Machine learning will not lead to 'End of work': 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).

"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.

ML is one element of what is known as artificial intelligence. Rapid advances in ML 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 ML will affect a particular job or profession can be difficult because ML tends to automate or semi-automate individual tasks, but jobs often involve multiple tasks, only some of which are amenable to ML 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 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," said Tom Mitchell from Carnegie Mellon University.

"People whose jobs involve human-to-human interaction are going to be more valuable because they can't be automated," Mitchell said.

Tasks that are amenable to ML include those for which a lot of data is available, researchers said.

To learn how to detect skin cancer, for instance, ML programmes were able to study more than 130,000 labelled examples of skin lesions.

Likewise, credit card fraud detection programs can be trained with hundreds of millions of examples.

ML can be a game changer for tasks that already are online, such as scheduling.

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 ML in the workforce is critical for understanding its likely economic impact, researchers said.