The Verifiable AI objectives are organized in four open research questions that constitute four dimensions of the grand challenge resulting from the emergent use of AI in safety-critical applications. These four dimensions also represent the natural way to organize the background material on Verifiable AI:
In the domains of aeronautics, automotive, energy, manufacturing and retail, Munich Innovation Hub for Applied AI proposes novel solutions to counter the complexity and dependability challenges resulting from distributed accountability, the need for more ...
A data set of 1.8 billion measurements from a mechanical wrist with three axes that can hold tools, for example, for spray painting in combination with a pump. The data set spans six months in 1-second intervals.
REST web service simulating the Wumpus World intelligent agent environment popularised by Russell & Norvig's seminal textbook Artificial Intelligence: A Modern Approach.
Descriptive and predictive analytics for manufacturing legacy and operational data related to maintenance, delivering insights on critical manufacturing operations.
Dockerized AI4EU Acumos component for uncertainty estimation for classification networks
Dezyne is a programming language and a set of tools to specify, validate, verify, simulate, document, and implement concurrent control software for embedded and cyber-physical systems.
Verifiable and Explainable RIsk Forecasting Artificial Intelligence Framework
Data preprocessing, Feature Engineering, Model Development, Evaluation & Selection for Predictive Maintenance in Manufacturing. Part of the development of this asset was supported by the AI REGIO project, which has received funding from the European U...
Provide a large (1 terabytes) Covid-19 chest CT-scan Dataset
Federated training of Graph Neural Networks (GNNs) with Local Differential Privacy