Applications of Machine Learning in the Real World

Applications of Machine Learning

Learn about four real-world applications of machine learning here.

When you travel to a new country, have you ever had to translate a word quickly? Or how about when you are sending a reply to an email using the Gmail application? Do you notice that there are recommendations of what to reply? The examples above make use of machine learning.

From computerized translators, to email recommendations, as well as movie suggestions on Netflix, to autonomous cars. All of this is possible due to machine learning. But what is machine learning?

Machine learning is a concept that falls under the category of artificial intelligence. It is the process by which computers change the way they perform tasks by learning from new data sets. In this process, algorithms mimic the way humans learn to gradually improve their accuracy.

Due to the advancements in technology, we are surrounded by products and services that utilize machine learning. Machine learning helps make our lives easier. It makes processes more efficient, reduces manual working time, can be applied to a wide range of work in different industries, and has excellent capability to learn how to handle large amounts of data.

Machine learning can be classified into two categories: supervised machine learning and unsupervised machine learning. There is usually an outcome variable in supervised machine learning that controls the learning process. In particular, it is characterized by using labelled data sets to train algorithms that can classify data or accurately predict outcomes. Labelled datasets take the form of raw data such as videos and images with attached labels for the computers to learn from the model created. Unsupervised learning involves creating models of unlabelled data without predefined classes or examples. There is no “human supervisor,” and learning must rely on heuristic guidance generated through the system by examining various sample data or the environment.

You’d be surprised where we can find these applications in the real world. Here are four examples where machine learning is applied. You will see how these systems work to make our lives easier.

1) Google Translate

When you don’t know the language in a new country, have you had difficulty asking for directions because you weren’t sure how? Your first thought was probably to use Google Translate. Google Translate quickly, easily, and effectively translates phrases from one language to another.

The software uses machine learning by the process called “the neural machine translation.” The system uses deep learning techniques to translate phrases into different languages in this process. Google recently introduced “Google’s Neural Machine Translation System (GNMT) consisting of both “eight encoder and eight decoder layers.” Connecting encoder and decoder layers allows for speedy translation “through inference computation.” The inference computation has the ability to recognize complex words and phrases through dividing words into sub word units.

However, a disadvantage of using Google Translate is that it is not always reliable. Many phrases can get lost in translation because the literal meaning in one language may not make sense when translating it into a different language. The translations often do not capture the true essence of the sentence and may thus be misinterpreted by another person.

Additionally, although the translation website can translate basic simple sentences, there may be a lot of errors if one aims to translate complicated words and phrases. Therefore, a person should always check the final texts.

2) Gmail Recommendations

Gmail can produce reply recommendations, subject recommendations, email closings; you name it. Gmail suggests responses users can give to emails through the “Smart Composition Personalization” process. For example, there are suggestions based on how a person can start and end the email. The recommendations are generated from the users’ past data records to suggest smart responses to emails.

Gmail also suggests ideas of what users can write in the body of the email by giving around three recommendations to choose from. Google effectively combines language generation models neural bag of words (BoW), Recurrent Neural Network-based Language Model (RNN-LM), (RNN), and ngram to produce speedy recommendations. Google makes these suggestions based on the combination of the subject line information and prior emails chains.

The benefit of using the smart composition personalization process for writing emails is that it allows users to send quick responses to the sender. On the other hand, a drawback is that auto-generated responses may seem that not much thought was given to the email. Therefore, it is recommended that the personalization tool should mostly be used to get an idea about what to write.

3) Netflix Recommendations

You are sitting with your friend, browsing through the different options through Netflix. But, how is Netflix able to generate these recommendations? Through machine learning, of course! The machine learning technology that Netflix uses to recommend shows to their users is called the “recommended engine” model.

The recommendation engine uses the historical data compiled based on your recent activity and other users’ activity similar to yours to recommend movies and TV shows accordingly.

This allows Netflix to recommend shows based on genre, actors, and directors that you may like. Recommending TV shows and movies users may want causes them to stay engaged on the website for extended periods.

However, with the “recommended engine” technique, it may not always be guaranteed that, for instance, if a person likes a particular movie from a specific director, that person will like another movie from the same director.

4) Tesla’s Autonomous Cars

The company Tesla uses machine learning for autonomous cars to make smart decisions. The compiled data is based on the movements of the vehicles on the road through the “imitation learning” system. Drivers’ reactions are gathered together to get a picture of an overall sense of how to act on the road. The sensors capture the drivers’ movements related to the use of the steering wheel, the use of accelerator, the use of brakes, and the indicator.

Tesla’s machine learning model uses neural networks to learn from incorrect movements of some drivers to learn about what went wrong. Images are saved of the incorrect actions to prevent the same mistakes from happening again in the future.

A potential advantage of autonomous cars is that parking becomes less of a hassle. This is especially true if vehicles have to be parked in tight spaces, something many people often struggle with. Additionally, with autonomous cars, human errors may be prevented. There may be times when people sometimes drive while they are on their phones which can be dangerous. There would be fewer distractions with self-driving vehicles, resulting in fewer accidents on the road.

However, it may be possible that with autonomous cars, unexpected faults may occur from the sensors, which poses a risk to the passengers’ safety. This is because sometimes machines cannot distinguish between what is right and what is wrong. It may therefore be hard for machines to make smart decisions. Tesla has faced challenges reporting unexpected car crashes in the past because drivers were not fully aware of what was happening in front of them on the road. There have also been cases where drivers sit in the back seat instead of the front seat when driving autonomous self-driving cars, which poses a risk to the safety of others on the road and themselves.

Based on the challenges encountered, Tesla will need to work on refining its imitation learning models so that it makes more accurate decisions. Further tests will have to be carried out to limit errors made by the cars.

Applications of Machine Learning in the Real World – Conclusion

Overall, machine learning has helped us to perform tedious tasks more effectively and efficiently. However, it is essential to note that human intervention is still needed to ensure that tasks are completed at a high-quality standard.
Further Read:

TechVidvan: Exploring the Advantages and Disadvantages of Machine Learning

Pratibha Roy: Examples of Machine Learning Applications in Real World

Pavan Vadapalli: How Netflix Uses Machine Learning & AI For Better Recommendation?

Bernard Marr: How Tesla Is Using Artificial Intelligence to Create The Autonomous Cars Of The Future

Laurie Clarke: How self-driving cars got stuck in the slow lane

Rebecca Heilweil: Elon Musk’s problematic plan for “full self-driving” Teslas

Natt Garun: How to enable and use Gmail’s AI-powered Smart Reply and Smart Compose tools

Yonghui Wu: Smart Compose: Using Neural Networks to Help Write Emails