Probabilistic Computing

The Probabilistic Computing Project at CIT Center for Radical Transformation builds on the work of the MIT Probabilistic Computing Project, which aims “to improve our ability to engineer artificial intelligence, reverse-engineer natural intelligence, and deploy applications that increase our collective intelligence and well-being.”

We are working on teaching the theory of probabilistic computing, developing neuro-symbolic generative AI systems and supporting the development and use of InferenceQL.

InferenceQL is an open-source, SQL-like language for querying probabilistic programs modeling tabular data. Based on InferenceQL, we are designing a curriculum for a data science course and research projects to take open data projects and provide inference interfaces to them.

Joi Ito leads the project at CIT. Cameron Freer is teaching a course on probabilistic computing at Keio and CIT, and Karthik Dinakar is working on applying probabilistic programming to clinical applications. We are in the process of recruiting other researchers and faculty at CIT and elsewhere as we ramp up this program over the next year.