. Vol. 10, No. 4. pp 463–518.
- Wardrip-Fruin, Noah and Nick Montfort, eds. The New Media Reader. Cambridge: MIT Press, 2003. ISBN 0-262-23227-8. "Lucasfilm's Habitat" pp. 663–677.
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