I will talk about an approach to human-computer interaction which makes the uncertainty in the computer's interpretation of the user's intentions tangible, supporting efficient and enjoyable interaction. I will present a 'liquid cursor' demonstration, as an example of making Bayesian inference concrete and visible, as evidence flows between the user and computer. I will present some current research challenges which I hope the BARK audience can engage with, including the use of complex models to shape interaction dynamics, and in measures of interaction between agents. Application examples from mobile interaction and Brain Computer Interaction will be used.