The Turing test and the robot necessity

arXiv (Cornell University), 2022

As AI algorithms increasingly participate in daily activities that used to be the sole province of humans, we are inevitably called upon to consider how much machines are really like us. To address this question, we turn to the Turing test and systematically benchmark current AIs in their abilities to imitate humans. We establish a methodology to evaluate humans versus machines in Turing-like tests and systematically evaluate a representative set of selected domains, parameters, and variables. The experiments involved testing 769 human agents, 24 state-of-the-art AI agents, 896 human judges, and 8 AI judges, in 21,570 Turing tests across 6 tasks encompassing vision and language modalities. Surprisingly, the results reveal that current AIs are not far from being able to impersonate human judges across different ages, genders, and educational levels in complex visual and language challenges. In contrast, simple AI judges outperform human judges in distinguishing human answers versus machine answers. The curated large-scale Turing test datasets introduced here and their evaluation metrics provide valuable insights to assess whether an agent is human or not. The proposed formulation to benchmark human imitation ability in current AIs paves a way for the research community to expand Turing tests to other research areas and conditions. All of source code and data are publicly available: here. Figure 1. Schematic illustration of Turing tests in six vision and language tasks. A Turing test works with a judge asking a test subject (either a human or an AI agent) a series of tasks. Each party is kept in a separate room, so no physical contact is allowed. The AI passes the Turing test if the judge is unable to distinguish the AI from another human being by using the responses collected from the given task presented to both. See Fig 2 for an overview of the six tasks.

Minds and Machines, 2020

AI Magazine, 2016

The Turing test, as originally conceived, focused on language and reasoning; problems of perception and action were conspicuously absent. To serve as a benchmark for motivating and monitoring progress in AI research, this article proposes an extension to that original proposal that incorporates all four of these aspects of intelligence. Some initial suggestions are made regarding how best to structure such a test and how to measure progress. The proposed test also provides an opportunity to bring these four important areas of AI research back into sync after each has regrettably diverged into a fairly independent area of research of its own.

New Generation Computing, 2013

Turing's landmark paper on computing machinery and intelligence is multifaceted and has an underemphasized ethical dimension. Turing's notion of "intelligence" and "thinking" was far more encompassing than the common anthropocentric view may suggest. We discuss a number of open and underrated problems that the common interpretation of the Turing test as a test of machine intelligence entails. We suggest that a more meaningful question than "Can machines think?" is whether modern computing machinery can amplify human intelligence. We cite examples ranging from traditional silicon-based environments to carbon-based, living organisms in order to illustrate that this kind of intelligence amplification is indeed happening today. We conclude that in its interpretation as a test of machine intelligence, the Turing test may indeed be harmful for artificial intelligence (AI); in its wider interpretation, however, it remains an inspiring source for philosophy and AI alike.

The Turing test: the elusive …, 2003

Journal of Information, Communication and Ethics in Society, 2015

086a4c9e-1fbb-4474-b9f3-1d653ba70bbf)/publications.html?filter=research) Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.

Journal of Computers

This paper addresses psychologists-psychoanalysts in particular-and engineers, as well as others with an interest in technology and a proclivity for critical thinking. We hope to grasp the attention of persons who are skeptical or even uneasy with regard to modern electronic machines. It is important to realize that the mathematician and information theoretician Alan Turing was the first to prompt mankind to consider a comparison between intelligent machines and humans from a scientific perspective. His test proposal piqued the interest of engineers as well as philosophers, and has caused many of them to question their concepts to this day. This treatise aims to show that Turing, a scientist rightfully (albeit belatedly) highly esteemed, formulated a fascinating research question which can be legitimately answered only if one deals intensively and seriously with the disciplines of psychoanalysis and computer engineering. The project SiMA (Simulation of Mental apparatus & Applications) at the Vienna University of Technology assembled a team of scientists who have come to a concise conclusion and formulated an answer to this question. The answer, however, is not what one might expect.

Forum Philosophicum

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.

Proceedings of the …, 2010

In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human. This test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement, with the human hand movement being a sophisticated demonstration of this function. Therefore, we propose a Turing-like handshake test, for machine motor intelligence. We administer the test through a telerobotic system in which the interrogator is engaged in a task of holding a robotic stylus and interacting with another party (human, artificial, or a linear combination of the two). Instead of asking the interrogator whether the other party is a person or a computer program, we employ a forced-choice method and ask which of two systems is more humanlike. By comparing a given model with a weighted sum of human and artificial systems, we fit a psychometric curve to the answers of the interrogator and extract a quantitative measure for the computer model in terms of similarity to the human handshake.