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Deeply Explainable Intelligent Machines

Descrizione del progetto

I robot dovranno spiegare le loro azioni

L’obiettivo del progetto DEXIM, finanziato dall’UE, consiste nel costruire un approccio di apprendimento profondo, completamente trasparente, addestrabile e spiegabile da punto a punto, per la comprensione della scena visiva. Per raggiungere questo obiettivo, ci avvarremo delle interazioni positive tra modalità di dati multipli, inseriremo vincoli di incertezza e di continuità temporale, così come meccanismi di memoria. I risultati di questa proposta avranno conseguenze dirette per molte applicazioni pratiche, in particolare per quanto riguarda la robotica mobile e il settore dei mezzi intelligenti. Questo progetto rafforzerà quindi la fiducia dell’utente in un mercato molto competitivo.

Obiettivo

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.

Meccanismo di finanziamento

ERC-STG - Starting Grant
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Coordinatore

EBERHARD KARLS UNIVERSITAET TUEBINGEN
Contribution nette de l'UE
€ 1 500 000,00
Indirizzo
Geschwister-scholl-platz
72074 Tuebingen
Germania

Mostra sulla mappa

Regione
Baden-Württemberg Tübingen Tübingen, Landkreis
Tipo di attività
Higher or Secondary Education Establishments
Collegamenti
Altri finanziamenti
€ 0,00

Beneficiari (1)