What Is Machine Learning?

For the past few millennia, learning has primarily been something we encourage in children. We send them to school for a dozen years, so they can learn all the necessary rules and experiential knowledge that will help them as adults. These days, however, we’re starting to do the same thing to machines.

As our technologically driven culture produces more and more data, someone has to figure out how to parse these exabytes for useful business intelligence. That someone is likely to be a machine. A combination of big data and machine learning is critical to a company’s successful transition into a data-rich age.

How to Teach a Machine?

Most learning falls into two categories: decision-making algorithms and rule-based processing. The tricky thing that humans do is learn how to generate new rules based on existing rules, expanding their corpus of rule-based learning. While we base many of our actions on decision-making algorithms, computers can actually process data faster than we can with regard to these algorithms. With big data and machine learning, data scientists are learning how to process raw data much faster than individuals can. The sheer volume — and availability — of data presents tremendous potential for machine learning.

For instance, when we teach a child about wombats, we might show the child pictures of a wombat. We may only have a handful of picture books with wombats, and we probably do not have a live wombat out in the yard for the child to study. Since the child is also seeing pictures of squirrels, airplanes, toasters, Mom’s face, and the like, it will take some time before the child can successfully identify a wombat when presented with one.

On the other hand, after feeding a computer thousands of digital pictures of wombats, the computer can, with a high degree of probability, tell the difference between a wombat and a marmot, even if this form of machine learning means simply labeling the marmot “not a wombat.”

Big data and machine learning allow businesses to sift through volumes of data and rapidly generate business intelligence and insights that would previously have taken too long to be useful. Machine learning’s predictive models enable a company to simultaneously consider multiple strategic models without the cost and resources involved in simulating the various projections.

Built for the Future

Machine learning creates opportunities for organizations to improve efficiency. By reducing redundancies across divisions, big data and machine learning can streamline an organization and reduce internal costs. Data-crunching algorithms can identify gaps as well as overages in the organization’s resource and production pipeline.

Machine learning gets better the more data you provide. As an organization embraces big data and machine learning, localized silos of disparate data sets stored on spreadsheet on discrete machines will vanish, reducing weaknesses within an organization’s infrastructure.

We are not building sentient computers yet, but we are learning how to use computational systems to combine big data and machine learning for complex calculations applied to highly diverse and deep data sets. In fact, machine learning is already sneaking into your everyday life. Every time you receive a recommendation from an online service such as Amazon, Hulu or your favorite virtual shoe store, that recommendation comes from machine learning. When you get an email from your credit card company alerting you to unusual activity, like business transactions at a Tiki bar in Barbados, a machine learning algorithm is working behind the scenes to keep your account safe. In fact, when we buy self-driving cars in the next five years, it will be because machine learning made the roads safe enough for us to go hands-free.

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