From Chatbots to Cybersecurity, Machine Learning Is Innovating Various Business Dimensions
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Machine Learning, a subset of Artificial Intelligence, has some revolutionary impact across several business operations and functions. Machine learning has many potential uses, including external (client-facing) applications like customer service, product recommendation, and pricing forecasts, but it is also being used internally to help speed up processes or improve products that were previously manual and time-consuming.
One of the most relevant consumer-based use for machine learning is voice assistants or chatbots that applies mostly to smartphones and smart home devices. The voice assistants on these devices use machine learning to understand what you say and craft a response. The machine learning models behind voice assistants were trained on human languages and variations in the human voice because it has to translate what it hears into words and then make an intelligent, on-topic response.
Millions of consumers use this technology, often without realizing the complexity behind the tool. The concept of training machine learning models to follow rules is fairly simple, but when you consider training a model to understand the human voice, interpret meaning, and craft a response, that is a heavy task.
Moreover, while the technology to gather and read data has existed for quite some time, teaching computer systems to actually understand what they’re looking at has proved to be a deceptively complicated problem. Thanks to machine learning applications, more and more devices now feature object recognition capabilities. An autonomous vehicle, for instance, knows another car when it sees one, even if programmers didn’t provide it with an exact example of that car to use as a reference. Retail stores are even using this technology to help speed up the checkout process. Cameras detect the items customers place in their cart and can automatically charge their accounts when they leave the store.
Machine learning algorithms have allowed cybersecurity efforts to keep pace with these rapid changes. Predictive analytics make it possible to identify and mitigate threats faster than ever, and machine learning can track user behavior within a network to spot irregularities and gaps in existing security measures.
Furthermore, machine learning doesn’t just help companies set prices; it also helps companies deliver the right products and services to the right areas at the right time through predictive inventory planning and customer segmentation. Retailers, for example, use machine learning to predict what inventory will sell best in which of its stores based on the seasonal factors impacting a particular store, the demographics of that region and other data points — such as what’s trending on social media, said Adnan Masood who as chief architect at UST Global specializes in AI and machine learning.
“Think of it as a recommendation engine built for retail,” he added.
Similarly, companies can use ML to better understand specific segments within their overall customer base; retailers, for instance, use the technology to gain insights into the buying patterns of specific groups of shoppers — whether a group based on similar ages or incomes or education levels, etc. — so they can better target their needs, such as stocking stores with the merchandise that the identified segment is most likely to want.
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Smriti is a Content Analyst at Analytics Insight. She writes Tech/Business articles for Analytics Insight. Her creative work can be confirmed @analyticsinsight.net. She adores crushing over books, crafts, creative works and people, movies and music from eternity!!
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