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.

When people hear the word "grammar", most of them still think about a set of syntactic rules to combine words (and their concepts) in a compositional fashion. Most NLP systems therefore consider grammar to be equal to syntactic parsing, so syntax is simply one of the components of a traditional pipeline that can be useful for downstream tasks. In the MUHAI project, we take a different approach inspired by cognitive-functional linguistics, in which grammar itself is meaningful: it expresses how people conceptualise reality, and which perspective they take on the events that they perceive in their daily lives.

Performing household activities such as cooking and cleaning have, until recently, been the exclusive provenance of human participants. However, the development of robotic agents that can perform different tasks of increasing complexity is slowly changing this state of affairs, creating new opportunities in the domain of household robotics. Most commonly, any robot activity starts with the robot receiving directions or commands for that specific activity. Today, this is mostly done via programming languages or pre-defined user interfaces, but this changes rapidly.

Over the last decade, conversational agents have slowly but surely become very common in our daily lives. Conversational agents - these are computer agents, robots or software which can understand and talk to a user in natural language - include assistants on our smartphones like Siri or Google Assistant, voice-controlled smart home devices like Google Home and Amazon Alexa, as well as online chatbots like the ones frequently used in customer service.

Babies, puppies, kittens may be bundles of joy but they are also agents of pure chaos. They knock things over, stick their fingers where they're not supposed to, and get a taste or sniff of anything they can, all just for fun of course. Or is it "just for fun"? In the moment, it is, but this playfulness of infants also allows them to build the intuitive models of the physical world which are needed to cope with that world "seriously".