Machine learning in everyday life – Six common applications

The term ‘machine learning’ is not one with high salience for the public. According to research by the Royal Society of London, only 9% of people recognize the use of machine learning (ML), a branch of artificial intelligence (AI) that allows computer systems to learn directly from examples, data, and experience.

However, many people are familiar with specific machine learning applications and interact with them every day without really being aware of the intelligence under the hood.

Machine learning is already deployed in a range of systems or situations that shape daily life, whether by detecting credit card fraud instances, providing online retail recommendations, or supporting search engine functions, virtual personal assistants, image processing, etc.

Many of the high-profile advances in ML are linked to computer gaming. However, the applications of machine learning are much broader. In a way, many of us interact with machine learning-based systems every day without necessarily realizing what a powerful technology is. This post will explore some of the applications of machine learning already encountered in everyday life.

1. Recommender systems: Suggesting products or services

Recommender systems are systems that recommend products or services based on previous choices. They are amongst the most widely recognized application of machine learning, even if familiarity with the underlying technology is low.

Consumption patterns and expressed preferences are used by recommender systems to predict which products or services are likely to be desirable to the user. It’s a type of machine learning that analyses data from previous purchases and the purchases of others to spot patterns and make predictions. Amazon and Netflix, for example, use such systems in their online retail environments. They can also promote specific types of content to social media users, such as news stories relevant to the user’s interests.

2. Organizing information: Search engines and spam filtering

Machine learning also aids in the retrieval of results from internet search engines like Google. These systems use the words entered in a search to find words and phrases with the same or very similar meanings and then use that information to predict which web pages will respond to that query.

Machine learning can be used by spam detection systems to filter emails. In this application, the system is trained to distinguish between spam and non-spam emails using a sample of documents classified as spam and non-spam. The system can learn how specific words, sender names, and other characteristics relate to whether an email is spam during this training process. When it’s put into production, it uses this knowledge to classify new emails, fine-tuning its training as users point out incorrect classifications.

3. Voice recognition and response: Virtual personal assistants

By distinguishing between the different audio footprints of these sounds, natural language processing (NLP) and speech recognition systems can match the sound patterns produced in human speech to words and phrases they have already encountered. They can then translate the words into text or carry out commands once they’ve identified the words.

Until recently, voice recognition systems had a poor track record of accuracy, making them difficult to use in many situations. These systems can now recognize speech much more accurately, converting the data patterns encoded within sound waves to text and carrying out the commands contained therein, thanks to recent advancements. As a result, many smartphones and other devices now include virtual personal assistants, such as Alexa, Google Assistant, or Siri, which respond to voice commands and provide answers.

4. Computer vision: Tagging photos and recognizing handwriting

Advanced image recognition systems and computer vision can benefit from machine learning. Computer vision necessitates the detection and analysis of visual images and the association of numerical or symbolic information with those images.

Image recognition can be used in social media applications to tag objects or people in photos that have been uploaded to a website. Similar image recognition systems can recognize scanned handwritten material, such as addresses on letters or digits on cheques.

Gaming systems that use computer vision to detect user movements or gestures as part of their gameplay also use machine learning. The system is taught to recognize a ‘body’ and then uses that knowledge to interact with its users.

5. Machine translation: Translating the text into different languages

Computer systems can automatically convert text or speech from one language to another using machine translation. Although efforts in this field date back to at least the early 1950s, recent advancements have made these techniques more widely applicable. There are now various methods for accomplishing this goal, including statistical, rule-based, and neural network-based approaches. Machine translation is now widely used in mobile phone translation apps, social and traditional media, and international organizations that need to reproduce documents in multiple languages.

6. Detecting patterns: Unusual financial activity

Machine learning can identify patterns in data that human analysts might miss because of its ability to analyze large datasets. The fraud detection systems associated with credit cards and other payment systems are common in their pattern recognition abilities. Algorithms are trained to recognize typical spending patterns using normal transaction data from many users. The location, magnitude, and timing of spending activity can be used to determine what makes a transaction more or less likely to be fraudulent. The system can then raise a flag if a user exhibits an unusual spending pattern, and the activity can be queried with the user.

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