

VERIFAI
Verifiable and Explainable RIsk Forecasting Artificial Intelligence Framework
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:
Verifiable and Explainable RIsk Forecasting Artificial Intelligence Framework
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 380 million measurements from a hydraulic pump that can be mounted on an industrial robot, for example, to pump liquid paint for spray painting. The data set spans two months in 1-second intervals.
This repository contains the base components necessary to bootstrap your own Constraint Object-Oriented Logic Action Programming as a Service (COOLAPS) application docker container.
State-of-the-art solver for logic programming under the answer set semantics.
A tailored Graph Neural Network architecture with Differential Privacy guarantees for both training and inference.
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.
Dockerized AI4EU Acumos component for uncertainty estimation for classification networks
Provide a large (1 terabytes) Covid-19 chest CT-scan Dataset
REST web service simulating the Wumpus World intelligent agent environment popularised by Russell & Norvig's seminal textbook Artificial Intelligence: A Modern Approach.