"I don't agree that big data poses unique challenges" Google does. IBM does too (Watson). Unique algorithms on unique hardware for the sake of ML. I was at a ma

"I don't agree that big data poses unique challenges" Google does. IBM does too (Watson). Unique algorithms on unique hardware for the sake of ML. I was at a machine learning conference with a speaker from Google (soz can;t remember who). They said they are not interested in any machine learning with complexity above O(nlogn) or that has to visit a training point more than once. That's a hell of alot of literature in the bin for the sake of big data (e.g. SVM, back propagation NNs). So the problems there are solving do have novel constraints (even if the overall function they are trying to approximate is the same) Yeah I agree probability is a logical inference scheme. I was just commenting on the kind of logic people think of on the term "LOGIC". That's classical, but you can also argue any system with symbols is logic. I was just trying to say that the Bayesian people think differently to the formal logic people. Thats what I meant by culture. They are very different sets of people normally (exception, see below) Bayesian Logic Networks:- http://ias.cs.tum.edu/_media/spezial/bib/jain09blns.pdf http://ias.cs.tum.edu/_media/spezial/bib/jain09blns.pdf probabilistic inference upon hard logic constraints. != Bayesian Belief Networks (standard Bayesian formulation)