Researchers say that machine learning may outperform people but that it cannot replace them

**Machine learning** (ML) systems, an element of what is known as Artificial Intelligence (AI), can **outperform people** in a number of tasks though they are unlikely to replace people in all jobs, say researchers. [caption id=“attachment_4276353” align=“alignleft” width=“380”]  Representational image. Reuters.[/caption] In a “Policy Forum” commentary published in the journal Science, Tom Mitchell of Carnegie Mellon University and Erik Brynjolfsson of Massachusetts Institute of Technology (MIT), both in the US, said that tasks that are amenable to ML include those for which a lot of data is available. ML can be a game changer for tasks that already are online, such as scheduling, but its is not a good option if the user needs a detailed explanation for how a decision was made, according to the authors. The researchers explained that machine learning tends to automate or semi-automate individual tasks, but jobs often involve multiple tasks, only some of which are amenable to ML approaches. Jobs that do not require dexterity, physical skills or mobility also are more suitable for ML, the researchers said. Earlier this year, for instance, researchers showed that a ML programme could detect skin cancers better than a dermatologist. That does not mean ML will replace dermatologists, who do many things other than evaluate lesions. To learn how to detect skin cancer, for instance, ML programmes were able to study more than 130,000 labeled examples of skin lesions. Likewise, credit card fraud detection programs can be trained with hundreds of millions of examples. “I think what’s going to happen to dermatologists is they will become better dermatologists and will have more time to spend with patients,” Mitchell said. “People whose jobs involve human-to-human interaction are going to be more valuable because they can’t be automated,” Mitchell added. Tasks that involve making quick decisions based on data are a good fit for ML programmes, but not so if the decision depends on long chains of reasoning, diverse background knowledge or common sense. In other words, ML might be better than a physician at detecting skin cancers, but a dermatologist is better at explaining why a lesion is cancerous or not. Understanding the precise applicability of ML in the workforce is critical for understanding its likely economic impact, the authors said. “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,” they pointed out. 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, the authors said.