If you (hypothetically) "look" at the plot of y = 1 + \Omega, where
\Omega is Chaitin's halting probability, even that would "look"
extremely simple, even though K(\Omega) = \infinity.
The reason why *any* straight line graph "looks" simple is not because
our brain can accurately determine the exact equation of the line, but
because our brain constructs a *simple approximation* of the equation
of the line.
>>> on SE.
>>> Potential applications includes making complexity measure as regularizer
>>> for Recurrent Neural Networks. It would be interesting to see if
>>> gradient descent on such cost function can approximate Solomonoff
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