Sunday 10 a.m.–1 p.m. in Expo Hall
Making Python libraries machine accessible
Zebulun Arendsee, Andrew Wilkey, Jennifer Chang
In a future with strong AI, what will be the role of the programmer? We believe programmers should work with machines by giving them the knowledge they need to reason about our programs. Natural language documentation is imperfect even for humans, since it can be ambiguous and can fall out of sync with the code base. In this poster, we will show our approach to layering an elegant, knowledge representation-based, type system on Python libraries without touching the Python source. We will show how this semantic information can be used to * formalize documentation and make it machine and human searchable * automatically generate runtime assertions * seamlessly integrate with other languages * and lots more Let humans do the fun work, describing problems and building algorithms, and let machines handle the details.