“Democratizing ML” is a hot topic these days - particularly in industry. Efficiency, composability and accessibility of machine learning technology are active areas of investment for many research and product groups. Unfortunately, while machine learning has the potential to fundamentally improve how software is constructed, opportunities to leverage machine learning to improve more conventional developer tools (languages, compilers, and IDEs for example) have largely gone untapped. This talk will detail the work our team has done to improve developer efficiency and resource utilization at Facebook - from updating the Hack programming language to support probabilistic programming techniques, to developing a new suite of AI-driven developer tools. I’ll describe the lessons we’ve learned along the way, as well as future opportunities we see to optimize or auto-tune other common pieces of developer infrastructure.
Joe Pamer has spent the last two decades developing cutting-edge compilers, programming languages, and IDEs across industry and academia. Before joining Facebook to help lead their programming language efforts, he was instrumental in the design and development of the F#, TypeScript, and Swift programming languages, and has contributed to many other major developer technologies ranging from .NET to VSCode to Clang. His current mission is to radically improve the way we approach developer tooling and infrastructure by applying machine learning techniques to every link in the developer toolchain.