Machine learning is a technique that helps to teach computers how to do what comes naturally to humans and animals, like learning from experience. Machine learning uses computational methods to “learn” information from data directly without relying on a predetermined equation as a model. These algorithms improve their performance as the number of samples become available from learning increases. Here are a few reasons machine learning is so essential to the world.

Why Does Machine Learning Matter?

Machine learning has become an essential technique for solving problems in areas with the rise in big data. Some necessary methods include:

Computational Finance – used for credit scoring and algorithmic trading.

Image Processing and Computer Vision – used for face recognition, motion detection, and object detection

Computational Biology – used for tumor detection, drug discovery, and DNA sequencing

Energy Production – used for price and load forecasting

Automotive, Aerospace, and Manufacturing – used for predictive maintenance

Natural Language Processing – used for voice recognition applications

Cognitive Computing

What is the driving power behind cognitive computing? Machine learning, of course. Machine learning applies different algorithms behind the scenes that provide powerful cognitive computing capabilities that enable apps to do remarkable things like seeing, listening, talking, and decision-making. What exactly are these capabilities? Those used most frequently include natural language processing, visual recognition, and face detection. Slightly more complex is the use of emotion detection, language translation, and even sentiment analysis. The main goal of all of this cognitive computing is to deliver better customer interactions and experiences. No matter what the industry, everyone is clamoring to provide the best possible customer experience.

Creativity versus Task Automation

Design is more complicated than other jobs that are task-driven and repetitive. These repetitive types of jobs are the ones that are most likely to be replaced by robots. As for design, it requires the ability to assess information within different contexts, establish a framework for a plan, and empathy for other users. Robots are not sophisticated enough to successfully perform jobs requiring that kind of perspective, compassion, and creativity. Still, they are well suited to automating repetitive tasks and making designers more efficient.

The Internet of Things

The Internet of Things has been evolving over the last several decades existing in several different forms. Today cloud computing is redefining the IoT with its data-driven platforms. These platforms can capture enormous amounts of data from various data types to querying, process, and analyze the data to identify significant trends and make the IoT more intelligent. One of the best case studies to demonstrate the value of machine learning on the IoT is predictive maintenance. Machine learning algorithms help create the best model best matched for understanding the patterns found in datasets generated by industrial devices. The models can search for anomalies in the datasets that vary from the predicted standard designs that can result in the failure or breakdown of equipment. The neat thing about machine learning is that these anomalies or faults can be identified and detected before a user ever notices them. This form of predictive maintenance is sturdy and opens the door for industrial IoT to evolve to the next level.

When and Why Should You Use Machine Learning?

A good time to use machine learning would be when you have a complicated task or problem involving many data and lots of variables, but you do not have an existing formula or equation. Machine learning is used in a large number of applications today. Facebook’s news feed is one of the most well known, and it uses machine learning to personalize each member’s feed. It recognizes when you scroll past certain things but stops to read individual articles or posts, and it will start to show more activity similar to what you were reading earlier in your news feed. It uses statistical analysis and predictive analytics to identify patterns in your data and uses those patterns to populate news feeds. When you no longer stop looking at certain things, it also collects that data and adjusts your newsfeed accordingly.

Machine learning is beneficial in today’s society to determine and analyze patterns in data. It also helps to teach computer things that humans and animals are already familiar with. Machine learning is essential and something that will continue to grow.

Nov 18 20
Christina Zumwalt
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