Outputs

The review of communication and dissemination activities related to Muhai.

The third volume, completing the Muhai book is dedicated to case studies that now focus on understanding society. It introduces more tools for narrative understanding, and then reports on experiments to understand society through demography, through social media analysis, and through art.

This prototypical memory bank contains a variety of social memories and is shared as open data on the Zenodo platform.

The canvas allows you to explore the codes and structures of Muhai's outputs.

The volume focuses on theoretical research and concrete case studies about understanding everyday activities in the real world. The case studies come from the domain of cooking: the preparation and execution of recipes and the invention of recipes by constructing variations of existing ones.

MUHAI is approaching its final stage, time to present main results to key audiences, including policy makers and fellow researchers. Discover this first final event with a picture carousel and video highlights.

This is the demonstration of a proactive intelligent observatory and analysis tools for social inequality. The observatory is based on both big data from historical archives as well as big data extracted from social media. It was developed as an interactive support tools for capturing and understanding narratives in socioe-conomic history research, as well as analysing and mapping the public perception of social phenomena and the dynamics of online debates and their narratives, such as on inequality. This is extremely useful in collecting the large data generated on social media and map the perception of users regarding inequality. Discover more about its composition and funcionality.

This is the demonstration of a proactive intelligent observatory and analysis tools for social inequality. The observatory is based on both big data from historical archives as well as big data extracted from social media. It was developed as an interactive support tools for capturing and understanding narratives in socioe-conomic history research, as well as analysing and mapping the public perception of social phenomena and the dynamics of online debates and their narratives, such as on inequality. This is extremely useful in collecting the large data generated on social media and map the perception of users regarding inequality. Discover more about its composition and funcionality.

The volume is in-depth and integrated panorama of narratives and understanding in different disciplines and fields.   The book, edited by Luc Steels, builds upon recent insights and findings from social and cognitive sciences, humanities and other fields for which narratives have been found to play a relevant role in human sense and decision-making processes. The book retraces the existing narrative-centric studies in order to identify the most promising research streams for tomorrow’s AI.  From conceptual foundations of human-centric AI to pragmatics of language, touching narratives in economic and social affairs, historical sciences, clinical trials, social neurosciences and art interpretation the human narratives are explored in their relationship with AI.

This benchmark for recipe understanding in autonomous agents aims to support progressing the domain of natural language understanding by providing a setting in which performance can be measured on the everyday human activity of cooking. For this goal, the benchmark provides a number of recipes written in natural (human) English that should be converted to a procedural semantic network of cooking operations that can be interpreted and executed by autonomous agents.  The full benchmark has been made available standalone and as part of the Babel toolkit. Both options provide the same benchmark functionalities, but the Babel toolkit also provides the option of extending the system.

“The ultimate purpose of social sciences is to furnish causal explanations of classes of observable events, which are, at least in part, generated by individual and collective agency/action.” [1] The aim of a digital assistant for social history research is therefore to support social scientists with the construction of such causal explanations for observable events, also theories or hypotheses.