Machine learning has become red-hot with hype, but the world of academic research is still worlds apart from the pragmatic demands of the industry. How does one make sense of the deluge of academic papers and cutesy demos, all claiming earth-shattering state-of-the-art results? What are the main obstacles on the path between a research idea and a robust, practical system?

This talk focuses on the barriers that the research and open source community face in becoming more widely applicable and useful outside of the academic niche. We'll explore the different incentive structures of research vs industry and see what both sides can do to make each other's life easier.