[ExI] GPT-4 on its inability to solve the symbol grounding problem

Brent Allsop brent.allsop at gmail.com
Sat Apr 8 19:53:01 UTC 2023

Great, thanks so much for this!!

Yes, Gordon, and Adrian are getting it, even extending the idea to other
implications, like things may appear to be grey in dark light, even though
they can know it is still red.
And Jason understands the fundamental issue, he just has some remaining

The fundamental idea is that the redness you experience is just a
physical fact.  Redness is a property of your knowledge of a strawberry.
And properties can represent information.

Jason was asking about the dictionary.
This has to do with the way we represent information in a substrate
independent way.
For example, anything that is not a redness property can still represent
redness, if you have a dictionary.  +5 volts can be thought of as
representing red, a punch in a paper tape can be thought of as
representing red.  Even a greenness property can be thought of as
representing red, if you have a dictionary that tells you this.  But the
transducing system that detects the +5 volts operates as the dictionary,
and it sends the correct meaning to whatever next property will be
representing that same 1 not 0 or red not green meaning.

A transducer can interpret +5 volts to represent 1.  Then a series of
transducers detecting voltages on wires can represent a string of 1s and 0s.
A dictionary can specify that a particular string of 1s and 0s represents
the letters "Red."
A further dictionary can say that the referent of the word 'Red' is the
quality your brain uses to represent red things with.

On Sat, Apr 8, 2023 at 1:02 PM Jason Resch via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

> My questions related to the diagram:
> 1. Why is the strawberry gray, is it supposed to be gray to signal that
> photons are colorless? Are all three seeing the same strawberry?

They are all looking at the same strawberry.  The fact that it is grey is
because all we know of the object is that it reflects 700 nm light.  But
this abstract description of that behavior of the strawberry tells us
nothing about what it is like.  It is something in your brain that has your
redness property, not the strawberry reflecting 700 nm light.  The
strawberry just seems to be red, because our brain falsely colors our
conscious knowledge of it to be red, to tell us that is the one we want to
pick.  Nobody knows the colorness quality of anything out there.  All we
know are the false colors things seem to be.  All we need to do to know the
true colors of things, is discover which of all our descriptions of stuff
in the brain, is a description of redness, so we will then have the
required dictionary.

2. The image file is called "functionally equal machines", but how are they
> functionally equal when they each have a different mental state from the
> same stimulus?

They all know the strawberry reflects 700 nm light.  And they will all tell
you the strawberry is red.  And they can all be equally intelligent.  They
just represent their information in different ways.

> 3. Why is the same person seeing a green strawberry? Is it meant be the
> same person or a different person with inverted qualia?

The only difference between the first two, is the second one has a red /
green signal inverter between his retina and the optic nerve.  This changes
the dictionary of which property it uses to represent red strawberries
with.  The first one represents red knowledge with redness, the second one,
because of the inverted dictionary, represents red knowledge with greenness.

> 4. What do you mean by a dictionary conveying the meaning of red?
> Dictionaries say nothing of the quale of red. They can only refer to things
> that look red, but we have no proof people even see colors the same as each
> other.

Let me know if what I've said above doesn't answer this question.

And finally, there is more to it than just there are different ways to
represent information (1: directly on physical properties, or 2: in a
substrate independent way, which requires a dictionary)

There are also different ways of doing computation.  First, there is
computing directly on physical qualities, using some kind of waves
<http://www.izhikevich.org/publications/pwc.pdf>, or quantum entanglement
that computationally binds the qualities into one composite qualitative
computational experience.  Second, there is computing with abstract binary
1s, and 0s (it doesn't matter what physical properties are representing the
1s or the 0s because you always have a transducing dictionary which tells
you which is which.  And you computationally bind groups of 1s and 0s in a
CPU where registers can be computationally bound with discrete logic
gates.  Both of these systems can do computation, and both can be
intelligent.  But what they are like, and the way they compute are very
One is phenomenally conscious, and the other is, though equally
intelligent, abstractly so.
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