Generative flow networks (GFlowNets) are generative policies trained to sample proportionally to a given reward function. If the reward function is a prior distribution times a likelihood, then the GFlowNet learns to sample from the corresponding posterior. Unlike MCMC, a GFlowNet does not suffer from the problem of mixing between modes, but like RL methods, it needs an exploratory training policy in order to discover modes. This can be conveniently done without any kind of importance weighting because the training objectives for GFlowNets can all be correctly applied in an off-policy fashion without reweighting. One can view GFlowNets also as extensions of amortized variational inference with this off-policy advantage. We show how training the GFlowNet sampler also learns how to marginalize over the target distribution or part of it, at the same time as it learns to sample from it, which makes it possible to train amortized posterior predictives. Finally, we show examples of application of GFlowNets for Bayesian inference over causal graphs, discuss open problems and how scaling up such methodologies opens the door to system 2 deep learning to discover explanatory theories and form Bayesian predictors, with the approximation error asymptotically going to zero as we increase the size and training time of the neural network.