Data Tools: Artificial Intelligence and Data Analytics

Machine learning, also known as Artificial Intelligence (AI) adds an intelligence layer to big data to handle the bigger sets of data to derive patterns from it that even a team of data scientist would find challenging (Maycotte, 2014; Power, 2015). AI makes their insights not by how machines are programmed, but how the machines perceive the data and take actions from that perception, essentially conducting self-learning (Maycotte, 2014).  Understanding how a machine perceives the big dataset is a hard task, which also makes it hard to interpret the resulting final models (Power, 2015).  AI is even revolutionizing how we understand what intelligence is (Spaulding, 2013).

So what is intelligence

At first, doing arithmetic was thought of as a sign of biological intelligence until the invention of the digital computers, which then shift biological intelligence to be known for logical reasoning, deduction and inferences to eventually fuzzy logic, grounded learning, and reasoning under uncertainty, which is now matched through Bayes Nets probability and current data analytics (Spaulding, 2013). So as humans keep moving the dial of what biological intelligence is to a more complex structure, if it requires high frequency and voluminous data, then it can be matched by AI (Goldbloom, 2016).  Therefore, as our definition of intelligence expands so will drive the need to capture intelligence artificially, driving change in how big datasets are analyzed.

AI on influencing the future of data analytics modeling, results, and interpretation

This concept should help revolutionize how data scientists and statisticians think about which hypotheses to ask, which variables are relevant, how do the resulting outputs fit in an appropriate conceptual model, and why do these patterns hidden in the data help generate the decision outcome forecasted by AI (Power, 2015). To figure out or make sense of these models would require subject matter experts from multiple fields and multiple levels of employment hierarchy analyzing these model outputs because it is through diversity and inclusion of thought will we understand an AI’s analytical insight.

Also, owning data is different from understanding data (Lapowsky, 2014). Thus, 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, which allows for a data scientist to gain a better understanding of their datasets (Lapowsky, 2014; Power, 2015).

AI on generating datasets and using data analytics for self-improvements

Data scientists currently collected, preprocess, process and analyze big volumes of data regularly to help provide decision makers with insights from the data to make data-driven decisions (Fayyad, Piatetsky-Shapiro, & Smyth, 1996).  From these data-driven decisions, data scientist then measure the outcomes to prove the effectiveness of their insights (Maycotte, 2014).   This analysis on how the results of data-driven decisions, will allow machine learning algorithms to learn from their decisions and actions to create better ways of searching for key patterns in bigger and future datasets. This is an ability of AI to conduct self-learning based off of the results of data analytics through the use of data analytics (Maycotte, 2014). Meetoo (2016), stated that if there is enough data to create accurate rules it is enough to create insights; because machine learning can run millions of simulations against itself to generate huge volumes of data to which to learn from.

AI on Data Analytics Process

AI is a result of the massive amounts of data being collected, the culmination of ideas from the most brilliant computer scientists of our time, and on an IT infrastructure that didn’t use to exist a few years ago (Power, 2015).  Given that data analytics processes include collecting data, preprocessing data, processing data, and analyzing the results, any improvements made for AI on the infrastructure can have an influence on any part of the data analytics process (Fayyad et al., 1996; Power, 2015).  For example, as AI technology begins to learn how to read raw data to turn that into information, the need for most of the current preprocessing techniques for data cleaning could disappear (Minelli, Chambers, & Dhiraj, 2013). Therefore, as AI begins to advance, newer IT infrastructures will be dreamt up and built such that data analytics and its processes can now leverage this new infrastructure, which can also change the way on how big datasets are analyzed.

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Data Tools: Artificial Intelligence and Decision Making

“Machines can excel at frequent high-volume tasks. Humans can tackle novel situations.” – Anthony Goldbloom

Jobs today will look drastically different in 30 years from now (Goldbloom, 2016; McAfee, 2013).  Artificial intelligence (AI) works on Sundays, they don’t take holidays, and they work well at high frequency and voluminous tasks, and thus they have the possibility of replacing many of the current jobs of 2016 (Goldbloom, 2016; Meetoo, 2016).  AI has been doing things that haven’t been done before: understanding, speaking, hearing, seeing, answering, writing, and analyzing (McAfee, 2013). Also, 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). Eventually, AI and machine learning will be commonly used as a tool to augment or replace decision makers.  Goldbloom (2016) gave the example that a teacher may be able to read 10,000 essays or an ophthalmologist may see 50,000 eyes over a 40-year period; whereas a machine can read millions of essays and see millions of eyes in minutes.

Machine learning is one of the most powerful branches to AI, where machines learn from data, similar to how humans learn to create predictions of the future (Cringely, 2013; Cyranoski, 2015; Goldbloom, 2016; Power, 2015). It would take many scientists to analyze a big dataset in its entirety without a loss of memory such that to gain insights and to fully understand how the connections were made in the AI system (Cringely, 2013; Goldbloom, 2016). This is no easy task because the eerily accurate rules created by AI out of thousands of variables can lack substantive human meaning, making it hard to interpret the results and make an informed data-driven decision (Power, 2015).

AI has been used to solve problems in industry and academia already, which has given data scientist knowledge on the current limitations of AI and whether or not they can augment or replace key decision makers (Cyranoski, 2015; Goldbloom, 2016). Machine learning and AI does well at analyzing patterns from frequent and voluminous amounts of data at faster speeds than humans, but they fail to recognize patterns in infrequent and small amounts of data (Goldbloom, 2016).  Therefore, for small datasets artificial intelligence will not be able to replace decision makers, but for big datasets, they would.

Thus, the fundamental question that decision makers need to ask is how is the decision reduced to frequent high volume task and how much of it is reduced to novel situations (Goldbloom, 2016).  Thus, if the ratio is skewed on the high volume tasks then AI could be a candidate to replace decision makers, if the ratio is evenly split, then AI could augment and assist decision makers, and if the ratio is skewed on novel situations, then AI wouldn’t help decision makers.  They novel situations are equivalent to our tough challenges today (McAfee, 2013).

Finally, Meetoo (2016), warned that it doesn’t matter how intelligent or strategic a job could be, if there is enough data on that job to create accurate rules it can be automated as well; because machine learning can run millions of simulations against itself to generate huge volumes of data to learn from.  This is no different than humans doing self-study and continuous practice to be subject matter experts in their field. But people in STEAM (Science, Technology, Engineering, Arts, and Math) will be best equip them for the future world with AI, because it is from knowing how to combine these fields that novel, infrequent, and unique challenges will arise that humans can solve and machine learning cannot (Goldbloom, 2016; McAfee, 2013; Meetoo, 2016).

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