PPAML (Probabilistic Programming for Advanced Machine Learning)
Galois is the team of domain experts working on this DARPA-funded project that is aimed at advancing the state of the art in machine learning.
Machine learning is at the heart of modern approaches to artificial intelligence. The field posits that teaching computers how to learn can be significantly more effective than programming them explicitly. Unfortunately, building effective machine learning applications currently still requires Herculean efforts on the part of highly trained experts in machine learning.
Probabilistic programming is a new programming paradigm for managing uncertain information. The goal of the PPAML program is to facilitate the construction of machine learning applications by using probabilistic programming to:
- Dramatically increase the number of people who can successfully build machine learning applications;
- Make machine learning experts radically more effective; and
- Enable new applications that are inconceivable today.
The PPAML program started in November 2013 and is scheduled to run 46 months, with three phases of activity through 2017.