Unless artificial consciousness can be proven formally, judgments of the
success of any implementation will depend on observation.
The Turing test is a proposal for identifying machine intelligence as
determined by a machine's ability to interact with a person. In the Turing
test one has to guess whether the entity one is interacting with is a
machine or a human. An artificially conscious entity could only pass an
equivalent test when it had itself passed beyond the imaginations of observers
and entered into a meaningful relationship with them, and perhaps with
fellow instances of itself.
A cat or dog would not be able to pass this test. It is highly likely
that consciousness is not an exclusive property of humans. It is likely
that a machine could be conscious and not be able to pass the Turing test.
As mentioned above, the Chinese room argument attempts to debunk the
validity of the Turing Test by showing that a machine can pass the test
and yet not be conscious.
Since there is an enormous range of human behaviours, all of which are
deemed to be conscious, it is difficult to lay down all the criteria by
which to determine whether a machine manifests consciousness.
Indeed, for those who argue for indirect perception no test of behaviour
can prove or disprove the existence of consciousness because a conscious
entity can have dreams and other features of an inner life. This point
is made forcibly by those who stress the subjective nature of conscious
experience such as Thomas Nagel who, in his essay, What is it like to
be a bat?, argues that subjective experience cannot be reduced, because
it cannot be objectively observed, but subjective experience is not in
contradiction with physicalism.
Although objective criteria are being proposed as prerequisites for testing
the consciousness of a machine, the failure of any particular test would
not disprove consciousness. Ultimately it will only be possible to assess
whether a machine is conscious when a universally accepted understanding
of consciousness is available.
Another test of AC, in the opinion of some, should include a demonstration
that machine can learn the ability to filter out certain stimuli in its
environment, to focus on certain stimuli, and to show attention toward
its environment in general. The mechanisms that govern how human attention
is driven are not yet fully understood by scientists. This absence of
knowledge could be exploited by engineers of AC; since we don't understand
attentiveness in humans, we do not have specific and known criteria to
measure it in machines. Since unconsciousness in humans equates to total
inattentiveness, an AC should have outputs that indicate where its attention
is focused at any one time, at least during the aforementioned test. By
Antonio Chella from University of Palermo. "The mapping between the
conceptual and the linguistic areas gives the interpretation of linguistic
symbols in terms of conceptual structures. It is achieved through a focus
of attention mechanism implemented by means of suitable recurrent neural
networks with internal states. A sequential attentive mechanism is hypothesized
that suitably scans the conceptual representation and, according to the
hypotheses generated on the basis of previous knowledge, it predicts and
detects the interesting events occurring in the scene. Hence, starting
from the incoming information, such a mechanism generates expectations
and it makes contexts in which hypotheses may be verified and, if necessary,