arXiv subjects

Sharon Temtsin

Publications and source records attributed to Sharon Temtsin.

3 recordsLinked to original sources

arXiv2026

Turing's First Imitation Game: Design Concepts and a Human-Approximates-Machine Reading

This paper examines Turing's 1948 report, "Intelligent Machinery", as an important conceptual source for the later imitation games. Its first contribution is to identify and integrate the design concepts underlying the 1948 chess-based imitation game: the possibility that intelligent machines may make mistakes, the exclusion of irrelevant physical features, the role of the human judge, and Turing's claim that intellectual activity consists mainly of search. The paper's second contribution is to argue that restricting the human contestant to a rather poor chess player increases the role of intellectual search and makes human behaviour more comparable to machine behaviour. This interpretation presents the 1948 game as a human-approximates-machine game and suggests that the imitation game framework can be used not only to ask whether machines imitate humans, but also to examine when human intelligence becomes machine-like under specific task constraints.

cs.HC↗

arXiv2025

A Rigorous Turing Test: a Foundation for Evaluating Artificial General Intelligence

Several studies claim that large language models have passed the Turing Test and hence can "think", yet none follow Turing's original instructions precisely. Passing the test holds significance as evidence that a machine demonstrates human-like intelligence, and as a marker for artificial-general intelligence in commercial and legal domains. We conducted Turing's three-player imitation game with an LLM by following the guidelines identified by Turing and applying scientific standards wherever detailed instructions were missing. We performed a computer-imitates-human game without duration constraints and a man-imitates-woman game as a benchmark. In the computer-imitates-human game, only one participant misidentified the large language model, indicating that claims of large language models' passing the Turing test are premature. Participants required over five minutes for both tasks, with the man-imitates-woman game taking longer; shorter time limits elsewhere may explain earlier positive results. We expect the Turing Test to remain central in assessing machine intelligence for years to come.

cs.HC↗

arXiv2023

Detecting The Corruption Of Online Questionnaires By Artificial Intelligence

Online questionnaires that use crowd-sourcing platforms to recruit participants have become commonplace, due to their ease of use and low costs. Artificial Intelligence (AI) based Large Language Models (LLM) have made it easy for bad actors to automatically fill in online forms, including generating meaningful text for open-ended tasks. These technological advances threaten the data quality for studies that use online questionnaires. This study tested if text generated by an AI for the purpose of an online study can be detected by both humans and automatic AI detection systems. While humans were able to correctly identify authorship of text above chance level (76 percent accuracy), their performance was still below what would be required to ensure satisfactory data quality. Researchers currently have to rely on the disinterest of bad actors to successfully use open-ended responses as a useful tool for ensuring data quality. Automatic AI detection systems are currently completely unusable. If AIs become too prevalent in submitting responses then the costs associated with detecting fraudulent submissions will outweigh the benefits of online questionnaires. Individual attention checks will no longer be a sufficient tool to ensure good data quality. This problem can only be systematically addressed by crowd-sourcing platforms. They cannot rely on automatic AI detection systems and it is unclear how they can ensure data quality for their paying clients.

cs.HC↗