A Closer Look at Machine Learning and Its Applications

In recent years, advances in the field of artificial intelligence (AI) have given rise to machine learning - a form of autonomous data analysis that allows computers to draw upon historical data in order to predict future outcomes. In theory, this gives organizations the ability to grow knowledge exponentially, reduce risk, make smarter decisions, and identify opportunities indiscernible to the human eye. In this article, we’ll explore the applications of machine learning within a working context and examine how it might affect the businesses of tomorrow.

Applications

In its short lifetime, machine learning has already found use across multiple sectors:

Chatbots are growing increasingly intuitive, learning more about human language with each conversation. Already, we can see their use within a domestic context as Alexa, Google Assistant, Siri, Watson Assistant, and other programs grow increasingly responsive.

For sales teams, machine learning has usage within Customer Churn Modeling, as it identifies when customer loyalty is starting to diminish and helps identify strategies to resolve the issue.

Risk management has benefitted from machine learning as algorithms are used to decipher behavior patterns, detecting any unusual anomalies. The ability of machine learning algorithms to adapt over time makes them particularly adept at defending against fraud, malware or hacking.

When it comes to operational matters we can see the use of machine learning to optimize processes. Process mining uses data to discover, validate and improve workflows in order to benefit a business holistically. Not only does this help companies to increase their sales, it also mitigates risk and helps us to uncover new opportunities.

Future Applications

We know the possibilities for machine learning are limitless, but what does the future of exponential information growth actually look like? For the medical sector, it could mean huge leaps in prediction and treatment as computers grow more adept at prognosticating and therefore preventing disease.

New discoveries in ‘deep learning’ (a subfield of the science) also mean that neural networks are expanding and are capable of accessing and learning from increasingly large data sets. For businesses that can get their hands on this early technology, growth could be accelerated tremendously.

This also rings true for quantum computing, which again boosts machine learning capabilities. Quantum computing allows the performance of simultaneous multi-state operations to enable faster data processing. Google’s own quantum processor, for example, is capable of performing tasks in 200 seconds that would take the world’s fastest supercomputer 10,000 years to complete. Although not yet available, quantum computers have received heavy investment and may be commercially available within a decade.

Learning About Machine Learning

If you’re interested in studying machine learning, you’ll find no shortage of opportunities to do so, and in ways that might not disrupt your current schedule. This online master’s in data science program, for example, could help you to develop your skills in machine learning, as well as data mining, data management, and database applications - all remotely. Just be certain to read testimonials from previous students, and don’t be afraid to reach out for more information on their prospectus. Alternatively, there are plenty of Youtube channels that provide educational video content to help new developers build on their knowledge of the science.

The future of Artificial Intelligence is both infinitely mysterious and exciting. With so many issues rising in prevalence (both in the working world and the external one), machine learning could provide key solutions and insights at a rate faster than any other in human history.

Kevin is an experienced web developer with extensive experience working with Ruby on Rails since 2012 and clients based across the United States. Learn more about his services at: www.kevinhq.com

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