Machine Learning is one of human civilization’s most recent forms of automation, which has been a part of industrial manufacturing and production since at least the Industrial Revolution of the 19th and 20th centuries and has existed as a concept since ancient Greek mythology.
Automation dramatically increased productivity in manufacturing,
particularly among automotive assembly lines during the 20th century, and Machine Learning seeks a similar effect on several industries.
Big Data
The commodification of data has motivated businesses of all types to collect, store and process it in many new ways. The phrase “Big Data” has become commonly used to describe the various industries that are united by their dependence on and proliferation of electronic data as a commodity. Big Data is defined by what is often referred to as the “3 Vs”: volume (the massive amounts of data processed), velocity (the accelerating pace of data transmission), and variety (the growing diversity in forms of data).
The ability to capture, store, transmit, and process massive and diverse sets of data is highly valuable to businesses in the modern ecosystem of an increasingly digitized economy. And the more companies can harness and control Big Data, the easier it will be to leverage Machine Learning in exponentially productive ways.
Deep Learning and AIWith the advancement of Machine Learning algorithms, neural networks can now be organized into several layers that form complex hierarchies. These hierarchies translate into information that allows for decision making and understanding on a much more profound level than more primitive Machine Learning models. Thus far, implementations of deep learning have included self-driving vehicles, black and white image colorization, and precision medicine.
While Machine Learning products are already widely used, the greater field of Artificial Intelligence is only beginning to expand into mainstream use. General AI might exist at some point on the horizon, but for now, businesses are most concerned with Machine Learning’s ability to help them understand the world and make better decisions.
Conclusion
The field of business intelligence has adopted Machine Learning over time first for its descriptive power (to explain things that happen) and then for its predictive power. The next stage of Machine Learning for business will come in the form of prescriptive analytics, which will inform companies what, in fact, they should actually do in order to achieve the most desirable outcomes.
Aloha Technology is at the forefront of this innovation. The evolution of Machine Learning is empowering companies worldwide with the ability not only to understand and even predict information and phenomena but also with the best possible courses of action. To neglect Machine Learning as an essential component to modern business intelligence is to forfeit future success to the competition.
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