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     ABSTRACT

     This paper describes the Machine Certainty Theorem (MCT) and it's
ramifications.

     The MCT states that a machine can not learn anything with perfect
certainty.
 
     This statement depends on the definitions of machine, learn, and
certainty.
 
     A machine is defined as any system of parts interacting via cause
and effect across a space time distance.

     A "space time distance" is later generalized to "dimension" which
is later generalized further to "difference", i.e.  two different
objects.

     To learn is defined as to come to know knowledge about.

     Knowledge is a state in the learner that represents, renders or
symbolizes a state in the learned about.

     Certainty is defined as perfect certainty.
 
     Perfect certainty is defined as learned knowledge which can not be
wrong, i.e.  knowledge one is sure is true without possibility of error.
 
     It is asserted that in the physical universe, all learning results
from being the effect of some cause.  The effect contains some data
about the nature of the cause, and that effect IS the learning that
takes place.

     This is called Learning by being an Effect.

     This is also called learning by indirect perception.  One learns
about A by looking at B, i.e.  A's causal imprint on B.  In this case B
is the machine learning about A.
 
     In the absence of an effect or causal imprint, there is no
learning.
 
     It is also asserted that "state does not prove prior state", i.e.
any state a machine might be in, will never contain certainty that it
was ever in a prior state.

     Thus it is concluded that a machine can not learn with certainty
that it has changed state.

     In the absence of certainty of a change in state, there must also
be an absence of certainty that an effect was received, which implies an
absence of certainty on any learning that might have been deduced from
that effect.

     It is further asserted that correlation does not prove causation.
 
     Thus effects do not prove cause, i.e.  changes in state 'here' do
not prove prior states 'there', either in the machine itself or
elsewhere.
 
     Remember 'here' and 'there' mean two different points in either
space or time or both.  It will be shown that the key term is 'two
different'.
 
     It is thus concluded that a machine can not be built to prove that
there is a cause merely by looking at effects, EVEN IF COULD BE CERTAIN
OF EFFECTS WHICH IT CAN'T.

     Since effects do not prove cause, and all a machine can do is BE an
effect, no machine can learn with certainty that there is cause.
 
     In other words it is offered that because a machine learns by being
an effect of causes, and because effect does not prove cause, a machine
can never learn with certainty if cause exists merely by studying
effects, let alone the nature of that cause.
 
     The concept of an effect is native to a machine, because machines
work by being an effect.
 
     But the concept of cause is non native to a machine.
 
     Cause and its nature forever remain a theory to a machine, not a
directly perceived perfect certainty.

     IN CONCLUSION

     Because a machine can only learn by being an effect, and
 
     because a machine can not learn with certainty that it has changed
state, and

     because a machine can not learn with certainty that a change in
state was caused,

     it is concluded that a machine can not learn with certainty about
anything, which includes cause, space, time, or the existence of
anything including itself.

     The important application of the MCT is in the reverse, to wit
since consciousness can learn with perfect certainty a number of things,
including its own existence, perception of two different colors, and
personal agency (causation), one is led to conclude that consciousness
is not a machine, that is not using indirect perception to learn about
itself, but instead using direct perception by looking at itself
directly.

      Direct perception is an oxymoron in the physical universe,
but is defined as learning about cause by looking at cause rather
than by looking at effects of that cause.

     Homer