Deep learning - deeper flaws?
In this post I summarise four lines of argument for why we should be skeptical about the potential of deep learning in its current form. I am fairly confident that the next breakthroughs in AI will come from some variety of neural network, but I think several of the objections below are quite a long way from being overcome. Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution - Pearl, 2018 Pearl describes three levels at which you can make inferences: association, intervention, and counterfactual. The first is statistical, identifying correlations - this is the level at which deep learning operates. The intervention level is about changes to the present or future - it answers questions like "What will happen if I do y?" The counterfactual level answers questions like "What would have happened if y had occurred?" Each successive level is strictly more powerful than the previous one: you can't figure out what the effects