Multi-Task Learning: Case Study and Applications

Machine learning is all about computers and their empowered abilities to perform cognitive actions. While this sentence alone is not enough to explain the true prowess of machine learning, a little bit can be definitely understood from it. 

 

A subfield of artificial intelligence, machine learning tools and techniques have not only enhanced the way technology plays a role in our lives, but it has also expanded the scope for more technological advancement. 

 

When it comes to machine learning alone, a number of subfields are well-suited for cutting-edge technology that we have adapted and accommodated in our lives. A subfield of machine learning, multi-task learning (MLT) is a newly emerged area of study that has made technology more versatile and reliable at the same time. 

 

Multi-task learning aims to enable computers to solve more than one task at the same time, by clubbing the common factors in multiple tasks and taking advantage of these similarities. 

 

Unlike conventional devices and smart gadgets that could only perform one task at a given time, MLT has induced a superpower in computers and machines that makes them perform multiple tasks simultaneously. 

 

Earlier, machine learning could only work with a single model trained well enough to perform a specific task. 

 

However, with the increasing ability of computers and machines, MLT creates a converging point for more than one machine learning algorithm to apply to a problem and perform tasks with a similar set of factors side by side. 

 

In a way, multi-task learning in machine learning is inductive learning that machines employ to narrow down the common factors of a set of problems. This leads to the generation of a broader machine learning model as compared to the earlier ones that are only ideal for a particular task or a problem. 

 

MTL plays the role of a regularizer by proposing inductive bias. It considerably minimizes the overfitting risk while also minimizing the ability of the model to coordinate random noise amidst training.

 

How does Multi Task Learning Work?

 

Even though the overall concept of MTL might sound extremely easy, it is simply one of the most complex processes that exist in machine learning to date. 

 

While we walk through the working of MTL, we shall come across numerous mechanisms that the concept depends upon and takes into consideration while pursuing its aim. Let us get started with how multi-task learning works.

 

To begin with, it is important to note that reducing overfitting in machine learning is one of the biggest auxiliary aims in MTL. As overfitting fails to make a model accommodative of other datasets, MTL works very well in reducing overfitting. 

 

Firstly, any multi task learning model begins with attention focusing that involves differentiating between important and unimportant variables. This can also be seen as a task of segregating primary factors and noise in machine learning. 

 

Not only does this help in clustering the common factors in a set of problems but it also leads to a more organized and focused approach toward MTL. From here on, the second step is to regularize. 

 

Among a set of multiple factors, MTL acts as a regularizer that promotes an inductive approach to problem-solving and reduces the chances of overfitting by a considerable margin. 

 

The next step is to monitor the progress of each and every task. Just like each and every finger of a hand is not similar, not all tasks have the same ability to keep up with a new feature. 

 

Herein, MLT takes to ‘eavesdropping’ or monitoring each and every task’s progress. This involves working and testing the new feature on all tasks separately and collectively. Steadily, all tasks are able to achieve the same proficiency level. 

 

The last step is to present a larger dataset to train the tasks effectively. Since the concern of overfitting is largely due to data-dependent noise, an augmentation in datasets presented to the machine learning models is the key to this problem. 

 

As more and more data samples are presented before the model, the training becomes enlarged and expansive in scope. As soon as the training is over, the major task presented before the model is to determine links between more than 2 tasks. 

 

This involves finding common features, narrowing them down, and grouping them to apply task functions effectively. 

 

Also Read | Underfitting and Overfitting in Machine Learning

 

Case Study of Multi-Task Learning

 

By using this instance, we can simply understand how MLT operates in the field of machine learning. Let us suppose that a group of words are present before a model. The task is to fit in the correct word in this given sentence and join it with other sentences. 

 

This calls for focusing attention on the important variables. Once attention is gathered on the important factors of a task, the model regularizes or induces the general factors in the given tasks. The inductive factor might be related to the topic or the subject of the sentence that the words are related to. 

 

As soon as the eavesdropping task is done and all tasks have caught up the speed to train themselves, more and more datasets (here, words) are provided for better performance. This results in reduced overfitting and greater chances of accurate task solutions. 

 

There are many uses of multi-task learning in the machine learning industry. From spam detection to self-driving vehicles, MLT is a revolutionary technology that has delivered high-quality results across a range of sectors. 

 

Also Read | Different Types of Learning in Machine Learning

 

Top Applications of MTL

 

Let us now move on to explore the top 5 multi-task learning applications across various industries. 

 

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Applications of Multi-Task Learning

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1. Spam Filtration

 

Filtering messages is not a cakewalk. From emails to digital letters, messages and texts can have crucial information and at the same time, may carry harmful or even useless information. 

 

Thus, spam filtration has become an essential task that can be done easily with the help of computers. Since the task of filtering messages is common to spam, primary, and other messages, tasks under MLT can be well-trained and executed. 

 

One of the biggest uses of MLT can be witnessed in spam filtration in emails and on other platforms. Even though clustering algorithms are enough for this purpose, spam filtration simply enhances the way we are presented with spam messages and related notifications. 

 

2. Stock Market Analysis

 

We all know what a stock market is. By trading in shares via a stock market broker or similar platforms, an individual can participate in the stock market by exchanging, selling, or buying stocks of a particular company. 

 

In relation to this, the stock market analysis aims to forecast the volatility in the prices of stocks. With the help of multi-task learning, stock markets can be analyzed, predicted, and presented before traders. 

 

As the task of analyzing different stocks is common to the model of MLT, simultaneous features like price rise, market ups and downs, and other such phenomena of the day-to-day business can be well predicted with the help of past records. 

 

That is the power of multi-task learning. Inspired by the human ability of multitasking, machines can also do the same thing with the help of deep learning and machine learning. 

 

3. Computer Sciences

 

Despite its ability to perform multiple tasks in specialized fields, the most renowned use of multi-task learning lies in computer sciences. 

 

From multi-tasking to deep learning, computer sciences have achieved a new dimension. With learned professionals contributing to the technology, multi-task learning is paving the way for future intelligence in computers and machines. 

 

MTL attempts to make use of effective information that is enclosed in multiple learning tasks in order to help gain a more precise learner for every task. 

 

4. Self-Driving Vehicles

 

The next application of multi-task learning goes as far as the automobile sector. Self-driving cars are a result of AI that has induced the capability of automation in cars. 

 

However, there is more to the story. Self-driving cars are backed by multi-task learning that prepares them to handle more than one task at the same time. 

 

From pulling off the brakes to driving in the right direction, self-driving cars are designed in a way that prepares them to focus attention on the important variables, narrow down the common features in the given tasks, and monitor the models so that they learn the process, and ultimately augment the dataset for better training. 

 

One of the biggest advantages of self-driving cars is that no human will be required to drive the car himself/herself. 

 

5. Facial Recognition

 

Another one of the biggest advantages of multi-task learning is that machines are now capable of recognizing your face and unlocking your credentials. We all have our smartphones that use facial recognition to unlock our phones. 

 

While the biggest technology behind this feature might seem to be none other than our front camera, AI is the cornerstone technology that has influenced such a technology. 

 

With the help of deep learning and MLT, our smartphones perform the task of recognizing our faces. As we register our face in our phone beforehand, the phone uses it as a dataset. 

 

Afterward, as we try to unlock our phone, it immediately gives us access. However, our phone is simultaneously capable of rejecting the face that is not registered with it. Hence, facial recognition is a well-known application of MLT. 

 

Summing Up

 

To sum up, multi-task learning is an elaborate process that takes a lot of training using datasets. Unlike performing one task at a time, machines have now become capable of solving multiple tasks or problems simultaneously. 

 

The key to this power is that machines rule out the common areas of interest in all problems and train themselves accordingly. 

 

They say that machines will soon take over humans one day. And by looking at MLT, this phrase seems more believable than ever. As more and more machines become empowered, humans are taking the back seat and reaping the fruit of their doings. 

 

Lastly, it is technologies like multi-task learning that have led to the ever-increasing greed among humans for speed, success, and growth.