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Buzz Lightyear 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 these problems.

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 http://www.culture.com.au/brain_proj/neur_net.htm and http://www.iasc-bg.org.yu/Papers/Work-97/work-97.html)

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 have.

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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." (from http://www.tu-graz.ac.at/igi/maass/PNN.html )


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 complex.
  • 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:
    • 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 processing documents
    • 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!
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A word of caution (and a funny story!):
  • 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: see http://vv.carleton.ca/~neil/neural/tank.html for a rather funny story about the government and neural networks.

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Want to find out more? Try these sites:
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