As always, comments and criticisms are very welcome.
Francis
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From World-Wide Web to Super-Brain
PRINCIPIA CYBERNETICA[1] WEB (C)[2]
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Author: F. Heylighen[3],
Date: Jan 5, 1995
Parent Node(s):
Super- and/or Meta-being(s)[4]
FROM WORLD-WIDE WEB TO SUPER-BRAIN
The present World-Wide Web, the distributed hypermedia interface to the
information available on the Internet, is in a number of ways similar to a
human brain, and is likely to become more so as it develops. The core
analogy is the one between hypertext and associative memory. Links between
hyperdocuments or nodes are similar to associations between concepts as they
are stored in the brain. However, the analogy goes much further, including
the processes of thought and learning.
Spreading activation
Retrieval of information can in both cases be seen as a process of
"spreading activation": nodes or concepts that are semantically "close" to
the information one is looking for are "activated". The activation spreads
from those nodes through their links to neighbouring nodes, and the nodes
which have received the highest activation are brought forward as candidate
answers to the query. If none of the proposals are acceptable, those that
seem closest to the answer are again activated and used as sources for a new
process of spreading. This process is repeated, with the activation moving
from node to node via associations, until a satisfactory solution is found.
Such a process is the basis for thinking[6]. In the present Web, spreading
activation is only partially implemented, since a user normally selects
nodes and links sequentially, one at a time, and not in parallel like in the
brain. Thus, "activation" does not really spread to all neigbouring nodes,
but follows a linear path.
A first implementation of such a "parallel" activation of nodes might be
found in WAIS-style search engines (e.g. Lycos[7]), where one can type in
several keywords and the engine selects those documents that contain a
maximum of those keywords. E.g. the input of the words "pet" and "disease"
might bring up documents that have to do with veterinary science. This only
works if the document one is looking for effectively contains the words used
as input. However, there might be other documents on the same subject using
different words (e.g. "animal" and "illness") to discuss that issue. Here,
again, spreading activation may help: documents about pets are normally
linked to documents about animals, and so a spread of the activation
received by "pet" to "animal" may be sufficient to select the searched-for
documents. However, this assumes that the Web would be linked in an
intelligent way, with semantically related documents (about "pets" and
"animals") also being close in hyperspace. To achieve this we need a
learning process.
Learning webs
In the human brain knowledge and meaning develop through a process of
associative learning[8]: concepts that are regularly encountered together
become more strongly connected (Hebb's rule for neural networks). At present
such learning in the Web only takes place through the intermediary of the
user: when a maintainer of a web site about a particular subject finds other
web documents related to that subject, he or she will normally add links to
those documents on the site. When many site maintainers are continuously
scanning the Web for related material, and creating new links when they
discover something interesting, the net effect is that the Web as a whole
effectively undergoes some kind of associative learning.
However, this process would be much more efficient if it could work
automatically, without anybody needing to manually create links. It is
possible to implement simple algorithms that make the web learn (in
real-time) from the paths of linked documents followed by the users. The
principle is simply that links followed by many users become "stronger",
while links that are rarely used become "weaker". Some simple heuristics
can then propose likely candidates for new links, and retain the ones that
gather most "strength". The process is illustrated by our "adaptive
hypertext experiment[9]", where a web of randomly connected words
self-organizes into a semantic network, by learning from the link selections
made by its users. If such learning algorithms could be generalized to the
Web as a whole, the knowledge existing in the Web could become structured
into a giant associative network which continuously adapts to the pattern of
its usage.
Answering Ill-Posed Questions
We can safely assume that in the following years virtually the whole of
human knowledge will be made available electronically over the networks. If
that knowledge is then semantically organized as sketched above, processes
similar to spreading activation should be capable to retrieve the answer to
any question for which an answer somewhere exists. The spreading activation
principle allows questions that are ill-posed: you may have a problem, but
not be able to clearly formulate what it is you are looking for, but just
have some ideas about things it has to do with.
Imagine the following situation: your dog is continuously licking mirrors.
You don't know whether you should worry about that, or whether that is just
normal behavior, or perhaps a symptom of some kind of disease. So you try to
find more information by entering the keywords "dog", "licking" and "mirror"
into a Web search. If there would be a "mirror-licking" syndrome described
in the literature about dog diseases, such a search would immediately find
the relevant documents. However, that phenomenon may just be an instance of
the more general phenomenon that certain animals like to touch glass
surfaces. A normal search on the above keywords would never find a
description of that phenomenon, but the spread of activation in a
semantically structured web would reach "animal" from "dog", "glass" from
"mirror" and "touching" from "licking", thus activating documents that
contain all three concepts. This example can be easily generalized to the
most diverse and bizarre problems. Whether it has to do with how you
decorate your house, how you reach a particular place, how you remove stains
of a particular chemical, what is the natural history of the Yellowstone
region: whatever the problem you have, if some knowledge about the issue
exists somewhere, spreading activation should be able to find it.
For the more ill-structured problems, the answer may not come immediately,
but be reached after a number of steps. Just like in normal thinking,
formulating part of the problem brings up certain associations which may
then call up others that make you reformulate the problem in a better way,
which leads to a clearer view of the problem and again a more precise
description and so on, until you get a satisfactory answer. The web will not
only provide straight answers but general feedback that will direct you in
your efforts to get closer to the answer.