A researcher trained AI to generate African masks

Artificial intelligence (AI) can generate eerily realistic faces, but what about tribal artwork? That's the question Victor Dibia, a human-computer interaction researcher and Carnegie Mellon graduate, sought to answer with an AI system trained on a dataset of African masks. As Dibia explains in a blog post, the work was inspired by a trip to the 2018 Deep Learning Indaba, an annual machine learning conference held at Stellenbosch University, South Africa, in September. Attendees were provided access to second-generation Tensor Processing Units (TPUs) -- Google-designed chips purpose-built for fast training or inference of AI models -- which Dibia used for training. He tapped Google's TensorFlow machine learning framework to get a generative adversarial network (GAN) -- a two-part neural network consisting of generators that produce samples and discriminators that attempt to distinguish between the generated samples and real-world samples -- up and running on the TPUs.