🚀 go-pugleaf

In article <1328@oravax.UUCP> ian@oravax.odyssey.UUCP (Ian Sutherland) writes:
>In article <11185@thor.acc.stolaf.edu> taplin@thor.stolaf.edu (Brad Taplin) writes:
>>In article <1326@oravax.UUCP> ian@oravax.odyssey.UUCP (Ian Sutherland) writes:
>>>>>>mamoth, fuzzy, almost "humane" computer...
>>Depends on the application.
 
>Indeed.  If you really DON'T have enough flesh and blood friends, and
>you prefer your friends to be mammoth, fuzzy and vague ;-) you might
>want such a machine.  The kind of application the original poster was
>talking about was one in which you wanted the machine to be
>INTELLIGENT...
SPOILER: Several screenfuls of some crazed AI terrorist's ideas!

Thanks for the reassuring ;-). I do have trouble finding trustworthy 
friends so I would want such a machine :-Y. But about INTELLIGENCE...

Understood. I counter your cliam by suggesting that SOME (albeit few)
of those "fuzzy" characteristics can actually aid "thinking", speed
problem solving. Your straight algorithms might lead directly to
complex solutions, but I can imagine situations in which a quick,
reliable guess beats a somewhat slower but perfect response.

Ever read "The Art of Motorcycle Maintenance"? Persig suggests that when
his excessively single-minded pursuits of "Truth" in the mountains got
bogged down he'd clear his head and let his attention drift. The
critical eye remained open (wrote Persig) but the focus became whatever
struck the imagination. I've tried such "lateral thinking" and found it
often led to useful and accurate ideas my previous trains of thought
might have never reached. If indeed the possibilities are as complex and
ever-changing in real-world scenarios as I imagine them to be, then
could not practical AI benefit from the "alchemy" Persig suggests?

Imagine I've designed a computer to take over the responsibilities of an
air traffic control tower. Some paniced pilot radios that they need an
emergency landing NOW, yet the "independent" mind in my computer knows
damn well that recalculating precisely which planes go where on a
crowded New York Friday will probably take way too long. My computer
then divies up its tasks into three: The first watches and prioritizes
everything, the second (top priority now) starts making "fuzzy"
educated guesses, the third calculates under a more methodical system
the best possible solution. 

Now, if the pilot needs an answer before program3 is done, program1
(judge, manager, communicator) takes the best program2 has yet to
offer and offers it on, along with a rough estimate on its chances
of working, to all planes, vehicles, and people involved. While prog2
spools up new ideas of ever-greater complexity prog1 keeps both the
thoughtful and quick progs informed. Once prog3 has decided the best
possible solution the pilot is informed, and if s/he thinks that ultimate
decision is still workable prog1 helps everyone execute the prog3 plan.
If not, then everything starts afresh and prog2 has a spool of untested
ideas waiting to be considered.

One might argue that all three algorithms in this crisis control
situation should be seen as arrow-straight, but I'm still under the
impression that the quick-thinking prog2 must work most efficiently
by being "softer", not just simpler, than prog3. Well-placed random
variables could result in a very workable solution, even if it ain't
the best, in this painfully strict timeframe.

Tell me if (and why) I'm barking up a felled tree.
-- 
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"...I've gotten two thousand fourteen times smarter since then..." -MCP
Brad Taplin, alum, magna sin laude, afloat?  taplin@thor.acc.stolaf.edu
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