We're on vacation in Spain and I finally have time to relax, catch up with my reading
list and spend some quality time with my lovely wife Nanna and my kids.
If you're interested in AI, watching kids will teach you about the difficulty
of seemingly simple real world tasks. Like filling, pressurizing, aiming and shooting
a water gun. It turns out pressurizing, aiming and shooting is hard wired, while filling the
damn thing is almost beyond the capability of a three year old boy. And talking about fun,
after a day at the beach, nothing beats a little recreational programming in Lisp and a glass
of wine.
Back to the reading list:
Over the last couple of weeks I collected numerous papers in a
Read This!
folder. This morning I read a paper written by Hans Moravec in 1975 titled:
The Role of Raw Power in Intelligence.
Thirty years after its publication it's still quite to the point:
The enormous shortage of ability to compute is distorting our
work, creating problems where there are none, making others impossibly
difficult, and generally causing effort to be misdirected. Shouldn't
this view be more widespread, if it is as obvious as I claim?
In the early days of AI the thought that existing machines
might be much too small was widespread, but there was hope that clever
mathematics and advancing computer technology could soon make up the
difference. Since then computers have improved by a factor of ten
every five years, but, in spite of reasonably diligent work by a
reasonable number of people, the results have been embarrassingly
sparse. The realization that available compute power might still be
vastly inadequate has since been swept under the rug, due to wishful
thinking and a feeling that there was nothing to be done about it
anyway and that voicing such an opinion could cause AI to be
considered impractical, resulting in reduced funding.
There is also an element of scientific snobbery. Many of the
most influential names in the field seem to feel that AI should be
like the theoretical side of physics, the essential problem being to
find the laws of universe relating to intelligence. Once these are
known, the thinking goes, construction of efficient intelligent
machines will be trivial. Suggestions that the problems are
essentially engineering ones of scale and complexity, and can be
solved by incremental improvements and occasional insights into
sub-problems, are treated with disdain.
This attitude is a variant of the philosophical notion that
all truth can be arrived at by pure thought, and is unfounded and
harmful. One wonders what state space travel would be in if the
Goddards and von Brauns had spent their time trying to find the
universal laws of rocket construction before trying to build space
ships. AI needs a stronger experimental base. Like other branches of
endeavor (notably physics, aeronautics and meteorology), we should
realize our desperate need for more computing, and do things about it.
