On Apr 6, 2008, at 11:58 AM, Richard Loosemore wrote:
Artificial Intelligence research does not have a credible science
behind it. There is no clear definition of what intelligence is,
there is only the living example of the human mind that tells us
that some things are "intelligent".
The fact that the vast majority of AGI theory is pulled out of /dev/
ass notwithstanding, your above characterization would appear to
reflect your limitations which you have chosen to project onto the
broader field of AGI research. Just because most AI researchers are
misguided fools and you do not fully understand all the relevant
theory does not imply that this is a universal (even if it were).
This is not about mathematical proof, it is about having a credible,
accepted framework that allows us to say that we have already come
to an agreement that intelligence is X, and so, starting from that
position we are able to do some engineering to build a system that
satisfies the criteria inherent in X, so we can build an intellgence.
I do not need anyone's "agreement" to prove that system Y will have
property X, nor do I have to accommodate pet theories to do so. AGI
is mathematics, not science. Plenty of people can agree on what X is
and are satisfied with the rigor of whatever derivations were
required. There are even multiple X out there depending on the
criteria you are looking to satisfy -- the label of "AI" is immaterial.
What seems to have escaped you is that there is nothing about an
agreement on X that prescribes a real-world engineering design. We
have many examples of tightly defined Xs in theory that took many
decades of R&D to reduce to practice or which in some cases have never
been reduced to real-world practice even though we can very strictly
characterize them in the mathematical abstract. There are many AI
researchers who could be accurately described as having no rigorous
framework or foundation for their implementation work, but conflating
this group with those stuck solving the implementation theory problems
of a well-specified X is a category error.
There are two unrelated difficult problems in AGI: choosing a rigorous
X with satisfactory theoretical properties and designing a real-world
system implementation that expresses X with satisfactory properties.
There was a time when most credible AGI research was stuck working on
the former, but today an argument could be made that most credible AGI
research is stuck working on the latter. I would question the
credibility of opinions offered by people who cannot discern the
difference.
And in case you are tempted to do what (e.g.) Russell and Norvig do
in their textbook...
I'm not interested in lame classical AI, so this is essentially a
strawman. To the extent I am personally in a "theory camp", I have
been in the broader algorithmic information theory camp since before
it was on anyone's radar.
It is not that these investors understand the abstract ideas I just
described, it is that they have a gut feel for the rate of progress
and the signs of progress and the type of talk that they should be
encountering if AGI had mature science behind it. Instead, what
they get is a feeling from AGI researchers that each one is doing
the following:
1) Resorting to a bottom line that amounts to "I have a really good
personal feeling that my project really will get there", and
2) Examples of progress that look like an attempt to dress a
doughnut up as a wedding cake.
Sure, but what does this have to do with the topic at hand? The
problem is that investors lack any ability to discern a doughnut from
a wedding cake.
J. Andrew Rogers
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singularity
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