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DEXIM

Financiado

Deeply Explainable Intelligent Machines

Explanations are valuable because they scaffold the kind of learning that supports adaptive behaviour, e.g. explanations enable users to adapt themselves to the situations that are about to arise. Explanations allow us to attain a... ver más

31/12/2025

HMGU

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2019-11-27

ERC-2019-STG: ERC Starting Grant

I+D

Cerrada hace 6 años

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3 Participantes

HMGU

1.11M€ | Lider

J.R. LICEAGA, S.L.

183.18K€ | Participante

391.25K€ | Participante

Últimas noticias

02-12-2024: AGENCIA-VALENCIANA-D... En las últimas 48 horas el Organismo AGENCIA VALENCIANA D... ha otorgado 1 concesiones

02-12-2024: CDTI En las últimas 48 horas el Organismo CDTI ha otorgado 4 concesiones

01-12-2024: REDES En las últimas 48 horas el Organismo REDES ha otorgado 1337 concesiones

Duración del proyecto: 73 meses Fecha Inicio: 2019-11-27
Fecha Fin: 2025-12-31

Presupuesto del proyecto 2M€

Descripción del proyecto Explanations are valuable because they scaffold the kind of learning that supports adaptive behaviour, e.g. explanations enable users to adapt themselves to the situations that are about to arise. Explanations allow us to attain a stable environment and have the possibility to control it, e.g. explanations put us in a better position to control the future. Explanations in the medical domain can help patients identify and monitor the abnormal behaviour of their ailment. In the domain of self-driving vehicles they can warn the user of some critical state and collaborate with her to prevent a wrong decision. In the domain of satellite imagery, an explanatory monitoring system justifying the evidence of a future hurricane can save millions of lives. Hence, a learning machine that a user can trust and easily operate need to be fashioned with the ability of explanation. Moreover, according to GDPR, an automatic decision maker is required to be transparent by law. As decision makers, humans can justify their decisions with natural language and point to the evidence in the visual world which led to their decisions. In contrast, artificially intelligent systems are frequently seen as opaque and are unable to explain their decisions. This is particularly concerning as ultimately such systems fail in building trust with human users. In this proposal, the goal is to build a fully transparent end-to-end trainable and explainable deep learning approach for visual scene understanding. To achieve this goal, we will make use of the positive interactions between multiple data modalities, incorporate uncertainty and temporal continuity constraints, as well as memory mechanisms. The output of this proposal will have direct consequences for many practical applications, most notably in mobile robotics and intelligent vehicles industry. This project will therefore strengthen the user’s trust in a very competitive market.

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