Machine Learning Poised to Drive
January 4, 2018
learning computer systems, which get better with experience, are poised
to transform the economy much as steam engines and electricity have in
the past. They can outperform people in a number of tasks, though they
are unlikely to replace people in all jobs.
So say Carnegie Mellon University's Tom Mitchell and MIT's Erik
Brynjolfsson in a Policy Forum commentary to be published in the Dec. 22
edition of the journal Science. Mitchell, who founded the world's first
Machine Learning Department at CMU, and Brynjolfsson, director of the
MIT Initiative on the Digital Economy in the Sloan School of Management,
describe 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," they write. 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.
"We don't know how all of this will play out," acknowledged Mitchell,
the E. Fredkin University Professor in CMU's School of Computer Science.
Earlier this year, for instance, researchers showed that a ML program
could detect skin cancers better than a dermatologist. That doesn't mean
ML will replace dermatologists, who do many things other than evaluate
"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."
Tasks that are amenable to ML include those for which a lot of data is
available, Mitchell and Brynjolfsson write. To learn how to detect skin
cancer, for instance, ML programs 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.
ML can be a game changer for tasks that already are online, such as
scheduling. Jobs that don't 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.
ML is not a good option if the user needs a detailed explanation for how
a decision was made, according to the authors. 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.
Work is underway, however, on "explainable" ML systems.
the precise applicability of ML in the workforce is critical for
understanding its likely economic impact, the authors say. Earlier this
year, a National Academies of Sciences, Engineering and Medicine study
on information technology and the workforce, co-chaired by Mitchell and
Brynjolfsson, noted that information technology advances have
contributed to growing wage inequality.
"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," they write.
"This will require considerable attention among policy makers, business
leaders, technologists and researchers."