56,091 research outputs found
Design of the Artificial: lessons from the biological roots of general intelligence
Our desire and fascination with intelligent machines dates back to the
antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought
(syllogism) and Heron of Alexandria's mechanical machines and automata.
However, the quest for Artificial General Intelligence (AGI) is troubled with
repeated failures of strategies and approaches throughout the history. This
decade has seen a shift in interest towards bio-inspired software and hardware,
with the assumption that such mimicry entails intelligence. Though these steps
are fruitful in certain directions and have advanced automation, their singular
design focus renders them highly inefficient in achieving AGI. Which set of
requirements have to be met in the design of AGI? What are the limits in the
design of the artificial? Here, a careful examination of computation in
biological systems hints that evolutionary tinkering of contextual processing
of information enabled by a hierarchical architecture is the key to build AGI.Comment: Theoretical perspective on AGI (Artificial General Intelligence
On the origin of ambiguity in efficient communication
This article studies the emergence of ambiguity in communication through the
concept of logical irreversibility and within the framework of Shannon's
information theory. This leads us to a precise and general expression of the
intuition behind Zipf's vocabulary balance in terms of a symmetry equation
between the complexities of the coding and the decoding processes that imposes
an unavoidable amount of logical uncertainty in natural communication.
Accordingly, the emergence of irreversible computations is required if the
complexities of the coding and the decoding processes are balanced in a
symmetric scenario, which means that the emergence of ambiguous codes is a
necessary condition for natural communication to succeed.Comment: 28 pages, 2 figure
Complexity, parallel computation and statistical physics
The intuition that a long history is required for the emergence of complexity
in natural systems is formalized using the notion of depth. The depth of a
system is defined in terms of the number of parallel computational steps needed
to simulate it. Depth provides an objective, irreducible measure of history
applicable to systems of the kind studied in statistical physics. It is argued
that physical complexity cannot occur in the absence of substantial depth and
that depth is a useful proxy for physical complexity. The ideas are illustrated
for a variety of systems in statistical physics.Comment: 21 pages, 7 figure
What Makes a Computation Unconventional?
A coherent mathematical overview of computation and its generalisations is
described. This conceptual framework is sufficient to comfortably host a wide
range of contemporary thinking on embodied computation and its models.Comment: Based on an invited lecture for the 'Symposium on
Natural/Unconventional Computing and Its Philosophical Significance' at the
AISB/IACAP World Congress 2012, University of Birmingham, July 2-6, 201
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