Machine Learning Will Not Take Away All The Jobs: Study

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, a study has found. 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).

The researchers found out that, although the economic effects of machine learning 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.

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. Machine Learning is one element of what is known as artificial intelligence. Rapid advances in Machine Learning have yielded recent improvements in facial recognition, natural language understanding and computer vision.

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. Predicting how machine learning will affect a particular job or profession can be difficult because machine learning tends to automate or semi-automate individual tasks, but jobs often involve multiple tasks, only some of which are amenable to machine learning approaches.

Earlier this year, for instance, researchers showed that a ML program could detect skin cancers better than a dermatologist. However, that does not mean machine learning will replace dermatologists, who do many things other than evaluate lesions.

Tom Mitchell from Carnegie Mellon University said, "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.”

Jobs that do not require dexterity, physical skills or mobility also are more suitable for ML. Tasks that involve making quick decisions based on data are a good fit for ML programs; not so if the decision depends on long chains of reasoning, diverse background knowledge or common sense.

Understanding the precise applicability of Machine Learning in the workforce is critical for understanding its likely economic impact, researchers said.