Online research in philosophy

No computer that had not experienced the world as we humans had could pass a rigorously administered standard Turing Test. We show that the use of “subcognitive” questions allows the standard Turing Test to indirectly probe the human subcognitive associative concept network built up over a lifetime of experience with the world. Not only can this probing reveal differences in cognitive abilities, but crucially, even differences in _physical aspects_ of the candidates can be detected. Consequently, it is unnecessary to propose even harder versions of the Test in which all physical and behavioral aspects of the two candidates had to be indistinguishable before allowing the machine to pass the Test. Any machine that passed the “simpler” symbols- in/symbols-out test as originally proposed by Turing would be intelligent. The problem is that, even in its original form, the Turing Test is already too hard and too anthropocentric for any machine that was not a physical, social, and behavioral carbon copy of ourselves to actually pass it. Consequently, the Turing Test, even in its standard version, is not a reasonable test for general machine intelligence. There is no need for an even stronger version of the Test.

The Turing Test (TT), as originally specified, centres on theability to perform a social role. The TT can be seen as a test of anability to enter into normal human social dynamics. In this light itseems unlikely that such an entity can be wholly designed in anoff-line mode; rather a considerable period of training insitu would be required. The argument that since we can pass the TT,and our cognitive processes might be implemented as a Turing Machine(TM), that consequently a TM that could pass the TT could be built, isattacked on the grounds that not all TMs are constructible in a plannedway. This observation points towards the importance of developmentalprocesses that use random elements (e.g., evolution), but in these casesit becomes problematic to call the result artificial. This hasimplications for the means by which intelligent agents could bedeveloped.

The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing''s ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the `other minds'' problem, and similar topics in philosophy of mind are discussed. We also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test. We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic.

This commentary attempts to show that the inverted Turing Test (Watt 1996) could be simulated by a standard Turing test and, most importantly, claims that a very simple program with no intelligence whatsoever could be written that would pass the inverted Turing test. For this reason, the inverted Turing test in its present form must be rejected.

Turing''s test has been much misunderstood. Recently unpublished material by Turing casts fresh light on his thinking and dispels a number of philosophical myths concerning the Turing test. Properly understood, the Turing test withstands objections that are popularly believed to be fatal.

The test Turing proposed for machine intelligence is usually understood to be a test of whether a computer can fool a human into thinking that the computer is a human. This standard interpretation is rejected in favor of a test based on the Imitation Game introduced by Turing at the beginning of "Computing Machinery and Intelligence.".

Turing’s Imitation Game is often viewed as a test for theorised machines that could ‘think’ and/or demonstrate ‘intelligence’. However, contrary to Turing’s apparent intent, it can be shown that Turing’s Test is essentially a test for humans only. Such a test does not provide for theorised artificial intellects with human-like, but not human-exact, intellectual capabilities. As an attempt to bypass this limitation, I explore the notion of shifting the goal posts of the Turing Test, and related tests such as the Total Turing Test, away from the exact imitation of human capabilities, and towards communication with humans instead. While the continued philosophical relevance of such tests is open to debate, the outcome is a different class of tests which are, unlike the Turing Test, immune to failure by means of sub-cognitive questioning techniques. I suggest that attempting to instantiate such tests could potentially be more scientifically and pragmatically relevant to some Artificial Intelligence researchers, than instantiating a Turing Test, due to the focus on producing a variety of goal directed outcomes through communicative methods, as opposed to the Turing Test’s emphasis on ‘fooling’ an Examiner.

The standard interpretation of the imitation game is defended over the rival gender interpretation though it is noted that Turing himself proposed several variations of his imitation game. The Turing test is then justified as an inductive test not as an operational definition as commonly suggested. Turing's famous prediction about his test being passed at the 70% level is disconfirmed by the results of the Loebner 2000 contest and the absence of any serious Turing test competitors from AI on the horizon. But, reports of the death of the Turing test and AI are premature. AI continues to flourish and the test continues to play an important philosophical role in AI. Intelligence attribution, methodological, and visionary arguments are given in defense of a continuing role for the Turing test. With regard to Turing's predictions one is disconfirmed, one is confirmed, but another is still outstanding.

The paper examines the nature of the behavioral evidence underlying attributions of intelligence in the case of human beings, and how this might be extended to other kinds of cognitive system, in the spirit of the original Turing Test (TT). I consider Harnad's Total Turing Test (TTT), which involves successful performance of both linguistic and robotic behavior, and which is often thought to incorporate the very same range of empirical data that is available in the human case. However, I argue that the TTT is still too weak, because it only tests the capabilities of particular tokens within a preexisting context of intelligent behavior. What is needed is a test of the cognitive type, as manifested through a number of exemplary tokens, in order to confirm that the cognitive type is able to produce the context of intelligent behavior presupposed by tests such as the TT and TTT.

Alan Turing devised his famous test (TT) through a slight modificationof the parlor game in which a judge tries to ascertain the gender of twopeople who are only linguistically accessible. Stevan Harnad hasintroduced the Total TT, in which the judge can look at thecontestants in an attempt to determine which is a robot and which aperson. But what if we confront the judge with an animal, and arobot striving to pass for one, and then challenge him to peg which iswhich? Now we can index TTT to a particular animal and its syntheticcorrelate. We might therefore have TTTrat, TTTcat,TTTdog, and so on. These tests, as we explain herein, are abetter barometer of artificial intelligence (AI) than Turing's originalTT, because AI seems to have ammunition sufficient only to reach thelevel of artificial animal, not artificial person.