Machines can learn and be smart. That is nice to know; however, what are the uses of machine learning? how is the world using them?
Below are a couple of things machine learning can be used for or is being used for in the world today.
Virtual personal assistants
We use machine learning is in virtual personal assistants gadgets. Some of these gadgets include Cortana, Google Now, Siri, Amazon Echo, among others. These are intelligent digital personal assistants that help you find useful information when you ask them over voice. To use them, all you have to do is to activate them and then ask them anything you want. For instance, you can ask questions like:
- “What is the weather going to look like today?”
- “At what time am I meeting Peter?”
- “Set an alarm for 4 PM today evening.”
- “Remind me to call my mother at 9 o’clock.”
The personal assistance will then respond by looking for the information that you need. It can do this by recalling related queries (uses this to know your schedule and plans), sending commands to other devices, and apps to collect the information you need (uses this to know the weather and get personal kinds of stuff from your phone) among other techniques.
Machine learning is what we normally use to make virtual personal assistants smart. Machine learning is the application that helps the virtual assistance collect huge tons of data from various sources that help them learn about their users, and become more accurate in helping the user track information and be organized.
Increasing efficiency in healthcare
The healthcare industry is a big part of the world’s economy. As essential as it is, it normally operates in inefficient legacy infrastructure. The health sector usually has a hard time finding a way that they can preserve their patient’s sensitive details and at the same time be able to optimize their system. Here is where machine learning comes in.
One of the outstanding things about machine learning algorithms is their ability to process large collections of data. Machine learning can boost efficiency in the healthcare industry by helping the industry to do what it has not been able to do in the first place, which is processing large amounts of data without breaching patient’s confidentiality.
The healthcare industry also uses Machine learning to analyze and understand diagnoses. In one study, doctors used a computer-assisted diagnosis (CAD) to assess the early mammography scans of women that succumbed to breast cancer later on in their lives. The computer-managed to detect 52% of the cancers one year before the actual diagnosis.
In conclusion, great uses of machine learning in the healthcare sector and for various things.
Online fraud detection
Estimates show that the cybercrime damage cost will go past $ 6 trillion yearly by the year 2021. As you can see, online fraud is a big problem in the world right now. Machine learning is one of the ways that organizations and companies use to detect fraud.
PayPal is currently using machine learning to protect itself from money laundering. It uses machine learning to collect, analyze, and categorize transactions as either legitimate or illegitimate.
Banks also use machine learning to protect themselves from online fraud. Have you ever received an email asking you if you authorized a certain transaction? The banks do this when they fear that fraud has been committed on your account. How do they detect that? They use machine learning through a computer trained to detect fraudulent transactions.
Machine learning finds heavy usage in social media services. Before machine learning, different social media platforms used to place adverts that sometimes did not apply to specific individuals using the social media platform. Since social media platforms started using machine learning, their advertisements are now better at targeting their audience. That is just one of the many places that we use machine learning on social media.
Here is a list of other ways social media has used machine learning and artificial intelligence:
- Similar pins: Social media platforms such as Pinterest normally use computer vision made by machine learning to gather information on a certain image and then use that to find similar images it can recommend to users.
- People you may know: You will normally see this on Facebook. Here, Facebook shows a user people he/ she might know on the platform so that they can “friend request” them. This application is normally a machine learning process because a machine studies people’s profiles activities among other things and comes up with a list of people that the person might know.
Movie and music recommendation services
Another area where machine learning finds great use is in entertainment apps and sites that have movies and music. Some of these apps and sites include YouTube, Netflix, and Pandora. Normally when you use these sites, you notice that the platform starts to recommend movies and music that are similar to what you have been watching. That process is a machine learning process.
Prediction in transport
Machine learning also finds great use in commuting predictions. One of the best examples that show the work of machine learning is how cars use GPS to know traffic predictions or to know the direction of a place. The GPS usually gets a feed of different routes and then trained to identify them, which enables it to show you the way when you ask.
Refining results on search engines
One of the most popular ways that we use machine learning is in improving the search results for you as a client. How does this happen?
Normally, a program watches you when you execute a search on Google. The program usually monitors your movements to see whether you found what you are looking for or not. For instance, if you click on a web page and stay there, the program will assume the search was successful as you got what you wanted.
On the other hand, if you opened a search but ended up not clicking any web page or opened the first page then moved to the second page and went out, the program will assume you did not get what you wanted and then it will learn from that and try to deliver a better outcome in the future.