Project description
Human-centric AI that will understand us better
Artificial intelligence (AI) has already made possible the communication between machines and humans, in addition to providing solutions to problems. But could AI grasp deeper meanings or motivations? The EU-funded MUHAI project will investigate by exploring a radically new approach in studies on human-centric AI technology focussing on meaning and understanding. Researchers will refer to the theory of personal dynamic memory to shed light on the process of reconstructing meanings of experiences. Based on recent advances in technical AI components, such as deep learning networks, it will build new ones, such as the mental simulation of actions using gamified virtual environments. The new approach will be tested in cases that require common sense and an understanding of social phenomena, as for example the persistence of inequality in our society.
Objective
The MUHAI project explores a radically new approach to push the envelope of human-centric AI technology to come to grips with meaning and understanding. Meanings are distinctions (categories) that are relevant for prediction, classification, communication, problem solving or other mental tasks. The meanings of an experience include what events, actors and entities play a role, the temporal, spatial and causal relations between events, intentions and motivations of the actors, and values that implicitly underly their behaviour. Understanding is the process of reconstructing these meanings and organizing them in terms of a coherent narrative that explains a new experience by linking it into a Personal Dynamic Memory.
The MUHAI project will advance our ability to capture more of the rich and complex understanding that humans are capable of. It critically relies on enormous recent advances in technical AI components (such as deep learning networks) and semantic resources (such as knowledge graphs). In addition, the project will build novel components, such as: mental simulation of actions using gamified virtual environments and a powerful context mechanism to deal with the complexity of accessing, expanding and managing a very big dynamic memory (> 10-100 billion of facts). All these components will be assembled in a library called CANVAS, made available through the AI4EU AI-on-demand platform.
The technical advances of the project will be developed and systematically tested in two challenging human-centric case studies emphasizing AI for the common good: (i) learning and using common sense every day knowledge on how to do things, in order to enhance well-being and empower common human activities, and (ii) learning about society through AI-supported analysis of historical sources and contemporary digital media streams, in order to understand social phenomena, specifically the origins and persistence of inequality in our society.
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Funding Scheme
RIA - Research and Innovation actionCoordinator
28359 Bremen
Germany