The Imitation Game According To Turing

The current cycle of hype and anxiety concerning the benefits and risks to human society of Artificial Intelligence is fuelled, not only by the increasing use of generative AI and other AI tools by the general public, but also by claims made on behalf of such technology by popularizers and scientists. In particular, recent studies have claimed that Large Language Models (LLMs) can pass the Turing Test-a goal for AI since the 1950s-and therefore can "think". Large-scale impacts on society have been predicted as a result. Upon detailed examination, however, none of these studies has faithfully applied Turing's original instructions. Consequently, we conducted a rigorous Turing Test with GPT-4-Turbo that adhered closely to Turing's instructions for a three-player imitation game. We followed established scientific standards where Turing's instructions were ambiguous or missing. For example, we performed a Computer-Imitates-Human Game (CIHG) without constraining the time duration and conducted a Man-Imitates-Woman Game (MIWG) as a benchmark. All but one participant correctly identified the LLM, showing that one of today's most advanced LLMs is unable to pass a rigorous Turing Test. We conclude that recent extravagant claims for such models are unsupported, and do not warrant either optimism or concern about the social impact of thinking machines.

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.