You’ve probably heard of machine learning. It may seem futuristic and out of everyone’s reach, but it is more present in our daily lives than you might imagine and, of course, in business.
Machine learning identifies complex patterns, predicts behaviour using an algorithm and improves your automated processes. Machine learning has already become an indispensable tool for companies in all types of sectors, and its applications are constantly evolving with the aim of improving the user experience. In this article, “Machine learning: everyday examples”, we present some of the most common technological tools that make use of machine learning.
Machine learning: examples and definition
Machine learning is the branch of artificial intelligence that gives machines the ability to “learn” from data analysis in order to identify patterns and support decision-making with minimal human intervention; people and machines work hand in hand. This ability enables the optimisation of day-to-day processes, to the extent that a machine can make a call to reserve a table in a restaurant without the caller realising that he or she is not talking to a person.
Having understood the concept, let’s give some examples of how it is used on a daily basis. One of the tools in which machine learning is most present is Gmail, the email service that has the option to help you write an email taking into account previous emails, either to autocomplete sentences, confirm that you want to send an email even when you have not attached the file, but when you have written it in the body of the email, etc. The sorting of emails in the inbox is also done by machine learning.
Spotify is a music, podcast and digital video streaming service that gives you access to millions of songs, and proposes playlists based on tastes or behavioural patterns. Spotify’s recommendation algorithms are among the best and most complex in the world. Its recommendations are made using three different models:
- Collaborative filtering model, by analysing your and others’ behaviour, opinions and plays of songs.
- Natural language processing model, using blogs and internet comments, they detect trending songs. They analyse text.
- Audio model, by analysing the songs that are added to the platform and comparing them with the most popular ones.
Personal assistants thanks to machine learning
If we talk about machine learning, we must not forget personal assistants such as Siri or Alexa, which use natural language processing or NLP, a mechanism using programmes that simulate communication. Large companies such as Google, Apple and Amazon are betting heavily on this technology. We already have Google Home and Amazon Echo, which are currently causing a furore. These types of assistants learn from the conversations they record with millions of users and, above all, from the owner of the device and the tasks it performs.
Other applications of machine learning
Transport companies such as UPS use machine learning to improve and optimise their work, specifically to programme their routes with the aim of minimising left turns, which are the most risky. Uber and Cabify also use these algorithms to reduce transport times with the lowest risk of accidents and, consequently, higher revenues.
In the banking sector, for example, machine learning makes it possible to reduce risk when granting loans, detect signs of non-payment or combat cyber-attacks. Chatbots are becoming increasingly common in sectors such as insurance companies. They offer a chat available for the user to ask questions and interact with them and try to ensure that they enjoy a good customer experience.
There is a wide variety of tools present in our daily lives that use machine learning, whether to categorise emails, offer recommendations, perform searches, generate routes or combat cyber-attacks. After reading this article, “Machine learning: everyday examples”, do you still think machine learning is a thing of the future?