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Machine learning uses algorithms and statistical models to perform specific tasks without human interaction
Humans are living in a truly global revolution of technology. Thanks to the advancements on computational power and machine learning applications. The first two decades of the 21st century have witnessed dramatic advancements in artificial intelligence (AI) research. Machine learning has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. Machine learning is a subset of artificial intelligence that involves the study and use of algorithms and statistical models for computer systems to perform specific tasks without human interaction. Machine learning utilisation opens door to futuristic technologies that people use in their daily life. Henceforth, Analytics Insight brings you the top 10 use cases that everyone should know about.
Voice assistants are ubiquitous right now. Popular voice assistants like Apple’s Siri, Google Assistant, Amazon’s Alexa, etc. are paving the way to be part of people’s general conversation. Machine learning algorithm works behind all these voice assistants to recognize the speech using Natural Language Processing (NLP). Then, it converts the speech into numbers using machine learning and formulates a response accordingly. NLP is also used to translate unclear legalese in contracts into plain language to prepare information. Researchers expect it to become phenomenally smarter in future as machine learning techniques get more advanced.
Technology is gaining ground in the marketing system. Using machine learning features, marketing industry segments customers based on behavioural and characteristic data. The digital advertisement platforms allow marketers to focus on the set of audience with relevant product influence. They understand customer requirements and serve with better product promotion accordingly.
Big companies involved in financial engagements and banks are using machine learning for fraud detection. This helps companies to keep consumers safe. Machine learning can also be valuable to companies that handle credit card transactions. The technology is trained to flag transactions that appear to be fraudulent based on certain criteria according to the company’s rules. By detecting such mishaps, companies can be prevented from falling prey to a big loss. Besides, an enterprise can also gain insights into its competitive landscape and consumer loyalty and forecast sales or demand in real-time with machine learning.
Self-driving cars are one of the fascinating technologies where machine learning is leveraged on a high-level. The beauty of self-driving cars is that all the three main aspects of machine learning namely supervised, unsupervised and reinforcement learning are used throughout the car’s design. Smart cars use machine learning features like detecting objects around the car, finding the distance between the car in the front, where the pavement is located, and traffic signal, evaluating the condition of the driver and scene classification. Machine learning can also give real-time advice about road conditions and traffic.
Companies working on making the transportation industry more technology-reliant are choosing machine learning as the primary source. Ride-hailing apps like Uber, Lyft, Ola, etc use machine learning throughout their many products from planning optimal routes to deciding prices. Dynamic price in travel adjusts the traveller’s prices to changing market conditions. The prices vary depending on factors like time, location, weather, customer demand, etc. Machine learning is also helping drivers to find the most optimal route to get passengers from point A to B.
Organisations can use machine learning models to predict the customer’s behaviour based on their past data. Companies look for what people are talking about in social media and then identify those who are searching for the given product or service. For example, Zappos uses analytics and machine learning to help provide personalized sizing and search result for customers, as well as predictive behavior models.
The value of machine learning in healthcare is its ability to process huge datasets beyond scope of human capability, and then reliably converts analysis of that data into clinical insights that aid physicians. Machine learning helps in planning and providing care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction. Computer-assisted diagnosis (CAD), an application of machine learning can also be used to review the mammography scans of women in predicting cancer.
Intelligent Process Automation (IPA) is the product of the convergence of AI and related technologies including computer vision, cognitive automation and machine learning. By bringing these technologies together under a single process, companies get a richer automation possibility, unlocking every business value for the enterprise. The machine learning algorithm can be used in automating error-free insurance risk assessment from manual data entry work.
Machine learning is helping customer support by leveraging chatbots that give relevant reply to consumer’s queries. Using concepts of Natural Language Processing (NLP) and sentiment analysis, machine learning algorithms are able to understand customer’s need and the tone they say it. Then the system redirects the query to appropriate customer support person.
Machine learning plays a pivotal role in providing security at large gatherings. The technology provides an asset to help avoid fake alarms and spots things that human screeners may miss out in security at big public events. For example, Evolv Technology claims to offer a physical security system that screens 600 to 900 people to walk through per hour.
This article has been published from a wire agency feed without modifications to the text. Only the headline has been changed.