By Alexander E.
With the evolution of machine learning, computers have the ability to learn without being programmed by using algorithms to iteratively learn from data. Machine learning is similar to data mining in that they both look for patterns in data. It then uses that data to perform tasks without being explicitly programmed. Machine learning has been developed in consumer products and services, which are items that are purchased by individuals or households for daily consumption. The impact of machine learning has increased the ability to anticipate consumer needs and help consumers make decisions without using their input. When dealing with consumer products and services, machine learning produces many benefits, utilizes tools and software for data to be processed, and faces challenges in the process.
Machine learning can be classified into two different types including supervised learning and unsupervised learning. In supervised learning, there are input variables and output variables in which the input data can predict the output for that data. This algorithm compares the actual and correct output to find errors so that it can modify it. Unsupervised learning is when the system has input variables without output variables and has to figure out what the data is showing. In this algorithm, there are no correct answers.
For machine learning to perform, it requires tools and software that provide functionality. According to the InfoWorld.com article, one of the open source tools used is called Shogun, which is a machine learning library written in C++ used in: Java, Python, C#, Ruby, R, Lua, Octave, and Matlab. (Yegulalp, 2014). Another tool is called Accord, which is a machine learning and signal processing framework for .Net. Signal processing is “a range of machine learning algorithms for images and audio that stitches together images or performing face detection.” (Yegulalp). H20 is used for business processes, including fraud or trend predictions.
Consumers can benefit from machine learning in many ways. For example, machine learning makes it possible for consumers to personalize their online experiences without having to input any data. In the Wired.com article, it mentions that “from a consumer perspective, everything that is performed online, every sales process, product interaction… is being tracked by various sources.” (“Use Data to Tell the Future: Understanding Machine Learning”, 2014). Machine learning can be effective when perceptions need to be found from Big Data that’s always changing and diverse. It can outperform traditional methods on accuracy, scale, and speed. These methods are highly superior when analyzing potential consumers across data from many sources including transactional and social media sources. (“Use Data to Tell the Future: Understanding Machine Learning”). There are services like Netflix and Facebook that predict future outcomes for the users. When watching favorite shows on Netflix, it can generate suggestions based on user preferences. Facebook and other social media have the ability to collect data from users and present suggestions of what friends to add or what pages to like.
An application that is using machine learning is The North Face, which is a U.S. outdoor apparel and retail store. The ComputerWorld.com article discusses how North Face created the Expert Personal Shopper that is an online shopping assistant. (Gaudin, 2016). This system asks questions based on what the consumer may be using his/her desired product for. It then offers the best choices based on the criteria selection. Another consumer service that is using machine learning is Amazon. According to TheNextWeb.com article, Amazon is using machine learning to make an improvement in product reviews. (Ghoshal, 2015). The company wants to upgrade the system so that reviews can be more helpful to consumers on products. Amazon also wants to help consumers to learn about the manufacturer changes of existing products that are yet to be announced or mentioned.
Machine learning deals with challenges in the system. It requires a huge amount of data from humans to understand different concepts. In TheVerge.com article by James Vincent, he talks about how big firms like Facebook or Google have a copious amount of data to improve machine learning systems. (Vincent, 2016). On the other hand, small tech firms don’t have the luxury in the same amount of data. Another challenge is having artificial intelligence be able to do multiple tasks at once. The current systems are able to be efficient in whatever task they are designed for, but are not able to fully multitask. The third major challenge is trying to understand how artificial intelligence comes to its conclusions. Machines aren’t fully advanced in thinking exactly like humans which limits its capabilities.
In conclusion, machine learning can benefit consumer needs in the future with the expansion of artificial intelligence. It has the potential to analyze what consumers desire and make recommendations or suggestions based on personalization. Consumers will be able to produce less and less input data for their personal needs with the growth of machine learning.