How does asemantic computing differ from traditional distributed computing?

UPDATE: submitted this as an article to arXiv. UPDATE 2: as of 21.10.2023 it appears as “on hold”.

This is an important question. It is also an example of argument from AI summary. Such an argument aims to establish if the communication of an idea is defective, by asking an AI to provide a summary. The summary is then vetted by the author of the argument, as proof that the communication of the idea is conformal with the intentions of the author.

With such proof, the author may ask then: if the AI gets it, why don’t you? [ie the dialogue partners]

Here is the AI answer to the question from the title and here is the archived answer.

The edits are only a link to the AI used and the replacement of the text “geeksforgeeks.org” with “asemantic-computing.md” for the links provided in the AI answer (only the links name are changed, not the links themselves, of course).

Start!

Answer | Phind V6 Model

Asemantic computing differs from traditional distributed computing in several key ways:

1. Global Control and Meaning: Traditional distributed computing systems require a global control and meaning. This is because they are designed to perform tasks that require a global understanding of the system’s state. In contrast, asemantic computing systems do not require a global control or meaning. They operate locally in time and space, without needing to understand the overall state of the system [asemantic-computing.md].
2. Semantics: Asemantic computing does not have semantics, which are the meanings of symbols in a system. This is a significant difference from traditional distributed computing systems, which rely on semantics to understand and interpret the data. In asemantic computing, the data is processed without any inherent meaning [asemantic-computing.md].
3. Turing Machines and Graph Rewrite Systems: Asemantic computing uses Turing machines and graph rewrite systems, which are local and do not require global control or meaning. In contrast, traditional distributed computing systems often use term rewrite systems, which are non-local and require global control [asemantic-computing.md].
4. Confluence: Asemantic computing does not require confluence, which is the property of a graph rewrite system where if we have a graph that can be rewritten into one without any further possible rewrites, then this final state is unique. This is undesirable for life-like distributed computations, where final states are to be interpreted as death and they have to be recycled somehow by another mechanism [asemantic-computing.md].
5. Extracting Meaning: The meaning we can extract from an asemantic computing system is only local. In traditional distributed computing systems, we can extract global measures of agreement and synchronization. However, in asemantic computing, we cannot extract precise global measures of agreement or synchronization [asemantic-computing.md].
6. Programming and Usage: Asemantic computing systems are unclear how to program or use them in the old ways. However, they can be used as if they are a living ecosystem, an extension of the meatspace [asemantic-computing.md].