Projects
Explainable AI for end users and developers PXL
Explainable, human-in-the-loop hybrid AI, resolving conflicting user feedback Ghent University
One of the biggest struggles hindering adoption of AI in industry (e.g. production processes) is the fact that they see AI as a fully data driven approach, requiring a lot of data, which is often not available. Hybrid AI softens the need for big data by including domain knowledge into the AI. Whereas today’s hybrid AI solutions embed objective knowledge into the AI, here, an approach is researched that captures the tacit knowledge of ...
CTRL schemes merged with eXplainable AI for t(h)rustworthy control of physical dynamic systems (CTRLxAI=T(H)RUST) Ghent University
As today’s industrial processes become more complex, controllers used
in drivetrains for vehicles, machines, robots, process facilities, and other
physical dynamic systems face increasing challenges with respect to e.g.
efficiency and quality. In an industry 4.0 setting, a higher level of
adaptivity and automation is required. Meanwhile, artificial intelligence
(AI) is a promising enabling ...
Design and evaluation framework for eXplainable AI KU Leuven
In recent years, we have witnessed the rapid adoption of AI to automate and solve different tasks. However, many of these systems do not explain their predictions to final users, which is critical to certain domains where decision-making needs to be well informed, such as medicine, finance and military. This problem has driven the development of eXplainable AI (XAI), and a diversity of methods have been proposed to construct explanations. ...
Design and Interpret: A New Framework for Explainable AI Vrije Universiteit Brussel
one important shortcoming: they are often considered as black boxes, as their inner processes and generalization capabilities are not fully understood. In this project, we aim at tackling this problem, by developing a new framework for AI that's explainable and interpretable. Two complementary research directions ...
Explainable AI KU Leuven
While eXplainable AI (XAI) is only recently gaining widespread visibility, the Machine Learning (ML), Artificial Intelligence (AI) and Recommender Systems literature contain a long history of work on explanations. The distinction was made early on between transparency that explains the inner logic of a model and justification that decouples the explanation from the model. The later category is also researched under the umbrella of ...
Design and Interpret: A New Framework for Explainable AI KU Leuven
Deep neural networks (DNNs) have shown tremendous performance improvement in a wide range of applications. However, they have one important shortcoming: they are often considered as black boxes, as their inner processes and generalization capabilities are not fully understood. In this project, we aim at tackling this problem, by developing a new framework for AI that's explainable and interpretable. Two complementary research directions will ...
SBO Project: CTRL schemes merged with eXplainable AI for t(h)rustworthy control of physical dynamic systems (CTRLxAI=T(H)RUST) Vrije Universiteit Brussel
in drivetrains for vehicles, machines, robots, process facilities, and other
physical dynamic systems face increasing challenges with respect to e.g.
efficiency and quality. In an industry 4.0 setting, a higher level of
adaptivity and automation is required. Meanwhile, artificial intelligence
(AI) is a promising enabling technology. However, ...