Application Areas of Machine Learning
Machine learning (ML) is referred to as the use of artificial intelligence (AI) that allows systems to learn and improve automatically from experience without being specifically programmed. The primary objective of ML is to enable computers to learn automatically and accordingly adjust actions without human assistance or intervention. ML build algorithms that receives or takes input data and perform statistical analysis for predicting output and updates output with the availability of new data (Michalski, Carbonel & Mitchell, 2013). Business intelligence (BI) is referred to as technology-based method that analyses data and present the litigable information to end users so that they can make informed decisions on business. BI uses ML to unlock the customer and corporate data that are used to accomplish decisions that can keep the company ahead of the competition (I??K, Jones & Sidorova, 2013). This essay discusses about the different application areas of machine learning along with specific applications of machine learning. Next, the essay discusses about the various methods of machine learning. Then the essay investigates about the emerging technologies as well as current developments in the area of machine learning. Finally, the essay predicts about the future of machine learning and mention its impact on human life.
The application areas of machine learning (ML) are as follows. One common application area of ML is virtual personal assistant. Such personal assistant helps in finding information when verbally asked. An important component of the virtual personal assistant is ML as it gathers and refine information based on one’s earlier involvement with them. Virtual personal assistant is incorporated into various platforms such as smart speakers, smartphones, mobile applications (Tanweer, Ali & Abdullah, 2017). ML helps in managing traffic by estimating the regions where congestion can occur on basis of daily experiences. Through prediction of rider demand the cab booking application uses ML to define hours of price surge. Nowadays, AI powered video surveillance system detect any sort of crime before it is going to happen. The system provides alert to human attendants or security personnel by tracking any unusual human behaviour and which helps in avoidance of mishaps. When such activities are counted and reported to be true then those services helps in improving the surveillance service with the help of ML (Lee & Nevatia, 2014).
Further ML has applications in different online activity areas of cyberspace, which are as follows. ML is used for recognizing friends or associated person or group on social media platform. ML is also the core component of computer vision technique that is used for identifying objects in videos and images through extraction of useful information (Goodfellow et al., 2016). ML empowers spam filtering and malware filtering techniques that detects the new spam and malware and offer protection against them. Most of the websites provides customer support through chatbot. Chatbot present information from website to customers. However, ML algorithms of chatbot make it to understand user queries in a better way and supply better answers to them (Hill, Ford & Farreras, 2015). ML are used by search engines to improve search results for the user. ML also recommend products to the customer based on the customer’s past purchases, his/her behaviour with the application or website such as items liked or specific brand preference or items added to cart. ML helps in making cyberspace a secure place. It does so by tracking online monetary fraud. For instance, online merchandise such as Paypal uses ML to differentiate legitimate and illegitimate transactions that takes place between sellers and buyers (Buczak & Guven, 2016).
Online Activity Areas of Cyberspace
ML finds its application in almost all sorts of industries. The applications of ML are as follows. In manufacturing industry, ML is used for condition monitoring and predictive maintenance. In retail industry, ML is used for cross-channel marketing and upselling. In healthcare industry, ML is used for identifying disease and risk satisfaction. In hospitality and travel industry, ML performs dynamic pricing. In the industry of financial services, ML is used for regulation and risk analysis ((Michalski, Carbonel & Mitchell, 2013).
Decision trees is a method of ML that in general allow business to make better decision and consequently make better profit. However, such trees may have large bias and large variance. To overcome this issue, various decision trees are combined together to make greater predictive performance which cannot be achieved through single decision tree. Another method of ML is reinforcement learning that enables machines and software agents in automatic determination of ideal behaviour within any particular context and maximization of its performance (Witten et al., 2016).
ML nowadays are used in releasing the potential of three-dimensional printing, blocking fraud and determining ways of coding curiosity into intelligent machines. Another emerging technology in ML is its inclusion in audience management tool to perform digital advertising in determining the demographic group or most profitable audience for any advertisement. Supervised learning technologies are developed today by combining techniques of ML with recurrent neural networks in order to detect any sort of suspicious activity of user or cyber-attacks. ML are also used today on the automation of robotic processes that automate human tasks for assisting the corporate processes. Such automation is extremely useful in cases where it is very expensive or inefficient to hire someone for a particular task or job (Low et al., 2014).
Deep learning is one of the most recent developments in the research of ML and the methods of deep learning has made great advancement in ML. Deep learning enables computational models to attain data representation that consist of multi-level abstraction (LeCun, Bengio & Hinton, 2015).
Future of machine learning and the impact of machine learning in human lives:
In future ML will play a pivotal role in the revolution of AI. ML plays a ML has a direct impact in many aspects of human lives. Many applications uses ML to suggest or recommend desired or preferred items to the appropriate user. In future ML will emerge as a global force as, it will enable the computer to see, to generate innovate art, to contribute in medical diagnosis and to translate different languages.
Conclusion:
From the above discussion in the essay, it draws conclusion the ML is a type of algorithm that are used in different software applications to predict results more accurately without specifically programming the applications. The essay also mentioned significance of ML in different application areas as well as different application of ML. In addition, the essay also outlined some methods of ML. Then the essay highlight about the emerging technologies and current developments in the field of ML. Finally, the essay states about the future of ML as well as its impact on human lives.
References:
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.
Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 49, 245-250.
I??K, Ö., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13-23.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
Lee, S. C., & Nevatia, R. (2014). Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system. Machine vision and applications, 25(1), 133-143.
Low, Y., Gonzalez, J. E., Kyrola, A., Bickson, D., Guestrin, C. E., & Hellerstein, J. (2014). Graphlab: A new framework for parallel machine learning. arXiv preprint arXiv:1408.2041.
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media.
Tanweer, M., Ali, A., & Abdullah, M. (2017). Virtual Personal Assistant.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
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