What Can Machines Do Today?

What Can Machines Do Today?

Tasks that are especially amenable to machine learning are typically those that involve an awful lot of data. We've already seen numerous examples of this in action, not least in areas such as healthcare, where algorithms that are trained on vast quantities of data are able to outperform trained clinicians. The authors are at pains to point out that this doesn't mean that those professionals will be out of a job, however.

"I think what's going to happen to dermatologists is they will become better dermatologists and will have more time to spend with patients," they say. "People whose jobs involve human-to-human interaction are going to be more valuable because they can't be automated."

It stands to reason, therefore, that tasks that are already performed online are something that machine learning can do well today, while those that require personal or physical skills are much less so. Likewise, if decisions need to be made quickly, then ML can certainly do this, but it struggles more with long chains of reasoning or common sense.

There are also clear limitations in the ability of ML to actually explain the decisions it came up with — and we're not just talking the kind of "show your workings" that might be required from a regulatory perspective. While it can detect cancer well, it's likely that the doctor will do a much better job of explaining why the patient has cancer.

It seems that there is a great deal of misunderstanding at the moment about just what kind of impact technology might have on society, and so long as that misunderstanding exists, it will be difficult to construct suitable policy responses. While not a full answer in itself, papers such as this one do provide a more balanced contribution to the debate, which is to be welcomed.

"Although there are many forces contributing to inequality, such as increased globalization, the potential for large and rapid changes due to ML, in many cases within a decade, suggests that the economic effects may be highly disruptive, creating both winners and losers," the pair conclude. "This will require considerable attention among policymakers, business leaders, technologists, and researchers."