Project description
Democratising automated machine learning with humans in mind
Automated machine learning (AutoML) is a matter of trust and interactivity. They are both key factors in supporting developers and researchers, but, despite the huge progress in recent years, democratisation of machine learning via AutoML is yet to be achieved. On the contrary, the EU-funded ixAutoML project is designed with human users at its heart in several stages. The team aims to bring the best of two worlds together: human intuition and generalisation capabilities for complex systems, and efficiency of systematic optimisation approaches for AutoML. They believe that their timely human-centred Interactive and Explainable Human-Centered AutoML – ixAutoML – will have significant impact on making machine learning accessible to a much wider base .
Objective
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.
Fields of science
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
ERC - Support for frontier research (ERC)Host institution
30167 Hannover
Germany