CONSCIOUSNESS

3. Cognitive Sciences and Psychology 137
3.11 Cognitive development
3.12 Artificial intelligence & robotics
186 When is a Robot Conscious? Peter Ford Dominey, Stephane Lallee
(Robot Cognition Laboraotry, INSERM Stem Cell and Brain
Research Institute, Bron, France)
In The Conscious Mind (1996) Chalmers argues that a zombie world can exist which is
physically indiscernible from our world, but entirely lacking conscious experience, and that
this leads to a logical inconsistency for physicalist theories of consciousness. Dennett and
others have argued that the strong notion of zombies is not feasible. Today, highly articulated
humanoid robots provide a new testing ground for these notions. In this context, we have developed
cognitive systems that allow humanoid robots to learn to (1) recognize and describe
new actions, (2) observe two humans performing coordinated cooperative tasks and then (3)
step in and participate, taking the role of either observed human. The robot has a shared intentional
plan that allows it to anticipate what the human will do, and help the human when
necessary. Such an ability to manipulate shared intentions is considered a hallmark of human
cognition (e.g. Tomasello et al. 2005). The robot can also describe what it and the others are
doing, and why, and ask questions when it does not understand things. The question can then
be posed, to what degree are these robots conscious? The talk will provide concrete examples
of humanoid robots performing cognitive tasks that are typically considered to require consciousness
in humans, and will address this question of robot consciousness in the context of
current perspectives in philosophy of psychology, and cognitive neuroscience. C5
187 Moving Bubbles of Attention: A Mechanism Enabling the Emergence of Self and
Focused Consciousness in Embodied Artificial General Intelligences Ben Goertzel
(CEO, Novamente LLC and Biomind, Rockville, MD)
A systematic conceptual and technical perspective on machine consciousness is outlined,
and explored in the specific context of the OpenCog Artificial General Intelligence (AGI)
system and its application to control virtual world agents and humanoid robots. Consciousness
is viewed from a panpsychist vantage, as a universally present “ambient” quality which
manifests itself differently in different entities, and which appears in a highly focused form in
certain embodied, self-modeling intelligent systems like humans. The question of how to create
AGI systems that give rise to similar forms of highly focused consciousness is posed; and a
solution is hypothesized, in the form of nonlinear-dynamical attention-allocation mechanisms
that give rise to a spontaneously self-organized “moving bubble of attention” correlated with
the system’s self-model. The specific implementation of this mechanism in the OpenCog system
using artificial economics and probabilistic inference is discussed. Speculations regarding
the potential underpinnings of similar phenomena in human and animal brains are also briefly
considered, including dendritic webs and strange attractors. PL4
188 What Could a Brain do with Quantum Algorithms? Hartmut Neven, Vasil S.
Denchev, Purdue University, Vdenchev@purdue.edu Geordie Rose And William G.
Macready, D-Wave Systems, Rose,wgm@dwavesys.com (Google,
Malibu, CA)
The tradition of the Tucson conferences was in part born out of the idea that quantum
mechanical processes are implicated in conscious experience. Proving this conjecture poses
formidable epistemological and experimental challenges. An aspect that has received less attention
but is more tractable is the question to what degree quantum mechanisms can assist an
intelligent system in performing key tasks. We want to contribute to the debate by reporting on
a series of theoretical and experimental studies designed to show how learning from examples
can benefit from employing quantum algorithms. A formulation is employed in which a binary
classifier is constructed as a thresholded linear superposition of a set of weak classifiers. The
weights in the superposition are optimized in a learning process that strives to minimize the