Does the Turing Test demonstrate intelligence or not?

The Turing Test has served as a defining inspiration throughout the early history of artificial intelligence research. Its centrality arises in part because verbal behavior indistinguishable from that of humans seems like an incontrovertible criterion for intelligence, a "philosophical conversation stopper" as Dennett says. On the other hand, from the moment Turing's seminal Mind article was published, the conversation hasn't stopped; the appropriateness of the Test has been continually questioned, and current philosophical wisdom holds that the Turing Test is hopelessly flawed as a sufficient condition for attributing intelligence. In this short article, I summarize for an artificial intelligence audience an argument that I have presented at length for a philosophical audience that attempts to reconcile these two mutually contradictory but well-founded attitudes towards the Turing Test that have been under constant debate since 1950.Engineering and Applied Science

A formal approach to exploring the interrogator's perspective in the Turing test

My aim in this paper is to use a formal approach to the Turing test. This approach is based on a tool developed within Inferential Erotetic Logic, so called erotetic search scenarios. First, I reconstruct the setting of the Turing test proposed by A.M. Turing. On this basis, I build a model of the test using erotetic search scenarios framework. I use the model to investigate one of the most interesting issues of the TT setting – the interrogator’s perspective and role in the test

Beyond Static Datasets: A Deep Interaction Approach to LLM Evaluation

Large Language Models (LLMs) have made progress in various real-world tasks, which stimulates requirements for the evaluation of LLMs. Existing LLM evaluation methods are mainly supervised signal-based which depends on static datasets and cannot evaluate the ability of LLMs in dynamic real-world scenarios where deep interaction widely exists. Other LLM evaluation methods are human-based which are costly and time-consuming and are incapable of large-scale evaluation of LLMs. To address the issues above, we propose a novel Deep Interaction-based LLM-evaluation framework. In our proposed framework, LLMs' performances in real-world domains can be evaluated from their deep interaction with other LLMs in elaborately designed evaluation tasks. Furthermore, our proposed framework is a general evaluation method that can be applied to a host of real-world tasks such as machine translation and code generation. We demonstrate the effectiveness of our proposed method through extensive experiments on four elaborately designed evaluation tasks

De FrameNet à la Théorie Sens-Texte : conversion et correspondance

Ce projet se décline en deux parties. Dans un premier temps, il s’agit de développer une méthode de conversion automatique des textes annotés selon la sémantique des cadres dans FrameNet en représentations sémantiques de la Théorie Sens-Texte, afin de développer davantage de ressources informatiques pour assurer le développement de différents projets, notamment le réalisateur de textes GenDR. Dans un second temps, cette conversion sera mise à profit pour effectuer une analyse comparative entre les deux formalismes. Nous retiendrons que ces formalismes ne sont pas incompatibles, mais diffèrent par leurs niveaux de granularité et leurs objectifs propres. Nous tracerons quelques parallèles entre les fonctions lexicales et les relations entre cadres, et proposerons une mise en commun des formalismes afin de les enrichir.This project is divided in two main parts. Firstly, a method allowing for an automatic conversion of FrameNet's Semantics-based text annotations into semantic representations, according to the Meaning-Text Theory framework, will be presented. This method will lead to an increased set of data usable to develop and improve various Meaning-Text Theory-based projects, including GenDR, a text realizer. Secondly, the conversion task will be used to do a comparative analysis of the two frameworks. We will conclude that the two frameworks are not incompatible, but differ in their granularity and goals. We will also draw parallels between the lexical functions and frame- to-frame relationships, and make some suggestions regarding changes to the frameworks in order to enrich them

Does the Turing Test Demonstrate Intelligence or Not?

The Turing Test has served as a defining inspiration throughout the early history of artificial intelligence research. Its centrality arises in part because verbal behavior indistinguishable from that of humans seems like an incontrovertibl