My intuition is that the approach Gamalon is using has more potential than deep learning.

I've been playing with the concept for a while however failing to get any good results. Debugging probabilistic programs is so damn long and difficult since bugs can show as subtle biases in output instead of clear cut deviations. (I described my approach here: https://www.quora.com/What-deep-learning-ideas-have-you-trie... ) For me, this is just a hobby.

Joshua B. Tenenbaum et al.'s group seem to name their approach program induction, I had called mine Bayesian Auto Programming. I see you are calling yours Bayesian Program Synthesis. Clearly we have similar intuition about the essence of the solution to AI.

I wish you better luck than me.