Systems, Man & Cybernetics - July 2015 - 9

Awareness
in Brain, Society,
and Beyond
A Bridge Connecting Raw Data
to Perception and Cognition
by Goutam Chakraborty, Robert Kozma, Tadahiko Murata, and Qiangfu Zhao

image licensed by ingram publishing

he goal of computational awareness (CA)
research is to build systems that are aware.
Awareness is related to a system's ability to
perceive, feel, or develop conscious experiences. For cybernetics, the study of awareness
is fundamental.
In this article, first, we describe awareness and perception in the brain and introduce spatiotemporal dynamic
models with intermittent phase transitions. Then, we
propose a three-valued logic model that can be useful to
interpret awareness mechanisms. Finally, CA applications in human societies are used as examples to show
the potential impact of CA in the age of big data. There
are still many theoretical and practical problems to solve.
This article describes our diverse views, exposing various
aspects of CA. We hope that this comprehensive review
helps to promote this emerging field and that interested
researchers will join CA research and the IEEE Systems,
Man, and Cybernetics Society (SMCS) Technical Committee on Awareness Computing so that we can promote this
emerging field together.
Introduction
The dream of building a humanlike intelligent machine
started during the middle of the 20th century. In 1947,
Turing [1] predicted that by the end of the century, intelligent computers would be built, and he proposed a test
for computer intelligence, famously known as the Turing
Digital Object Identifier 10.1109/MSMC.2015.2470197
Date of publication: 4 March 2016

test. In 1955, McCarthy first coined the term artificial
intelligence (AI) as the science and engineering of making intelligent machines.
The Early State of AI Research-Initial Euphoria
Starting in the mid-1950s, fundamental research on AI
was initiated by leading scientists like Newell, Simon,
and McCarthy. AI generated a lot of interest, and many
believed that a machine with humanlike intelligence would
soon be created. In 1957, Simon wrote,
It is not my aim to surprise or shock you-but the
simplest way I can summarize is to say that there are
now in the world machines that think, that learn, and
that create. Moreover, their ability to do these things
is going to increase rapidly until-in a visible future-
the range of problems they can handle will be coextensive with the range to which [the] human mind has
been applied [2].
The Era of Expert Systems
In the beginning, AI research was centered around logic,
reasoning, formal rules, knowledge representations, and
general problem solving. Functional programming languages, such as Lisp, were widely used to model expert
systems. A common belief was that even intuition can
be understood and expressed by rules and facts. It was
thought that the experts would be able to explicitly
express their decision-making processes, which, in turn,
could be replicated in respective knowledge bases.
A notable project, named Dendral, was started by
Feigenbaum et al. at Stanford University. Dendral was
Ju ly 2015

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