Descripción del proyecto
Democratización del aprendizaje automático automatizado teniendo en cuenta las personas
El aprendizaje automático automatizado (AutoML, por sus siglas en inglés) es una cuestión de confianza e interactividad. Ambos son factores fundamentales para apoyar a los programadores e investigadores, pero, a pesar de los enormes avances de los últimos años, la democratización del aprendizaje automático a través de AutoML aún no se ha logrado. En cambio, el proyecto ixAutoML, financiado con fondos europeos, se ha concebido teniendo en cuenta a los usuarios humanos en varias etapas. El equipo pretende reunir lo mejor de dos mundos: la intuición y la capacidad de generalización de las personas para los sistemas complejos y la eficacia de los métodos de optimización sistemática para el AutoML. Creen que su oportuno AutoML interactivo y explicable centrado en el ser humano (ixAutoML, por sus siglas en inglés) tendrá una repercusión significativa en hacer que el aprendizaje automático sea accesible para una base mucho más amplia.
Objetivo
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.
Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
30167 Hannover
Alemania