RE: Re[2]: Immortality and Resources

Jim Legg (income@ihug.co.nz)
Mon, 3 Feb 1997 13:27:04 +1300


------ =_NextPart_000_01BC11DD.567230A0
Content-Type: text/plain; charset="us-ascii"
Content-Transfer-Encoding: quoted-printable

> Guru George asks: What the fuck is "intrinsic value"? How is it to =
be estimated?

A system that relies on captive consumers who can't notice or question =
the nature of the non-local variables hidden in 'price' is similar to a =
classic backward propagation Artificial Neural Network. For example, in =
accepting that classic AI is not going to provide a direct interface to =
the mind, the modern AI trend looks like a constructionist model that =
uses graded (i.e. intrinsic) values for feed-forward unsupervised =
learning. The simplest form of intrinsic value is a unit of frequency. =
The result of using analog or hidden variables in systems is that they =
cannot be examined, backed-up and eventually can't be trusted.

The good news is that it is easy to prune any analog 'beanstalk-like' =
system into the bigger and more efficient (i.e. digital) constructionist =
mapping method with its independent components. There is a priceless =
inference engine called "The Ingrid Thought Processor" which I have been =
involved with for the last eighteen years. It's available as freeware =
from my website to further explain constructionism.=20

I don't know how many readers have seen the elaborate economic charts =
put out by the Millenium Institute, but I challenge anyone to show that =
these charts cannot be improved by reconstructing them without the red =
bits.=20

Best,

Jim Legg http://homepages.ihug.co.nz/~income

------ =_NextPart_000_01BC11DD.567230A0
Content-Type: application/ms-tnef
Content-Transfer-Encoding: base64
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------ =_NextPart_000_01BC11DD.567230A0--