Making the Agora Vanish | OSINT distributed haven (Intellagora)

[Ray Dillinger]

On Sat, 14 Apr 2001, Aimee Farr wrote:


>That is an over-simplification, but yes. Intelligence is not headlines. To a
>large extent, "what's happening" is not analyzed correctly, because the
>intelligence community lacks sufficient expert analysis to cope with the
>dataload. This capability is in the private sector. These information flows,
>between the government sector and the private sector, are unmapped.

This is not true any more.  The automated analysis of trawled data has 
advanced considerably beyond keyword searching at this point; there are 
programs out there now specifically looking for much more subtle and 
complicated things, which were formerly the domain of intelligence 
analyists, and they are actually pretty damn good.  The simple keyword 
searchers and keyphrase searchers you hear about with echelon are only 
the front line; they pass their data back to much more sophisticated 
AI programs that analyze content, and synthesize information gleaned 
from massive numbers of such missives.   

Every time a situation like the Aum Shenrikyo (spelled?) subway 
attack happens, if the automated analysis suite didn't point it 
out first, human analysts come in and check out the dataflows 
that ran before it and around it, and create a new auto-analysis 
program.  And then later, when another group that has anything 
like the same rhetoric and seems to be going through the same 
logistical steps pops up, the auto-analysis finds it without human 
help. 

I do not speak of specific known programs here; but my primary 
background is in AI and expert systems, and I can state unequivocally 
that intelligence analysis funded most of the research in the field 
for a very long time, and that programs such as I described above 
are well within the current state of the art.  It is unusual for 
them to be deployed very widely in private industry because in 
private industry there is a real problem of retaining personnel 
with the proper expertise to work on them.  They tend to be delicate 
in their operation -- you go to make a minor change in the data 
or the rules or the schemas and the performance of all other parts 
of the system degrades unless you are extremely careful, well-trained, 
and, let's face it, consistently just plain smarter than normal people.  
But when they are in tune, and their vocabulary tables are up-to-date, 
they are highly accurate.

The problem of keeping these systems in tune is what drives most 
practical AI research today; the systems are effective, but brittle 
and unable to cope with subtle changes and variations very well. 
"Fuzzy" approaches like ANN's and Genetic Algorithms are attempts 
to get past this problem by making self-adjusting systems, but the 
volumes of data required to get self-adjustment working using such 
approaches are a problem; you'd have to have data from  hundreds 
of Aum Shenrikyo type attacks before your GA or ANN really had a 
good chance of picking out what parts of the dataflow were relevant.

So here's my speculation: human analysts are probably called in only 
after something takes the automatic tools by surprise, or when there 
is an administrative need for specific analysis that the automatic 
tools do not provide.


				Bear