Many people are familiar with machine learning from shopping on the internet and being served ads related to their purchase. This happens because recommendation engines use machine learning to personalize online ad delivery in almost real time. Beyond personalized marketing, other common machine learning use cases include fraud detection, spam filtering, network security threat detection, predictive maintenance and building news feeds.
Now lets brief about Machine Learning
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
And, the good news is, Latest technology is advancing consistently and revolutionizing every facet of our routines. Humans had their first brush-up with Machine Learning when voice-controlled personal assistants — Amazon’s Echo and Alexa — were launched. These devices are a new normal with the trend of smart homes picking up. Driverless cars, which were a quintessential sci-fi fantasy, aren’t something of the far-off future now. These new-age vehicles, aimed at cutting down human labor, are tested across the world for their utility benefits.
Let’s dive in how Machine Learning is revolutionizing our lives.
Machine Learning can empower surgical robots to help doctors in medical procedures while ensuring minimal invasion and high precision. This achievement can improve the success rates of surgical procedures and accelerate turnaround time with cost benefits. In the context of healthcare, ML can be a critical enabler to efficient diagnosis, research, and treatment, thereby underlining the holistic transformation of the sector.
From administration, record-keeping to fully fledged diagnosis and treatments, ML has the capability to analyze the crisis at hand and compare it with numerous other scenarios for the right treatment and procedure. This comparison saves time and paves a strategic path for the decisive medical approach.
Today’s transportation industry is highly influenced by Machine Learning. The technology has been instrumental in eradicating the threat posed by reckless driving through the deployment of sensory management and automation. This has intelligized vehicles to understand the surrounding parameters and take precautionary action whenever required to ensure passenger safety.
China is testing driverless buses for its city while Google and Rolls Royce have collaborated to design and launch the world’s first self-driving ship by 2020. The ship will be leveraging Google’s Cloud Machine Learning Engine to understand the sea and objects surrounding it. This will enable the remote controlling of the vessel and end up reducing the resources involved in the transportation.
Beyond vehicles, Machine Learning can soon by deployed for traffic management and preventing traffic congestions on roads. China and Singapore, at the moment, are leading the innovation and creating algorithms that can help drivers choose routes cleared off traffic.
Several banks and financial institutions are using ML-based complex algorithms to analyze and predict loan risks and assess the quality of the application. Therefore, reinforcing them to take informed decisions. It’s also helping in the detection of frauds and scams via analytics.
The otherwise traditional industry is favoring Machine Learning to mitigate its transformational bottlenecks and create better user engagement for the stakeholders, including the customers. Voice Recognition, Chatbots, and Predictive Analysis are helping bridge the gap between financial establishments and potential customers. Today, customers can contact a business anytime around the day and rest assured of instant, real-time replies from Chatbots. This saves time as well as improves the customer experience for the organizations.
It’s true that teachers cannot be replaced by bots, but they sure can be assisted by the diversity. Machine Learning can provide to their methods of teaching and education on a whole. ML capabilities are being used to assess the child’s academic understanding, analyze the way he/she perceives knowledge, and create a customized academic plan that can help focus on the wholesome attribute of that specific student.
The algorithms analyze test results and create a unique grading system that can free up the teachers’ time and help refine the education modules for children. Not to mention, ML can be extremely helpful for students with disabilities and learning gaps.
ML, in addition to teaching, is simplifying administrative duties. It helps educational institutions create an organized means of administering students and staff via its automated responses, customized software and more. It’s all about breaking down the processes and making organizing an institution less of a burden.
Lawyers remain busy. So, automating their day-to-day activities can free them from the redundant tasks and help them focus on creating liable solutions to the cases at hand.
Machine Learning brings forth this automation and adds a new twist to the otherwise conventional industry. It augments documents and their processing. It also analyzes them for proofs and research, which can help the lawyers extract the relevant information without having to expend time in pouring over books and legal documents. They can also help systemize the operational tasks, such as staying abreast with the hearings and keeping note of the impending dates.
ML can strategize how lawyers review their cases and hopefully, can speed up the cases that otherwise take months and years to resolve.