Many people are unsure about exactly what machine learning is. But the reality is that it is already part of everyday life.
A form of artificial intelligence, it allows computers to learn from examples rather than having to follow step-by-step instructions.
The Royal Society believes it will have an increasing impact on people's lives and is calling for more research, to ensure the UK makes the most of opportunities.
Machine learning is already powering systems from the seemingly mundane to the life-changing. Here are just a few examples.
1. On your phone
Using spoken commands to ask your phone to carry out a search, or make a call, relies on technology supported by machine learning.
Virtual personal assistants - the likes of Siri, Alexa, Cortana and Google Assistant - are able to follow instructions because of voice recognition.
They process natural human speech, match it to the desired command and respond in an increasingly natural way.
The assistants learn over a number of conversations and in many different ways.
They might ask for specific information - for example how to pronounce your name, or whose voice is whose in a household.
Data from large numbers of conversations by all users is also sampled, to help them recognise words with different pronunciations or how to create natural discussion.
2. In your shopping basket
Many of us are familiar with shopping recommendations - think of the supermarket that reminds you to add cheese to your online shop, or the way Amazon suggests books it thinks you might like.
Machine learning is the technology that helps deliver these suggestions, via so-called recommender systems.
By analysing data about what customers have bought before, and any preferences they have expressed, recommender systems can pick up on patterns in purchasing history. They use this to make predictions about the products you might like.
3. On your TV
Similar systems are used to recommend films or TV shows on streaming services like Netflix.
Recommender systems use machine learning to analyse viewing habits and pick out patterns in who watches - and enjoys - which shows.
By understanding which users like which films - and what shows you have watched or awarded high ratings - recommender systems can identify your tastes.
They are also used to suggest music on streaming services, like Spotify, and articles to read on Facebook.
4. In your email
Machine learning can also be used to distinguish between different categories of objects or items.
This makes it useful when sorting out the emails you want to see from those you don't.
Spam detection systems use a sample of emails to work out what is junk - learning to detect the presence of specific words, the names of certain senders, or other characteristics.
Once deployed, the system uses this learning to direct emails to the right folder. It continues to learn as users flag emails, or move them between folders.
5. On your social media
Ever wondered how Facebook knows who is in your photos and can automatically label your pictures?
The image recognition systems that Facebook - and other social media - uses to automatically tag photos is based on machine learning.
When users upload images and tag their friends and family, these image recognition systems can spot pictures that are repeated and assigns these to categories - or people.