Amy Heineike is the Principal Product Architect at Primer, building machines that read and write text, leveraging NLP, NLG, and a host of algorithms to augment human analysts. On Primer’s founding team, she built their data science and engineering teams and recently moved to England where she will grow their UK presence. Previously, she developed technology for visualizing large document sets as network maps, and worked in London modeling cities. A Cambridge mathematician, Amy is fascinated by complex human systems and the algorithms and data that help us understand them.
June 25, 2020 05:00 PM PT
Adam Paszke - PyTorch: a modern machine learning research and production platform (PyTorch) -5:00
Amy Heineike - Science vs Covid, lessons from Covid19Primer.com (Primer) - 26:26
PyTorch: A Modern Machine Learning Research and Production Platform
Over the past two years PyTorch has become one of the most popular libraries used in machine learning research, with many of the groundbreaking advancements appearing alongside their PyTorch implementations immediately. Unfortunately, the adoption within the industry has been rather slow compared to the research community, and so one of the goals overarching current development is enabling easier transfer of ideas from academia to industry. This includes enabling easy model packaging and export, simple mobile deployments and Python-free execution - all while retaining the laser focus on great user experience. In this talk I’ll cover the fundamental ideas behind the library, highlight recent advancements, present exciting upcoming features and talk about a few success stories to showcase the progress that has been made so far.
Science vs Covid, Lessons From Covid19Primer.com
The exponential growth of scientific research about the novel coronavirus is one of the truly inspiring and hope filled stories of this crisis – but it’s also a story of overwhelming data volume. AI has a crucial role to play in making information accessible and putting it in context. We built covid19primer.com to connect the research to the news and social conversations about it, and to discover trends and highlight commentary. What have we learnt so far, and what comes next?