Research
Ought is somewhere between a research lab and a startup.
We do conceptual and empirical research on supporting deliberation using machine learning, and share our findings openly.
This research is guided by a vision for tools and applications that we hope will eventually help millions of people think through the questions and choices they face every day.
We're incorporated as a non-profit, so we can afford to take the long view.
Projects
Our focus is on mechanisms for training ML algorithms to answer questions in cases where it's difficult or impossible to get empirical feedback on the quality of the answers.
Help people answer questions about the future by making qualitative reasoning steps explicit and using language models to incrementally automate those steps
Integrate model-based and judgmental forecasting so that we get the best of both worlds, automated forecasts that are sensitive to qualitative arguments
Solve difficult problems by assembling small and mostly context-free contributions from individual agents who don't know the big picture