The development of robots that closely resemble human beings can contribute to cognitive research. An android provides an experimental apparatus that has the potential to be controlled more precisely than any human actor. However, preliminary results indicate that only very humanlike devices can elicit the broad range of responses that people typically direct toward each other. Conversely, to build androids capable of emulating human behavior, it is necessary to investigate social activity in detail and to develop models of the cognitive (...) mechanisms that support this activity. Because of the reciprocal relationship between android development and the exploration of social mechanisms, it is necessary to establish the field of android science. Androids could be a key testing ground for social, cognitive, and neuroscientific theories as well as platform for their eventual unification. Nevertheless, subtle flaws in appearance and movement can be more apparent and eerie in very humanlike robots. This uncanny phenomenon may be symptomatic of entities that elicit our model of human other but do not measure up to it. If so, very humanlike robots may provide the best means of pinpointing what kinds of behavior are perceived as human, since deviations from human norms are more obvious in them than in more mechanical-looking robots. In pursuing this line of inquiry, it is essential to identify the mechanisms involved in evaluations of human likeness. One hypothesis is that, by playing on an innate fear of death, an uncanny robot elicits culturally-supported defense responses for coping with death’s inevitability. An experiment, which borrows from methods used in terror management research, was performed to test this hypothesis. [Thomson Reuters Essential Science Indicators: Fast Breaking Paper in Social Sciences, May 2008]. (shrink)

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)

Computation is interpretable symbol manipulation. Symbols are objects that are manipulated on the basis of rules operating only on theirshapes, which are arbitrary in relation to what they can be interpreted as meaning. Even if one accepts the Church/Turing Thesis that computation is unique, universal and very near omnipotent, not everything is a computer, because not everything can be given a systematic interpretation; and certainly everything can''t be givenevery systematic interpretation. But even after computers and computation have been successfully distinguished (...) from other kinds of things, mental states will not just be the implementations of the right symbol systems, because of the symbol grounding problem: The interpretation of a symbol system is not intrinsic to the system; it is projected onto it by the interpreter. This is not true of our thoughts. We must accordingly be more than just computers. My guess is that the meanings of our symbols are grounded in the substrate of our robotic capacity to interact with that real world of objects, events and states of affairs that our symbols are systematically interpretable as being about. (shrink)

This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing.

Mahadevan (2018, AAAI Conference. https://people.cs.umass.edu/~mahadeva/papers/aaai2018-imagination.pdf) proposes that we are at the cusp of imagination science, one of whose primary concerns will be the design of imagination machines. Programs have been written that are capable of generating jokes (Kim Binsted’s JAPE), producing line-drawings that have been exhibited at such galleries as the Tate (Harold Cohen’s AARON), composing music in several styles reminiscent of such greats as Vivaldi and Mozart (David Cope’s Emmy), proving geometry theorems (Herb Gelernter’s IBM program), and inducing quantitative (...) laws from empirical data (Pat Langley, Gary Bradshaw, Jan Zytkow, and Herbert Simon’s BACON). In recent years, Dartmouth has been hosting Turing Tests in creativity in three categories: short stories, sonnets, and dance music DJ sets. In this post, I will provide a brief and non-exhaustive survey of some plausible responses to these imagination machines and the related prospects for our understanding of the imagination. (shrink)

A series of imitation games involving 3-participant (simultaneous comparison of two hidden entities) and 2-participant (direct interrogation of a hidden entity) were conducted at Bletchley Park on the 100th anniversary of Alan Turing’s birth: 23 June 2012. From the ongoing analysis of over 150 games involving (expert and non-expert, males and females, adults and child) judges, machines and hidden humans (foils for the machines), we present six particular conversations that took place between human judges and a hidden entity that produced (...) unexpected results. From this sample we focus on features of Turing’s machine intelligence test that the mathematician/code breaker did not consider in his examination for machine thinking: the subjective nature of attributing intelligence to another mind. (shrink)

The grammatical manipulation and production of language is a great deceiver. We have become habituated to accept the use of well-constructed language to indicate intelligence, understanding and, consequently, intention, whether conscious or unconscious. But we are not always right to do so, and certainly not in the case of large language models (LLMs) like ChapGPT, GPT-4, LLaMA, and Google Bard. This is a perennial problem, but when one understands why it occurs, it ceases to be surprising that it so stubbornly (...) persists. This paper will have three main sections. In the Introduction I will say a little about language, its aetiology, and useful sub-divisions into natural and cultural. In the second section I will explain the current situation with regard to large language models and fill in the background debates which set the problem up as one of increased complexity rather than one of a qualitatively different kind from narrow or specific AI. In the third section I will present the case for the missing phenomenological background and why it is necessary for the co-creation of shared meaning in both natural and cultural language, and I will conclude this section by presenting a rationale for why this situation arises and will continue to arise. Before we do any of this, I need to clarify one point: I do not wish to challenge the ascription of artificial general intelligence (AGI) to LLMs, indeed I think agnosticism is best in this respect, but I do challenge the more serious, and erroneous, ascription of understanding, intention, reason, and consciousness to them. And so, I am making two points: an epistemological one about why we fall into error in our ascription of a mental life to LLMs, and an ontological one about the impossibility of LLMs being or becoming conscious. (shrink)

In the last 20 years the Turing test has been left further behind by new developments in artificial intelligence. At the same time, however, these developments have revived some key elements of the Turing test: imitation and adversarialness. On the one hand, many generative models, such as generative adversarial networks, build imitators under an adversarial setting that strongly resembles the Turing test. The term “Turing learning” has been used for this kind of setting. On the other hand, AI benchmarks are (...) suffering an adversarial situation too, with a ‘challenge-solve-and-replace’ evaluation dynamics whenever human performance is ‘imitated’. The particular AI community rushes to replace the old benchmark by a more challenging benchmark, one for which human performance would still be beyond AI. These two phenomena related to the Turing test are sufficiently distinctive, important and general for a detailed analysis. This is the main goal of this paper. After recognising the abyss that appears beyond superhuman performance, we build on Turing learning to identify two different evaluation schemas: Turing testing and adversarial testing. We revisit some of the key questions surrounding the Turing test, such as ‘understanding’, commonsense reasoning and extracting meaning from the world, and explore how the new testing paradigms should work to unmask the limitations of current and future AI. Finally, we discuss how behavioural similarity metrics could be used to create taxonomies for artificial and natural intelligence. Both testing schemas should complete a transition in which humans should give way to machines—not only as references to be imitated but also as judges—when pursuing and measuring machine intelligence. (shrink)

To perceive an affordance is to perceive an object or situation as presenting an opportunity for action. The concept of affordances has been taken up across wide range of disciplines, including AI. I explore an interesting extension of the concept of affordances in robotics. Among the affordances that artificial systems have been engineered to detect are affordances to deliberate. In psychology, affordances are typically limited to bodily action, so the it is noteworthy that AI researchers have found it helpful to (...) extend the concept to encompass mental actions. I propose that psychologists can learn from this extension, and argue that human subjects can perceive mental affordances, such as affordances to attend, affordances to imagine and affordances to count. (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)

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)

A robot that is functionally indistinguishable from us may or may not be a mindless Zombie. There will never be any way to know, yet its functional principles will be as close as we can ever get to explaining the mind.

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- 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)

We introduce a construct called “humanness sensitivity,” which we define as the ability to recognize uniquely human characteristics. To evaluate the construct, we used a “concurrent study design” to conduct an internet-based study with a convenience sample of 42,063 people from 88 countries (52.4% from the U.S. and Canada).We sought to determine to what extent people could identify subtle characteristics of human behavior, thinking, emotions, and social relationships which currently distinguish humans from non-human entities such as bots. Many people were (...) surprisingly poor at this task, even when asked simple questions about human relationships or anatomy. Participants were best at identifying subtle aspects of human cognition and worst at identifying subtle aspects of human communication. Test scores were good predictors of whether someone was employed and modest predictors of other self-reported criterion measures. We also found that people identifying themselves in marginal societal categories (e.g., in the “other” category for gender or sexual orientation) identified themselves as less human and also scored lower on our test. As computers continue to become more human-like, our study suggests that the vast majority of humankind will likely have great difficulty distinguishing them from people. Can methods be devised for improving this ability? Might humanness sensitivity help people to make such distinctions? Will people who excel at differentiating humans and non-human entities—like the “blade runners” in the 1982 and 2017 feature films—someday hold a special place in society? (shrink)