Meeting notice: The 04.July.20 meeting will be held at 7:30 P.M. at 
the Royal East (782 Main St., Cambridge), a block down from the corner 
of Main St. and Mass Ave.  If you're new and can't recognize us, ask 
the manager. He'll probably know where we are. More details below.

Suggested topic of the week:  Strong AI and where is it?

Half of what we talk about -- at least when we get within shouting
distance of our raison detre -- depends on the development of something called 'strong AI'.

The term was invented by the philosopher John Searle to refer to 
computers with minds or consciousnesses.  The meaning we intend when 
we use the term was (I think) articulated by Ray Solomonoff: the 
ability to do science, math, and engineering as well as a reasonably 
competent human.  (At least I first heard this usage from Ray.)

Note that within this definition a strong AI doesn't have to perform 
better than humans or even as well as the best human.  For Solomonoff 
the test is generality: a strong AI has to be able to find its 
bearings and operate competently in a very wide range of fields, from 
physics to engineering, from biotech to electrical, from protein 
folding to biosynthesis.  Once trained to a field, it has to be able 
to handle the same range of problems over the same variety of contexts 
as a professional engineer does during his or her work day, without 
taking any more time to do so than that human would.

I think a reasonable but not definitive argument can be made that over 
the past thirty years progress on this issue -- generality -- has been 
zero to negative.  (You get negative progress when people give up and 
leave the field.) What progress has been achieved on the AI agenda has 
come from evading the problem. Chess programs compensate for their 
inability to recognize strong and weak positions as well as even 
middling players by doing much better at calculating lookaheads. 
Speech recognizers work by compiling huge databases of phoneme 
patterns instead of recognizing semantics.  Machine vision is making 
some progress (with very simple applications, like license plate 
recognition) because installations can be lit with LEDs, thus 
eliminating a source of variation that humans are mostly unaware of 
but which was killing the technology.

Agenda items whose variability can not be evaded, like Go or Natural
Language Recognition or for that matter lots of machine vision apps,
like counting the number of people in a crowd or extracting spatial
measurements from a photo, are going nowhere, or nowhere fast,
assuming the standard of reasonably competent human performance.  Not
that that standard can't be lowered. Train a pigeon on a leaf and it
will recognize leaves of that kind in all kinds of orientations, light
levels, distances, life cycle stages, and even disrepair.  Even
defined down like this, such accomplishments are still way out of
reach for our machines.

Any solution to the generality problem might cascade overnight into an 
immensely powerful technology.  I see no clear way of distinguishing 
between the variability that must be mastered in decoding a scene 
visually, handling objects, and solving low-level problems like 
tolerances and structural integrity and manufactureability, and 
managing the high- level variations involved with moving from 
electrical to mechanical engineering or from engineering to physics. A 
solution to the variation problem on one level might work on all of 
them.

On the other hand, perhaps our failure to date is pointing to 
something deep about the difference between digital and analog 
systems, though Lord knows what.

In any event, if we can't build machines that can solve reasonably 
hard engineering problems on their own, unattended, all manner of 
complex engineering systems will be out of reach for a long time, from 
the "Scientist's Assistant" (an automated first-year grad student) to 
most of the high- end nanotech apps, with the assembler first on the 
list.

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In twenty years half the population of Europe will have visited the
moon.

                                -- Jules Verne, 1865

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Announcement Archive: http://www.pobox.com/~fhapgood/nsgpage.html.

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Legend:

"NSG" expands to Nanotechnology Study Group.  The Group meets on the
first and third Tuesdays of each month at the above address, which
refers to a restaurant located in Cambridge, Massachusetts.

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