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Lrn2Cre8

Financiado

Cerrado

Learning to Create

Lrn2Cre8 aims to understand the relationship between learning and creativity by means of practical engineering, theoretical study, and cognitive comparison. We begin from the position that creativity is a function of memory, that... ver más

30/09/2016

QMUL

3M€

Presupuesto del proyecto: 3M€

Líder del proyecto

QUEEN MARY UNIVERSITY OF LONDON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 6 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2016-09-30 No tenemos la información de la convocatoria

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

6 Participantes

QMUL

0.00€ | Lider

UPV

N.D. | Participante

OFAI

N.D. | Participante

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Líder del proyecto

QUEEN MARY UNIVERSITY OF LONDON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 3M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Lrn2Cre8 aims to understand the relationship between learning and creativity by means of practical engineering, theoretical study, and cognitive comparison. We begin from the position that creativity is a function of memory, that generates new structures based on memorised ones, by processes which are essentially statistical. Thus, the project is situated in the tradition of frequentist statistical models of mind, and it builds on statistical understandings of perception of sequence (these are not naive 1st-order statistics) to consider its domain at the level of sequence processing in terms of percepts. Representations of these percepts are also learned, in parallel with the structural information in the data itself, and the guiding principle is one of information efficiency: the representations conspire to produce the most efficient possible representation of the data in memory. In Lre2Cre8, we wish to avoid the problem of extrinsic domain reasoning (e.g., physics in the real world) so we use music as our domain; extrinsic reasoning for music is small in comparison. We propose to build systems that take musical data as input, both in continuous and discrete forms, and learn the necessary representations and structure to memorise it efficiently. We hypothesise that this is a cognitive model of human musical behaviour, and we will test our hypthesis though empirical studies and experiments that compare the behaviour of our computational models with human behaviours. We will study the relationship between our well-understood and cognitively validated learning mechanisms and creative behaviour, in musical composition and performance. We aim to devise new methods for evaluation of creative behaviour in machines and humans, and to apply them, comparatively, to creative processes and outputs of the project. We aim ultimately to produce music which will be of genuine interest to society, and we will launch a record label to promote it as part of our evaluation methods.

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