The proposition of machine learning on several industries, professional field and the personnel are considered astounding by some and appalling. Ones outlook will largely depend on your profession and the work one does.
Machine learning has the possibility to mechanize a large segment of expert manual labor, but the quantity to which this affect a personnel depends on the level of complexity concerned in the job. Machine learning at this age allows the automation of singular tasks, whereas many jobs involve multiple tasks and even multitasking at level machine learning isn't capable of yet.
Teaching and Learning Industry
Computers can be programmed to determine individual study plans, specific to each student's needs. Algorithms can analyze test results, drastically reducing the time teachers spend in their leisure time on grading.
A student's attendance and academic history can help determine gaps in knowledge and learning disabilities. These applications won't necessarily translate to a teacher less classroom though there is that hypothetical, as well, but will facilitate the teaching and learning environments to enhance the outcomes and ease the burden on both teacher and student.
Legal and law industry
Law firms are increasingly turning to machine learning to process massive amounts of data related to legal precedents. J.P. Morgan, for example, uses a software program dubbed Control Intelligence to review documents and previous cases in seconds that would otherwise take 360,000 hours.
As with our teachers above, its unlikely machine learning or AI will replace lawyers any time soon, given the necessity of rebuttal and human logic or appeal, but the incorporation of machine learning will surely reduce the time taken to put together a case, and it could expedite trials, speeding up the processes of the court.
The automation of industries is the most obvious shift we can expect from machine learning. Functions and tasks that were once undertaken by trained workers are increasingly being mechanized; in particular jobs that involve some element of danger or potential harm, such as work in factories and mining.
There are already driverless trucks operating in mining pits in Australia, operated remotely from a distant control center.More and more machinery is taking the place of labor. You need only visit your local supermarket to see more self service kiosks and fewer staff. But, here again, there is a limit to how far a person is willing to deal with a machine, and the human ability to quickly fix a problem isn't something machines are capable of yet.
Machine learning is taking a bigger part in our health and well being on a daily basis, and it is already being used for faster patient diagnosis. Even the prevention of illness in the first place have been aided by predicting the potential health problems one may be susceptible to, based on age, socio economic status, genetic history, etc.
The use of programs to analyze and cross reference symptoms against databases containing millions of other cases and illnesses has led to faster diagnoses of illness and disease, saving lives through quicker treatment and decreasing the time a patient spends in the health system. Hospitals are currently using AI algorithms to more accurately detect tumors in radiology scans and analyze different moles for skin cancer, and machine learning is being adapted to accelerate research toward a cure for cancer.
The self control of our transport industries is steadily becoming more reliant on machine learning and AI, and it is expected that within the next decade, the majority of our shipping and rail networks will be controlled autonomously. China is currently testing driver less public buses.
Rolls Royce and Google have teamed up to design and launch the world's first self driving ship by 2020. The vessel will use Google's Cloud Machine Learning Engine to track and identify objects at sea. While Google's self driving car replaces one driver, the autonomous ship's AI will need to carry out the tasks usually requiring a crew of 20.