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5 Ways Machine Learning is Changing the Game — an article on the Smart Living Network
April 30, 2019 at 3:43 AMComments: 0 Faves: 0

5 Ways Machine Learning is Changing the Game

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Machine learning has affected most industries with an effort to automate tasks and increase efficiency. While this technology is replacing workers in some industries, it is enhancing the work of professionals in other sectors. Machine learning technology helps computers to learn so that they can see, read, listen, talk, and write so as to do some of the tasks that are done by humans. Here is how machine learning is changing the gam

1. Transport


The transport industry is becoming more dependent on AI and machine learning. Research and predictions show that over the next ten years, most of the rail and shipping networks are going to be controlled autonomously. While China is testing driverless public buses, the major automotive companies are looking to release self-driving cars. Also, Google and Rolls Royce are collaborating on designing and launching the first self-driving ship in the world by 2020. The vessel will employ Cloud Machine Learning Engine by Google in tracking and identifying objects at sea. While the companys self-driving car replaces a single driver, the AI in this ship will replace 20 members of the crew. Many Canadian aviation firms are also heavily investing in the development of pilotless commercial airplanes. Additionally, NASA has already seen success in launching and landing an autonomous space shuttle.


2. Health


Machine learning is playing a significant part in your well-being and health every day. It is being utilized in diagnosing patients faster. It is also being employed in preventing illnesses through prediction of potential health issues that an individual may experience depending on age, genetic history, and socio-economic status, among others. The use of machine learning and AI-powered software assists in analyzing and cross-referencing symptoms and signs against databases that have millions of other conditions and cases to result in faster diagnoses. Hence, numerous lives can be saved via faster treatment, and there is reduction of time spent by a patient in the health system. Moreover, physicians are utilizing AI algorithms in the precise detection of tumors in radiology scans and analysis of various moles for skin cancer. Also, machine learning has been adapted to increase the speed of research toward cancer treatment.


3. Manual and Skilled Labor


Machine learning has enabled the automation of industries. Tasks and functions that were at one point undertaken by trained employees are quickly being automated, especially mining and factory jobs that involve potential danger and harm. For instance, Australia is using driverless trucks in mining pits, and the trucks are run remotely from a control center that is miles away. Whats more, supermarkets have replaced staff with self-service kiosks. However, more development is required in this area as there is a need for the human ability to solve issues quickly.


4. Law


Legal organizations are speedily employing machine learning in the processing of vast amounts of information linked to legal precedents. For instance, J.P. Morgan is using a software called Control Intelligence in reviewing documents as well as previous cases in seconds instead of the previous 360,000 hours. While this technology is making legal work easier, hastening court processes, speeding up trials, and minimizing the time for preparing a case, lawyers are unlikely to be replaced. This is because there is a need for human logic during a legal process.


5. Education


Teachers are required to be referees, mentors, analysts, educators, allies, counselors, diplomats, and more. Hence, it is not possible for a computer to carry out all these functions. However, machine learning has enabled the automation of some of these functions. The technology has enabled computers to be programmed in the determination of individual study plans that are particular to the needs of each student. Machine learning algorithms are being used in analyzing test results, which quickly minimizes the time taken by teachers to grade. Moreover, these algorithms can use the academic history and attendance of a student to find learning disabilities and gaps in knowledge. This software will not replace teachers but will facilitate learning environments and teaching to enable the outcomes and reduce the burden on both the student and teacher.


Conclusion


Whether it is health, education, legal, or any other industry, machine learning technology is increasing productivity and efficiency. Getting insight into machine learning through machine learning with python can enable individuals to understand and leverage it. If people do not adapt and embrace the technology to their benefit, it will replace them.


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