How Machine Learning is Evolving

The problems that can be solved by Machine Learning technology are virtually limitless. Businesses are already benefiting from its prediction power, as well as its ability to personalize customer experience in unprecedented ways. Although the technology might seem like it’s too expensive for the average business to adopt, its cost-cutting potential is simply profound.

Machine Learning is a subfield of Artificial Intelligence that is powered by algorithms. Its ability to recognize patterns allows it to predict possible outcomes and even improve upon its own programming. The technology is often broken down into the following categories:

• Supervised learning: The process of training machines to learn by example, by providing them with points of reference against which to measure their own input-output relationships.


• Unsupervised learning: The process by which machines independently detect patterns among data sets, unassisted by reference data provided through human intervention.


• Semi-supervised learning: A combination of the previous two methods where the former is used to assist the latter in pattern detection among unlabeled data.


• Reinforcement learning: A training system that provides the machine with positive feedback upon successfully achieved tasks so that it is encouraged to make similar or identical decisions.

Early History of Machine Learning

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.

Around the 1930s and 1940s, a computer scientist by the name of Alan Turing began laying the groundwork for Machine Learning technology by theorizing how computer systems could mimic human intelligence and decision making. By the 1950s, this theoretical human brain simulation started to materialize in the form of what is often called the “Artificial Neural Network.” And with the subsequent rise of digital technology and the internet, data has gradually become a highly commodified resource (with some even calling it the “new oil” of the 21st century) that is driving Machine Learning innovation.

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). [Watch this video to know how businesses can deliver improved services]

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 AI

With 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 (just to name a few).

While Machine Learning (and to a slightly lesser extent, deep learning) products are already widely used, the greater field of Artificial Intelligence is only beginning to expand into mainstream use. General AI (the hypothetical evolution of AI into perfect human brain simulation and beyond) 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 (to anticipate what might happen). 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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In order to take advantage of Machine Learning, it’s important to understand its history as well as the progress that it has made over the years. If companies can leverage the benefits of Machine Learning early on, then they can stay ahead of the competition.