Every day, technology advances and computers become more powerful. They are now capable of doing much more than simply executing commands. They can be taught how to acquire and interpret information on their own, without human assistance. This is, in a nutshell, what machine learning means.
A Few Words on Machine Learning and Artificial Intelligence
Machine learning is closely connected with artificial intelligence. Smart machines are not a novelty: industrial manufacturing robots are a basic and crude example of artificial intelligence. At the extreme end, the robot Sophia represents the highest level of sophistication reached by computers.
But all ranges of artificial intelligence entities use machine learning as the basic principle of performing their tasks. How widespread are these AI entities powered by machine learning? They are currently behind some of the most popular web and mobile apps people use on a daily basis.
Let’s take a look at the most innovative and practical ways in which machine learning was used in such web and mobile apps. These examples serve as a way of understanding how machine learning can be implemented in other types of apps, creating smarter business and retail tools for companies worldwide.
1. Netflix – Accurate Predictive User Behavior
One of the fascinating features of Netflix is its uncanny ability of guessing what each user enjoys watching and making very accurate recommendations. This is not lucky guesswork. Instead, it is based on concepts such as Linear regression and Logistic regression–all machine learning algorithms.
Netflix uses a very detailed classification of its content by genre, year of release, actors, directors, reviews, duration of the movie and more. The machine learning algorithms monitor user consumption of such content. The algorithms learn which genres, actors, content length, etc., each user favors, and which are their least favorites.
This type of machine learning is useful in shopping apps, for instance, where the capacity to learn a consumer’s needs, preferences and buying behavior are of the greatest importance.
2. Swiftkey Neural – Context Based Text Prediction
Every mobile phone user has experienced the perils of auto-correct. Each mobile OS has a component which predicts what users want to type and suggests a word or even changes a typed word to something else. A lot of the time, these suggestions are extremely inaccurate.
Swiftkey Neural app is different in this respect because it implements machine learning algorithms which are capable of understanding not only the language, but also the context of the conversation. The suggestions made by the app are different, for instance, during a personal conversation and a business one, even if the basic topic is the same.
In this instance, machine learning is useful as a business tool, helping companies develop collaborative tools and proprietary direct messaging apps.
3. Facebook Messenger – A New Way of Talking to Machines
Facebook Messenger chatbots are now a regular presence in the cyber world. On many occasions when users interact with a business by private message, they are not exchanging texts with a human, but an artificial intelligence entity.
Some of these chatbots are so advanced, thanks to machine learning, that their conversational style is indistinguishable from a human’s. The secret behind these advanced chatbots is the fact that Facebook allows developers to submit their own AI creations to be included in the social media network.
This example showcases the core principle behind machine learning: collaboration. A strong collaborative environment has the key ingredients necessary for the development of successful apps.
4. Baidu – High Precision Voice Recognition
A Chinese internet technology company which offers users a search engine and a social media network, Baidu, may be less known globally, but the way it uses machine learning is impressive. The machine learning algorithm it uses is called natural language processing, and it consists of 5 steps:
- Voice pickup
- Speech recognition
- Natural language understanding
- Natural language generation
- Speech synthesis
The most advanced result of this type of machine learning is Deep Voice, a deep neural work developed in the R&D lab of Baidu which can generate human voices that are almost indistinguishable from an actual human’s.
The applications of this type of machine learning are tremendous: from virtual shop assistants, to voice recognition logins, to assisting visually impaired users with web and mobile app interactions.
5. Snapchat Filters – Accurate Facial Detection and Recognition
Snapchat filters are one of the key reasons why this social network is so popular among young people. They let users add cat or dog ears and noses to their faces, turn into a flower, or a superhero. These augmented reality filters allow the holographic images to move naturally, in real time, following the movements of the head, eyelids and lips.
This is the result of machine learning: computers cannot “understand” the precise characteristics of a human face, but by prolonged exposure to a large number of faces they can discriminate between each specific feature.
Facial recognition is on the rise–as an unlocking mechanism for mobile phones or even as a replacement for a password to log in to various accounts. The more developers learn to incorporate machine learning into apps, the more accurate the facial recognition functions will become, leading to more secure web and mobile apps.