The_Future_of_Employment

pletely automated (Phua, et al., 2010). In a similar manner, the comparative
advantages of computers are likely to change the nature of work across a wide
range of industries and occupations.
In health care, diagnostics tasks are already being computerised. Oncologists
at Memorial Sloan-Kettering Cancer Center are, for example, using IBM’s
Watson computer to provide chronic care and cancer treatment diagnostics.
Knowledge from 600,000 medical evidence reports, 1.5 million patient records
and clinical trials, and two million pages of text from medical journals, are used
for benchmarking and pattern recognition purposes. This allows the computer
to compare each patient’s individual symptoms, genetics, family and medication
history, etc., to diagnose and develop a treatment plan with the highest
probability of success (Cohn, 2013).
In addition, computerisation is entering the domains of legal and financial
services. Sophisticated algorithms are gradually taking on a number of tasks
performed by paralegals, contract and patent lawyers (Markoff, 2011). More
specifically, law firms now rely on computers that can scan thousands of legal
briefs and precedents to assist in pre-trial research. A frequently cited example
is Symantec’s Clearwell system, which uses language analysis to identify
general concepts in documents, can present the results graphically, and proved
capable of analysing and sorting more than 570,000 documents in two days
(Markoff, 2011).
Furthermore, the improvement of sensing technology has made sensor data
one of the most prominent sources of big data (Ackerman and Guizzo, 2011).
Sensor data is often coupled with new ML fault- and anomaly-detection algorithms
to render many tasks computerisable. A broad class of examples can be
found in condition monitoring and novelty detection, with technology substituting
for closed-circuit TV (CCTV) operators, workers examining equipment
defects, and clinical staff responsible for monitoring the state of patients in intensive
care. Here, the fact that computers lack human biases is of great value:
algorithms are free of irrational bias, and their vigilance need not be interrupted
by rest breaks or lapses of concentration. Following the declining costs of digital
sensing and actuation, ML approaches have successfully addressed condition
monitoring applications ranging from batteries (Saha, et al., 2007), to aircraft
engines (King, et al., 2009), water quality (Osborne, et al., 2012) and intensive
care units (ICUs) (Clifford and Clifton, 2012; Clifton, et al., 2012). Sensors can
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