Re: virus: Re: virtuality

Alex Williams (thantos@decatl.alf.dec.com)
Sat, 28 Dec 1996 19:41:23 -0500 (EST)


> This is a fun mental exercise-- rminds me of the "what if you are a
> brain in a jar?" discussions that are designed to just freak you out.

Who /says/ I'm not just a brain in a jar? Er, sorry, didn't mean to
give it away ...

> Personally-- sorry to sound like a stick in the mud-- I don't think,
> whether you choose a reality that's virtual or real, it would have any
> impact on memetics.

I agree, the knowledge of the status of your perceptions is just
another meme. How readily it spreads, what it interacts positively
and negatively with and what complexi it forms are all subject to
memetic study.

> It just puts you at one level of abstraction-- it's an aloof position, a
> seperation from the "world" that reminds me of monastic practices.

Actually, it doesn't even do that. It adds a new element, just as
monastic practices are an element. Its a meme-complex that changes
what memes you'll be exposed to and which ones are likely to stick,
but still a complex all the same.

> That being said-- I ahve to also say-- because I now know what you do
> for a living Alex-- that you cannot build an artificial memeshpere or
> virtual reality. I have gone into that in greater detail in a previous
> post.

Turing tests for AMemespheres? If its spoor gives me good memes and
if my feedback to it improves or changes the good memes I get out of
it, it it memesphere /enough/ to be abstracted so? This is orthogonal
to intelligence, in this case. A really good learning search engine
might qualify as a really simple memesphere if it operates as an
autonomous agent and has memes in contention which provide its model
of its `environment.'

> The thing that is missing from your A-memesphere is the memesphere. Just
> like the AfterDark aquarium screensaver is missing two things-- water
> and fish.

The question is is the AfterDark model accurate enough to tell us
something about the processes involved in fish in an aquarium? Like
the Byrds algorithm, which takes three simple rules and, applied
iteratively to a group of objects in a simulation, provides an
accurate model of flocking behaviour and motion, a model can give
insight into the mechanisms that might underlie the reality, even a
simplified model.