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Monday, August 22, 2016

Algorithm/Problem Categories for AI and Data Science

Like this statement of solution categories:  10 Algorithm Categories for A.I., Big Data, and Data Science by Chris Pehura:  

Crunchers:  These algorithms use small repetitive steps guided with simple rules to number crunch a complex problem ... 
Guides: These algorithms guide us on how to best navigate a policy, process, or workflow ... 
Advisors:  These algorithms advise us on our best options ... 
Predictors: These algorithms predict future human behaviors and events by using small repeatable decisions ... 
Tacticians:  These algorithms tactically anticipate short-term behaviors and react accordingly. ... 
Supervisors: These algorithms strategically anticipate behaviors and plan accordingly. ... 
Lifters:These algorithms help us by automating our mundane and repetitive work freeing us to do what we’ve been hired to do. ...  
Partners: These algorithms bring out the best in us. They have a large amount of subject matter expertise in our area allowing us to be more productive and more focused ...  
Okays:  These algorithms have subject matter expertise in multiple areas allowing groups of us to do all our foundational analytical work ...
Supervisors:  These algorithms have key subject matter expertise for how our business works. They manage us and our efforts  ... 

Details are debatable, I would have kept it simpler, five would have done it sufficiently, and there will always be overlaps. but does make you think of the activity involved, and data and methods needed.   plus value represented.   Good descriptions at the links, with much further supporting information.

Original article with much more detail.  http://bizcatalyst360.com/10-algorithm-categories-for-a-i-big-data-and-data-science

DSC Quote, with more background:  http://www.datasciencecentral.com/profiles/blogs/10-algorithm-categories-for-a-i-big-data-and-data-science

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