Opis projektu
Roboty uzasadniające swoje działania
W ramach unijnego projektu DEXIM powstanie w pełni transparentne, kompleksowe, możliwe do trenowania i dające się wyjaśnić podejście uczenia głębokiego, które umożliwi urządzeniom rozumienie scen wizualnych. Aby osiągnąć ten cel, zespół projektu wykorzysta pozytywne interakcje pomiędzy wieloma metodami przetwarzania danych, a także ograniczenia dotyczące niepewności i ciągłości czasowej oraz mechanizmy pamięci. Wyniki projektu znajdą szereg praktycznych zastosowań, w szczególności w branży robotyki mobilnej oraz sektorze inteligentnych pojazdów. Realizacja projektu pozwoli zatem zwiększyć zaufanie użytkowników do tego bardzo konkurencyjnego rynku.
Cel
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.
Dziedzina nauki
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringrobotics
- social scienceslaw
Słowa kluczowe
Program(-y)
Temat(-y)
System finansowania
ERC-STG - Starting GrantInstytucja przyjmująca
85764 Neuherberg
Niemcy