The use of Machine Learning is growing at an accelerated rate. According to Statista, the market size of the machine learning industry will reach around 503.40 billion USD by 2030, which is 79.29 billion USD in 2024. Most people find themselves using this technology without even realizing it. Indeed, using Google Maps, Alexa, social networking sites, and even Siri is all about machine learning.
This post will tell you about the most popular Machine Learning uses and the future of Machine Learning.
The 11 Most Popular Uses of Machine Learning
The uses of Machine Learning can be varied, and the limit only lies in the creativity of the programmers. Today, DigitalCruch brings you the list of the 11 most popular uses of Machine Learning:
1. Image Recognition
The most frequent use of Machine Learning is associated with image recognition. Yes, people can identify images, find new places, or recognize objects with the help of ML and AI.
Facebook is a clear example, as it has a function to suggest friends when you are about to tag a photo. In this case, Facebook’s technology uses facial detection and recognition algorithms through Machine Learning.
Face recognition is widely used in the airline industry to detect the presence of criminals and works for big corporations to pass the employees based on visual identification. To know how to introduce face recognition for your project, check this page.
2. Voice Recognition
Speech recognition is another well-known use of machine learning (ML) and artificial intelligence (AI). Whether you open your smart lock through a verbal command or use Google Assistance or Cortana, it is all about the application of ML.
Voice recognition technology has been one of the fastest-growing uses of Machine Learning in the last decade. Statista also anticipates that the global market size of the voice recognition industry will reach up to 50 billion USD by 2029. This figure was only 10.7 billion USD in 2020.
3. Traffic Prediction
Do you usually depend on Google Maps to travel from one place to another place, or send them ‘pin locations’ to share your current location? If yes, it is also a perfect example of using AI and ML to activate GPS technology. Yes, GPS technology and Machine Learning have merged to provide a better service with digital maps. Nowadays, apps can predict traffic and suggest the best route to get there in less time.
Applications such as Google Maps, Waze, and other digital maps collect real-time information from users and process it to help the application and users find the most optimal route.
4. Product Recommendation
Have you ever utilized an OTT or streaming platform or purchased something online? Companies like Alibaba, Netflix, YouTube, Hulu, and Amazon trust Machine Learning algorithms to improve their search recommendations.
When you search for something, consume content, or buy something, the sites collect information from your interactions, process it, and feed it to a Machine Learning algorithm to recommend something for your next interaction.
It’s not just products; Google uses recommendation algorithms to offer a better search experience.
5. Virtual Assistants
This guide has already mentioned virtual assistants in the speech recognition point, but using Machine Learning in virtual assistants encompasses much more than just speech recognition.
The construction of a virtual assistant can be as complex as its developer wants. Using different Machine Learning algorithms results in a better experience, suggesting songs, improving your schedule, and even taking a call for you through smart home devices.
6. Language Translations
Google Neural Machine Translation is a Machine Learning program that translates text from almost any language to another. We know it better than Google Translate.
More companies have their own algorithms for translation. Millions of people use these services. Many of us often use free and paid translation or proofreading services as a paper writer, such as DeepL Translator or Grammarly. There are even browsers already with the function of translating by Machine Learning and can translate entire sites into your language. Fascinating.
7. Autonomous Vehicles
We are not so far from living in a science fiction movie; one of the most exciting uses of Machine Learning can be found in autonomous vehicles. Likewise, renowned electric car manufacturer Tesla claimed to use ML to enhance its self-driving features.
8. Email Filters
How do the email service providers know what is important and what is spam? When we receive an email, the email goes through an automatic filter to reach our inbox, while other emails go straight to spam.
Thanks to different Machine Learning algorithms such as decision tree algorithms or Bayesian algorithms, spam and malware detection are detected in time to avoid filling our mailboxes with junk.
9. Medical Diagnostics
One of the most promising uses of machine learning is in the medical field. The diagnosis of diseases with the help of machine learning is growing rapidly. Solutions exist to build 3D models to find the exact position of the brain.
Machine learning technology that locates brain lesions helps to find brain tumors and other types of brain-related diseases more easily.
10. Personalized Marketing
Marketing strategies have always had one goal: to learn more about customers, serve their needs, and sell more.
Machine Learning technology has helped many businesses achieve their goals. Digital ad platforms use Machine Learning to create better strategies and techniques for finding customers.
Also, Machine Learning technology in marketing sends personalized emails and recommendations.
11. Fraud Detection
Machine Learning is a key part of banking security since one of the most popular uses of Machine Learning in the financial industry is the detection of fraudulent transactions.
Fraud detection with machine learning is possible thanks to recognizing fake accounts and stolen IDs and even identifying stolen money transactions.
The Future of Machine Learning
The applications of Machine Learning that were shown to you were unfathomable just a few years ago. Still, with the rate of technological innovation and scientific discoveries, we are continually astounded.
So, what is the next step, and what is the future of Machine Learning? Machine Learning’s future tendencies are efficiency, security, and less data utilization.
More Efficient Machine Learning
However, ML and AI still have much room for improvement. Fast-paced advances will allow us to see Machine Learning optimized to be more efficient in a few years.
The trend is to implement artificial intelligence to recognize its mistakes and improve based on them without human supervision.
Protecting Against Cyberattacks with Machine Learning
As technology advances, so does cybercrime, forcing companies to reevaluate their processes for protecting online systems. Artificial intelligence and machine learning represent key pieces of monitoring, preventing, and responding to cyber-attacks. One day, database theft and digital fraud will probably be a thing of the past.
Fewer Data for Machine Learning
Machine Learning needs less data to achieve its programmed function as machines have more processing power and become more efficient.
The trend is towards training Machine Learning models with less data, resulting in models that learn with much smaller data sets.
Hopefully, this article will assist our readers in thinking or working more passionately about using ML and AI approaches to introduce something new. You can also share your thoughts about Machine Learning and its uses by commenting below!
