Weidi Zhang, PhD student & Jieliang Luo, PhD MAT (2020).
An exploratory journey inside a neural network in VR
Lavin is a conceptual response to Ground Truth in the modern AI age. From a neural network (NN) trained to recognize thousands of objects to a NN can only generate binary outputs, each NN, like human beings, has its own understanding of the real world, even the inputs are the same. LAVIN provides an immersive responsive experience to visually explore one understanding of a NN in which the real world is mapping to 50 daily objects. LAVIN constantly analyzes the real world via a camera and outputs semantic interpretations, which navigate the audience in a virtual world consisting of all the fluid abstract structures that designed and animated based on the photogrammetry of daily objects that the NN can recognize.
The current AI technique allows a neural network to recognize over 20 thousand different categories of objects. Meanwhile, countless neural networks are trained for different applications that the outputs of each neural network is a unique projection of its own understanding of the real world. Regardless of the complexity of the projection, it shapes the “world value” of the neutral network it belongs. Therefore, an interesting question arises as to what ground truth is in the modern AI age, given the fact that the most complex neural network model cannot inclusively represent the real world. This VR project tries to address this question by providing an immersive responsive experience to evoke people’s awareness of regrading values and beliefs.