We'll discuss Project MagLev, NVIDIA's internal end-to-end AI platform for developing its self-driving car software, DRIVE. We'll explore the platform that supports continuous data ingest from multiple cars producing TB of data per hour. We'll also cover how the platform enables autonomous AI designers to iterate training of new neural network designs across thousands of GPU systems and validate the behavior of these designs over multi PB-scale data sets. We will talk about our overall architecture for everything from data center deployment to AI pipeline automation, as well as large-scale AI dataset management, AI training, and testing.
Twitter is a unique source of real-time information, offering amazing opportunities for automatic content understanding. The format of this content is diverse (tweets, photos, videos, music, hyperlinks, follow graph, ...), the distribution of topics ever-changing (on a weekly, daily, or sometimes hourly basis), and the volume ever-growing; making it very challenging to automatically and continuously expose relevant content. Manually defining features to represent this data is showing its limits. In this talk, I provide an overview of how automated, content-driven representationsenabled by modern deep-learning algorithmsenables us to build adaptive systems which capture the richness of this content. Specifically, the presentation focuses on deep representations for images and images+text.