Machine learning is not the same thing that it was once ten years ago, when traditional machine learning systems were able to take care of basic math, record keeping and data transmission, and had built-in algorithms to predict future behavior (“Machine Learning: What it is and why it Matters”).
These traditional systems were used in a multitude of companies, as software that could simply “get the job done”, but when the internet became a world-wide phenomenon, and the exchange of data between servers grew enormous, these simple and short-sighted systems no longer sufficed (“Machine Learning: What it is and why it Matters”).
As consumers became more educated, they started taking advantage of technology for their personal means. This reduced paperwork and increased the need for systems that could understand and quickly process dramatically changing information. In essence, mankind helped themselves to the advances in technology, and the world was able to move forward, and favor new technology over old technology.
over old money
The demand for these systems was so great, that “machine learning”, as the leading companies at that time knew it, underwent a major transformation, and is today, a work in progress, as engineers are working hard on FBLearner Flow, and Google Machine Learning (Dunn, 2016).
Machine learning today is the ability of systems to make predictions based on data and conduct deep learning. Deep learning uses neural networks to learn complex relationships between inputs and outputs and data activities (“The Evolution of Machine Learning”, 2017). Deep learning makes it easier, for machine learning to make real-time predictions, saving leading industry professionals thousands of dollars in time and money.
Machine learning today can find glitches in real-time data that traditional systems lack the ability to do and can prevent terrorism.
Machine learning can also help sales and marketing professionals. Applications and websites cover the world-wide web today, and user experience engineers are constantly working on these sites, to attract more users, and get more user data.
Big data is growing exponentially, on the web, and has the capacity to help the health-care, telecommunications, government, finance, e-commerce, fashion industries and etc (“Machine Learning: What it is and why it Matters”). Big data will be explosive in 2018 and is getting hotter.
Unwittingly, mankind has almost no control.
“So many completely automatic decisions are taking place all around us, in fact, that the economist Brian Arthur use the image of a “second economy,” in which transactions happen with no human involvement in a “vast, silent, connected, unseen, and autonous” fashion. Over time, this automatic second economy is encroaching into the familiar human-mediated one, with algorithms taking over from experts and HiPPOs. As more and more of the world’s information becomes digitized, it provides a plethora of data for improving decisions making, converting intuition into data-driven decision making (McAfee & Brynjolfsson, 2017).
Much of the world has become induced into this “second-economy,” and is like a big computer, processing, analyzing and working behind the scenes, while the other half of the world, conducts business sleepily (McAfee & Brynjolfsson, 2017).
Dong, Catherine, “The Evolution of Machine Learning.” TechCrunch. Retrieved from https://techcrunch.com/2017/08/08/the-evolution-of-machine-learning/
Dunn, Jeffrey, “Introducing FBLearner Flow: Facebook’s AI backbone.” May, 9, 2016. Retrieved from https://code.facebook.com/posts/1072626246134461/introducing-fblearner-flow-facebook-s-ai-backbone/.
“Machine Learning: What it is and why it Matters.” Retrieved from https://www.sas.com/en_us/insights/analytics/machine-learning.html. Accessed on September 25, 2017.
McAfee, Andrew & Brynjolfsson. (2017). “Harnessing Our Digital Future: Machine Platform Crowd.” New York: W. W. Norton & Company.