The
study of consciousness and its origins has experienced
radical breakthroughs with the advent of emerging technologies
in the field of neuroscience. Once overcast with a veil
of mystery, the inner workings of the brain have undergone
profound illuminations made possible by rapid advances
in the computing industry, coupled with insights gleaned
through medical science. New technologies such as MRI
scanning have enabled scientists to understand the neurological
correlates of mental processes in ever finer detail, allowing
them to glimpse the interior of a fully-functioning brain
in real-time. However, even with these exponential advances
in the field of consciousness, laying a theoretical foundation
to explain precisely how it arises currently remains a
cryptic and insurmountable task. The most important scientific
discovery of our time will be when this problem is resolved.
Proposed Theories of Consciousness
The
scientific community has generally accepted that consciousness
is an emergent system-level feature of neurophysiological
processes. Exactly how our individual subjective experiences
arise has been a matter of long-standing debate in both
scientific and philosophical circles, but there are a
number of currently proposed theories that attempt to
resolve the mystery of consciousness. Among these theories
are (1)
consciousness is a feature of synchronized resonance within
neurons in the frontal cortex, (2)
consciousness results through the mechanism of quantum
coherence in neuron microtubules, and (3)
consciousness is an emergent property of complex systems.
Resonance
The neural resonance hypothesis was first explored by
Francis Crich of the Salk Institute and Christof Koch
of the California Institute of Technology. They observed
that certain areas of the brain critical for awareness
fire in complex, organized patterns. Large groups of neurons
in the frontal cortex fire in synchronous pulses — in
the visual cortex this occurs at 40 cycles per second.
In much the same way as a symphony composition is produced
by a fusion of complementary melodies, consciousness may
arise as harmonic standing waves are formed among immense
numbers of neurons spread throughout the brain. These
waves may form a kind of working memory that allows for
the formation of a unified consciousness.
Coherence
An
alternative, and controversial, explanation of consciousness
was originally put forward by renowned mathematician Sir
Roger Penrose. Penrose teamed with anesthesiologist Stuart
Hameroff, positing that consciousness is a product of
quantum interactions occurring within microtubules - small,
slender structures that form the skeleton of all eukaryotic
cells. Fluctuations occurring at the quantum level could
produce quantum coherence capable of influencing neuron
activity at the macroscopic scale.
A
common objection made in the scientific community is that
under normal conditions these types of quantum effects
have only been seen to occur near absolute zero. At the
temperatures found within biosystems, the level of random
noise found within the system would likely rule out coherence
unless some unknown factor comes into play. However, nature
may have evolved to overcome this obstacle; it is possible
that biological systems exhibit novel physical properties.
Experiments underway by the Tuscynski Biophysics Group
at the University of Alberta are examining the physics
of microtubule coherence in vivo.
Emergence
It is no more possible to track the cause-effect path
from neuron activity to higher functions such as language
and discovery than it is to track the path from a water
molecule to the curl of a beach wave. Unfortunately,
appeals to emergence always leave an unsatisfying gap
in any attempt to produce a complete explanation, but
nature is full of such examples.
Cauller
and Penz
A third mechanism
put forward to explain the mystery of consciousness holds
it to be an emergent property of complex systems. Ernest
Nagel and Brain McLaughlin cite Mill's 'Of the Composition
of Causes' chapter of System of Logic (1843)
as the locus classicus on the notion of emergence.
As applied to neuroscience, consciousness originates at
a fundamental level involving information processing and
is expressed when this processing reaches a certain level
of complexity.
High-level feedback mechanisms that evolved to respond
to rapidly-changing sensory input may give rise to a subjective
awareness of mental activity. This coherent, unified sense
of self - arising from the interaction of many unrelated
subsystems - would play a role in evolution by ensuring
the survival of the individual through long-term decision
making and goal-oriented behaviors. Consciousness is critical
for abstract reasoning and long-term planning. A thinking,
conscious individual can better evaluate and adapt to
changes in its surrounding environment. Once a system
reaches a critical level of complexity, this integration
of neurological functioning may allow the formation of
consciousness.
The Potentials of Artificial
Intelligence
Given
that our sense of self arises from and is dependent upon
the brain’s physiological processes, the possibility of
engendering consciousness into alternative substrates
becomes much more realistic. The human brain can be viewed
as a biomechanical machine whose development is governed
by the interplay of tens of thousands of different genes.
This interplay determines synaptic growth, neurotransmitter
production, and a myriad array of other functions that
constitute the brain.
However,
the human nervous system is a remarkable instrument of
bewildering complexity - the most advanced system in the
known universe. To recreate the human mind, one must successfully
emulate its processes — a daunting task. When examining
the operations of the mind at work, it is necessarily
true that introspective decision-making processes are
more accessible than are those that underlie awareness.
Those that are available to conscious examination can
be formally defined.
Analysis
of the decision processes involved in chess playing has
proven much more decipherable to artificial intelligence
researchers than the processes involved in vision. Consequently
we are capable of building chess-playing computers that
can best the top players in the world, whereas building
a robot that can navigate open terrain has proven a notoriously
difficult task. There are many schools of thought in the
field known as artificial intelligence, but they can generally
be broken down into two approaches: the top-down and the
bottom-up.
The
top-down approach attempts to mimic human intelligence
by the application of precise rules to the thinking process.
One example of this is expert systems: programs which
are given a large body of information about a specific
subject and generally perform well within their parameters,
but fail miserably outside a given subject range. Conversely,
the bottom-up approach to AI attempts to build models
from the ground up, modeling neurons to simulate the process
of learning.
Limitations of the Top-Down
Approach
Of
the two, the top-down approach showed impressive results
at first but has disappointedly failed to produce significant
recent advancements. The number of ‘rules’ governing behavior
is simply too vast and undefined, frequently manifesting
what appear to be conflicting parameters of operation.
This is in large part due to the fact that the brain itself
is a continuously evolving, massively parallel-processing
system that merges digital and analog computation and
does not follow the black-and-white decision-making process
of digital programming. Information is instead processed
via cascading waves of electrical signals that are augmented
and controlled by hundreds of neurotransmitters and hormones,
forming a vastly complex system that is nearly opaque
to the probing of modern science.
Rise of the Bottom-Up Approach
Due
to the complexity of the systems that comprise the human
brain, the bottom-up approach of AI was initially far
too formidable for AI researchers. The vast numbers of
neurons - upwards of 100 billion - and associated interconnections
- averaging 10,000 per neuron - found in the human brain
were simply too complex to fathom. Processing power was
not up to task for such a mammoth undertaking.
But
given the current state of computing technology and the
pace at which it continues its exponential growth, this
is an approach that shows much promise in taking up the
slack where the top-down approach has been unable to succeed.
New approaches in parallel-distributed processing break
larger problems into manageable pieces and allow researchers
to solve previously intractable problems in a relatively
short amount of time. A powerful tool made practical by
recent advances in technology is artificial evolution.
Artificial Evolution
Artificial
evolution utilizes computers to evolve complex systems
from simple initial states following the same rules as
Darwinian evolution. Recall that all life on earth can
trace its origins to the random interaction of simple
molecules early in the earth’s history. These molecules
combined to form complex proteins, and those that were
easily reproducible naturally overran the untapped environment.
From those early protein strings evolved DNA, which can
itself be seen as a protein computer which processes information
in base4 as opposed to base2 coding
language. All life - including sentient, self-aware and
eternally questioning human beings - evolved from this
primal soup of molecules.
Self-organization of complex, thinking creatures from
initial disorder is an extremely powerful example of chaos
theory, in which complex behaviors arise from a set of
simple initial conditions. In computer simulations with
artificial life, this trend towards increasing complexity
not only occurs routinely, it is accomplished more easily
and is more inevitable than is usually considered. This
holds profound implications for the question of life elsewhere
in the cosmos and suggests that the mathematical architecture
of the laws governing the universe is not only capable
of supporting life but is predisposed to evolving it.
Genetic Algorithms
The
primary tool AI employs for the evolution of complexity
is a genetic algorithm— the software equivalent of genes
found in nature. Specific software instructions can be
viewed as the equivalent of chromosomes found in DNA.
A problem is approached by the creation of a sample population
of GAs, usually selected randomly. This process is repeated
in parallel until a suitable solution is found. Each algorithm
is rated at a certain fitness level according to how well
it approaches the problem and then the highest scoring
algorithms are paired and mated, creating the successive
generation. Random mutations are introduced at the time
of pairing, introducing new possibilities into the evolution
process.
Most
algorithms will not meet fitness standards and quickly
die off, but a few will survive and pass their genes to
the successive generation. Over a period of many generations,
the process can quickly be fine-tuned to produce a remarkably
efficient solution for the given problem. Currently, state-of-the-art
GAs can be evolved not only on software, but in physical
medium on reprogrammable chips called field programmable
gate arrays, or FPGAs, which use reprogrammable logic
gates. This allows for a diverse array of contributing
effects to emerge that would otherwise be unseen, due
to subtle variances in the physics of the electromagnetic
fields that the chips generate. Researchers have found
that GA-based FPGAs sometimes utilize these effects to
their advantage in finding solutions to a given problem
— another example of the potential creative capacities
of modeling Darwinian evolution.
Simulating Evolution
in silico
At this
moment, computers show no sign of intelligence. This
is not surprising, because our present computers are
less complex than the brain of an earthworm. But it
seems to me that if very complicated chemical molecules
can operate in humans to make them intelligent, then
equally complicated electronic circuits can also make
computers act in an intelligent way.
Stephen W. Hawking
Employing computers
to simulate evolution provides many advantages over the
process that nature utilized to develop complexity. In
nature, the development process depends on random interactions
between molecules in the environment and occurs on a time
scale of millennia. Mutation and progress occur at a snail’s
pace when viewed from the time frame of silicon-based
evolution, and mutation frequently brings about changes
counter-productive to the survival of the species.
In
a silicon-based medium, genes are allowed to pair and
mutate at the rate of thousands of generations per second.
Variables can be manipulated to increase the rate at which
the system moves towards producing a desired solution.
Computers will allow us to reproduce evolution at an exponential
rate that increases in parallel to our processing speed.
The
current trend of exponential growth we are now experiencing
is a self-propagating feedback loop that will allow the
development of goal-oriented complex systems which will
rival the computing power of the human brain within a
generation: following Moore’s Law, a parallel-distributed-processing
network will rival human memory capacity by 2010.
A
primary stumbling block to successfully reproducing the
behavior of the human brain lies in the fact that neurons
are in themselves complex processors that merge analog
and digital computation to reach decisions. A single neuron
works on a digital fire-or-none decision process but is
influenced by the input of surrounding neurons and ambient
level of neurotransmitter molecules in the surrounding
vicinity.
Further,
the sheer number of excitatory and inhibitory neural pathways
that influence neural functioning is dazzling in its complexity.
Traditional models of neural nets mimic the characteristics
of biological neural nets to develop connections,8
but fail to incorporate the analog computations that influence
the molecular-scale behavior of their carbon-based counterparts.
However,
research advances have allowed this area much progress
through the implementation of composite analog-digital
neural models: this problem should recede as they grow
increasingly more adept at modeling the large number of
neurotransmitters and hormones that influence cognitive
function. Studying the hundreds of specialized areas which
control the functioning of the human brain has been another
wall which limits developmental progress in artificial
neural networks, but is one which will quickly be decoded
as the capabilities of MRI scanning increase.
Soon
it will be possible to achieve a level of resolution precise
enough to view individual neurons firing in real-time
models. This will be a boon for neuroscientists and AI
programmers alike as neural pathways can be precisely
mapped to analyze their functioning. It is increasingly
apparent that the development of artificial brains with
comparable abilities to ours will become a feasible goal.
Brain Modeling
Whatever one man is
capable of imagining, other men will prove themselves
capable of realizing.
Jules Verne
A prominent
example of artificial evolution is taking place in Brussels,
Belgium at a private blue-sky research laboratory, Starlab
-- a 75 million neuron artificial brain that, if
successful, holds the potential to transform the face
of artificial intelligence.
The
massively parallel neural network consists of roughly
one million modules of cellular automata, which grow and
evolve at electronic speeds inside special hardware called
a CAM Brain machine, or CBM. The CBM updates cellular
automata cells at a rate of 130 billion per second, and
can evolve a fully-operational neural net module in about
one second. These modules are then assembled into human-defined
architectures, which are downloaded into a large memory
space updated in real time by the CBM. This massive artificial
brain will be capable of complex behaviors in response
to external sensory stimuli.
Though at present these neural models are simple in comparison
to their biological counterparts, the increasing pace
of our technological developments requires the examination
of the potential societal implications of artificial intelligence
research. Future advances in technology will enable us
to create silicon-based intelligence with many times our
own capabilities and memory capacity. The French Senate
has taken notice - in the summer of 2001 organizing an
international hearing to address these questions.
" If one maverick researcher can get this
amazing AI hardware created, imagine what could be done
with a concerted effort to get real AI working by the
governments, universities and corporations of the world.
"
Ben Goertzel
Deep Future
As our models
of the brain become increasingly indistinguishable from
their carbon-based counterparts and develop more complex
behavioral patterns, consciousness may become possible
through the interaction of many smaller-scale systems,
just as it does in the biological brain - bringing about
an age of ubiquitous intelligence.
Silicon-based
intelligence possesses a number of advantages over carbon-based
intelligence, including the capability of redesigning
its own architecture to maximize efficiency. It could
evolve at an astonishingly rapid pace, catalyzing a global
paradigm shift as it swiftly rises to become more capable
at human-dominated tasks.
In
the coming decades the distinction between man and machine
will blur, with the possibility of humankind merging with
its own creations. Mankind may be compelled to evaluate
the possibility of granting recognition to a new species
of sentient, silicon-based lifeforms of our own creation.
This
and many other questions pose themselves to us as we stand
on the brink of a chasm that may prove to be the most
profound forward leap ever confronted in our collective
memory. We are faced with the possibility of a new era:
the dawn of an age in which mankind is no longer the most
advanced species on the planet.
The best way to predict the future is to invent
it.
Richard Feynman
References
Cauller and
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Cambridge, Massachusetts, 1995.
Mill,
John Stuart. (1843). System of Logic. London: Longmans,
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Moravec, Hans. Mind Children, Harvard University
Press, Cambridge, Massachusetts, 1988.
Paul, Gregory S. and Cox, Earl D. Beyond Humanity:
Cyber Evolution and Future Minds, Charles River Media,
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Penrose, Roger. Shadows of the Mind: A Search for the
Missing Science of Consciousness, Oxford University
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Sayre, Kenneth M. Consciousness: A Philosophical Study
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1969. Torrance, Steve. The Mind and the Machine: Philosophical
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New York, 1984.
Winston,
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