Taming the Machine (Learning)

The one thing everyone agreed on was the need for Decision Velocity. manmachine.jpg

Increasing it, that is. Make decisions faster.

Chris Taylor says that there is simply too much data out there for humans to use effectively. So bring in the machines. Machine Learning is where raw computing power is let loose on stacks of data in order to find useful patterns there.

Fair point. The machines are coming.

Opher Etzion points out the issues with relying on Machine Learning alone – cannot be done in real-time; cannot scale since every use case needs its own ‘learning’; and, finally it needs to be supplemented by humans who can provide a future vision that machines that learn from past data cannot.

Sound assessment of the current situation. No doubt the technology to support real time machine learning will improve – using Event Processing infrastructure as pointed out by Opher. Better tools will emerge that allow generalized learning across multiple use cases – promoting reusability and thereby providing scalability.

The open question is how to get humans to participate in machine learning.

This is where Decision Management Technology comes in. It includes Advanced Analytics like Machine Learning as well as Business Rules Management Systems (BRMS). The latter is where Human Expertise can be explicitly stated and managed. So, ideally we need to create a Decision Service black box that holds the Machine Learning model surrounded by Human Expertise expressed as business rules. And viola, this Decision Service can now automate most operational decisions – and you have increased Decision Velocity. Building Decision Management systems does require explicit decision modeling and the need to start there first.

Let us tame the Machine by giving it business rule Prime Directives.