If I had to guess I would say that:<p>- It involves generative models (needed to make inferences from very few examples)<p>- It is still a connectionist approach (typical probabilistic programming is great if you have a lot of insight into the model, but not if you&#x27;re trying to solve a general case... unless you&#x27;re doing program induction but you need to represent programs...)<p>- It doesn&#x27;t involve MCMC sampling for inference, because that&#x27;s too slow or even intractable.<p>Some type of variational program induction where programs are represented as differentiable neural networks would be in that corner. Or it could be something totally different, but speculating is fun.