Comment by opticalflow

I hate the term "AI" (even though I am CTO of a company with "AI" in its name, but since we use machine learning/DCNNs in our systems, it’s very trendy). The problem with "AI" is the "intelligence" part. Intelligence is a construct like "porn", like in the famous words of Justice Stewart about defining the latter "...but I know it when I see it". At best, it's very ambiguous -- and misleading at worst. They have been many attempts to quantitatively and qualitatively define intelligence -- none of which I find particularly satisfying and neither do any three given scientists in a room agree on a single interpretation. My problem with TFA is that it is comparing apples to oranges; deep convolutional networks are very different tools useful for a subset of problems than the ones using Bayesian inference and other statistical methods. Brute force methods like image morphology, object counting, and transforms are useful for even yet an entire Other set of problems. To say that one has displaced another is an error, in fact in most useful, modern, production systems a combination of all three is utilized, each to their purpose. To make direct comparisons between them while implying the historical decisions to use one or the other are due to Moore's Law is a false equivalence.

I clearly need my morning coffee.