In my PhD research I am building computer systems for artistic image generation/stylization using deep learning neural network technology. I am advancing new techniques for better fine-grained and fluid controllability of imagery, as well as computational modeling of the human emotional/aesthetic impact of different image qualities. Such advancements enable both “apprentice” AI software tools as well as as well as autonomous artificial artist systems.
Position: PhD Researcher
Using Cognitive Science as a basis for our work, we attempt to model aspects of human creativity in AI. Specially we are using Neural Networks (and evolutionary systems) in the form of Deep Learning, CNNs, RNNs and other modern techniques to model aspects of human expression and creativity. We are known for modelling expression semantics and generation of visual art (stills, videos, VR) but have extended our work into expressive forms of linguistic (word based) narrative.
What is abstraction? Can you use AI techniques to model the semantics of an idea, object, or entity, where that understanding allows for abstraction of the meaning? We use several AI techniques including genetic programming, Neural Nets and Deep Learning to explore abstraction in its many forms. Mainly here in the visual and narrative arts.
Adaptation of an Autonomous Creative Evolutionary System for Real-World Design Application Based on Creative Cognition
Conference Proceedings: Fourth International Conference on Computational Creativity, 2013