A framework for Thinking about Distributed Cognition

As is often the case when scientific or engineering fields emerge, new concepts are forged or old ones are adapted. When this happens, various arguments rage over what ultimately turns out to be conceptual misunderstandings. At that critical time, there is a need for an explicit reflection on the meaning of the concepts that define the field. In this position paper, we aim to provide a reasoned framework in which to think about various issues in the field of distributed cognition. We argue that both relevant concepts, distribution and cognition, must be understood as continuous. As it is used in the context of distributed cognition, the concept of distribution is essentially fuzzy, and we will link it to the notion of emergence of system-level properties. The concept of cognition must also be seen as fuzzy, but for different a reason: due its origin as an anthropocentric concept, no one has a clear handle on its meaning in a distributed setting. As the proposed framework forms a space, we then explore its geography and (re)visit famous landmarks

Grounding Abstract Word Definitions In Prior Concrete Experience

Longman’s Dictionary of Contemporary English (LDOCE) and Cambridge International Dictionary of English (CIDE) have a defining vocabulary of 2000 words from which most but not all of the words are defined. We developed an algorithm, applicable to any dictionary, for recursively deleting words not used in any definition until the corpus is reduced to a subset – a grounding kernel – from which all the words of the complete dictionary are reachable by definition alone. We compared LDOCE’s and CIDE’s defining vocabulary (DV) and grounding kernel (GK) against the rest of its words, on (1) concreteness, (2) imagery and (3) age of acquisition based on the MRC psycholinguistic database [Wilson 1988]. Both GK and DV proved significantly higher on all three scales (p<0.001) for all but 2 of the 12 comparisons (no difference in concreteness in LDOCE for either GK or DV). The difference was also consistently greater using our automatically computed GK than using the DV defined by the compilers of LDOCE and CIDE

Grounding Abstract Word Definitions In Prior Concrete Experience

Longman’s Dictionary of Contemporary English (LDOCE) and Cambridge International Dictionary of English (CIDE) have a defining vocabulary of 2000 words from which most but not all of the words are defined. We developed an algorithm, applicable to any dictionary, for recursively deleting words not used in any definition until the corpus is reduced to a subset – a grounding kernel – from which all the words of the complete dictionary are reachable by definition alone. We compared LDOCE’s and CIDE’s defining vocabulary (DV) and grounding kernel (GK) against the rest of its words, on (1) concreteness, (2) imagery and (3) age of acquisition based on the MRC psycholinguistic database [Wilson 1988]. Both GK and DV proved significantly higher on all three scales (p&lt;0.001) for all but 2 of the 12 comparisons (no difference in concreteness in LDOCE for either GK or DV). The difference was also consistently greater using our automatically computed GK than using the DV defined by the compilers of LDOCE and CIDE

Hierarchies in Dictionary De

A dictionary defines words in terms of other words. Definitions can tell you the meanings of words you don't know, but only if you know the meanings of the defining words. How many words do you need to know (and which ones) in order to be able to learn all the rest from definitions? We reduced dictionaries to their "grounding kernels" (GKs), about 10% of the dictionary, from which all the other words could be defined. The GK words turned out to have psycholinguistic correlates: they were learned at an earlier age and more concrete than the rest of the dictionary. But one can compress still more: the GK turns out to have internal structure, with a strongly connected "kernel core" (KC) and a surrounding layer, from which a hierarchy of definitional distances can be derived, all the way out to the periphery of the full dictionary. These definitional distances, too, are correlated with psycholinguistic variables (age of acquisition, concreteness, imageability, oral and written frequency) and hence perhaps with the ``mental lexicon" in each of our heads

Déclaration de Montréal pour un développement responsable de l'intelligence artificielle

Ce document est une partie du Rapport de la Déclaration de Montréal pour un développement responsable de l'intelligence artificielle 2018.La Déclaration de Montréal pour un développement responsable de l'intelligence artificielle (IA) propose un cadre éthique pour orienter un développement et un déploiement de l'IA qui soit au service du bien-être de tout un chacun. La Déclaration est une œuvre collective, fruit du travail d'une équipe scientifique pluridisciplinaire et interuniversitaire qui s'appuie sur un processus délibératif inclusif. Elle a été co-construite à partir d'un dialogue entre citoyens, experts, responsables publics, parties prenantes de l'industrie, des organisations de la société civile et des ordres professionnels. Les dix grands principes éthiques qui composent la Déclaration s'adressent aux responsables politiques ainsi qu'à toute personne, toute organisation de la société civile et toute compagnie désireuses de participer au développement de l'IA de manière responsable.The Montréal Declaration for a Responsible Development of Artificial Intelligence (AI) proposes an ethical framework to guide the development and deployment of AI for the well-being of all people. The Declaration is a collective effort, resulting from the work of a multidisciplinary and inter-university scientific team that relies on an inclusive deliberative process. It has been co-constructed through a dialogue between citizens, experts, public officials, industry stakeholders, civil society organisations and professional bodies. The ten main ethical principles that compose the Declaration are addressed to policy makers as well as to any individual, civil society organisation or company wishing to participate in the development of AI in a responsible manner

Dialogue entre agents naturels et agents artificiels. Une application aux communautés virtuelles

In order to set up mixed communities in which natural agents (humans) and artificial agents are brought together, it is necessary to ensure smooth interactions. This thesis presents models of interaction and agent, based on the hypothesis that a message carries a potential meaning -subject to discussion- which has to be interpreted, and not one meaning -set and precisely defined- which only needs to be decoded. This hypothesis implies the two cornerstones of this thesis: contextual enhancement and conversational focus. The first point, contextual enhancement, associates a message to its context in a process of interpretation, instead of a simple decoding process, as is usual in typical information systems. Symmetrically, an expression process replaces the encoding. The second point, conversational focus, is a representation of the topic of the interaction. It is collectively built by the people involved in the interaction and subject to discussion, which naturally leads to conversational follow-up. A conversational agent for movies recommendation in a 3D virtual shop has been designed following the presented models, and several interactions between this agent and a human customer are analysed.Cette thèse s'inscrit dans le cadre de la constitution de communautés mixtes rassemblant des agents naturels (humains) et des agents artificiels. Dans cet objectif, la thèse présentée traite des interactions pouvant exister entre ces types d'agents lorsqu'ils sont mis en présence au sein d'une communauté virtuelle. Les modèles d'agent et d'interaction proposés reposent sur l'hypothèse que chaque message est porteur d'un potentiel de sens -négociable- qu'il est nécessaire d'interpréter et non pas porteur d'un sens -unique et précis- qu'il suffit de décoder. Ceci implique les deux points centraux suivants : l'enrichissement contextuel du message et le focus conversationnel. Le premier point, l'enrichissement contextuel, associe un message avec son contexte de façon à permettre l'interprétation de ce message plutôt qu'un simple décodage comme cela est le cas dans les applications informatiques classiques. De manière duale, un processus d'expression remplace l'encodage. Le deuxième point, le focus conversationnel, représente l'objet de la discussion, lui aussi négociable et construit de manière collective par l'ensemble des interactants, leurs différentes interventions entraînant l'émergence du dialogue. Un agent conversationnel dédié à la recommandation de films, dans le cadre d'une boutique virtuelle 3D, constitue le cadre technologique et applicatif de la thèse défendue qui se conclut sur l'analyse de plusieurs dialogues entre cet agent et un client humain

Dialogue entre agents naturels et agents artificiels (une application aux communautés virtuelles)

Cette thèse s'inscrit dans le cadre de la constitution de communauté mixtes rassemblant des agents naturels (humains) et des agents artificiels. Dans cet objectif, la thèse présentée traite des interactions pouvant exister entre ces types d'agents lorsqu'ils sont mis en présence au sein d'une communauté virtuelle. Les modèles d'agent et d'interaction proposés reposent sur l'hypothèse que chaque message est porteur d'un potentiel de sens - négociable- qu'il est nécessaire d'interpréter et non pas porteur d'un sens- unique et précis- qu'il suffit de décoder. Ceci implique les deux points centraux suivants: l'enrichissement contextuel du message et le focus conversationnel. Le premier point, l'enrichissement contextuel, associé un message avec son contexte de façon à permettre l'interprétation de ce message plutôt qu'un simple décodage comme cela est le cas dans les applications informatiques classiques. De manière duale, un processus d'expression remplace l'encodage. Le deuxième point, le focus conversaionnel, représente l'objet de la discussion, lui aussi négociable et construit de manière collective par l'ensemble des interactants, leurs différentes interventions entraînant l'émergence du dialogue. Un agent conversationnel dédié à la recommandation de films, dans le cadre d'une boutique virtuelle 3D, constitue le cadre technologique et applicatif de la thèse défendue qui se conclut sur l'analyse de plusieurs dialogues entre cet agent et un client humain.GRENOBLE1-BU Sciences (384212103) / SudocGRENOBLE-MI2S (384212302) / SudocSudocFranceF

Hierarchies in dictionary definition space

A dictionary defines words in terms of other words. Definitions can tell you the meanings of words you don't know, but only if you know the meanings of the defining words. How many words do you need to know (and which ones) in order to be able to learn all the rest from definitions? We reduced dictionaries to their "grounding kernels" (GKs), about 10% of the dictionary, from which all the other words could be defined. The GK words turned out to have psycholinguistic correlates: they were learned at an earlier age and more concrete than the rest of the dictionary. But one can compress still more: the GK turns out to have internal structure, with a strongly connected "kernel core" (KC) and a surrounding layer, from which a hierarchy of definitional distances can be derived, all the way out to the periphery of the full dictionary. These definitional distances, too, are correlated with psycholinguistic variables (age of acquisition, concreteness, imageability, oral and written frequency) and hence perhaps with the "mental lexicon" in each of our heads

Implementing Software Process Improvement: An Empirical study

‘The definitive version is available at www3.interscience.wiley.com '. Copyright John Wiley &amp; Sons, Ltd. [Full text of this article is not available in the UHRA]In this paper we present survey data characterizing the implementation of SPI in 85 UK companies. We aim to provide SPI managers with more understanding of the critical success factors of implementing SPI. We present an analysis of the critical implementation factors identified in published case studies. We use a questionnaire to measure the use of these factors in ‘typical’ software companies. We found that many companies use SPI but the effectiveness of SPI implementation is variable. Many companies inadequately resource SPI and fail to evaluate the impact of SPI. On the other hand, companies show a good appreciation of the human factors associated with implementing SPI. Copyright © 2002 John Wiley & Sons, Ltd.Peer reviewe