Last week, I had the chance to spend five days at Schloss Dagstuhl learning, talking, and thinking about “Human-Like Neural-Symbolic Computation”.
Usually, one can distinguish two kinds of approaches in artificial intelligence: Symbolic approaches are based on symbols and rules, mostly in the form of logic. They are good for encoding explicit knowledge as for example “all cats have tails”. Neural approaches on the other hand typically work on raw numbers and use networks of artificial neurons. They are good for learning implicit knowledge, e.g., how to recognize cats in an image. All participants of this invitation-only seminar are actively working on combining these two strands of research.
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Last week, I participated in this year’s interdisciplinary college (https://www.interdisciplinary-college.de). In the course of this spring school, I was able to meet many other students working on exciting research projects. By taking lectures, I acquired basic knowledge of neuroscience, some ideas about creativity (both from the behavioral/neural viewpoint as from the AI perspective) and many impulses on language grounding in robotics.
I was also able to present my overall PhD research project (“Concept Formation in Conceptual Spaces”) both during the poster session and as a “rainbow course” lecture. On both occasions I got very valuable feedback and stimulating impulses for my further research. I’ve uploaded the respective resources in case you are interested: the pdf file of my poster and the slides of my presentation.
Long story short: it was a great week with a lot of input and impulses. 🙂