Increasing AI Programmer Productivity


July 18, 2019


Markus Weimer, Sarah Bird, Ce Zhang, Matei Zaharia, Tianqi Chen, Gustavo Alonso


Microsoft Research, Microsoft Research, ETH Zurich, Stanford University, University of Washington, ETH Zurich


With the advent of machine learning techniques, programmer productivity is poised to significantly improve. The job of a software engineer is changing into one where they learn a model for a function by using vast amounts of data, and then apply this model to predict or infer the value of this function on new and unknown data.

But for this new model of software development to become the dominant approach, we will need advances in several areas, including in program synthesis, compilers, high-performance computer systems, and neural network architectures. This session will identify the new programmer paradigm and identify what is needed to realize the potential productivity improvements it promises.


  • Portrait of Markus Weimer

    Markus Weimer

    Principal Scientist

  • Portrait of Sarah Bird

    Sarah Bird

    Principal Program Manager, Responsible AI Lead, Emerging Technology and Research Strategy Lead, Azure AI