AI & Digital Humanities
The first workshop will be about potentially setting up 3 different clusters.
Deep Learning in Machines and Humans
Recent deep learning advances have introduced powerful ways for machines to learn complex tasks, but machine learning still differs from human learning in at least two fundamental ways: machines need immense amounts of data as compared to humans (but what do we actually quantitatively know about human input?), and humans learn many of their complex behaviors (such as language) in social and interactive settings. Also, many complex human behaviors in humans are culturally shared and transmitted and do not spring from the mind of a single individual brain, but are subject to self-organisation and dynamically evolving. Modeling more realistic social and evolutionary settings for learning may therefore benefit machine learning as well. Bringing together researchers from the fields of child (language) acquisition, machine learning, NLP, evolutionary robotics, self-organization and more, we will find collaborative ways to bridge these gaps.
AI in Digital Humanities
Recruiting AI techniques to answer questions in the humanities using data sets available in various fields. This could for instance include using text mining and NLP methods to identify the emergence of certain concepts or debates in historical texts, using 3D modelling to explore data from archeology, using machine learning to find patterns in the relations between dictionary glosses or using computer vision techniques to quantify aspects of sign language structure and evolution in video data.
Modeling the Human Mind
Bringing together researchers who are interested in creating machine intelligence inspired by or copying aspects of human intelligence that are still hard to grasp by machines such as emotions (psychology, affective computing), mind-reading and storytelling (linguistics, psychology), computational creativity (arts, CS), meta-control (psychology, cognitive robotics), curiosity (psychology, CS), rhythm and music (psychology, linguistics, CS)
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