What is Machine Learning?
Machine learning is an interface for artificial intelligence (AI) that automatically learns and builds skills without being directly programmed (Simon, 1983). Machine learning focuses on the development of applications that can display and use data (Delnevo et al., 2019). The learning process begins with observations or inputs to recognize data patterns and make better decisions based on the features learned (Raina et al., 2007). The main aim is to encourage computers to learn and automatically modify their actions without human intervention or assistance (Whitehill et al., 2014). Machine learning is also a study of computer algorithms that automatically advance through learning (Das et al., 2015). The algorithms build a sample-based model, called training data, to simulate and determine without planning (Burns & Brock, 2005). Machine learning techniques, including email and computer vision, are being used for various purposes (Kaelbling et al., 1996). It is challenging or unlikely to construct conventional algorithms to conduct the necessary tasks (Deo, 2015).
Applications of Machine Learning
In several sectors, machine learning plays a rising function, from engineering to project planning (Langley & Simon, 1995). The latest architecture paradigm is primarily focused on computer-aided methodologies (Giannitelli et al., 2014). Engineering structures are closely tested during the design process by using models to include detail on stress regions, displacement, and load-bearing power (Wilson et al., 2008). Several engineers use an approach to the finite element as a primary tool of study (Steif & Gallagher, 2004). In the case of finite element mesh construction, machine learning may be a critical factor in enhancing efficiency and the accuracy of measured solutions (Kolandaivelu et al., 2015).
Another engineering modeling of machine learning has been carried out in constructing structures such as traffic density forecasting in road and road infrastructure (Hofleitner et al., 2012). There are also many other technical uses for data mining technology, including malfunction diagnostics, entity recognition, and computer or sensor configuration (Sun et al., 2018). Classification can be part of the fault diagnosis process (Chen et al., 2018). Besides design, deep learning methodologies, such as neural networks and case-based inference, are widely utilized to handle project engineering in an environment where significant multinationals require tremendous time and budget plans (Menon et al., 2004).
Machine learning application is strongly linked to computer statistics, the estimation of which focuses on computers, but statistical learning is not all machine learning (Iniesta et al., 2016). The analysis of mathematical optimization provides techniques, philosophy, and areas of application in machine education (Olden et al., 2008). Data mining is a related area of science which focuses on unattended learning in the exploratory data processing (Raihan et al., 2018). Machine learning is often known as statistical analysis in the sense of business problems (Makridakis et al., 2018).
The development of massive datasets coupled with improved algorithms and explosive growth of computational resources in recent years has also contributed to an unprecedented increase in emphasis on machine learning (L’Heureux et al., 2017). Today, the classification, regression, clustering, or dimensional reduction of extensive collections of particularly high-dimensional input data has been used efficiently in machine learning (McCallum et al., 2000). Machine learning has outperformed human abilities in several fields (Holzinger, 2016). As a result, most of our daily lives are guided by machine learning algorithms such as image and speech recognition, online searches, fraud detection, emails, spam filtering, credit scores, and many others (Das et al., 2015).
Machine learning algorithms have revolutionized other sectors, including the recognition of images (Rogan et al., 2008). In order to achieve meaningful results in material science, it is clear that one must not only use machine learning methods (Shepperd et al., 2014). Since machine learning approaches are still new, several reported implementations are elementary in nature and complexity (Batista et al., 2004). They also require models to be fitted into incredibly limited training sets or machine learning techniques to map spaces in hundreds of hours (Ganapathi et al., 2009). Of course, machine learning algorithms can be used as an easy-to-use process for small, low-dimensional datasets (Mullainathan & Spiess, 2017). However, this is not enough to allow us to reproduce effective machine learning methods in other fields (Butler et al., 2018).
The downside of Machine Learning
Deep learning is one of the popular growing machine learning techniques used in various applications (Ardabili et al., 2020). Deep neural networks can learn features without extensive supervision (Jing & Tian, 2020). However, neural networks with one or two completely interlinked secret layers challenge new-themed researchers to explain deep learning algorithms' functions (Najafabadi et al., 2015). Another main challenge of machine learning algorithms in research is the lack of new rules, understanding, and information resulting from their use (Zhou et al., 2017). This is because machine learning algorithms are usually seen as black boxes (Koh & Liang, 2017). After all, concept computers are too abstract and foreign for people to understand (Boden, 2009). The validity of the criticism and the multiple reactions to the problem will be addressed (Kessler et al., 2019).
Machine learning is utilized for many reasons due to its ability to produce similar outcomes as a human can do. However, building an effective machine learning algorithm can be a challenging task due to its complexity. Nevertheless, machine learning is still widely used to obtain more accurate results in many scientific fields by researchers worldwide, and this intervention will dominate any other methods in the future.
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