ML has a fantastic array of uses in today's business, and it can only increase and improve over time. The subfields of ML include social media and product recommendations, image recognition, health diagnosis, language translation, speech recognition, and data mining, to name a few.
Social media platforms, like Facebook, Instagram, or LinkedIn use ML to suggest pages to follow or groups to join based on the posts that you like. It takes historical data of what others have liked or what posts are similar to what you’ve liked, makes those suggestions to you, or adds them to your feed.
It is also possible to use ML on an eCommerce site to make product recommendations based on previous purchases, your searches, and other users’ actions similar to yours.
A significant use for ML today is for image recognition. Social media platforms have recommended tagging people in your photos based on ML. Police have been able to use it, looking for suspects in pictures or videos. With the plethora of cameras installed in airports, stores, and doorbells, it is possible to figure out who committed a crime or where the criminal went.
Health diagnoses are also a good use of ML. After an event like a heart attack, it is possible to go back and see warning signs that were overlooked. A system used by doctors or hospitals could be fed medical records from the past and learn to see the connections from the input (behavior, test result, or symptom) to the output (e.g., a heart attack.) Then when the doctor feeds their notes and test results into the system in the future, the machine can spot the heart attack symptoms much more reliably than humans so that the patient and doctor can make changes to prevent it.
Language translation on web pages or apps for mobile platforms is another example of ML. Some apps do a better job than others, which comes down to the ML model, technique, and algorithms they utilise.
An everyday use today for ML is in banking and credit cards. There are signs of fraud that ML can detect quickly and would take humans a long time to discover, if at all. The plethora of transactions that have been examined and labeled (fraud or not) can allow ML to learn to spot fraud in a single transaction in the future. ML that is terrific for this is data mining.
Data mining is a type of ML that analyses data to make predictions or discover patterns within big data. The term is a bit misleading as it does not require anyone, be it a bad actor or employee, rooting around in your data to find a piece of data that would be useful. Instead, the process involves discovering patterns in data helpful for making decisions in the future.
Take, for example, a credit card company. If you have a credit card, your bank has likely notified you of a suspicious activity on your card at some point. How does the bank spot such activity so quickly, sending a nearly instantaneous alert? It’s the continuous data mining that enables this fraud protection. As of early 2020, there are over 1.1 trillion cards issued in the US alone. The number of transactions from those cards produce diverse data for mining, pattern searches, and learning to identify suspicious transactions in the future.