Sublime

Microsegments are based on machine learning and clustering algorithms and can be much larger in number than traditional segments.

Hal Varian noted this problem in 1995, writing in a paper called “Economic Mechanism Design for Computerized Agents” that “to function effectively, a computerized agent has to know a lot about its owner’s preferences: e.g., his maximum willingness-to-pay for a good. But if the seller of a good can learn the buyer’s willingness-to-pay, he can make

ensemble methods, commodity modeling, and analysis of text data.

As knowledge grows, scientists specialize ever more narrowly, but no one is able to put the pieces together because there are far too many pieces. Scientists collaborate, but language is a very slow medium of communication. Scientists try to keep up with others’ research, but the volume of publications is so high that they fall farther and farther

“The algorithms know you better than you know yourself,”

So if instead of trying to fit a straight line to all the data, you just fit it to the points near the query point, you now have a very powerful nonlinear regression algorithm.

A very advanced way of working with segmentation is to have algorithms put together behavioural clusters dynamically. Within retail, this typically translates to ‘people who buy certain products’.

The Master Algorithm is the unifier of machine learning: it lets any application use any learner, by abstracting the learners into a common form that is all the applications need to know.

Pedro Domingos so memorably put it, “People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.”