10 Algorithm Categories for A.I.

10 Algorithm Categories for A.I., Big Data, and Data Science

10 Algorithm Categories for A.I., Big Data, and Data Science

This article was written by a Data-centric Executive Management, Chris Pehura. Chris is a management consultant with a data emphasis helping Fortune 100/1000 companies strategically evolve and reinvent their businesses to maximize their revenue growth.

Are algorithms taking over our jobs? Yes, yes they are… and that a good thing.

An algorithm is a series of steps with rules that help us solve problems and accomplish goals. And when we structure these steps and rules the right way we can automate the algorithm to establish Artificial Intelligence (A.I.). And it is this A.I. that helps us do our analytical heavy lifting so we can focus our time on doing the things that we’re good at… the things we were hired to do.

A.I. is changing our jobs, our work styles, and our business cultures. A.I. helps us discover and focus on the key subject matter expertise that makes our human capital good, really good at what they do. But using A.I. in the work place does get complicated. It gets complicated because there are different levels of algorithms used to implement A.I., each varying in their use and impact. To better balance our human capital with our A.I. capital, here are the top 10 algorithm categories used to implement A.I., Big Data, and Data Science.

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Here are the 5 Algorithm Categories for A.I., Big Data, and Data Science:

1 – Crunchers. These algorithms use small repetitive steps guided with simple rules to number a complex problem. We give these algorithms the data, and they come back with an answer.

 



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