Machine learning applications are everywhere now. Chances are, you’ve interacted with a machine learning application today. Image and voice recognition, medical diagnosis, and data extraction all use machine learning. Your computer uses it. So does your doctor’s office. As the world becomes more complex, machine learning applications are showing up in more sectors.
Why is this?
Machine learning simplifies the complex. That’s what makes it so useful. It can quickly identify patterns in data and make decisions on that data, often without human intervention. It achieves this faster and better than any human could.
Think of machine learning as a thousand Sherlock Holmes, always working to find an answer.
If you’re unsure how the technology works, take a look at our previous tutorial on machine learning. Machine learning applications have become prevalent and important. Here are some real-world examples.
Give a machine learning application unstructured data and it will extract structured information from it. Machine learning algorithms automate dataset annotation (labeling data) so it can more easily be used by predictive analytics tools.
In other words, the algorithm–the ML–does the work to label people, places, things, numbers, times, dates, whatever the algorithm needs to make a decision. Then it uses that information to make a decision faster than a human could. If you’ve ever spent time tagging Facebook photos, you can imagine how much time this can save (see below).
One real-world application: automated invoice processing. Businesses can use data extraction to keep up with billing, record-keeping, and accounting processes. Further, the same data can easily transfer over to yearly reports, giving in-depth financial insights into the business year.
Remember that scene in Bambi when he calls the little skunk flower because he’s sitting in a field of flowers? Obviously, little Bambi had no classification skills.
Bambi understood that things in the field were living. But at first, he was unable to quantify a dataset for plants (flowers) and one for non-plants (skunk).
But machine learning algorithms have mad classification skills. They’re great at putting data into groups using rules set by analysts. Once the algorithm finishes classifying all data, analysts can calculate probabilities. This has all kinds of real-world applications.
Algorithms like this often show up in places like banking and real estate. Machine learning can predict if transactions are fraudulent or real. It can also predict if real estate prices will go up or down. And it can predict whether a neighborhood is improving or declining using crime and sound disturbance data, as well as information on the rate and type of businesses opening and closing nearby.
Machine Learning Applications in Medical Diagnosis
Medical diagnosis relies on seeing patterns in symptoms (data). Physicians now use machine learning algorithms for everything from mental health to cancer detection to preventive genetics.
Real-world applications in medicine include helping with gene research in the fight against Cystic Fibrosis, using chatbots to identify groups at high risk of social isolation and possible suicide, and mapping skin lesions to separate melanomas from benign issues.
Medical use of ML algorithms is only beginning. This area is expected to take off in the coming decades.
“Alexa, play my music.”
“Okay. Shuffling your music.”
“Alexa, what’s the weather going to be like tomorrow?”
“Tomorrow there will be clouds and a chance of showers, with a high of 79 degrees.”
Many of us interact with Alexa, Google, or Siri this way. But it wouldn’t be possible without machine learning. ML converts our live voice into text file commands. The system then receives a correct output–usually. This is sent back to the device as an audio file.
Every time we talk to Alexa (or Siri, or Google) we become part of the training model, helping the algorithm learn. In 2018, Alexa understood 95% of the words it heard. That percentage continues to grow.
As the algorithm learns from us collectively, the times we hear “Sorry, I don’t know that” will happen less and less.
Do you tag friends and family in your Facebook photos or other applications? Ever noticed how you only need to tag someone a few times before the algorithm recognizes them?
That’s machine learning, too.
Image recognition is one of the most controversial sectors of machine learning. As helpful as it is on Facebook or when looking for a particular stock image on Pixabay or Unsplash, many people are concerned over possible privacy issues related to facial recognition software. That deep subject deserves its own post, however.
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