Machine learning is a subset of artificial intelligence that deals with the construction and study of algorithms that can learn from and make predictions on data. It has been used in many domains, such as computer vision and natural language processing. Recently, many researchers have been exploring the use of machine learning techniques in the automotive industry and in industrial automation.
There is a lot of excitement surrounding machine learning models and automated machines. Data science and deep learning are two areas where machine learning models are being used extensively.
Industrial automation is a field that deals with the design, application, and control of automated systems. Autonomous machines are used extensively in industrial automation to improve accuracy, efficiency, and production time.
What is Machine Learning in Automation
Machine learning is a technique of using statistical intelligence to predict future events. In this process, we use the data that we have collected to estimate the future and then make predictions on real objects or data. In addition to predicting future events, machine learning can also be used in other areas.
Almost 3/4 of respondents believe that increasing investment in machine learning and automation would help their firm accomplish its existing goals.
For example, we may use machine learning to identify patterns in historical data to improve the accuracy of predictions. We can use this technique in many areas such as marketing, finance and risk management, and many others.
In this context, machine learning refers to the application of statistical techniques for prediction, classification, and regression analysis. In other words, we can say that machine learning uses a set of rules to provide the possibility of making correct or correct predictions.
As a result, there are data pre-processing and learning algorithms; both of these processes are used to obtain new knowledge from the data. The data pre-processing process is one that consists of cleaning and transforming (or processing) the raw data into a form that makes an accurate prediction easier than before. The process also includes reducing noise and inaccuracy in the data or extracting insights from it in some way.
Automated Machine Learning
Machine learning is a process that enables computers to learn from raw data. By using algorithms, computer programs can automatically improve given tasks by learning from experience.
This process is also known as natural language processing and is used extensively in artificial intelligence research. Machine learning solutions are constantly being developed to tackle real-world problems.
Automated machine learning is important for data scientists because it allows them to leverage their extensive programming knowledge and domain knowledge to build models that can learn from data. Machine learning is a powerful tool for making predictions, and automated machine learning makes it easier to use by automating the process of building models.
The Different Types of Automated Machine Learning
Given all the possible advantages of automated machine learning, why don’t we see this technology in more applications? There are several reasons. Automated machine learning is still in the early stage of its development.
This means that most algorithms are not sophisticated enough to provide high performance for some problems. Also, the development of automated machine learning does not have the same level of funding as traditional methods and is more focused on fundamental issues than implementation.
In the United States, the machine learning application market received $37 billion in funding in 2019. This section describes some types of automated machine learning:
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Descriptive Models
These models indicate how data is distributed or what types of data belong to a domain. Each model is useful for describing the data or detecting patterns in the data. This type of learning is concerned with the properties and properties of data, which are used to describe and understand the data.
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Classification Models
These models indicate how we can use machine learning algorithms to classify objects into different classes. We can also use this type of model to identify patterns in instances using classification (for example, a pattern that a person has a certain job title) or rule-based systems (for example, when we have a lot of letters, then there is a very high probability that it’s an e-mail).
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Regression Models
These models estimate the relative relationship between variables. We can use this model to create rules to predict some variables from others (for example, we can expand the amount of time it takes to complete a task based on our experience).
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Clustering Models
These models indicate how we can organize objects into different groups. We can also use this type of model to create rules for the prediction of future data; for example, if the characteristics of an object are similar to that of another object, then there is a very high probability that its properties will be similar.
The Challenges of Automated Machine Learning
Industrial automation utilizes a lot of data collected from sensors and other input devices to make decisions. “Data engineering” refers to the process of gathering, cleaning, and arranging this data. In 2020, each person produced 1.7 gigabytes in a single second.
The use of data engineering has increased the opportunities for machine learning in industrial automation because it has made machine learning algorithms universally available to be used for any purpose. However, there are several challenges in automated machine learning based on big data sources.
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High Speed
The first challenge is that the big data sources are generated at a high speed, which makes them difficult to be categorized manually or by human experts.
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Size of Big Data
The second challenge is the growing size of big data. The size of the data used for machine learning is growing at an increasing rate, which makes it hard to process and store.
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Imperfect Quality of Big Data
The third challenge is the imperfect quality of big data. Because the amount of available training data is limited, most machine learning algorithms require a large amount of training data to avoid overfitting (that is, when an algorithm can perfectly model some specific training data but cannot generalize well to unseen data).
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Diversity of Tasks
The fourth challenge is the high complexity and diversity of tasks that require machine learning algorithms. Each task requires a unique algorithm with its own parameters to be optimized. It can be very tricky to find an appropriate algorithm for each task.
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Instability of Algorithms
The fifth challenge is the instability of machine learning algorithms. If a machine learning algorithm is not designed correctly, its cost can significantly increase. It often takes a long time to optimize the parameters of each machine learning algorithm for different tasks.
These five challenges, sometimes called “the curse of big data,” are common to almost all industrial fields that deal with big data and have caused many problems in the past. It is important to overcome these challenges if we want to use machine learning technologies in industrial automation productively.
Benefits of Automated Machine Learning
Automated machine learning is a powerful technology that enables us to solve the problems in industrial automation more effectively. According to a Deloitte 2020 global executive poll, there is a 58 percent rise in intelligent automation initiatives underway compared to the prior year.
Here are seven advantages of using automated machine learning:
1. Machine Learning is a Good Match for Big Data
To understand the benefits of automated machine learning, we must first describe how big data comes about and how it can benefit industrial automation.
Data usually consists of two types: structured data (for example, books and articles in a library) and unstructured data (for example, images, sound clips, and video files). Structured data is mainly stored in databases and column-oriented files. Unstructured data is stored in file formats such as .csv (comma separated values), .txt (.text), .xml (.eXtensible Markup Language), and .zip (.ZIP).
Big data is unstructured data that is analyzed using machine learning algorithms and is created at a high speed by sensors, mobile devices and the internet of things. As mentioned above, there are many challenges in using big data for machine learning.
If we cannot overcome these challenges, then it will be impossible to use machine learning to create industrial products with high predictive accuracy. Thus, a good solution is required to fulfill various tasks without additional costs caused by the messy nature of big data.
2. It is Cost-Effective to use Automated Machine Learning
To efficiently exploit big data, 48% of firms employ deep learning, machine learning, natural language processing, and data analysis.
When we utilize automated machine learning, our requirement to solve a problem is often reduced to a single mathematical equation, which is much easier to optimize and so the cost of solving it is reduced.
For example, if you want to make a decision based on the quality levels of products produced by two different factories, then you will compute the overall output quality of all products produced by each factory and compare the results. Another method would be to evaluate the quality directly of each product before processing them.
One method uses automation, but it is more costly than the other because it requires human intervention in most cases. In the latter method, you need to examine all products produced by both factories. In automated machine learning, you only need one equation to make a decision on which factory is better.
The first type of decision-making method is not only costly but also extremely slow as well. Human intervention takes time and automation takes even more time because a human expert must check the results generated by the system before they can be used.
With automated machine learning, we can use computing power to quickly find an equation that matches our required criteria, and then solve it in seconds or minutes instead of hours or even days. It is without a doubt much more efficient than comparing the quality of products manually or by using traditional methods based on heuristic rules.
3. Machine Learning Technology is Developed for Automated Machine Learning
If we want to use automated machine learning in industrial automation, we must first understand how to define what we mean by “the technology” in order to provide more benefits. In general, the technology that can be used to solve a problem is defined as a specific set of algorithms and methods. The more sophisticated the problem and the more varied the data, the more advanced the technology needed.
In automated machine learning, the algorithm is used to achieve automatization (that is, create computer software that can solve problems automatically). These algorithms are also called “machine learning algorithms. They are designed to solve problems based on some algorithmic rules that can be understood and used in code by machine learning programmers.
Machine learning is an important technology for achieving automation in industrial automation. The fact that most of these algorithms were developed by machine learning researchers and programmers indicates that we need to use them in automated machine learning.
4. Machine Learning Training Data is Sensible
It is impossible to use unstructured data for automatic machine learning if we do not have a large amount of training data for each algorithm, which has been created by experts on creating training sets for each algorithm. In other words, the more trainable data we have, the more accurate our results can be.
Although most training data belongs to structured domains such as texts (for example, content on articles and books), images, and video sequences, there are also many examples of unstructured data that can be used for machine learning:
Machine learning training sets are available for all types of data. Data that is not used in training sets cannot be used in automated machine learning. The role of automated machine learning is to use this type of educated, pure, and simple data to create classified patterns or rules (that is, decision criteria) that can be applied to different kinds of problems for automation.
5. Automated Machine Learning Has Fewer Problems than Traditional Methods
AI is used by 37% of enterprises and organisations. Traditional methods are also machine learning methods, but they are usually not used in automated machine learning because they involve too much human intervention because match only a limited set of heuristic rules. These heuristic rules can be obtained from human experts, which is extremely time-consuming and expensive.
One advantage of automated machine learning is that we may use the same algorithm for many kinds of problems with different requirements, which results in fewer errors—that is, less overlooking or error detection—than traditional methods. This decrease in errors means less downtime for production lines, fewer production costs, and shorter product delivery times.
6. Automated Machine Learning Can Be Used in Real-Time
With automated machine learning, we can create real-time decision tools without human intervention and it is also possible to make decisions with a speed that is not possible with traditional methods. This is often a big advantage for automated machine learning because we may make decisions to speed up production lines or to improve the accuracy of predictions.
It should be noted that it is difficult to make the decision in real-time, but it can be done by cutting off the data from some traditional methods and loading it into an automated system.
This process can be done in milliseconds, which is usually faster than a human being can react. Some types of automated machine learning systems that can be used in real-time include reactive control and model-based control.
7. Automated Machine Learning Allows You to Make Changes Easily
The main purpose of automated machine learning is to optimize the use of data for making decisions with little human intervention. Because it does not require human intervention, it is possible to change the “decision criteria” or “algorithms” without major changes. It also allows you to use or change data in a way that is not possible with traditional methods. As it is done in a completely transparent way, we don’t have to worry about human errors.
For example, if we have a set of training data, the decision criteria can be changed automatically (that is, without human interference) by selecting another type of software algorithm that can be used to make decisions based on the same training data; And if the first experiment was not successful, we can easily change the training data and repeat our experiments again and again until we finally get the results that meet our requirements.
Last Updated on October 12, 2023 by Priyanshi Sharma
