Normality and the Turing Test
Alexandre Kabbach

TL;DR
This paper reinterprets the Turing test through the lens of normality, arguing it assesses average human intelligence and that current AI models like ChatGPT target exceptional intelligence, thus questioning their status as true artificial intelligence.
Contribution
It introduces a novel perspective by framing the Turing test as a measure of normality and analyzes how modern AI models deviate from this goal.
Findings
Large language models target exceptional intelligence, not normal intelligence.
The Turing test's structure objectivizes normative ideals of behavior.
Current AI models are models of artificial smartness, not true intelligence.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEthics and Social Impacts of AI · Embodied and Extended Cognition · Philosophy and Theoretical Science
