Sun.5.SEP.2010 -- Looking Before We Leap
As we gear up for self-referential thought in autonomous robots, we want each robot AI Mind to be able to handle questions in three different formats, as exemplified by the following examples.
1. What do robots make?
2. What do robots do?
3. What are robots?
The questions listed above go from the very specific to the very general. The first question, "What do robots make?", is an example of the "what-do-X-VERB?" format, where the "verb" slot may be filled with any suitable verb, such as "think" or "need". We use the verb "make" here because it will allow the AI to recall a long list of the direct objects of the noun-verb combination, "robots make".
The second question is an example of the more
general "what-do-X-DO" format, where no particular verb is
supplied and the AI Mind is free to come up with a long
list of verbs +/- objects that would complete a thought
beginning with "robots" as a subject.
The third sentence, "What are robots?", is included
here only for completeness in the consideration of
questions that intelligent robots might be called upon to
answer. We are concerned today with the answering of "what-
do-X-VERB" and "what-do-X-DO" questions. It may seem to
the casual peruser of these AI Lab Notes that such
questions are ridiculously simple and should present no
difficulty at all to any True AI worthy of the name, but a
reality check is in order here because how a software
program deals intelligently with such simple questions is
itself a profound question requiring devilishly deep
thought to answer. And if you did not smile at the mention
deep thought in the previous sentence, then you have
no business here and you are really Joe Sixpack, not Joe
Now excuse us for a moment, because we have had to
respond urgently to the travails of some young graduate
student who has become lost on the Web and needs the help
of a webfooted wizard at the prestigious AI Forum. We found what he
was looking for, and the guy was beside himself with
astonishment and thanks. In order to wring the last drop
of memetic advantage out of the rescue-episode, we propose
to follow up with the following tongue-in-cheek
It's so outstanding to hear from you again,
How totally bodacious for you to do work on neural nets.
Bright and shining your future must be,
for you stick with your awesome goals and
fail-or-no-fail you care not.
To answer your further questions ready am I.
Just ask the Old AI Dude when the going gets ungoogly.
Sun.5.SEP.2010 -- Natura Non Facit
When we ask the AI, "What do robots make?", the responses could include cars, tools, parts, and even more robots. We need to change the AI Mindgrid in such a way that the AI will be able to make statement after statement until the possible answers have been exhausted in the knowledge base (KB). Somehow we need a way to make each succeeding answer drop out of the queue, so that the next answer may surface in consciousness. We may need to create an InHibit mind- module that will lower the activation on a particular node on the quasi-fiber of the verb (such as "make") figuring in the responses to the query.
Suddenly we see a way to achieve our goal of enabling
multiple answers to a what-do-X-VERB query. It will
involve radical changes perhaps not to the underlying
MindGrid, but certainly to several mind-modules
operating across the
At the heart of the solution is the brand-new idea
that, during a query-response, after a verb-node wins
selection into a thought, the entire verb-concept shall
psi-damped down to zero, but rather only the selection-
winning node shall be inhibited down to a negative level
of activation, such as minus-fifteen or lower.
PsiDecay module shall be made to work in two
directions, both downwards towards zero and upwards
towards zero, so that any inhibited node shall gradually
lose its inhibition. Mind-modules that try to zero out an
entire range of concepts, shall be rewritten ("Get me Re-
Write!") to zero out only positive activations on
concepts, and to leave negative activations alone. At the
same time as all these changes are in effect, the subject
of the query shall have a special status of persistence,
so that the AI shall try to issue a series of statements
about the subject in combination with the query-verb,
until all pertinent nodes on the query-verb have been
knocked down into a sub-zero inhibition. At that point,
any thought beginning with the query-subject will surely
fail to connect with the query-verb, and may or may not
find a different verb for the generation of a KB-valid
sentence. We may let the special status of the query-
subject persist only so long as valid thoughts emerge in
connection (in synergy) with the query-verb, with a
release-mechanism to dislodge the subject from its special
status when the knowledge base has been exhausted.
The beauty of inhibiting serial same-verb nodes down to
a definitely negative level of activation lies in the
realization that the sentence-generation process will
continue to work the old-fashioned way. The
VerbPhrase module will flush out the next same-verb
node to win thought-selection, oblivious to the fact that
one node is now out of commission at a deep level (deep
unthought) of negative activation. There is some elegance
to a solution in which you change one phenomenon (the post-
selection activation-level) while everything else still
works in the same old way. It is like evolution, which
does not make massive saltations all at once, but only
makes one tiny mutation at a time.