Text synthesizer converts thought
into text though.
Input: set of active concepts.
Simple implementation of text synthesizer
all active concepts by their “strength”
attribute value (optionally by desirability attribute value).
first active concept from the sorted list of active
the most relevant text concept for the
selected active concept.
the found text concept into piece of text using word dictionary or phrase dictionary.
the piece of text into text result.
next active concept.
starting from step 3 until all active concepts
will be processed.
Advanced implementation of text synthesizer
Advanced text synthesizer uses the same basic principles as
simple text synthesizer. The following advanced features could improve quality
of synthesized text.
Feature 1. Pre-sort active concepts by softcoded
Strong AI should try to create
its own softcoded routines for sorting active concepts. Eventually experimenting will find out what softcoded routines
are more efficient for text synthesizing.
Text synthesizing experiment scenario:
to “sort text” by a softcoded routine.
text output back into collection of active concepts. Use text parser for that.
Compare original set of active concepts with the final collection of
(Similar round-trip test may be applied as well: “initial text thought”
-> “thought” -> “resulting text thought”. Ideally “initial text
thought” and “resulting text thought” should be the same).
text results by external expert (another intelligence system --- could be
a human). Check if external expert understand the text results. Does the
expert like the text results?
Hint: Internet chatters can be used as external experts. They are free
and large in number.
ratings of softcoded routines based on self-evaluation (c) and expert
Hint: Use Event correlation
analyzer to evaluate experts’ feedback.
Feature 2. Order synthesized text to look
After set of text concepts is prepared to final text rendering,
the set is ordered in accordance with data from text pair.
This text pair data is gathered
during strong AI reading experience. Basically text pairs keep information about usual order of text
concepts in text.