Machine learning uses algorithms and statistical models to process the raw data and convert them into specific tasks without human interaction. Humans are living in a global revolution of technology. The first two decades of the 21st century have witnessed major advancements in artificial intelligence research.
Machine learning is one of the most successful applications of technology, affecting a large number of industries and impacting billions of users every day. Machine learning is connected to 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 utilization opens door to future technologies that people use in their daily life.
Voice assistants are found everywhere right now, voice assistants like Apple’s Siri, Google Assistant, Amazon’s Alexa, etc. have made the way to be part of people’s general conversation. The machine learning algorithm is used in all these voice assistants to recognize the speech using Natural Language Processing (NLP). Then, it converts the speech into numbers using machine learning and forms a response accordingly. Researchers expect it to become smarter in the future as machine learning techniques get more advanced.
Self-driving cars are one of the interesting technologies where machine learning is used on a high-level. When we look into self-driving cars, all three main aspects of machine learning namely supervised, unsupervised and reinforcement learning is used throughout the car’s design. These self-driving cars use ML features like detecting objects around the car, finding the distance between the cars in the front, evaluating the condition of the driver, etc. Machine learning can also advise about road conditions and traffic.
Big companies who are into financial engagements and banks are using ML for fraud detection. This helps companies to keep consumers safe. ML can also be valuable to companies that handle credit card transactions. The technology is trained to help transactions that appear to be fraudulent based on certain criteria according to the company’s rules. By detecting such frauds, companies can prevent them from falling prey to a big loss. This also helps an enterprise to gain insights into its competitive landscape and consumer loyalty and forecast sales or demand in real-time with machine learning.
Machine learning is helping customer support by giving chatbots relevant replies to consumer’s queries. With the help of Natural Language Processing (NLP) and sentiment analysis, machine learning algorithms can understand customer’s needs and the tone they say it. This will redirect the system queries to the appropriate customer support person.
The value of machine learning in healthcare is its ability to process a large amount of data beyond scope of human capability and then convert this data into clinical insights that help physicians. ML helps in planning and providing care, which leads to better outcomes, lower costs of care, and increased patient satisfaction. Computer-assisted diagnosis (CAD), an application of ML can also be used to review the mammography scans of women in predicting cancer.