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Network routing is probably the most natural application for the complexity approach. First, routers are junctions of a network (in itself a central concept in complexity). Secondly, there is no conceivable practical way in which the network routing problem can be solved by a hierarchical, global algorithm. Thus routing is solved through the use of local routers that know where to send the information that is flowing through them. However, due to the fact that the development of routing algorithms still resides exclusively in the realm of computer engineering and computer science, the routing algorithms themselves do not pass the line of triviality. Billions of dollars are lost every year in damage due to bottlenecks, congestions, Denial Of Service caused by malicious attacks, negligence or simply by mistake or mis-design. Many other applications and businesses do not get transferred to the Internet due to these problems. Building systematic algorithms, which utilize the fact that the Internet has grown big enough to be thought of as a statistical ensemble, promise to create a more flexible, vibrant, trustworthy Internet.

Customized Information Providers and the Distributed Cognitive Space

On Passover, Jews are supposed to tell their children about the Exodus. The fit way to do it is exemplified in the Passover 'Hagada'(=the 'telling') by 4 cases:

1. the case of the wise child which asks 'what are the rules we were instructed to follow on this occasion ?'.
2. the naughty, which asks 'why this effort to you?'
3. the naive / simple: 'What's this?'
4. the child that doesn't know how to ask.

For each of them a different answer is prescribed.

This situation is typical for every transfer of information. It is not enough to have in the data-base the correct answer and to deliver it. In order to be meaningful, the answer has to take into account the previous knowledge of the questioner its conceptual structure, its preconceptions and feelings and the situational context in which the question is asked. This background is given away in great measure by the very formulation of the question (or the lack of formulation in the case 4 above).

Consequently, there is a 'large' infinity of answers on the same subject, each of them fitting a certain questioner and context. Yet the amount of existing documents written already on the subject is usually very finite. While all the elements for giving the 'fit' answer to a current question may be available in the data-base, the answer itself is usually not.

It is a difficult, but increasingly compelling task to design 'machines' which can do exactly this it: given a subject and a questioner to give the 'fit' formulation of the answer(s). While in principle such a machine might require inputs from psychology, pedagogy, AI, the hope is that a host of adaptive agents might be able to 'learn' the profile of the questioners population and the procedures to tailor documents fit to their individual needs.

Bringing the Rosetta Stone into the Chinese Room

The translation capability became one of the stumbling stones of AI. The belief that translation capability is tantamount to artificial understanding leaves one with a very bleak prospect for achieving it soon or easily. Yet the proliferation and globalization of knowledge makes massive online translation and processing of documents, especially on the Internet, an unavoidable and ubiquitous task.

The problem is wider than simple translations from one national language to another. One may equally refer to various technical jargons as technical 'languages' and to writing executive abstracts as translations from verbose texts to their terse versions. 'Translations' may also be the transformation of plain unstructured text into some structured canonical template (code to be used as input to further processing by specialized programs).

One possible approach is the bootstrap one: using the abundance of already translated documents as a resource for new translations. The bulk of documents written in a given 'language' can be used as an operative definition of that language. Documents expressing in different 'languages' the same thing can be used as operative models for 'translations' between those 'languages'. This would be the automatic version of the techniques applied at the time for translating the old Egyptian hieroglyphs using the Rosetta stone [On that stone, the same text appeared written in hieroglyphic script and in parallel, in Greek and Demotic].

Rather than designing heavy AI machines with large databases and sophisticated 'intelligent' algorithms, all one has to do is to let a host of adaptive Internet agents to adapt the chunks of text of the already existing 'translations' to new texts. If the result is not satisfactory (may be due to the lack of a large previous pool of 'translations' between the two 'languages'), the human corrections necessary to improve it, will insure that the current document can be used in the future as a basis for better performance (supervised learning). Of course, the agents may be endowed also with the capabilities to use transitivity: given a set of translations from a 'language' A to a 'language' B and a set of 'translations' (in roughly the same subject) from B to C, they will produce automatic 'A-to-C translations'.

Instead of having to repeat each time the 'translation' process, one would use the Internet with all the previously created documents as a global intelligent Chinese Room populated by many cooperating adaptive agents and furnished with a huge Rosetta stone.

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