Descrizione del progetto
Democratizzare l’apprendimento automatico automatizzato pensando agli esseri umani
L’apprendimento automatico automatizzato (AutoML) è una questione di fiducia e interattività. Sono entrambi fattori chiave nel supportare sviluppatori e ricercatori, ma, nonostante gli enormi progressi negli ultimi anni, la democratizzazione dell’apprendimento automatico tramite AutoML deve ancora essere raggiunta. Al contrario, il progetto ixAutoML, finanziato dall’UE, pone gli utenti umani come fulcro in più fasi. Il team si propone di riunire il meglio di due mondi: l’intuizione e le capacità di generalizzazione per sistemi complessi umane e l’efficienza degli approcci di ottimizzazione sistematica per l’AutoML. Ritengono che il loro tempestivo AutoML interattivo e spiegabile incentrato sull’uomo, l’ixAutoML, avrà un impatto significativo nel rendere l’apprendimento automatico accessibile a un bacino molto più ampio.
Obiettivo
Trust and interactivity are key factors in the future development and use of automated machine learning (AutoML), supporting developers and researchers in determining powerful task-specific machine learning pipelines, including pre-processing, predictive algorithm, their hyperparameters and--if applicable--the architecture design of deep neural networks. Although AutoML is ready for its prime time after it achieved impressive results in several machine learning (ML) applications and its efficiency improved by several orders of magnitudes in recent years, democratization of machine learning via AutoML is still not achieved. In contrast to previously purely automation-centered approaches, ixAutoML is designed with human users at its heart in several stages. First of all, the foundation of trustful use of AutoML will be based on explanations of its results and processes. Therefore, we aim for:
1. Explaining static effects of design decisions in ML pipelines optimized by state-of-the-art AutoML systems.
2. Explaining dynamic AutoML policies for temporal aspects of dynamically adapted hyperparameters while ML models are trained.
These explanations will be the base for allowing interactions, bringing the best of two worlds together: human intuition and generalization capabilities for complex systems, and efficiency of systematic optimization approaches for AutoML. Concretely, we aim for:
3. Enabling interactions between humans and AutoML by taking human's latent knowledge into account and learning when to interact.
4. Building first ixAutoML prototypes and showing its efficiency in the context of Industry 4.0.
Perfectly aligned with the EU's AI strategy and recent efforts on interpretability in the ML community, we strongly believe that this timely human-centered ixAutoML will have a substantial impact on the democratization of machine learning.
Campo scientifico
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
ERC - Support for frontier research (ERC)Istituzione ospitante
30167 Hannover
Germania