Data Tools: Artificial Intelligence

Big data Analytics and Artificial Intelligence

Artificial Intelligence (AI) is an embedded technology, based off of the current infrastructure (i.e. supercomputers), big data, and machine learning algorithms (Cyranoski, 2015; Power, 2015). Though previously, AI wasn’t able to come into existence without the proper computational power that is provided today (Cringely, 2013).  AI can make use of data hidden in “dark wells” and silos, where the end-user had no idea that the data even existed, to begin with (Power, 2015).  The goal of AI is to use huge amounts of data to draw out a set of rules through machine learning that will effectively replace experts in a certain field (Cringely, 2013; Power, 2015). Cringely (2013) stated that in some situations big data can eliminate the need for theory and that AI can aid in analyzing big data where theory is either lacking or impossible to define.

AI can provide tremendous value since it builds thousands of models and correlations automatically in one week, which use to take a few quantitative data scientist years to do (Dewey, 2013; Power, 2015).  The thing that has slowed down the progression of AI in the past was the creation of human readable computer languages like XML or SQL, which is not intuitive for computers to read (Cringely, 2013).  Fortunately, AI can easily use structured data and now use unstructured data thanks to everyone who tags all these unstructured data either in comments or on the data point itself, speeding up the computational time (Cringely, 2013; Power, 2015).  Dewey (2013), hypothesized that not only will AI be able to analyze big data at speeds faster than any human can, but that the AI system can also begin to improve its search algorithms in phenomena called intelligence explosion.  Intelligence explosion is when an AI system begins to analyze itself to improve itself in an iterative process to a point where there is an exponential growth in improvement (Dewey, 2013).

Unfortunately, the rules created by AI out of 50K variables lack substantive human meaning, or the “Why” behind it, thus making it hard to interpret the results (Power, 2015).  It would take many scientists to analyze the same big data and analyze it all, to fully understand how the connections were made in the AI system, which is no longer feasible (Cringely, 2013).  It is as if data scientist is trying to read the mind of the AI system, and they currently cannot read a human’s mind. However, the results of AI are becoming accurate, with AI identifying cats in photographs in 72 hours of machine learning and after a cat is tagged in a few photographs (Cringely, 2013). AI could be applied to any field of study like finance, social science, science, engineering, etc. or even play against champions on the Jeopardy game show (Cyranoski, 2015; Cringely, 2013; Dewey, 2013; Power, 2015).

Example of artificial intelligence use in big data analysis: Genomics

The goal of AI use on genomic data is to help analyze physiological traits and lifestyle choices to provide a dedicated and personalized health plan to treat and eventually prevent disease (Cyranoski, 2015; Power, 2015).  This is done by feeding the AI systems with huge amounts of genomic data, which is considered big data by today’s standards (Cyranoski, 2015). Systems like IBM’s Watson (an AI system) could provide treatment options based on the results gained from analyzing thousands or even millions of genomic data (Power, 2015).  This is done by analyzing all this data and allowing machine learning techniques to devise algorithms based on the input data (Cringely, 2013; Cyranoski, 2015; Power, 2015).  As of 2015, there is about 100,000 individual genomic data in the system, and even with this huge amounts of data, it is still not enough to provide the personalized health plan that is currently being envisioned based on a person’s genomic data (Cyranoski, 2015).  Eventually, millions of individuals will need to be added into the AI system, and not just genomic data, but also proteomics, metabolomics, lipidomics, etc.


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