Large Language Models (LLMs) have been transformative. They are pre-trained foundational models that are self-supervised and can be adapted with fine tuning to a wide range of natural language tasks, each of which previously would have required a separate network model. This is one step closer to the extraordinary versatility of human language. GPT-3 and more recently LaMDA can carry on dialogs with humans on many topics after minimal priming with a few examples. However, there has been a wide range (...) of reactions and debate on whether these LLMs understand what they are saying or exhibit signs of intelligence. This high variance is exhibited in three interviews with LLMs reaching wildly different conclusions. A new possibility was uncovered that could explain this divergence. What appears to be intelligence in LLMs may in fact be a mirror that reflects the intelligence of the interviewer, a remarkable twist that could be considered a Reverse Turing Test. If so, then by studying interviews we may be learning more about the intelligence and beliefs of the interviewer than the intelligence of the LLMs. As LLMs become more capable they may transform the way we interact with machines and how they interact with each other. Increasingly, LLMs are being coupled with sensorimotor devices. LLMs can talk the talk, but can they walk the walk? A road map for achieving artificial general autonomy is outlined with seven major improvements inspired by brain systems. LLMs could be used to uncover new insights into brain function by downloading brain data during natural behaviors. (shrink)

Information security is perceived as an important and vital aspect for the survival of any business. Preserving user identity and limiting the access of web resources only to the humans and restricting ‘bots’ is an ever challenging area of study. With the increase in computing power and development of newer approaches towards circumvention and reverse-engineering, the recognition gap present between the machines and the humans is said to be decreasing. Turing test and its modified versions are in place (...) to deal with such problems and ways to resolve them by developing complex algorithms for bot prevention systems like CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart). This paper will deal with the use of “Machine Vision” for judging the ability of the machines to compete with humans in breaking sequences of security systems like CAPTCHA. Reverse Turing test will be put to practise here. Complex image recognition technologies and novel approaches towards using Human interactive proofs (HIP) are discussed. The progress of Turing test over the past 60 years has been paid due attention at the end. After all this experimentation, it can be said that the current machine vision is quite poor and is far worse than it is expected to be. (shrink)

Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical (...) underdetermination of the theory by the data. Level t1 is clearly too underdetermined, T2 is vulnerable to a counterexample (Searle's Chinese Room Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3 is the appropriate target level for cognitive science. When it is reached, however, there will still remain more unanswerable questions than when Physics reaches its Grand Unified Theory of Everything (GUTE), because of the mind/body problem and the other-minds problem, both of which are inherent in this empirical domain, even though Turing hardly mentions them. (shrink)

As a writer, it is hard to defend oneself from the accusation of being a robot. Even though the argument is ad hominem it perhaps is too difficult to create a “reversed” Turing test. It is suggested in this article that editors of flesh and blood still are necessary.

The Assumption of Agency Theory revisits the Turing Test and€examines what Turing's assessor knew. It asks important questions about how machines vis à vis humans have been characterized since Turing, and seeks to reverse the trend of looking closely at the machine by asking what humans know in interaction and how they know it.€This book€characterizes a non-human agent that shows itself in interaction but is distinct from human agency: an agent acting with us in our ongoing (...) reproduction and transformation of structure. Turing predicted that at the end of the twentieth century, w. (shrink)

This paper makes the case for a sharper terminology regarding AIs cognitive abilities. In arguing that thinking requires more than content production, I offer a definition of meaning drawing on a clear distinction between living and machine intelligence. A pivotal argument is the re-use of the Turing Test (TT) for understanding which theories of meaning and consciousness are no longer plausible—because they have been reproduced by software without thereby gaining conscious experience. In following the few theories that have (...) not (yet) failed this reversed Turing Test (RTT), the focus turns towards rethinking the human condition in times of AI along the lines of three questions: What if a machine developed consciousness? What if AI proceeded without developing a consciousness? What, if machinic and human intelligence merged? These three questions in the end lead to examining three related possible futures of humanism as now determined by the relation between Human Intelligence and AI. (shrink)

This article contributes to the debate around the abilities of large language models such as GPT-3, dealing with: firstly, evaluating how well GPT does in the Turing Test, secondly the limits of such models, especially their tendency to generate falsehoods, and thirdly the social consequences of the problems these models have with truth-telling. We start by formalising the recently proposed notion of reversible questions, which Floridi & Chiriatti propose allow one to ‘identify the nature of the source of (...) their answers’, as a probabilistic measure based on Item Response Theory from psychometrics. Following a critical assessment of the methodology which led previous scholars to dismiss GPT’s abilities, we argue against claims that GPT-3 completely lacks semantic ability. Using ideas of compression, priming, distributional semantics and semantic webs we offer our own theory of the limits of large language models like GPT-3, and argue that GPT can competently engage in various semantic tasks. The real reason GPT’s answers seem senseless being that truth-telling is not amongst them. We claim that these kinds of models cannot be forced into producing only true continuation, but rather to maximise their objective function they strategize to be plausible instead of truthful. This, we moreover claim, can hijack our intuitive capacity to evaluate the accuracy of its outputs. Finally, we show how this analysis predicts that a widespread adoption of language generators as tools for writing could result in permanent pollution of our informational ecosystem with massive amounts of very plausible but often untrue texts. (shrink)

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 Turing test, which is a verbal test of the indistinguishability of machine and human intelligence, is a historically important idea that has set a way of thinking about the AI (artificial intelligence) project that is still relevant today. According to it, the benchmark/blueprint for AI is human intelligence, and the key skill of AI should be its communicative proficiency – which includes explaining decisions made by the machine. Passing the original Turing test by a (...) machine does not guarantee that the machine is, from the point of view of a human, trustworthy; on the contrary, the idea of a machine capable of “fooling” or “outsmarting” a human is inherent within the concept of the test. We postulate, that a test that guarantees a high level of trust should be: a) non-imitative, b) non-behavioral, c) focused on the ability to explain, taking into account, however, the design and rules of operation of the machine (and not just human expectations), d) focused on the machine’s ability to learn. (shrink)

Charles Babbage began the quest to build an intelligent machine in the nineteenth century. Despite finishing neither the Difference nor the Analytical engine, he was aware that the use of mental language for describing the functioning of such machines was figurative. In order to reverse this cautious stance, Alan Turing postulated two decisive ideas that contributed to give birth to Artificial Intelligence: the Turing machine and the Turing test. Nevertheless, a philosophical problem arises from regarding intelligence (...) simulation and make-believe as sufficient to establish that programmed computers are intelligent and have mental states, especially given the nature of mind and its characteristic first-person viewpoint. The origin of Artificial Intelligence is undoubtedly linked to the accounts that inspired John Searle to coin the term strong AI ―or the view that simply equates computers and minds. Especially emphasising the divergence between algorithmic processes and intentional mental states, the Chinese Room thought experiment shows that, since the mind is embodied and able to realise when linguistic understanding takes place, mental states require material implementation, a point that directly conflicts with the accounts that reduce the mind to the functioning of a programmed computer. The experience of linguistic understanding with its typical quale leads to other important philosophical issues. Searle’s theory of intentionality holds that intentional mental states have conditions of satisfaction and appear in semantic networks; thus people know when they understand and what terms are about. In contrast, a number of philosophers maintain that consciousness is only an illusion and that it plays no substantial biological role. However, consciousness is a built-in feature of the system. Moreover, neurological evidence suggests that conscious mental states, qualia and emotions enhance survival chances and are an important part of the phenomenal side of mental life and its causal underpinnings. This renders an important gap between simulating a mind and replicating the properties that allow having mental states and consciousness. On this score, the Turing test and the evidence it offers clearly overestimate simulation and verisimilar make-believe, since such evidence is insufficient to establish that programmed computers have mental life. In summary, this dissertation criticises views which hold that programmed computers are minds and minds are nothing but computers. Despite the arguments in favour of such an equation, they all fail to properly reduce the mind and its first-person viewpoint. Accordingly, the burden of proof still lies with the advocates of strong AI and with those who are willing to deny fundamental parts of the mind to make room for machine intelligence. (shrink)

Stuart M. Shieber’s name is well known to computational linguists for his research and to computer scientists more generally for his debate on the Loebner Turing Test competition, which appeared a decade earlier in Communications of the ACM. 1 With this collection, I expect it to become equally well known to philosophers.

The Turing test has been studied and run as a controlled experiment and found to be underspecified and poorly designed. On the other hand, it has been defended and still attracts interest as a test for true artificial intelligence (AI). Scientists and philosophers regret the test’s current status, acknowledging that the situation is at odds with the intellectual standards of Turing’s works. This article refers to this as the Turing Test Dilemma, following the (...) observation that the test has been under discussion for over seventy years and still is widely seen as either too bad or too good to be a valuable experiment for AI. An argument that solves the dilemma is presented, which relies on reconstructing the Turing test as a thought experiment in the modern scientific tradition. It is argued that Turing’s exposition of the imitation game satisfies Mach’s characterization of the basic method of thought experiments and that Turing’s uses of his test satisfy Popper’s conception of the critical and heuristic uses of thought experiments and Kuhn’s association of thought experiments to conceptual change. It is emphasized how Turing methodically varied the imitation game design to address specific challenges posed to him by other thinkers and how his test illustrates a property of the phenomenon of intelligence and suggests a hypothesis on machine learning. This reconstruction of the Turing test provides a rapprochement to the conflicting views on its value in the literature. (shrink)

This paper provides a survey of philosophical discussion of the "the Turing Test". In particular, it provides a very careful and thorough discussion of the famous 1950 paper that was published in Mind.

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 paper was reprinted in: The Turing Test: The Elusive Standard of Artificial Intelligence, J. H. Moor, ed., Kluwer Academic, pp. 23-78, 2003.). (shrink)

Discussions of artificial intelligence have been shaped by two brilliant thought-experiments: Turing’s Imitation Test for thinking systems and Searle’s Chinese Room Argument. In many ways, debates about large language models (LLMs) struggle to move beyond these original, opposing thought-experiments. So, in this paper, I ask whether we can move debate forward by exploring the features Sceptics about LLM abilities take to ground meaning. Section 1 sketches the options, while Sections 2 and 3 explore the common requirement for a (...) robust relation between linguistic signs and external objects. Section 3 argues that concerns about worldly connections can be met and thus that outputs of LLMs should be viewed as genuinely meaningful. Section 4 then turns to the argument that the kind of derived meaning explored in Section 3 is insufficient for LLMs to count as meaningful systems per se, looking at the claim that they must possess original intentionality before we admit them to the space of meaning. I argue that this demand is not a prerequisite for meaning per se, but rather for some kinds of agency or conscious understanding. I suggest that the demands for original intentionality are not currently met by LLMs and that we should be cautious about whether we want them to be. (shrink)

The Turing Test, originally proposed as a simple operational definition of intelligence, has now been with us for exactly half a century. It is safe to say that no other single article in computer science, and few other articles in science in general, have generated so much discussion. The present article chronicles the comments and controversy surrounding Turing's classic article from its publication to the present. The changing perception of the Turing Test over the last (...) fifty years has paralleled the changing attitudes in the scientific community towards artificial intelligence: from the unbridled optimism of 1960's to the current realization of the immense difficulties that still lie ahead. I conclude with the prediction that the Turing Test will remain important, not only as a landmark in the history of the development of intelligent machines, but also with real relevance to future generations of people living in a world in which the cognitive capacities of machines will be vastly greater than they are now. (shrink)

The Turing Test is routinely understood as a behaviourist test for machine intelligence. Diane Proudfoot has argued for an alternative interpretation. According to Proudfoot, Turing’s claim that intelligence is what he calls ‘an emotional concept’ indicates that he conceived of intelligence in response-dependence terms. As she puts it: ‘Turing’s criterion for “thinking” is…: x is intelligent if in the actual world, in an unrestricted computer-imitates-human game, x appears intelligent to an average interrogator’. The role of (...) the famous test is thus to provide the conditions in which to examine the average interrogator’s responses. I shall argue that Proudfoot’s analysis falls short. The philosophical literature contains two main models of response-dependence, what I shall call the transparency model and the reference-fixing model. Proudfoot resists the thought that Turing might have endorsed one of these models to the exclusion of the other. But the details of her own analysis indicate that she is, in fact, committed to the claim that Turing’s account of intelligence is grounded in a transparency model, rather than a reference-fixing one. By contrast, I shall argue that while Turing did indeed conceive of intelligence in response-dependence terms, his account is grounded in a reference-fixing model, rather than a transparency one. This is fortunate, because, as an account of intelligence, the transparency model is arguably problematic in a way that the reference-fixing model isn’t. (shrink)

The Turing Test is claimed by many to be a way to test for the presence, in computers, of such ``deep'' phenomena as thought and consciousness. Unfortunately, attempts to build computational systems able to pass TT have devolved into shallow symbol manipulation designed to, by hook or by crook, trick. The human creators of such systems know all too well that they have merely tried to fool those people who interact with their systems into believing that these (...) systems really have minds. And the problem is fundamental: the structure of the TT is such as to cultivate tricksters. A better test is one that insists on a certain restrictive epistemic relation between an artificial agent A, its output o, and the human architect H of A – a relation which, roughly speaking, obtains when H cannot account for how A produced o. We call this test the ``Lovelace Test'' in honor of Lady Lovelace, who believed that only when computers originate things should they be believed to have minds. (shrink)

There has been considerable optimistic speculation on how well ChatGPT-4 would perform in a Turing Test. However, no minimally serious implementation of the test has been reported to have been carried out. This brief note documents the re-sults of subjecting ChatGPT-4 to 10 Turing Tests, with different interrogators and participants. The outcome is tremendously disappointing for the optimists. Despite ChatGPT reportedly outperforming 99.9% of humans in a Verbal IQ test, it falls short of passing the (...) Turing Test. In 9 out of the 10 tests conducted, the interroga-tors successfully identified ChatGPT-4 and the human participant. The probability of obtaining this result from a process in which the interrogator is really no better than chance at correct identification is calculated to be less than 1%. An additional question was posed to the interrogators at the end of each test: What led them to distinguish between the human and the machine? The interrogators, who effectively filtered out ChatGPT-4 from passing the Turing Test for intelligence, stated that they could identify the machine because it, in effect, responded more intelligently than the human. Subsequently, ChatGPT-4 was tasked with differentiating syntax from semantics and self-corrected when falling for the fallacy of equivocation. The curious situation is arrived at that passing the Turing Test for intelligence remains a challenge that ChatGPT-4 has yet to overcome, precisely because, as per the inter-rogators, its intellectual abilities surpass those of individual humans. (shrink)

This paper discusses some difficulties in understanding the Turing test. It emphasizes the importance of distinguishing between conceptual and empirical perspectives and highlights the former as introducing more serious problems for the TT. Some objections against the Turingian framework stemming from the later Wittgenstein’s philosophy are exposed. The following serious problems are examined: 1) It considers a unique and exclusive criterion for thinking which amounts to their identification; 2) it misidentifies the relationship of speaking to thinking as that (...) of a criterion; 3) it neglects the “natural” course of the development in semantics. However, these considerations suggest only that it is problematic to label a successful chatbot as a “thinking entity” without further qualifications, but not necessarily and once and for all incorrect. Philosophy has only little to say about the technical possibility of creating such an effective program. (shrink)

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen et al. (Exp Theor Artif Intell 352:24–28, 2004) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to (...) their source. Remarkably, they rated the AI’s moral reasoning as superior in quality to humans’ along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans’ raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality. (shrink)

In this commentary, we discuss the nature of reversible and irreversible questions, that is, questions that may enable one to identify the nature of the source of their answers. We then introduce GPT-3, a third-generation, autoregressive language model that uses deep learning to produce human-like texts, and use the previous distinction to analyse it. We expand the analysis to present three tests based on mathematical, semantic, and ethical questions and show that GPT-3 is not designed to pass any of them. (...) This is a reminder that GPT-3 does not do what it is not supposed to do, and that any interpretation of GPT-3 as the beginning of the emergence of a general form of artificial intelligence is merely uninformed science fiction. We conclude by outlining some of the significant consequences of the industrialisation of automatic and cheap production of good, semantic artefacts. (shrink)

Brain damage can cause massive changes in consciousness levels. From a clinical and ethical point of view it is desirable to assess the level of residual consciousness in unresponsive patients. However, no direct measure of consciousness exists, so we run into the philosophical problem of other minds. Neurologists often make implicit use of a Turing test-like procedure in an attempt to gain access to damaged minds, by monitoring and interpreting neurobehavioral responses. New brain imaging techniques are now being (...) developed that permit communication with unresponsive patients, using their brain signals as carriers of messages relating to their mental states. (shrink)

The 20th Century is the starting point for the most ambitious attempts to extrapolate human life into artificial systems. Norbert Wiener’s Cybernetics, Claude Shannon’s Information Theory, John von Neumann’s Cellular Automata, Universal Constructor to the Turing Test, Artificial Intelligence to Maturana and Varela’s Autopoietic Organization, all shared the goal of understanding in what sense humans resemble a machine. This scientific and technological movement has embraced all disciplines without exceptions, not only mathematics and physics but also biology, sociology, psychology, (...) economics etc. New terms were developed, such as “information”, “organization”, “entropy”, “communication”, “encryption”, “computation” and “algorithmics” to mention a few, all which had an enormous impact on artificial systems. Our work follows this historical track but with a reversed order of priorities. Instead of deducing a reduced group of human acts into an artificial environment, we deduce the human aspects of machines by extrapolating algorithmic methodologies into everyday life acts. The present informational theories were insufficiently powered to achieve this objective without developing a new theoretical approach. Hence, our research is directed towards an expansion of the current informational theories. (shrink)

In 2014, widespread reports in the popular media that a chatbot named Eugene Goostman had passed the Turing test became further grist for those who argue that the diversionary tactics of chatbots like Goostman and others, such as those who participate in the Loebner competition, are enabled by the open-ended dialog of the Turing test. Some claim a new kind of test of machine intelligence is needed, and one community has advanced the Winograd schema competition (...) to address this gap. We argue to the contrary that implicit in the Turing test is the cooperative challenge of using language to build a practical working understanding, necessitating a human interrogator to monitor and direct the conversation. We give examples which show that, because ambiguity in language is ubiquitous, open-ended conversation is not a flaw but rather the core challenge of the Turing test. We outline a statistical notion of practical working understanding that permits a reasonable amount of ambiguity, but nevertheless requires that ambiguity be resolved sufficiently for the agents to make progress. (shrink)

In the classical Turing test, participants are challenged to tell whether they are interacting with another human being or with a machine. The way the interaction takes place is not direct, but a distant conversation through computer screen messages. Basic forms of interaction are face-to-face and embodied, context-dependent and based on the detection of reciprocal sensorimotor contingencies. Our idea is that interaction detection requires the integration of proprioceptive and interoceptive patterns with sensorimotor patterns, within quite short time lapses, (...) so that they appear as mutually contingent, as reciprocal. In other words, the experience of interaction takes place when sensorimotor patterns are contingent upon one’s own movements, and vice versa. I react to your movement, you react to mine. When I notice both components, I come to experience an interaction. Therefore, we designed a “minimal” Turing test to investigate how much information is required to detect these reciprocal sensorimotor contingencies. Using a new version of the perceptual crossing paradigm, we tested whether participants resorted to interaction detection to tell apart human from machine agents in repeated encounters with these agents. In two studies, we presented participants with movements of a human agent, either online or offline, and movements of a computerized oscillatory agent in three different blocks. In each block, either auditory or audiovisual feedback was provided along each trial. Analysis of participants’ explicit responses and of the implicit information subsumed in the dynamics of their series will reveal evidence that participants use the reciprocal sensorimotor contingencies within short time windows. For a machine to pass this minimal Turing test, it should be able to generate this sort of reciprocal contingencies. (shrink)

This paper aims to present and discuss an argumentation against the Turing test (TT), which we shall call the CCSC (Complete Conversation System Claim). Exemplary arguments of the CCSC type include Lem’s “Space Gramophone,” the “machine equipped with a dictionary” proposed by Shannon and McCarthy, Block’s “Aunt Bubbles,” and Searle’s “Chinese Room” argument. CCSC argumentation is constructed to show that the TT is not properly designed and, consequently, is not a good hallmark of intelligence. Based on the original (...) TT rules reconstruction, I argue that CCSC-type argumentation seems to be aimed at a certain interpretation of the TT, which, as I demonstrate, commits the straw man fallacy. In light of the results presented by Łupkowski and Wiśniewski, I also discuss whether a complete conversation system is theoretically possible. (shrink)

I have discussed the frame problem and the Turing test at length, but I have not attempted to spell out what I think the implications of the frame problem ...

In 1950, Alan Turing proposed his eponymous test based on indistinguishability of verbal behavior as a replacement for the question "Can machines think?" Since then, two mutually contradictory but well-founded attitudes towards the Turing Test have arisen in the philosophical literature. On the one hand is the attitude that has become philosophical conventional wisdom, viz., that the Turing Test is hopelessly flawed as a sufficient condition for intelligence, while on the other hand is the (...) overwhelming sense that were a machine to pass a real live full-fledged Turing Test, it would be a sign of nothing but our orneriness to deny it the attribution of intelligence. The arguments against the sufficiency of the Turing Test for determining intelligence rely on showing that some extra conditions are logically necessary for intelligence beyond the behavioral properties exhibited by an agent under a Turing Test. Therefore, it cannot follow logically from passing a Turing Test that the agent is intelligent. I argue that these extra conditions can be revealed by the Turing Test, so long as we allow a very slight weakening of the criterion from one of logical proof to one of statistical proof under weak realizability assumptions. The argument depends on the notion of interactive proof developed in theoretical computer science, along with some simple physical facts that constrain the information capacity of agents. Crucially, the weakening is so slight as to make no conceivable difference from a practical standpoint. Thus, the Gordian knot between the two opposing views of the sufficiency of the Turing Test can be cut. (shrink)

I advocate a theory of syntactic semantics as a way of understanding how computers can think (and how the Chinese-Room-Argument objection to the Turing Test can be overcome): (1) Semantics, considered as the study of relations between symbols and meanings, can be turned into syntax – a study of relations among symbols (including meanings) – and hence syntax (i.e., symbol manipulation) can suffice for the semantical enterprise (contra Searle). (2) Semantics, considered as the process of understanding one domain (...) (by modeling it) in terms of another, can be viewed recursively: The base case of semantic understanding –understanding a domain in terms of itself – is syntactic understanding. (3) An internal (or narrow ), first-person point of view makes an external (or wide ), third-person point of view otiose for purposes of understanding cognition. (shrink)

Why did the plan of using zombie manufacture as a means of studying consciousness ever seem plausible? Why does it impress so many people today? The immediate reason surely lies in fascination with the Turing Test -- the suggestion that computer programs would be proved to be conscious if they managed to carry on conversations in a way that made them seem conscious to a naive observer.

In this paper we discuss the views on the Turing test of four influential thinkers who belong to the tradition of analytic philosophy: Ludwig Wittgenstein, Noam Chomsky, Hilary Putnam and John Searle. Based on various beliefs about philosophical and/or linguistic matters, they arrive at different assessments of both the significance and suitability of the imitation game for the development of cognitive science and AI models. Nevertheless, they share a rejection of the idea that one can treat Turing (...) test as a test for “machine thinking.” This seems to stem from a concern for the proper use of language —one that is a fundamental methodological commitment of analytic philosophy. (shrink)

The main factor of intelligence is defined as the ability tocomprehend, formalising this ability with the help of new constructsbased on descriptional complexity. The result is a comprehension test,or C- test, which is exclusively defined in computational terms. Due toits absolute and non-anthropomorphic character, it is equally applicableto both humans and non-humans. Moreover, it correlates with classicalpsychometric tests, thus establishing the first firm connection betweeninformation theoretical notions and traditional IQ tests. The TuringTest is compared with the C- (...) class='Hi'>test and the combination of the two isquestioned. In consequence, the idea of using the Turing Test as apractical test of intelligence should be surpassed, and substituted bycomputational and factorial tests of different cognitive abilities, amuch more useful approach for artificial intelligence progress and formany other intriguing questions that present themselves beyond theTuring Test. (shrink)

The Turing Test is best regarded as a model to test for intelligence, where an entity’s intelligence is inferred from its ability to be attributed with ‘human-likeness’ during a text-based conversation. The problem with this model, however, is that it does not care if or how well an entity produces a meaningful conversation, as long as its interactions are humanlike enough. As a consequence, the TT attracts projects that concentrate on how best to fool the judges. In (...) light of this, I propose a new version of the TT: the Questioning Turing Test. Here, the entity has to produce an enquiry rather than a conversation; and it is parametrised along two further dimensions in addition to ‘human-likeness’: ‘correctness’, evaluating if the entity accomplishes the enquiry; and ‘strategicness’, evaluating how well the entity accomplishes the enquiry, in terms of the number of questions asked. (shrink)

This commentary attempts to show that the inverted Turing Test 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.

In his 1950 paper “Computing Machinery and Intelligence,” Alan Turing proposed that we can determine whether a machine thinks by considering whether it can win at a simple imitation game. A neutral questioner communicates with two different systems – one a machine and a human being – without knowing which is which. If after some reasonable amount of time the machine is able to fool the questioner into identifying it as the human, the machine wins the game, and we (...) should conclude that it thinks. This imitation game, now known as the Turing Test, has been much discussed by philosophers of mind, and for more than half a century now there has been considerable debate about whether it is an adequate test for thinking. But what has not been much discussed are the sexed presuppositions underlying the test. Too often forgotten in the philosophical discussion is the fact that Turing’s imitation game is modeled on an imitation game in which a neutral questioner communicates with two different humans – one a man and one a woman – without knowing which is which. In this original imitation game, the man wins the game if he is able to fool the questioner into identifying him as the woman. In this paper, I explore the implications of this set-up. As I argue, the fact that the Turing test was modeled on a man/woman imitation game seems to have led us astray in various ways in our attempt to conduct an effective investigation and assessment of computer intelligence. (shrink)

This paper argues against the moral Turing test as a framework for evaluating the moral performance of autonomous systems. Though the term has been carefully introduced, considered, and cautioned about in previous discussions :251–261, 2000; Allen and Wallach 2009), it has lingered on as a touchstone for developing computational approaches to moral reasoning :98–109, 2015). While these efforts have not led to the detailed development of an MTT, they nonetheless retain the idea to discuss what kinds of action (...) and reasoning should be demanded of autonomous systems. We explore the flawed basis of an MTT in imitation, even one based on scenarios of morally accountable actions. MTT-based evaluations are vulnerable to deception, inadequate reasoning, and inferior moral performance vis a vis a system’s capabilities. We propose verification—which demands the design of transparent, accountable processes of reasoning that reliably prefigure the performance of autonomous systems—serves as a superior framework for both designer and system alike. As autonomous social robots in particular take on an increasing range of critical roles within society, we conclude that verification offers an essential, albeit challenging, moral measure of their design and performance. (shrink)