For example:


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

     The MCT states that a machine can not learn anything with perfect
     This statement depends on the definitions of machine, learn, and
perfect 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

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

     The object that learns is the learner.

     Objects have states described by their object quality set, the set
of all qualities of that object, both alone and in relation to other

     Knowledge is a state in the learner object 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, ie knowledge one is sure is true without possibility of error.
     We discriminate false certainties where in the learner THINKS there
is no possibility of error, from true certainties where there IS no
possibility of error.

     It is asserted that in the physical universe, all learning results
from being the effect of some cause.
     The object which is cause is called the original referent.

     The object which is effect is called the symbol of final authority.

     The cause produced by the referent and its qualities produces the
effect which is a change in the qualities of the symbol.

     The effect contains some data about the nature of the cause, and
that effect IS the learning that takes place in the symbol about the

     This is called Learning by being an Effect.

     In the absence of being an effect there can be no learning.

     Since all learning is from effects brought about by a cause,
all learning is about cause.

     Thus if cause is not involved, no learning can take place.

     This is also called learning by indirect perception.
     One learns about A by looking at B, ie 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 on B by A, there is
no learning.
     It is also asserted that "state does not prove prior state", ie 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 that it has
changed state with certainty.

     The absence of certainty of a change in state implies 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.
     By 'prove' mean only to demonstrate perfect certainty of.

     We do NOT mean merely to provide possible evidence for.

     Thus effects do not prove cause, therefore changes in state 'here'
do not prove prior states 'there', either in the machine itself or
     '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

     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 even 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.


     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,

     and therefore because a machine can not learn with certainty that
it has been affected,

     it is concluded that a machine can not learn with certainty about
anything, which includes cause, effect, 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 using indirect perception to learn about
itself, but instead is using direct perception by looking at itself

     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.