I just joined this list after Amara Angelica from KurzweilAI pointed out
that there was some talk about GAC in this group. I've looked at some of the
posts and would like to make some comments:
1 - GAC is a black box. I have made no disclosures on what it uses for
pattern matching (not that it should matter if you place any value in the
Turing Test which bars knowledge of the internals of a system.) But, I am
doing experiments now with SOMs and SRNs.
2 - The primary purpose of GAC is to build a fitness test for humanness in a
binary response domain. This will in the future allow GAC to babysit a truly
evolving artificial consciousness, rewarding and punishing it as needed at
machine speeds.
2.5 - The key to evolving anything is the fitness test. If I want to evolve
a picture of the Mona Lisa, then I need a fitness test for the Mona Lisa. A
good fitness test for the Mona Lisa would be a copy of an image of the Mona
Lisa. To rate the quality of an evolving picture, I would just need to
compare pixels. The next best thing to using an image would be random
sample of pixels; the larger the sample, the better will be the evolved
copy. Right now, GAC is a 50,000+ term fitness test for humanness. At each
one of those points GAC knows what it should expect it were testing an
average human, because for each one of those points GAC has made at least 20
measurements of real people.
3 - Any contradictions in GAC are real contradictions in us. It can't
believe anything that hasn't been confirmed by at least 20 people.
4 - GAC is science. Over 8 million actual measurements of human consensus
have been made. There are at least two other projects that claim to be
collecting human consensus information - CYC and Open Mind - neither has
actually done the science to verify that what is in their databases is
actually consensus human fact. It's all hearsay until the each item is
presented to at least 20 people (central limit theorem.)
Chris McKinstry
Director, Mindpixel Digital Mind Modeling Project
http://www.chrismckinstry.com
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