Why machine learning struggles with causality
The article makes a big unfounded assertion in one of the first few sentences "We learn them at a very early age, without being explicitly instructed by anyone and just by observing the world." - we do not learn these inferences "just by observing the world", we learn these inferences by acting on the world and observing the results, and arguably they are simply not learnable by pure observation alone. There's some theoretical grounding for that (IIRC Judea Pearl's book Causality might be a good source for that) and some experimental grounding for that (e.g. the two kitten experiment for which I can't quickly find the actual paper but it's described at https://io9.gizmodo.com/the-seriously-creepy-two-kitten-expe... ), also other fields like medicine have a good understanding of limitations of what knowledge can be gained from purely observational studies versus experimental interventions. So it seems a bit misleading to write a whole article about "why machine learning struggles with causality" without even addressing the key difference of learning from observations vs learning from interaction, which IMHO is a much more fundamental obstacle than everything the article menstions.