URL to an interesting article in The Daily Galaxy
The term, Artificial Intelligence was coined in 1956 by John McCarthy at
Massachusetts Institute of Technology. This year, computer scientists
celebrate the 100th anniversary of the birth of the mathematical genius Alan
Turing. Turing set the basis for digital computing in the 1930s to anticipate
our current technilogical age. The quest still remains to create a machine as
adaptable and intelligent as the human brain.
Computer scientist Hava Siegelmann of the University of Massachusetts
Amherst, an expert in neural networks, has taken Turing's work to its next
logical step by translating her 1993 discovery of "Super-Turing" computation
into an adaptable computational system that learns and evolves, using input
from the environment in a way much more like our brains do than classic
Turing-type computers. She and her post-doctoral research colleague Jeremie
Cabessa report on the advance in the current issue of Neural Computation.
"This model is inspired by the brain," she says. "It is a mathematical
formulation of the brain's neural networks with their adaptive abilities." The
authors show that when the model is installed in an environment offering
constant sensory stimuli like the real world, and when all stimulus-response
pairs are considered over the machine's lifetime, the Super Turing model
yields an exponentially greater repertoire of behaviors than the classical
computer or Turing model. They demonstrate that the Super-Turing model is
superior for human-like tasks and learning.
"Each time a Super-Turing machine gets input it literally becomes a
different machine. Classical computers work sequentially and can only operate in
the very orchestrated, specific environments for which they were programmed.
They can look intelligent if they've been told what to expect and how to
respond, Siegelmann says. But they can't take in new information or use it
to improve problem-solving, provide richer alternatives or perform other
In 1948, Turing himself predicted another kind of computation that would
mimic life itself, but died without developing his concept of a machine that
could use what he called "adaptive inference." In 1993, Siegelmann, showed
independently in her doctoral thesis that a very different kind of
computation, vastly different from the "calculating computer" model and more like
Turing's prediction of life-like intelligence, was possible. She published
her findings in Science and in a book shortly after.
Siegelmann says that the new Super-Turing machine will not only be flexible
and adaptable but economical. This means that when presented with a visual
problem, for example, it will act more like our human brains and choose
salient features in the environment on which to focus, rather than using its
power to visually sample the entire scene as a camera does. This economy of
effort, using only as much attention as needed and is another hallmark of
high artificial intelligence."
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