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 min read

Everyday examples of artificial intelligence and machine learning

Silvana Paneva
June 28, 2019

Mobile applications are improving every day. And if you're a developer you know that artificial intelligence and machine learning have an impact on this.

AI is the imitation of human behaviour and intelligence processes by machines and computer systems. These processes include learning, reasoning, and self-correction.ML is the study of algorithms and statistical models that computer systems use to perform tasks without using specific instructions. They rely on models and inference instead. Nowadays, AI and ML are in almost every application, site and device which you used daily.

However, if you don’t have a clue about programming most probably you haven’t even noticed.


1. Search Engines

The point of search engine optimisation is to improve the ability of learning and finding patterns.

In the past few years, search engines algorithms have been improved. In 2015, Google has enhanced its algorithm with RankBrain - a machine learning algorithm that determines which results are interesting for a given search.

Now all search engines use AI to separate high-quality content from low quality (spam). Thus AI protects from manipulation but it also helps with ranking algorithms and relatable articles are more likely to pop-up, frequently asked questions with their answers. Furthermore, image and voice searching are becoming more and more accurate again because of ML and AI. Google predicts that by 2020 more than 50% of all searches will be done by voice.


2. Personal Assistants

Personal assistants such as Siri, Bixby, and Alexa can do amazing things for you! Do research, play music, suggest movies, write a text message, call someone and many more - all with a simple verbal request.They can do these things thanks to machine learning and natural language processing. These AI subsets help computers to understand speech, people’s habits, and develop their own personality. In this way, people overcome discomfort when interacting with a machine.

That’s why Amazon added ‘hmms’ and ‘ums’ into Alexa’s responses and Siri makes jokes. To do this, companies are hiring writers to help build an assistant’s personality. By using machine learning algorithms, the bot can learn to produce authentic responses on cue from the basis the writer creates.


3. Image recognition

Machine Learning takes in data, pushes it through algorithms, and then makes a prediction. Deep Learning differs in how it’s able to determine if the conclusions are correct all on its own. This is important for image recognition because, for example, people want a self-driving car to differentiate a signpost and a pedestrian.In order to train a deep learning model, you need a dataset. The model practices making predictions from the information in a dataset and use that experience in real-world situations. Image recognition is used for recognising faces, landscapes, animals and so on.

We use image recognition in our app PlantSnap to detect a plant (flower, tree, cacti, etc) and identify its species. And currently, it can recognise 90% of all species of trees and plants!


4. Entertainment and social apps

Netflix predictive technologies analyse hundreds of records so it can suggest movies and TV shows similar to those you already have seen and rated positively. It also considers other aspects such as the time of day and what day it is, so the suggested movie can be more suitable.

Spotify uses AI to create your daily playlists and mixes, based on what you’ve been listening lately. It also suggests new artists that you might like. Spotify generates statistical summaries of all the activity from its users, and it can find out which are their favourite genres, artists, decade, languages, and come up with the right music for them.

AI is the reason Facebook can add attractive and relevant content to your News Feed based on your preferences. It does this by analysing your behaviour and interactions - what type of posts you like more, share and comment.


5. App Marketing

ML helps marketers understand the preferences of the users and the purchase pattern. Recommendations are based on customers search behaviour, purchase history, age, gender, location and frequency of use of the application.

For example, Amazon’s predictions and suggestions have become so good thanks to AI that now one-third of Amazon sales come from their recommendations. This technology can find out what your preferences and shopping behaviours are with incredible precision.



Thanks to machine learning and artificial intelligence, the mobile applications are far more useful, efficient and effective. Because of them, the human interaction with devices is becoming better and better. These technologies are fast and safe, and definitely will be a significant part of future mobile development. So it is a good idea to employ those technologies for your next app.