The_Future_of_Employment

can be accomplished by machines, while non-routine tasks are not sufficiently
well understood to be specified in computer code. Each of these task categories
can, in turn, be of either manual or cognitive nature – i.e. they relate to
physical labour or knowledge work. Historically, computerisation has largely
been confined to manual and cognitive routine tasks involving explicit rulebased
activities (Autor and Dorn, 2013; Goos, et al., 2009). Following recent
technological advances, however, computerisation is now spreading to domains
commonly defined as non-routine. The rapid pace at which tasks that were defined
as non-routine only a decade ago have now become computerisable is
illustrated by Autor, et al. (2003), asserting that: “Navigating a car through city
traffic or deciphering the scrawled handwriting on a personal check – minor
undertakings for most adults – are not routine tasks by our definition.” Today,
the problems of navigating a car and deciphering handwriting are sufficiently
well understood that many related tasks can be specified in computer code and
automated (Veres, et al., 2011; Plötz and Fink, 2009).
Recent technological breakthroughs are, in large part, due to efforts to turn
non-routine tasks into well-defined problems. Defining such problems is helped
by the provision of relevant data: this is highlighted in the case of handwriting
recognition by Plötz and Fink (2009). The success of an algorithm for handwriting
recognition is difficult to quantify without data to test on – in particular,
determining whether an algorithm performs well for different styles of writing
requires data containing a variety of such styles. That is, data is required
to specify the many contingencies a technology must manage in order to form
an adequate substitute for human labour. With data, objective and quantifiable
measures of the success of an algorithm can be produced, which aid the continual
improvement of its performance relative to humans.
As such, technological progress has been aided by the recent production
of increasingly large and complex datasets, known as big data. 16 For instance,
with a growing corpus of human-translated digitalised text, the success of a
machine translator can now be judged by its accuracy in reproducing observed
translations. Data from United Nations documents, which are translated by hu-
16 Predictions by Cisco Systems suggest that the Internet traffic in 2016 will be around 1
zettabyte (1×10 21 bytes) (Cisco, 2012). In comparison, the information contained in all books
worldwide is about 480 terabytes (5 × 10 14 bytes), and a text transcript of all the words ever
spoken by humans would represent about 5 exabytes (5×10 18 bytes) (UC Berkeley School of
Information, 2003).
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