We aim to improve our ability to engineer artificial intelligence, reverse-engineer natural intelligence, and deploy applications that increase our collective intelligence and well-being.
Our work integrates probabilistic inference, generative models, and Monte Carlo methods into the building blocks of software, hardware, and other computational systems. For example, we have developed high-level probabilistic programming languages, automated Bayesian data modeling systems, Bayesian inverse graphics approaches to 3D computer vision, and near-optimal algorithms, circuits, and hardware architectures for Monte Carlo. We test our results by collaborating with domain experts on practical applications.
In addition to core academic research and teaching, we mentor engineers and entrepreneurs, develop open source software, and lead hands-on AI workshops for industry. These activities have led to: VC-backed startups acquired by Salesforce (2012) and Tableau (2018), the founding in 2020 of Common Sense Machines, and a new Intel Center for Probabilistic Computing. We also carry out joint development and field testing with partners from both the public and private sectors.
Contact us to get involved in testing or contributing.