IEEE Technology and Society Magazine - Fall 2014 - 57

A very appealing example is the
reCaptcha system [9]. Captchas are
programs used for understanding
whether the user of an ICT service/
application is a machine or a human,
typically employed for preventing access from bots. In most cases
this is accomplished by showing a
distorted text, which humans can
correctly interpret but machines
cannot. While interpreting captchas,
humans are actually performing a
computational task, the potential of
which can be exploited for supporting the digitalization of book content. Indeed, many projects around
the world are scanning old books,
making them available in digital format to future generations. Typically,
after the scanning phase, the content
is passed through software for optical character recognition (OCR) in
order to transform the images resulting from the scan into text. OCR
software, however, is not perfect,
and - in particular when dealing
with old books that can be physically corrupted - may suffer from
an inability to correctly read some
words. By using such distorted
images as captchas it is possible to
leverage the human ability to correctly decipher distorted text to turn
the scanned version of a given word
into the corresponding text.1
Another example is chess. Chess
has been long considered a benchmark task for measuring the power
of artificial intelligence systems.
After the seminal victory by Deep
Blue over the then World Champion Garry Kasparov in 1997, it
became clear that - for such tasks -
machines could compete with
humans. Yet, how many resources
are consumed for that? A human
brain typically consumes around
25W of power, while Deep Blue's
1

This clearly requires additional mechanisms
for checking the correctness of the answer provided to the captcha. Typically two methods are
used. Either the image is provided to a number of
different users and a majority voting scheme is
employed to decide on the correct image, or the
distorted image is used in connection with one for
which the correct answer is already known.

power consumption was orders of
magnitude higher. Similar reasoning
applies to IBM Watson and the Jeopardy! victory [10]. Also in this case,
intelligent software agents, powered
by massive hardware architecture,

locate the balloons would win. The
winning team, based at M.I.T., was
able to correctly identify the location of all balloons in less than nine
hours. What was most interesting
was the strategy underpinning the

A new form of intelligence can
be devised to tackle complex and
challenging problems, by exploiting
the complementary strengths of
human and machines.
were able to compete (and actually
outperform) humans at a complex
and wicked task. Yet, and in spite of
the recent attention on "greening"
ICT, this came at a cost (in terms of
resources) that was extremely high.2
So it is not just about whether humans
or computers are best at carrying out
a given task, but also at what cost. In
terms of energy efficiency, humans
still largely outperform machines at
playing chess or Jeopardy!.
Some other interesting examples
are emerging in the field of citizens'
science. A well-known one is FoldIt
(https://fold.it/), an online video
game about protein folding. By playing the game, users actually have
helped discover structural configurations of relevant proteins. More than
240 000 users are currently registered. The new foldings for proteins
that were discovered by users led to
a publication in Nature where more
than 57 000 users ("Foldit players")
are cited as co-authors [11].
In all the previous examples, no
social construct was explicitly present. One interesting case in this sense
is the Network Challenge, launched
in 2009 by DARPA. The challenge
was organized as follows. On a given
day, ten red weather balloons would
be released at unknown locations in
the U.S. The first team to correctly
2

Unconfirmed estimates are in the 150-200 kW
range.

IEEE TECHNOLOGY AND SOCIETY MAGAZINE

FALL 2014

M.I.T. team's solution [12]. They
set up a public web site and fostered
viral recruitment of participants
through a recursive financial incentive scheme (based on getting a share
of the overall prize by DARPA). In
this way social dynamics, coupled
with appropriate incentive structures,
enabled the team to mobilize a sufficiently large number of motivated
users to provide accurate data.
In 2012 a revised version of the
same challenge was proposed in
the Tag Challenge (http://www.
tag-challenge.com/). The Tag Challenge was also a distributed search
problem, but this time it was about
locating and photographing, within
twelve hours, five persons in five
different cities (Washington DC,
New York, London, Stockholm,
Bratislava). The "suspects" were
wearing a T-shirt with a special
logo, and a booking photograph of
them was posted online on the day
of the competition. The main difference with the Red Balloon challenge
was the spatial distribution of the
locations involved (four countries,
two continents), which required the
ability to build on a geographically
dispersed social network of users.
In addition, the fact that "targets"
were moving made the data curation
(in particular, identification of false
positives) much more challenging.
The winning team used a strategy
similar to the one that won the Red
|

http://www http://www.tag-challenge.com/ https://www.fold.it/

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - Fall 2014