Types of machine learning applications in industry

In 1950, Alan Turing developed the Turing test to answer the question “can machines think?”. Since then, machine learning has gone from being just a concept, to a process relied on by some of the world’s biggest companies.

Machine learning is a field that falls under the umbrella of artificial intelligence (AI). In this area, computers can learn and perform tasks without being explicitly programmed to do so.

They do this by learning from experience, using algorithms, and discovering patterns and insights from data. Machines no longer require programming for repetitive tasks. They can now independently identify and correct errors in a process.

Many machine learning applications are quickly expanding across a variety of business sectors. In fact, the machine learning market worldwide increased beyond 150 billion U.S. dollars in 2023 [1]. Forecasts indicate that the market will expand at a consistent rate throughout the next decade. It is estimated that it will add approximately 50 billion U.S. dollars to the market each year.
One of the main reasons for its growing use is that businesses are collecting big data, from which they need to obtain valuable insights. Machine learning is an efficient way of making sense of this data. For example, it can be used to analyse the data sensors collect on the condition of machines on the factory floor.

[2] The global machine-learning market grew nearly 120 per cent in 2023, making up for a staggering drop of 46 per cent in 2022. Following these turbulent years, it is expected to grow at a steady rate of around 20 per cent through most of the decade until 2030.

As the market develops and grows, new types of machine learning will emerge and allow new applications to be explored. However, many examples of current machine learning applications fall into two categories; supervised learning and unsupervised learning.

What is supervised learning?

One of the types of machine learning that is widely used is called supervised learning. It is commonly employed in applications that rely on historical data to create training models for predicting future events. For instance, determining whether or not a credit card transaction is fraudulent.

This is a type of machine learning that identifies inputs and outputs and trains algorithms using labelled examples. To identify patterns, supervised learning uses methods like gradient boosting, classification, regression, and prediction. It then uses these patterns to predict the values of the labels on the unlabeled data.

Where is supervised learning used?

The process of drug research and development is now being carried out with the assistance of machine learning. It has various applications, such as target validation, identification of biomarkers, and the analysis of digital pathology data in clinical trials. Machine learning is a powerful tool that promotes data-driven decision-making. It has the potential to speed up the drug discovery and development process while improving success rates.

[3] In 2023, the global market for AI in drug discovery was expected to reach some 1.5 billion U.S. dollars. However, in the next decade, the market is expected to increase nearly ninefold. This statistic shows a projection of the global use of AI in the drug discovery market from 2023 to 2032.

What is unsupervised learning?

The next type of downtime is unsupervised learning. In contrast to supervised learning, unsupervised machine learning can function on datasets that do not contain any historical data. Instead, it looks for patterns and structures in the data that has been collected. 

Factories are now utilising unsupervised machine learning for predictive maintenance purposes. Machines have the ability to learn the data and algorithms that lead to system faults. They can then use this knowledge to proactively identify and address problems before they occur.

Using machine learning can help manufacturers reduce unplanned downtime. By predicting breakdowns in advance, they can order replacement parts from an automation equipment supplier. This results in saving both time and money.

According to a survey by Deloitte, the use of machine learning technologies in the manufacturing sector has been found to drastically decrease unplanned machine downtime. This reduction can range between 15 and 30%, resulting in a related 30% saving in maintenance costs.

Where is unsupervised learning used?

If you're working with a huge, complicated dataset and human labelling isn't an option, this type of machine learning will be crucial. Unsupervised learning can find insights into data that human analysts might overlook by examining it without any prior knowledge.

Common techniques employed in unsupervised learning include clustering, association rule learning, and dimensionality reduction. In clustering, data points that are similar to one another are grouped. Whilst in association rule learning, correlations between variables are sought after. Dimensionality reduction simplifies complex data by reducing the number of features while preserving essential information.

Unsupervised learning finds use in many different fields. It can improve product design, forecast failures, and find novel materials by analysing unlabelled data. It can also categorise suppliers, predict demand, identify anomalies, and optimise supply chain operations.

You can find connections and patterns that weren't there before by using a data-driven approach. As a result, this improves decision-making across the board, which in turn increases efficiency and decreases costs.

Machines, like Google's Duplex, have advanced to the point where they can now pass the Turing test. In turn, this demonstrates that they are capable of independent thought alongside humans. Manufacturers can use these machine learning applications to improve maintenance processes. With the help of data-driven insights, they can make smart decisions in real time.

At EU Automation, we understand the transformative power of machine learning. We make it possible for manufacturers all around the world to gain access to a global library of automation parts. As your trusted partner, we are dedicated to providing you with the most up-to-date technologies. Our goal is to ensure that your operations run smoothly and efficiently.

Get in touch today and discover how we do our part to keep your production lines moving.

Citations:

[1] https://www.statista.com/forecasts/1449854/machine-learning-market-size-worldwide

[2] https://www.statista.com/forecasts/1449852/growth-of-machine-learning-market-worldwide

[3] https://www.statista.com/statistics/1428832/ai-drug-discovery-market-worldwide-forecast/