> Most compression tests are like defining intelligence as the ability to
> catch mice. They measure the ability of compressors to compress specific
> files. This tends to lead to hacks that are tuned to the benchmarks. For the
> generic intelligence test, all you know about the source is that it has a
> Solomonoff distribution (for a particular machine). I don't know how you
> could make the test any more generic.


IMO the test is *too* generic  ... I don't think real-world AGI is mainly
about being able to recognize totally general patterns in totally general
datasets.   I suspect that to do that, the best approach is ultimately going
to be some AIXItl variant ... meaning it's a problem that's not really
solvable using a real-world amount of resources.  I suspect that all the AGI
system one can really build are SO BAD at this general problem, that it's
better to characterize AGI systems

-- NOT in terms of how well they do at this general problem

but rather

-- in terms of what classes of datasets/environments they are REALLY GOOD at
recognizing patterns in

I think the environments existing in the real physical and social world are
drawn from a pretty specific probability distribution (compared to say, the
universal prior), and that for this reason, looking at problems of
compression or pattern recognition across general program spaces without
real-world-oriented biases, is not going to lead to real-world AGI.  The
important parts of AGI design are the ones that (directly or indirectly)
reflect the specific distribution of problems that the reeal world presents
an AGI system.

And this distribution is **really hard** to encapsulate in a text
compression database.  Because, we don't know what this distribution is.

And this is why we should be working on AGI systems that interact with the
real physical and social world, or the most accurate simulations of it we
can build.

-- Ben G



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agi
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