[cerrelio]

> support vector machines and Bayesian learning have been around since the 70s/80s (ninja edit: SVM's since 1963! Markov Chains 1950s, Bayesian Learning/Pattern recognition sine the 1950's), but adoption has been slow due to the nature of business, which is now drooling over it since neural networks beat a few algorithms.

This is one of the things I find hardest about convincing managers and leads of. They think things like CRFs and Markov models are "new" methods and too risky. So they opt for explicit rule-based systems that use old search methods (e.g. A*, grid search), which hog tons of memory and processor. Those methods rarely ever work on interesting problems of the modern day.

They can understand the rule-based methods easily. They have a hard time leaping to "the problem is just a set of equations mapping inputs to outputs, and the mapping is found by an optimization method."

[godmodus]

I explain it using the infinitesimal method, which if done right using the hill climbing metaphor, often delivers. But it does take away the magic of "wooo, neural" :p