To Neural Networks and Beyond!
Neural Networks and Consciousness
So, neural networks are very good at a wide variety of problems, most
of which involve finding trends in large quantities of data. They
are better suited than traditional computer architecture to problems that
humans are naturally good at and which computers are traditionally bad
at ? image recognition, making generalizations, that sort of thing.
And researchers are continually constructing networks that are better at
But will neural networks ever fully simulate the human brain?
Will they be as complex and as functional? Will a machine ever be
conscious of its own existence?
Simulating human consciousness and emotion is still the realm of science
fiction. It may happen one day, or it may not ? this is an issue
we won't delve into here, because, of course, there are huge philosophical
arguments about what consciousness is, and if it can possibly be simulated
by a machine... do we have souls or some special life-force that is impossible
to simulate in a machine? If not, how do we make the jump from, as
one researcher puts it, "an electrical reaction in the brain to suddenly
seeing the world around one with all its distances, its colors and chiaroscuro?"
Well, like I said, we won't delve into it here; the issue is far too deep,
and, in the end, perhaps irresolvable... (if you want to delve, check out
Perhaps NNs can, though, give us some insight into the "easy problems"
of consciousness: how does the brain process environmental stimulation?
How does it integrate information? But, the real question is, why and how
is all of this processing, in humans, accompanied by an experienced inner
life, and can a machine achieve such a self-awareness?
Of course, the whole future of neural networks does not reside in attempts
to simulate consciousness. Indeed, that is of relatively small concern
at the moment; more pressing are issues of how to improve the systems we
Recent advances and future applications of NNs include:
Integration of fuzzy logic into neural networks
Fuzzy logic is a type of logic that recognizes more than simple true and
false values, hence better simulating the real world. For example,
the statement today is sunny might be 100% true if there are no clouds,
80% true if there are a few clouds, 50% true if it's hazy, and 0% true
if rains all day. Hence, it takes into account concepts like -usually,
somewhat, and sometimes.
Fuzzy logic and neural networks have been integrated for uses as diverse
as automotive engineering, applicant screening for jobs, the control of
a crane, and the monitoring of glaucoma.
See www.fuzzytech.com for more information
Pulsed neural networks
"Most practical applications of artificial neural networks are based on
a computational model involving the propagation of continuous variables
from one processing unit to the next. In recent years, data from neurobiological
experiments have made it increasingly clear that biological neural networks,
which communicate through pulses, use the timing of the pulses to transmit
information and perform computation. This realization has stimulated
significant research on pulsed neural networks, including theoretical analyses
and model development, neurobiological modeling, and hardware implementation."
Hardware specialized for neural networks
Some networks have been hardcoded into chips or analog devices ?
this technology will become more useful as the networks we use become more
The primary benefit of directly encoding neural networks onto chips
or specialized analog devices is SPEED!
NN hardware currently runs in a few niche areas, such as those areas where
very high performance is required (e.g. high energy physics) and in embedded
applications of simple, hardwired networks (e.g. voice recognition).
Many NNs today use less than 100 neurons and only need occasional training.
In these situations, software simulation is usually found sufficient
When NN algorithms develop to the point where useful things can be done
with 1000's of neurons and 10000's of synapses, high performance NN hardware
will become essential for practical operation. (from http://www.particle.kth.se/~lindsey/HardwareNNWCourse/
Improvement of existing technologies
All current NN technologies will most likely be vastly improved upon in
the future. Everything from handwriting and speech recognition to
stock market prediction will become more sophisticated as researchers develop
better training methods and network architectures.
NNs might, in the future, allow:
A word of caution (and a funny story!):
robots that can see, feel, and predict the world around them
improved stock prediction
common usage of self-driving cars
composition of music
handwritten documents to be automatically transformed into formatted word
trends found in the human genome to aid in the understanding of the data
compiled by the Human Genome Project
self-diagnosis of medical problems using neural networks
and much more!
Although neural networks do seem to be able to solve many problems,
we must put our exuberance in check sometimes ? they are not magic!
Overconfidence in neural networks can result in costly mistakes:
for a rather funny story about the government and neural networks.
Want to find out more? Try these sites: