I strongly agree. There are two claims here. The weak one is that, if you hold complexity constant, directed acyclic graphs (DAGs; Bayes nets or otherwise) are not necessarily any more interpretable than conventional NNs because NNs are DAGs at that level. I don’t think anyone who understands this claim would disagree with it.
But that is not the argument being put forth by Pearl/Marcus/etc. and arguably contested by LeCun/etc.; they claim that in practice (i.e., not holding anything constant), DAG-inspired or symbolic/hybrid AI approaches like Neural Causal Models have interpretability gains without much if any drop in performance, and arguably better performance on tasks that matter most. For example, they point to the 2021 NetHack Challenge, a difficult roguelike video game where non-NN performance still exceeds NN performance.
Of course there’s not really a general answer here, only specific answers to specific questions like, “Will a NN or non-NN model win the 2024 NetHack challenge?”
I strongly agree. There are two claims here. The weak one is that, if you hold complexity constant, directed acyclic graphs (DAGs; Bayes nets or otherwise) are not necessarily any more interpretable than conventional NNs because NNs are DAGs at that level. I don’t think anyone who understands this claim would disagree with it.
But that is not the argument being put forth by Pearl/Marcus/etc. and arguably contested by LeCun/etc.; they claim that in practice (i.e., not holding anything constant), DAG-inspired or symbolic/hybrid AI approaches like Neural Causal Models have interpretability gains without much if any drop in performance, and arguably better performance on tasks that matter most. For example, they point to the 2021 NetHack Challenge, a difficult roguelike video game where non-NN performance still exceeds NN performance.
Of course there’s not really a general answer here, only specific answers to specific questions like, “Will a NN or non-NN model win the 2024 NetHack challenge?”
That was a good succinct statement and useful links, thanks.