Kyle discusses Google’s recent open sourcing of ALBERT, a variant of the famous BERT model for natural language processing. ALBERT is more compact and uses fewer parameters.
George leads a discussion about the paper Explainable Artificial Intelligence: Understanding, visualizing, and interpreting deep learning models by Samek, Wiegand, and Muller. This work introduces two tools for generating local interpretability and a novel metric to objectively compare the quality of explanations.
Lan talks about her experience generating new Seinfeld scripts using GPT-2.