According to a recent study findings, machine learning computer systems, which get better with experience, can outperform people in a number of tasks, though they are unlikely to replace people in all jobs. Researchers from Carnegie Mellon University and Massachusetts Institute of Technology (MIT) in the US found 21 criteria to evaluate whether a task or a job is amenable to machine learning (ML).
“Although the economic effects of ML are relatively limited today, and we are not facing the imminent ‘end of work’ as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound,”
researchers said. The skills people choose to develop and the investments businesses make will determine who thrives and who falters once ML is ingrained in everyday life, they argue.
Advantages of machine learning
ML is one of the elements of artificial intelligence. Because of advancement in machine learning, improvements in facial recognition, natural language understanding and computer vision have been observed. It already is widely used for credit card fraud detection, recommendation systems and financial market analysis, with new applications such as medical diagnosis on the horizon.
Does machine learning affect your profession?
It is hard to predict how machine learning will affect a particular job or profession because it tends to automate or semi-automate individual tasks, but jobs often involve multiple tasks, only some of which are amenable to ML
Earlier this year, for instance, researchers showed that a ML programme could detect skin cancers better than a dermatologist. But, that does not mean ML will replace dermatologists, who do many things other than evaluate lesions.
“I think what’s going to happen to dermatologists is they will become better dermatologists and will have more time to spend with patients. People whose jobs involve human-to-human interaction are going to be more valuable because they can’t be automated,” said Tom Mitchell from Carnegie Mellon University.
To learn how to detect skin cancer, for instance, ML programmes were able to study more than 130,000 labelled examples of skin lesions. ML can be of great help for tasks such as scheduling, jobs that do not require dexterity, physical skills or mobility and tasks that involve making quick decisions based on data.