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78 Biomimetics: Biologically Inspired Technologies

What will occur during the darkest days . . .
Research scientists have made astounding breakthroughs.
What will occur within the industry itself. . .

What will occur during the darkest days . . .
The vacation should be very exciting.
What will occur during Christmas season when . . .

What will occur during the darkest days . . .
I would like to go skiing.
What will occur during my winter vacation . . .
What will occur during the darkest days . . .
There’s no way to be certain.
What will occur if we do nothing . . .

When the Union Bank launched another 100 . . .
She loved her brother’s Southern hospitality.
When the Union flag was raised again . . .

When the Union Bank launched another 100 . . .
New York City theater is on Broadway.
When the Union Square Theater in Manhattan . . .

A good analogy for this system is a child learning a human language. Young children need not
have any formal knowledge of language or its structure in order to generate it effectively. Consider
what this architecture must ‘‘know’’ about the objects of the world (e.g., their attributes and
relationships) in order to generate these continuations; and what it must ‘‘know’’ about English
grammar and composition. Is this the world’s first AI system? You decide.
Note that in the above examples the continuation of the second sentence in context was
conducted using an (inter-sentence, long-range context) knowledge base educated via exposure
to meaning-coherent sentence pairs selected by an external agent. When tested with context, using
completely novel examples, it then produced continuations that are meaning-coherent with
the previous sentence (i.e., the continuations are rarely unrelated in meaning to the context
sentence). Think about this for a moment. This is a valuable general principle with endless
implications. For example, we might ask: how can a system learn to carry on a conversation?
Answer: simply educate it on the conversations of a master human conversationalist! There is no
need or use for a ‘‘conversation algorithm.’’ Confabulation architectures work on this monkey-see/
monkey-do principle.
This sentence continuation example reveals the true nature of cognition: it is based on ensem-
bles of properly phased confabulation processes mutually interacting via knowledge links.
Completed confabulations provide assumed facts for confabulations newly underway. Con-
temporaneous confabulations achieve mutual ‘‘consensus’’ via rapid interaction through
knowledge links as they progress (thus the term consensus building). There are no algorithms
anywhere in cognition. Only such ensembles of confabulations. This illustrates the truly
alien nature of cognition in comparison with existing neuroscience, computer science, and AI
concepts.
In speech cognition (see Section 3.4), elaborations of the architecture of Figure 3.3 can be used
to define expectations for the next word that might be received (which can be used by the acoustic
components of a speech understanding system); based upon the context established by the previous
sentence and previous words of the current sentence which have been previously transcribed. For
text generation (a generalization of sentence continuation, in which the entire sentence is completed
with no starter), the choices of words in the second sentence can now be influenced by the context