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280 Are the Androids Dreaming Yet?
David Malament and Mark Hogarth of the University of California,
Irvine have proposed a form of space-time called the Kerr Metric. This
allows a machine to break the Turing limit, but has the drawback that as
it does so it falls through the event horizon and is sucked into the black
hole. We might discover new information but are now trapped inside the
event horizon unable to communicate it — a form of cosmic censorship.
Candidates for a hyper-computer that could fit inside a human
brain include mathematical curiosities which stretch the concept
of infinity. The easiest to understand is the Zeno machine. In a Zeno
machine a computer runs each successive step of a calculation in half the
time of the previous step. The computer can pack an infinite quantity of
computation into each finite time interval and can therefore outperform
a Turing machine. This theory fails at a practical level because we simply
cant build such a machine.
There are numerous weird suggestions for mathematical super-
Turing machines, and many are described on the Internet. They all fit
broadly within the two models above: modifications to space-time or
peculiar mathematical paradoxes. The inspiration for the true solution
to super-Turing thought may lay in there somewhere, but there are some
more plausible proposals to look at next.
Plausible Ideas
I have characterized the next set of ideas as plausible, but they may still
be highly controversial. My only criteria for plausibility are that the
mechanism must outperform a machine limited to counting numbers,
and it might fit inside our skulls. No black holes allowed.
One interesting proposal for a super-Turing machine that could
fit inside our skulls is the Adaptive Recurrent Neural Network, ‘ARNN’
proposed by Hava Siegelmann of the University of Massachusetts,
Amherst. An ARNN is a neural network with real number weights. As
you recall, real numbers are equivalent to the continuum infinity, a larger
infinity than that of counting numbers.
This is the infinity that defeats a Turing machine, and Siegelmann
harnesses it as the basis of her computing machine. She argues that,
although the machine cannot be programmed as it is impossible to write
real numbers down, once it is running, the weights diverge and real
numbers will be used within the network. These real numbers allow the
machine to compute using numbers that are not, themselves, computable
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