The original literal Moore's Law was just that the number of transistors would double every two years. Physics limits that so the continuation of progress requires thinking outside the box. Below is a list of technologies that can help that:
Remember the "business rules" craze of the early 2000s? They were popular especially with mortgage lenders. An example ILOG JRules decision table for mortgage lending.
No doubt you've encountered the image below from Gizmodo in some PowerPoint somewhere this year.
But that same PowerPoint likely didn't bother to answer the next logical question:
How to get to causality?
Somewhat of a sequel to my earlier post on causality, where do hypotheses come from?
The ideal hypothesis:
We see it all the time when reading scientific papers, "controlling for confounding variables," but how do they do it? The term "quasi-experimental design" is unknown even to many who today call themselves "data scientists." College curricula exacerbate the matter by dictating that probability be learned before statistics, yet this simple concept from statistics requires no probability background, and would help many to understand and produce scientific and data science results.
When constructing a recommender system and selecting algorithms, there is more to consider than just "accuracy". The most "accurate" recommender system would recommend the same items (whether those "items" are books, websites, options available to a software end user, etc.) over and over again, focused on a narrow topic area, and ignorant of context. Below are features of various recommender systems that, if combined, would perhaps form the ideal recommender system to produce "useful" rather than "accurate" results.
