Our software can be accessed via the Open Probabilistic Programming Stack. The core components of the stack are:
BayesDB, a probabilistic programming platform for AI-assisted data science.
Venture, a prototype general-purpose probabilistic computing platform.
Gen, a probabilistic meta-programming platform and high-performance run-time system suitable for production engineering in probabilistic robotics.
Video Talks + Podcasts
2018-12-04. Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning – Joshua Tenenbaum and Vikash Mansinghka at NIPS 2017.
2017-06-04. AI Assisted Data Analysis for Humanitarian Causes – Vikash Mansinghka at Effective Altruism (EA) Global, Boston.
2016-12-01: Introducing model-based thinking into AI systems – Vikash Mansinghka and Ben Lorica on The O’Reilly Data Show Podcast.
2016-10-06: Probabilistic Programming for Augmented Intelligence – Vikash Mansinghka at the Simons Institute for Theory of Computing.
2016-03-15: Probabilistic Programming for Augmented Intelligence – Vikash Mansinghka at the MIT Media Lab.
2015-10-28: BayesDB: Query the Probable Implications of Data – Marco Cusumano-Towner and Feras Saad at the Future Programming Workshop.
2015-09-24: An Overview of Probabilistic Programming – Vikash Mansighka at Strange Loop.
2015-09-24: BayesDB: Query the Probable Implications of Data – Richard Tibbetts at Strange Loop.
2015-02-17: A Survey of Probabilistic Programming – Vikash Mansinghka at Columbia Data Science
Papers, books, and essays serve as a shared basis of knowledge for the Probabilistic Computing Project.