[timboy03]

One tricky thing about Bongard problems is that for any given problem there are likely many different rules that could distinguish the six positive examples from the six negative examples.

For example, maybe a problem that is "really" about circles vs. triangles also happens to have more black pixels in the left images than in the right images.

A key skill in solving these problems is not just to find a compact and discriminating description, but to find such a description that is also one that a human Bongard problem designer would be likely to think was a cool and elegant puzzle that needs an "Aha" moment to recognize. If you find such a description, then you're very likely to be right.

I suspect that that last part (recognizing when you have found a solution that is pleasing enough to be the answer) is likely to be the biggest challenge for ML-based approaches to Bongard problems