As clever as machine learning is, there's one common problem: you frequently have to train the AI on thousands or even millions of examples to make it effective. What if you don't have weeks to spare? If Gamalon has its way, you could put AI to work almost immediately. The startup has unveiled a new technique, Bayesian Program Synthesis, that promises AI you can train with just a few samples. The approach uses probabilistic code to fill in gaps in its knowledge. If you show it very short and tall chairs, for example, it should figure out that there are many chair sizes in between. And importantly, it can tweak its own models as it goes along -- you don't need constant human oversight in case circumstances change.