The_Future_of_Employment

obots can now reproduce some aspects of human social interaction, the realtime
recognition of natural human emotion remains a challenging problem, and
the ability to respond intelligently to such inputs is even more difficult. Even
simplified versions of typical social tasks prove difficult for computers, as is
the case in which social interaction is reduced to pure text. The social intelligence
of algorithms is partly captured by the Turing test, examining the ability
of a machine to communicate indistinguishably from an actual human. Since
1990, the Loebner Prize, an annual Turing test competition, awards prizes to
textual chat programmes that are considered to be the most human-like. In
each competition, a human judge simultaneously holds computer-based textual
interactions with both an algorithm and a human. Based on the responses, the
judge is to distinguish between the two. Sophisticated algorithms have so far
failed to convince judges about their human resemblance. This is largely because
there is much ‘common sense’ information possessed by humans, which
is difficult to articulate, that would need to be provided to algorithms if they are
to function in human social settings.
Whole brain emulation, the scanning, mapping and digitalising of a human
brain, is one possible approach to achieving this, but is currently only a
theoretical technology. For brain emulation to become operational, additional
functional understanding is required to recognise what data is relevant, as well
as a roadmap of technologies needed to implement it. While such roadmaps exist,
present implementation estimates, under certain assumptions, suggest that
whole brain emulation is unlikely to become operational within the next decade
or two (Sandberg and Bostrom, 2008). When or if they do, however, the employment
impact is likely to be vast (Hanson, 2001).
Hence, in short, while sophisticated algorithms and developments in MR,
building upon with big data, now allow many non-routine tasks to be automated,
occupa tions that involve complex perception and manipulation tasks,
creative intelligence tasks, and social intelligence tasks are unlikely to be substituted
by computer capital over the next decade or two. The probability of an
occupation being automated can thus be described as a function of these task
characteristics. As suggested by Figure I, the low degree of social intelligence
required by a dishwasher makes this occupation more susceptible to computerisation
than a public relation specialist, for example. We proceed to examining
the susceptibility of jobs to computerisation as a function of the above described
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