Normality and the Turing Test
- URL: http://arxiv.org/abs/2508.21382v2
- Date: Sat, 08 Nov 2025 09:17:07 GMT
- Title: Normality and the Turing Test
- Authors: Alexandre Kabbach,
- Abstract summary: It argues that the Turing test is a test of normal intelligence as assessed by a normal judge.<n>It argues that the objectivization of normal human behavior in the Turing test fails due to the game configuration of the test.
- Score: 51.56484100374058
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper proposes to revisit the Turing test through the concept of normality. Its core argument is that the Turing test is a test of normal intelligence as assessed by a normal judge. First, in the sense that the Turing test targets normal/average rather than exceptional human intelligence, so that successfully passing the test requires machines to "make mistakes" and display imperfect behavior just like normal/average humans. Second, in the sense that the Turing test is a statistical test where judgments of intelligence are never carried out by a single "average" judge (understood as non-expert) but always by a full jury. As such, the notion of "average human interrogator" that Turing talks about in his original paper should be understood primarily as referring to a mathematical abstraction made of the normalized aggregate of individual judgments of multiple judges. Its conclusions are twofold. First, it argues that large language models such as ChatGPT are unlikely to pass the Turing test as those models precisely target exceptional rather than normal/average human intelligence. As such, they constitute models of what it proposes to call artificial smartness rather than artificial intelligence, insofar as they deviate from the original goal of Turing for the modeling of artificial minds. Second, it argues that the objectivization of normal human behavior in the Turing test fails due to the game configuration of the test which ends up objectivizing normative ideals of normal behavior rather than normal behavior per se.
Related papers
- AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games [63.29377274531968]
We introduce the AI GameStore, a scalable and open-ended platform to synthesize new representative human games.<n>We generate 100 such games based on the top charts of Apple App Store and Steam, and evaluate seven frontier vision-language models (VLMs) on short episodes of play.<n>The best models achieved less than 10% of the human average score on the majority of the games, and especially struggled with games that challenge world-model learning, memory and planning.
arXiv Detail & Related papers (2026-02-19T18:17:25Z) - Stuck in the Turing Matrix: Inauthenticity, Deception and the Social Life of AI [0.0]
In an age of generative AI, the Turing test describes the positions we humans occupy.<n>The essay uses data from Reddit postings about AI in broad areas of social life.<n>Even though the Turing Test may not tell us much about the achievement of AGI or other benchmarks, it can tell us a great deal about the limitations of human life in the Matrix.
arXiv Detail & Related papers (2026-01-09T17:00:15Z) - Wow, wo, val! A Comprehensive Embodied World Model Evaluation Turing Test [62.17144846428715]
We introduce the Embodied Turing Test benchmark: WoW-World-Eval (Wow,wo,val)<n>Wow-wo-val examines five core abilities, including perception, planning, prediction, generalization and execution.<n>For the Inverse Dynamic Model Turing Test, we first use an IDM to evaluate the video foundation models' execution accuracy in the real world.
arXiv Detail & Related papers (2026-01-07T17:50:37Z) - Evaluating Intelligence via Trial and Error [59.80426744891971]
We introduce Survival Game as a framework to evaluate intelligence based on the number of failed attempts in a trial-and-error process.<n>When the expectation and variance of failure counts are both finite, it signals the ability to consistently find solutions to new challenges.<n>Our results show that while AI systems achieve the Autonomous Level in simple tasks, they are still far from it in more complex tasks.
arXiv Detail & Related papers (2025-02-26T05:59:45Z) - The Imitation Game According To Turing [0.0]
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"<n>We conducted a rigorous Turing Test with GPT-4-Turbo that adhered closely to Turing's instructions for a three-player imitation game.<n>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.
arXiv Detail & Related papers (2025-01-29T13:08:17Z) - Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated [48.70176791365903]
This study explores how bias shapes the perception of AI versus human generated content.<n>We investigated how human raters respond to labeled and unlabeled content.
arXiv Detail & Related papers (2024-09-29T04:31:45Z) - On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of 'I don't know' [79.69412622010249]
Consistent reasoning, which lies at the core of human intelligence, is the ability to handle tasks that are equivalent.
CRP asserts that consistent reasoning implies fallibility -- in particular, human-like intelligence in AI necessarily comes with human-like fallibility.
arXiv Detail & Related papers (2024-08-05T10:06:53Z) - People cannot distinguish GPT-4 from a human in a Turing test [0.913127392774573]
GPT-4 was judged to be a human 54% of the time, outperforming ELIZA (22%) but lagging behind actual humans (67%)
Results have implications for debates around machine intelligence and, more urgently, suggest that deception by current AI systems may go undetected.
arXiv Detail & Related papers (2024-05-09T04:14:09Z) - The Human-or-Machine Matter: Turing-Inspired Reflections on an Everyday
Issue [4.309879785418976]
We sidestep the question of whether a machine can be labeled intelligent, or can be said to match human capabilities in a given context.
We first draw attention to the seemingly simpler question a person may ask themselves in an everyday interaction: Am I interacting with a human or with a machine?''
arXiv Detail & Related papers (2023-05-07T15:41:11Z) - The Turing Deception [0.0]
This research revisits the classic Turing test and compares recent large language models such as ChatGPT.
The question of whether an algorithm displays hints of Turing's truly original thoughts remains unanswered and potentially unanswerable for now.
arXiv Detail & Related papers (2022-12-09T16:32:11Z) - Can Machines Imitate Humans? Integrative Turing Tests for Vision and Language Demonstrate a Narrowing Gap [45.6806234490428]
We benchmark current AIs in their abilities to imitate humans in three language tasks and three vision tasks.
Experiments involved 549 human agents plus 26 AI agents for dataset creation, and 1,126 human judges plus 10 AI judges.
Results reveal that current AIs are not far from being able to impersonate humans in complex language and vision challenges.
arXiv Detail & Related papers (2022-11-23T16:16:52Z) - When to Make Exceptions: Exploring Language Models as Accounts of Human
Moral Judgment [96.77970239683475]
AI systems need to be able to understand, interpret and predict human moral judgments and decisions.
A central challenge for AI safety is capturing the flexibility of the human moral mind.
We present a novel challenge set consisting of rule-breaking question answering.
arXiv Detail & Related papers (2022-10-04T09:04:27Z) - Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles.
This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing.
The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
arXiv Detail & Related papers (2020-11-30T18:29:25Z) - Self-recognition in conversational agents [0.5156484100374058]
Sustaining the idea of self within the Turing test is still possible if the judge decides to act as a textual mirror.
It is possible that a successful self-recognition might pave way to stronger notions of self-awareness in artificial beings.
arXiv Detail & Related papers (2020-02-06T16:32:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.