Social media have a societal value, working as modern agoras but there is a need for understanding public concerns and the perceptions of anthropogenic phenomena at a time when user data is increasingly monetized. Part 3 of 3.

Social media have a societal value, working as modern agoras but there is a need for understanding public concerns and the perceptions of anthropogenic phenomena at a time when user data is increasingly monetized. Part 2 of 3.

Social media have a societal value, working as modern agoras but there is a need for understanding public concerns and the perceptions of anthropogenic phenomena at a time when user data is increasingly monetized. Part 1 of 3.

The fact that AI-generated entries have won art and photo contests sparked a debate in the art world. The blog reflects on what are the possible paths for art prizes in a world in which AI generators are commonly used.

Trust is an important factor in human-centric artificial intelligence – especially for the success and effectiveness of a collaborative task in which the participants rely on each other to achieve specific sub-goals. For example, in household environments, such as a kitchen, mistakes can be made by either party that could not only lead to failure to complete the task, but even to injury through various hot or sharp appliances. Trust in a new system or technology is critical to its success, since people tend to employ systems that they trust, and reject systems that they do not trust.

We are pleased to announce that MUHAI researcher Katrien Beuls is now Lecturer in Artificial Intelligence at the University of Namur (Belgium) and we are happy to welcome UNamur in the MUHAI consortium! Read her interview for the University of Namur's magazine "Omalius". ©UNamur (Photo credits: ©Christophe Danaux)

Imagine you're at a house party. You've just met someone new and you want to make a good impression. So, you ask them "How's it going?". To a machine, this phrase might seem like a simple question, but to a human it has a whole range of meanings. It could be a polite greeting, a genuine inquiry about how the person is doing, or even a subtle way of asking them to leave. The machine might not be able to pick up on the nuances of the conversation, but a human would be able to decipher the true meaning behind the words. That's pragmatics in action!

A recurring theme of AI research has turned out to be that what should be easy often is not. Consider this question – "what can I cut a stick of butter with?" You probably already thought of an answer, and if pressed, you could invent more creative ones. A string might do, or the edge of a glass perhaps if no knife is available – though you might protest if I suggested a jar's edge. It will be annoying to get the butter rests out of the threading.

Scientific discovery aims to explain the mechanisms that govern our world. In the social sciences and humanities, scientists are interested in our social world; societies and the individuals within them. They research, for instance, the mechanisms that cause social divides. Why do some groups die younger than others? Why do women earn less than men? How do the occupations of your (grand)parents influence your own career?

Any natural language expression of a set of facts — that can be represented as a knowledge graph — will more or less overtly assume a specific perspective on these facts. In this work we see theconversion of a given knowledge graph into natural language as the construction of a narrativeabout the assertions made by the knowledge graph.

What is the secret ingredient of recipes? In recipes, we talk about ingredients, sometimes many of them, which get moved into different containers and transformed in a thousand different ways, thus turning into other things (such as a dough, or a puree). Moreover, these ingredients and ‘resultant objects’ are often unmentioned, as in ‘Bake until crispy and golden.’ ‘Pour until saturated’. Yet, we are always capable of understanding what that specific step or instruction is talking about. How?

Through the Foundations for Meaning and Understanding in Human-centric AI, the MUHAI project offers an in-depth and integrated overview of narratives and understanding in different disciplines and research fields.  The volume 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 understanding and decision-making processes. This, to map the state-of-the-art of narrative-centric studies and to identify the most promising research streams for tomorrow’s AI.