Artificial Human Intelligence


We are interested in the development of computational models of human intelligence and in using the results of this research for the construction of architectures and systems which show a human-like behavior. The scientific aspects of this research are motivated by our belief that computational models of intelligence, if well informed by the results in neuroscience, the social sciences and the other relevant disciplines, are the best way to actually understand how the brain works. The engineering aspects are motivated by our belief that the future artificial intelligence systems should all show human-like intelligence. To us machine human-like intelligence is a necessary condition for the construction of systems which understand the world in the way humans do, and that can exploit this fact to help humans towards a better quality of life and society. We are not interested in machines which substitute humans but, rather in machines, which empower humans in their personal and social activities.

The main focus of this research is to build systems which live in an open world, which adapt to the unpredicted diversity which appears almost every second in our everyday lifes, and which evolve thanks to what they learn while adapting. The further requirement is that the evolution should be compliant to the human needs, understandable by humans and explainable to them, and such that machines are in any moment capable of understanding the feedback from humans, while not necessarily always accepting it. This is what we mean when we support the vision of a human interaction with the human-in-the-loop.

Research Directions

In the years, this activity has consisted of presentations, documents, initiatives, whose goal has been that of identifying and progressively focalizing relevant research topics, both scientific and engineering which would inform research of the group. Relevant items are:

  • Managing diversity in Knowledge [2006] [download] [CITE1], where, for the first time, the crucial role of diversity in knowledge representation and the consequent need for adaptation was identified. This work generated the seeds which later lead to the DataScientia initiative.
  • The future of AI: a few insights into the possible futures of Artificial Intelligence [2008] [download, [CITE2], which described the disatisfaction for some fundamental choices made, more or less explicitly, by the research in AI.
  • Computational Humanism [2017] [download] [CITE3], which identified social relations as one of the next big research challenges for AI in a world where the Internet allows zero-time infinite-space interactions between people. This work was the main outcome of the EC FET Project Smart Society and motivated the follow-up EC FET project WeNet – The Internet of us.

Main Publications

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    • Knowledge Representation and Reasoning
    • Machine Learning
    • Cognitive Modeling
    • Neuromorphic computation
    • Philosophy of Language
    • Social Sciences
    • Physics

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