Reverse Engineering Creativity into Interpretable Neural Networks

  • Marilena OitaEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)


In the field of AI the ultimate goal is to achieve generic intelligence, also called “true AI”, but which depends on the successful enablement of imagination and creativity in artificial agents. To address this problem, this paper presents a novel deep learning framework for creativity, called INNGenuity. Pursuing an interdisciplinary implementation of creativity conditions, INNGenuity aims at the resolution of the various flaws of current AI learning architectures, which stem from the opacity of their models. Inspired by the neuroanatomy of the brain during creative cognition, the proposed framework’s hybrid architecture blends both symbolic and connectionist AI, inline with Minsky’s “society of mind”. At its core, semantic gates are designed to facilitate an input/output flow of semantic structures and enable the usage of aligning mechanisms between neural activation clusters and semantic graphs. Having as goal alignment maximization, such a system would enable interpretability through the creation of labeled patterns of computation, and propose unaligned but relevant computation patterns as novel and useful, therefore creative.


Creativity Neural networks Imagination Semantic networks Knowledge Interpretability Neural architecture 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.The Swiss AI Lab IDSIA, SUPSI, USILuganoSwitzerland

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