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.
- Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37. Retrieved from: http://www.aaai.org/ojs/index.php/aimagazine/article/download/1230/1131/
- Goldbloom, A. (2016). The jobs we’ll lose to machines – and the ones we won’t. TED Talks. Retrieved from https://www.youtube.com/watch?v=gWmRkYsLzB4
- Lapowsky, I. (2014). 4 Big opportunities in Artificial Intelligence. Inc.com. Retrieved from http://www.inc.com/issie-lapowsky/4-big-opportunities-artificial-intelligence.html
- Maycotte, H. O. (2014). Why Big Data and AI needs each other — and you need them both. Forbes. Retrieved from http://www.forbes.com/sites/homaycotte/2014/12/16/why-big-data-and-ai-need-each-other-and-you-need-them-both/#721461dd2dc8
- Meetoo, A. (2016). Jobs of the future and how we can prepare for them. TEDx Talks. Retrieved from https://www.youtube.com/watch?v=OI5eO2CSib8
- Minelli, M., Chambers, M., & Dhiraj, A. (2013).Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. VitalBook file.
- Power, B. (2015). Artificial Intelligence is almost ready for business. Harvard Business Review. Retrieved fromhttps://hbr.org/2015/03/artificial-intelligence-is-almost-ready-for-business
- Spaulding, S. (2013). How AI is changing the way we view intelligence, the world, and ourselves. TEDx Talks. Retrieved from https://www.youtube.com/watch?v=qXtCApIxap8