On Sat, Aug 17, 2013 at 7:51 PM, Craig Weinberg <whatsons...@gmail.com> wrote:
> Coincidental post I wrote yesterday:
> It may not be possible to imitate a human mind computationally, because
> awareness may be driven by aesthetic qualities rather than mathematical
> logic alone. The problem, which I call the Presentation Problem, is what
> several outstanding issues in science and philosophy have in common, namely
> the Explanatory Gap, the Hard Problem, the Symbol Grounding problem, the
> Binding problem, and the symmetries of mind-body dualism. Underlying all of
> these is the map-territory distinction; the need to recognize the difference
> between presentation and representation.
> Because human minds are unusual phenomena in that they are presentations
> which specialize in representation, they have a blind spot when it comes to
> examining themselves. The mind is blind to the non-representational. It does
> not see that it feels, and does not know how it sees. Since its thinking is
> engineered to strip out most direct sensory presentation in favor of
> abstract sense-making representations, it fails to grasp the role of
> presence and aesthetics in what it does. It tends toward overconfidence in
> the theoretical.The mind takes worldly realism for granted on one hand, but
> conflates it with its own experiences as a logic processor on the other.
> It’s a case of the fallacy of the instrument, where the mind’s hammer of
> symbolism sees symbolic nails everywhere it looks. Through this intellectual
> filter, the notion of disembodied algorithms which somehow generate
> subjective experiences and objective bodies, (even though experiences or
> bodies would serve no plausible function for purely mathematical entities)
> becomes an almost unavoidably seductive solution.
> So appealing is this quantitative underpinning for the Western mind’s
> cosmology, that many people (especially Strong AI enthusiasts) find it easy
> to ignore that the character of mathematics and computation reflect
> precisely the opposite qualities from those which characterize
> consciousness. To act like a machine, robot, or automaton, is not merely an
> alternative personal lifestyle, it is the common style of all unpersons and
> all that is evacuated of feeling. Mathematics is inherently amoral, unreal,
> and intractably self-interested – a windowless universality of
> A computer has no aesthetic preference. It makes no difference to a program
> whether its output is displayed on a monitor with millions of colors, or
> buzzing out of speaker, or streaming as electronic pulses over a wire. This
> is the primary utility of computation. This is why digital is not locked
> into physical constraints of location. Since programs don’t deal with
> aesthetics, we can only use the program to format values in such a way that
> corresponds with the expectations of our sense organs. That format of
> course, is alien and arbitrary to the program. It is semantically ungrounded
> data, fictional variables.
> Something like the Mandelbrot set may look profoundly appealing to us when
> it is presented optically as plotted as colorful graphics, but the same data
> set has no interesting qualities when played as audio tones.
Ok, but this might be because our visual cortex is better equipped to
deal with 2D fractals. Not too surprising.
> The program
> generating the data has no desire to see it realized in one form or another,
> no curiosity to see it as pixels or voxels. The program is absolutely
> content with a purely quantitative functionality – with algorithms that
> correspond to nothing except themselves.
> In order for the generic values of a program to be interpreted
> experientially, they must first be re-enacted through controllable physical
> functions. It must be perfectly clear that this re-enactment is not a
> ‘translation’ or a ‘porting’ of data to a machine, rather it is more like a
> theatrical adaptation from a script. The program works because the physical
> mechanisms have been carefully selected and manufactured to match the
> specifications of the program. The program itself is utterly impotent as far
> as manifesting itself in any physical or experiential way. The program is a
> menu, not a meal. Physics provides the restaurant and food, subjectivity
> provides the patrons, chef, and hunger. It is the physical interactions
> which are interpreted by the user of the machine, and it is the user alone
> who cares what it looks like, sounds like, tastes like etc. An algorithm can
> comment on what is defined as being liked, but it cannot like anything
> itself, nor can it understand what anything is like.
> If I’m right, all natural phenomena have a public-facing mechanistic range
> and a private-facing animistic range.
I am willing to entertain this type of hypothesis.
> An algorithm bridges the gap between
> public-facing, space-time extended mechanisms, but it has no access to the
> private-facing aesthetic experiences which vary from subject to subject.
But why not? Why don't algorithms get the private-facing stuff? How do
you explain this natural vs. artificial distinction?
> definition, an algorithm represents a process generically, but how that
> process is interpreted is inherently proprietary.
I don't understand what you mean here. Can you elaborate?
> On Friday, August 16, 2013 3:21:11 PM UTC-4, cdemorsella wrote:
>> Telmo ~ I agree, all the Turing test does is indicate that a computer,
>> operating independently -- that is without a human operator supplying any
>> answers during the course of the test -- can fool a human (on average) that
>> they are dialoging with another person and not with a computer. While this
>> is an important milestone in AI research -- it is just a stand in for any
>> actual potential real intelligence or awareness.
>> Increasingly computers are not programmed in the sense of being provided
>> with a deterministic instruction set - no matter how complex and deep.
>> Increasingly computer code is being put through its own Darwinian process
>> using techniques such as genetic algorithms, automata etc. Computers are in
>> the process of being turned into self learning code generation engines that
>> increasingly are able to write their own operational code.
>> An AI entity would probably be able to easily pass the Turing test - not
>> that hard of a challenge after all for an entity with almost immediate
>> access to a huge cultural memory it can contain. However it may not care
>> that much to try.
>> Another study -- I think by Stanford researchers, but I don't have the
>> link handy though -- has found that the world's top super computers (several
>> of which they were able to test) are currently scoring around the same as an
>> average human four year old. The scores were very uneven across various
>> areas of intelligence that the standardized IQ tests or four year olds tries
>> to measure, as would be expected (after all a super computer is not a four
>> year old person).
>> Personally I think that AI will let us know when it has arisen by whatever
>> means it chooses to let us know. That it will know itself what it wants to
>> do, and that this knowing for itself and acting for itself will be the
>> hallmark event that AI has arrived on the scene.
>> -Chris D
>> From: Telmo Menezes <te...@telmomenezes.com>
>> To: everyth...@googlegroups.com
>> Sent: Friday, August 16, 2013 8:04 AM
>> Subject: Re: When will a computer pass the Turing Test?
>> On Fri, Aug 16, 2013 at 3:42 PM, John Clark <johnk...@gmail.com> wrote:
>> > On Wed, Aug 14, 2013 at 7:09 PM, Chris de Morsella <cdemo...@yahoo.com>
>> > wrote:
>> >> > When will a computer pass the Turing Test? Are we getting close? Here
>> >> > is
>> >> > what the CEO of Google says: “Many people in AI believe that we’re
>> >> > close to
>> >> > [a computer passing the Turing Test] within the next five years,”
>> >> > said Eric
>> >> > Schmidt, Executive Chairman, Google, speaking at The Aspen Institute
>> >> > on July
>> >> > 16, 2013.
>> > It could be. Five years ago I would have said we were a very long way
>> > from
>> > any computer passing the Turing Test, but then I saw Watson and its
>> > incredible performance on Jeopardy. And once a true AI comes into
>> > existence
>> > it will turn ALL scholarly predictions about what the future will be
>> > like
>> > into pure nonsense, except for the prediction that we can't make
>> > predictions
>> > that are worth a damn after that point.
>> I don't really find the Turing Test that meaningful, to be honest. My
>> main problem with it is that it is a test on our ability to build a
>> machine that deceives humans into believing it is another human. This
>> will always be a digital Frankenstein because it will not be the
>> outcome of the same evolutionary context that we are. So it will have
>> to pretend to care about things that it is not reasonable for it to
>> I find it a much more worthwhile endeavour to create a machine that
>> can understand what we mean like a human does, without the need to
>> convince us that it has human emotions and so on. This machine would
>> actually be _more_ useful and _more_ interesting by virtue of not
>> passing the Turing test.
>> > John K Clark
>> > --
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