Protocol-Free Communication
If we create a new contact on our phone, if we book a flight on a website or if we use an app to get the latest weather data we are communicating using a protocol. The details do not really matter, but it is almost certainly some sort of protocol or predefined data structure.
The emphasis here is on predefined. If you want to communicate with an app, website or service you need to know and precisely follow a protocol.
This does not apply to human communication. We can call into a call center and without much protocol explain to the agent that we need to book a flight. As long as the agent speaks the same language and you know the important trigger words you will most likely be able to book that flight no matter if you begin the communication with “Good Morning Sir” or not.
While digital communication protocols are necessary today, they make communication inflexible, implementations expensive and can be a hindrance for business-to-business communications. For customers they can mean reduced service quality because available features depend on the features the protocol supports.
Communication in Havel – be it human-to-computer or computer-to-computer – can be more akin natural language than traditional, structured protocols are.
In many cases, requests (or replies) do not need to be expressed exactly in a predefined way in order to be understood by a recipient. For a human there is no critical difference between the two requests “Hi there, I’m John, would you mind telling me the time?” and “This is John, what’s the time?” Human are able to read a hand-written fax as well as a well-formatted printout from an ERP system, even if they have not seen that particular format before.
As human language, Havel allows the same information to be expressed in different ways. The words or format do not matter that much as long as the meaning can be understood. It is what allows protocol-free communication. Havel, being an expressive and semantic language, enables protocol-free communications.
Havel also allows for automatic content-translations. A message that uses a custom format can contain or point to a translation-expression that allows the recipient to understand the contents. For example, sending an order to a web shop can include a semantic description of the contents; using this information the web shop can understand a format it had never encountered before and process the order.
Furthermore, a Samarai brain allows for a kind of semantic conversation: When a message was not conclusive, the recipient simply sends a reply asking for clarification – very much like human conversations – the protocol-free communication goes both ways.
In end-effect, Havel allows for collaborative computers that can execute complex tasks autonomously.
A more technical explanation of protocol-free communication can be found here.
This post is part of a comprehensive introduction into SRDM, graph languages and Havel.
Continue reading: Semantic Social Networks
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